Arihant Techno Solutions

ANDROID Project Titles 2016-2017

ATS_AND16_001 - Intelligent Hands Free Speech based SMS System on Android
            Over the years speech recognition has taken the market. The speech input can be used in varying domains such as automatic reader and for inputting data to the system. Speech recognition can minimize the use of text and other types of input, at the same time minimizing the calculation needed for the process. A decade back speech recognition was difficult to use in any system, but with elevation in technology leading to new algorithms, techniques and advanced tools. Now it is possible to generate the desired speech recognition output. One such method is the hidden Markov models which is used in this paper. Voice or signaled input is inserted through any speech device such as microphone, then speech can be processed and convert it to text hence able to send SMS, also Phone number can be entering either by voice or you may select it from contact list. Voice has opened up data input for a variety of user's such as illiterate, handicapped, as if the person cannot write then the speech input is a boon and other's too which can lead to better usage of the application.

ATS_AND16_002 - An Exploration of Geographic Authentication Schemes
            We design and explore the usability and security of two geographic authentication schemes: GeoPass and GeoPass- Notes. GeoPass requires users to choose a place on a digital map to authenticate with (a location password). GeoPassNotes—an extension of GeoPass—requires users to annotate their location password with a sequence of words that they can associate with the location (an annotated location password). In GeoPassNotes, users are authenticated by correctly entering both a location and an annotation. We conducted user studies to test the usability and assess the security of location passwords and annotated location passwords. The results indicate that both variants are highly memorable, and that annotated location passwords may be more advantageous than location passwords alone due to their increased security and the minimal usability impact introduced by the annotation.

ATS_AND16_003 - A Shoulder Surfing Resistant Graphical Authentication System
            Authentication based on passwords is used largely in applications for computer security and privacy. However, human actions such as choosing bad passwords and inputting passwords in an insecure way are regarded as ”the weakest link” in the authentication chain. Rather than arbitrary alphanumeric strings, users tend to choose passwords either short or meaningful for easy memorization. With web applications and mobile apps piling up, people can access these applications anytime and anywhere with various devices. This evolution brings great convenience but also increases the probability of exposing passwords to shoulder surfing attacks. Attackers can observe directly or use external recording devices to collect users’ credentials. To overcome this problem, we proposed a novel authentication system PassMatrix, based on graphical passwords to resist shoulder surfing attacks. With a one-time valid login indicator and circulative horizontal and vertical bars covering the entire scope of pass-images, PassMatrix offers no hint for attackers to figure out or narrow down the password even they conduct multiple camera-based attacks. We also implemented a PassMatrix prototype on Android and carried out real user experiments to evaluate its memorability and usability. From the experimental result, the proposed system achieves better resistance to shoulder surfing attacks while maintaining usability.

ATS_AND16_004 - STAMP: Enabling Privacy-Preserving Location Proofs for Mobile Users
            Location-based services are quickly becoming immensely popular. In addition to services based on users' current location, many potential services rely on users' location history, or their spatial-temporal provenance. Malicious users may lie about their spatial-temporal provenance without a carefully designed security system for users to prove their past locations. In this paper, we present the Spatial-Temporal provenance Assurance with Mutual Proofs (STAMP) scheme. STAMP is designed for ad-hoc mobile users generating location proofs for each other in a distributed setting. However, it can easily accommodate trusted mobile users and wireless access points. STAMP ensures the integrity and non-transferability of the location proofs and protects users' privacy. A semi-trusted Certification Authority is used to distribute cryptographic keys as well as guard users against collusion by a light-weight entropy-based trust evaluation approach. Our prototype implementation on the Android platform shows that STAMP is low-cost in terms of computational and storage resources. Extensive simulation experiments show that our entropy-based trust model is able to achieve high  (>0.9) collusion detection accuracy.

ATS_AND16_005 - Understanding Smartphone Sensor and App Data for Enhancing the Security of Secret Questions
            Many web applications provide secondary authentication methods, i.e., secret questions (or password recovery questions), to reset the account password when a user’s login fails. However, the answers to many such secret questions can be easily guessed by an acquaintance or exposed to a stranger that has access to public online tools (e.g., online social networks); moreover, a user may forget her/his answers long after creating the secret questions. Today’s prevalence of smartphones has granted us new opportunities to observe and understand how the personal data collected by smartphone sensors and apps can help create personalized secret questions without violating the users’ privacy concerns. In this paper, we present a Secret-Question based Authentication system, called “Secret-QA”, that creates a set of secret questions on basic of people’s smartphone usage. We develop a prototype on Android smartphones, and evaluate the security of the secret questions by asking the acquaintance/stranger who participate in our user study to guess the answers with and without the help of online tools; meanwhile, we observe the questions’ reliability by asking participants to answer their own questions. Our experimental results reveal that the secret questions related to motion sensors, calendar, app installment, and part of legacy app usage history (e.g., phone calls) have the best memorability for users as well as the highest robustness to attacks.

ATS_AND16_006 - SBVLC: Secure Barcode-based Visible Light Communication for Smartphones
            2D barcodes have enjoyed a significant penetration rate in mobile applications. This is largely due to the extremely low barrier to adoption-almost every camera-enabled smartphone can scan 2D barcodes. As an alternative to NFC technology, 2D barcodes have been increasingly used for security-sensitive mobile applications including mobile payments and personal identification. However, the security of barcode-based communication in mobile applications has not been systematically studied. Due to the visual nature, 2D barcodes are subject to eavesdropping when they are displayed on the smartphone screens. On the other hand, the fundamental design principles of 2D barcodes make it difficult to add security features. In this paper, we propose SBVLC-a secure system for barcode-based visible light communication (VLC) between smartphones. We formally analyze the security of SBVLC based on geometric models and propose physical security enhancement mechanisms for barcode communication by manipulating screen view angles and leveraging user-induced motions. We then develop three secure data exchange schemes that encode information in barcode streams. These schemes are useful in many security-sensitive mobile applications including private information sharing, secure device pairing, and contactless payment. SBVLC is evaluated through extensive experiments on both Android and iOS smartphones.

ATS_AND16_007 - Privacy-Preserving Location Sharing Services for Social Networks
            A common functionality of many location-based social networking applications is a location sharing service that allows a group of friends to share their locations. With a potentially untrusted server, such a location sharing service may threaten the privacy of users. Existing solutions for Privacy-Preserving Location Sharing Services (PPLSS) require a trusted third party that has access to the exact location of all users in the system or rely on expensive algorithms or protocols in terms of computational or communication overhead. Other solutions can only provide approximate query answers. To overcome these limitations, we propose a new encryption notion, called Order-Retrievable Encryption (ORE), for PPLSS for social networking applications. The distinguishing characteristics of our PPLSS are that it (1) allows a group of friends to share their exact locations without the need of any third party or leaking any location information to any server or users outside the group, (2) achieves low computational and communication cost by allowing users to receive the exact location of their friends without requiring any direct communication between users or multiple rounds of communication between a user and a server, (3) provides efficient query processing by designing an index structure for our ORE scheme, (4) supports dynamic location updates, and (5) provides personalized privacy protection within a group of friends by specifying a maximum distance where a user is willing to be located by his/her friends. Experimental results show that the computational and communication cost of our PPLSS is much better than the state-of-the-art solution.

Arihant Techno Solutions

Electrical / Power Electronics / Power Systems / Power Factor Correction - Project Titles 2016-2017

ATS_PED16_001 - A Compact Coupled Inductor for Interleaved Multiphase DC-DC Converters

            A compact coupled inductor structure is proposed for interleaved multiphase synchronous buck converters used to power computer processors and memories that require high current and fast current flew rate. The new coupled inductor structure reduces the winding resistor power loss and makes it possible to utilize Ferrite magnetic material with low core loss. In the letter, several proposed converter implementations to achieve inverse inductor coupling are illustrated, and inductance and coupling coefficient variations are studied through a simplified reluctance model and Maxwell magnetic simulation. The inductor structure not only can be extended to multiple phases but also can be simplified to single phase. The operation of the inductor is experimentally verified in a two-phase synchronous buck converter at switching frequency of one Mega-Hertz.

ATS_PED16_002 - A Multi-Level Converter with a Floating Bridge for Open-Ended Winding Motor Drive Applications

            This paper presents a dual three phase open end winding induction motor drive. The drive consists of a three phase induction machine with open stator phase windings and dual bridge inverter supplied from a single DC voltage source. To achieve multi-level output voltage waveforms a floating capacitor bank is used for the second of the dual bridges. The capacitor voltage is regulated using redundant switching states at half of the main dc link voltage. This particular voltage ratio (2:1) is used to create a multi-level output voltage waveform with three levels. A modified modulation scheme is used to improve the waveform quality of this dual inverter. This paper also compares the losses in dual inverter system in contrast with single sided three-level NPC converter. Finally, detailed simulation and experimental results are presented for the motor drive operating as an open loop v/f controlled motor drive and as a closed loop field oriented motor controller.

ATS_PED16_003 - Adapted NSPWM for Single DC-Link Dual-Inverter Fed Open-End Motor with Negligible Low-Order Harmonics and Efficiency Enhancement

            In this paper, the Near-State PWM (NSPWM), adapted to be implemented in dual-VSI fed open-end motor, is proposed with the aim of mitigating low-order harmonics (which lead to current THD minimization). Two proposed methods are studied in detail such as: (a) fixing Phase Angle Displacement (PAD) between two Voltage Source Inverters (VSIs) to 120° while adjusting Modulation Index (MI); and (b) fixing MI to the pre-determined value (wherein low-order harmonics are highly mitigated) while adjusting PAD. Furthermore, the proposed approaches enhance efficiency by limiting the number of commutations within switching interval. The paper also presents the mathematical approaches to accurately determine low-order harmonic components and switching losses for dual-VSI structure. The experimental setup, including dual-VSI and open-end induction motor, is assembled in the laboratory to evaluate performance of the proposed method. Finally, the simulation results, carried out in the MATLAB/Simulink environment, are found to be in a close agreement with experimental data.

ATS_PED16_004 - Advanced Design and Operation Consideration for Close-connected Winding Permanent Magnet Brushless DC Machine

            Advanced design and operation consideration for the close-connected winding permanent-magnet (PM) brushless DC machine [1] is proposed in this paper, including circulating current elimination, fault tolerant control for the breakdown of power electronic switch, and sensorless control method. With the finite-element analysis (FEA), the influence of machine design parameters on circulating current are investigated. By properly controlling the status of power electronic switches, the coils being connected into the equivalent circuit can be changed to avoid the failure switch. The sensorless control is explored by detecting the zero-crossing point of electromotive force (EMF).

ATS_PED16_005 - An Active Cross-Connected Modular Multilevel Converter (AC-MMC) for Medium-Voltage Motor Drive

            This paper presents an active cross-connected modular multilevel converter (AC-MMC) based on series-connected half-bridge modules. It is intended for completely enhancing the performance of a medium-voltage motor drive system in the full speed range from standstill to rated speed under all load conditions. The proposed AC-MMC circuit is characterized by the cross connection of upper and lower arm middle taps through a branch of series-connected half-bridge converters, which have an identical voltage and current rating with the sub-modules in upper and lower arms. This cross-connected branch provides a physical power transfer channel for upper and lower arms. By properly controlling the amount of high-frequency current flowing through the cross-connected branch, the power balance between the upper and lower arms is achieved even at a zero/low motor speed under constant torque condition. Meanwhile, no common-mode voltage is introduced in the whole speed range. A control strategy with focus on submodule capacitor voltage control is also proposed in this paper to guarantee the normal converter operation. Simulation results obtained from a 4160-V 1-MW model verify  the feasibility of the proposal. Experiments on a downscaled prototype also confirm the validity of the novel circuit and the associated control strategy.

ATS_PED16_006 - Adaptive Maximum Power Point Tracking Control Algorithm for Wind Energy Conversion Systems

            This paper presents an adaptive maximum power point tracking (MPPT) algorithm for small-scale wind energy conversion systems (WECSs) to harvest more energy from turbulent wind. The proposed algorithm combines the computational behavior of hill climb search, tip speed ratio, and power signal feedback control algorithms for its adaptability over wide range of WECSs and fast tracking of maximum power point. In this paper, the proposed MPPT algorithm is implemented by using buck– boost featured single-ended primary inductor converter to extract maximum power from full range of wind velocity profile. Evaluation of the proposed algorithm is done on a laboratory-scaled dc motor drive-based WECS emulator. TMS320F28335, 32-bit floating point digital signal controller, is used to execute the control schemes of the in-lab experimental setup. Experimental results show that tracking capability of the proposed algorithm under sudden and gradual fluctuating wind conditions is efficient and effective.

ATS_PED16_007 - A Quasi-Z-source Integrated Multi-port Power Converter with Reduced Capacitance for Switched Reluctance Motor Drives

            This paper presents a quasi Z-source integrated multiport converter (ZIMPC) for switched reluctance motor (SRM) drives to reduce the dc link capacitance. In conventional SRM drives, employing multi-phase asymmetrical H-bridge (ASHB) topology, large capacitors are necessary to absorb the transient energy during phase current commutation. However, electrolytic capacitors would affect the lifetime, cost and power density of the drive system. With switch multiplexing technique, a Z-source integrated multiport power converter is derived to achieve power ripple reduction using relatively small capacitance. Corresponding control method is designed and developed for the proposed SRM drive. Also, the ZIMPC can boost the equivalent phase exciting voltage and widen the constant power speed range (CPSR). At last, simulation and  experimental results verify the feasibility of the proposed ZIMPC and its superior performance with smaller capacitance as compared with ASHB.

ATS_PED16_008 - A Novel Technique for Two-Phase BLDC Motor to Avoid the Demagnetization

            Conventional permanent magnet motors operate in both magnetizing (pull) process and reversible demagnetizing (push) process on the recoil line of magnets. Therefore, thin surface permanent magnets may easily undergo a risk of demagnetization at the push process under certain fault conditions, which leads to deterioration of motor performance. Thus, thick magnets, whereas contributing the significantly high cost, are usually used to minimize this risk in the permanent magnet motors. In this paper, a novel operation technique, that involves only the pull process, has been proposed for a unique design of two-phase brushless DC (BLDC) motor to avoid the irreversible demagnetization of the magnets. The motor, operated only in the pull process, is kept away from the push process of the operation. Therefore, the motor sustains its initial magnetic operating point above the knee point during the normal operation as well as under the short circuit fault conditions. Finite element analysis is performed to validate the concept of the proposed technique.

ATS_PED16_009 - An Improved Model Predictive Control Scheme for the PWM Rectifier-Inverter System Based on Power-Balancing Mechanism

            The DC-link voltage fluctuation caused by the change of working state of the load motor has been one of the key issues in the PWM rectifier-inverter system. In this study, an improved model predictive control (MPC) scheme is proposed to address this problem. The MPC is applied to both the rectifier stage and the inverter stage in the system. Direct power control is used in the rectifier stage and the direct torque control is employed in the inverter stage, with the key novelty of the active power reference values being defined by both real-time and periodic compensation power based on the system-level power balance model. Meanwhile, a MPC algorithm based on a two-step prediction is introduced to compensate for the delay of a digital controller. Comparison has been conducted between the proposed scheme and three other methods. Simulation and experimental results show that the proposed control scheme exhibits good performance in both the rectifier stage and the inverter stage with improved dynamic response and suppressed voltage fluctuation of the DC-link voltage.

ATS_PED16_0010 - Dual Inverter Fed Pole-Phase Modulated Nine-Phase Induction Motor Drive with Improved Performance

            Typical value of rated phase voltage of pole phase modulated multiphase induction motor (PPMMIM) drives with wider speed range is in the order of few hundreds of kilo volts. This high value of phase voltage for high power density applications results in higher dc link voltage requirement and switch voltage rating of twolevel multiphase inverter. Further, using multiphase space vector pulse width modulation (SVPWM) yields less dc link utilization. Methods to increase dc link utilization using SVPWM with offset value of third harmonic order introduces dominant lower order harmonic currents into phase windings. One more major problem in high pole mode of PPMMIMs is higher torque pulsation due to decrease in phase number. To address these problems  this paper proposes a dual inverter based multilevel voltage excitation scheme for nine-phase PPMMIM with 1:3 speed ratio. In four-pole mode a simple phase grouping technique to eliminate lower order harmonic currents in the phase windings is proposed. In addition each inverter feeding these phase groups is modulated using carrier based three-phase SVPWM to achieve higher dc link utilization. This paper also proposes a multilevel voltage generation scheme for twelve-pole mode of operation using carrier phase shifted PWM for inherently available equal voltage profile coils (EVPCs) with the same dual inverter structure. The torque ripple using phase shifted carriers PWM and single carrier PWM are compared. Finite element method (FEM) model of ninephase PPMMIM is developed in Ansys Maxwell twodimension (2-D) and is co-simulated with three threephase dual inverters in Simplorer environment. Experimental validation is done for linear and over modulation case on 9ФIM fed from three three-phase dual inverters controlled using Spartan 6 field programmable gate array (FPGA) board programed in VHDL.

ATS_PED16_0011 - Extremely Sparse Parallel AC-Link Universal Power Converters

            Parallel ac-link universal power converters are a relatively new class of power converters that can be configured as dc-dc, dc-ac, ac-dc, and ac-ac. These converters are extensions of a buck-boost converter in which the current of the inductor is alternating and the input and output can have any number of phases with any forms, voltage amplitude, or frequency. By placing a small capacitor in parallel with the link inductor all the switches can benefit from soft switching. The main limitation of the parallel ac-link universal power converter is its large number of switches. A three-phase ac-ac configuration requires 24 unidirectional switches. This paper proposes two topologies based on the parallel ac-link universal power converters that significantly reduce the number of switches. One of the proposed topologies reduces the number of switches in a three-phase ac-ac configuration to 16. The other topology reduces the number of switches to 10. The latter can offer galvanic isolation with only a single-phase high frequency transformer. This paper presents the principles of the operation of the proposed topologies and evaluates them through simulation and experiments.

ATS_PED16_0012 - Energy Consumption of Geared DC Motors in Dynamic Applications: Comparing Modeling Approaches

            In recent years, many works have appeared which present novel mechanical designs, control strategies or trajectory planning algorithms for improved energy efficiency. The actuator model is an essential part of these works, since the optimization of energy consumption strongly depends of the accuracy of this model. Nevertheless, various authors follow very different approaches, often neglecting speed- and load-dependent losses and inertias of components such as the motor and the gearbox. Furthermore, there is no consensus on how negative power affects power consumption. Some authors calculate energy consumption by integrating the electrical power entirely, by integrating its absolute value, or by integrating only positive power. This paper assesses how well commonly used models succeed in predicting the energy consumption of an 80 W geared DC motor performing a dynamic task, by comparing the results they produce to experimental baseline measurements.

ATS_PED16_0013 - Derivation of Dual-Switch Step-Down DC/DC Converters with Fault-Tolerant Capability

            This letter presents a graph theoretic approach to deriving a family of dual-switch step-down dc/dc converters with fault-tolerant capability. The constraints set in the derivation process ensure minimum additional component is used to achieve fault-tolerant operation. The operation of converters derived is flexible. Under normal operating conditions, one of the two switches can serve as a main switch to control the power flow (i.e. single-switch converter operation) and the other switch is in stand-by mode. When a fault occurs on the main switch, the other switch will be activated to provide an alternate current path to continue converter operation and maintain output regulation. The fault-tolerant converters are derived by integrating a buck converter with a buck-boost converter. They share all the components except for the power switches. Due to different duty cycles required between the two operating conditions, a feedback controller is necessary to adjust the duty cycle for tight output regulation. The derivation procedure and experimental results on fault occurence are reported. The converter derivation approach is able to identify reported topologies and can be extended to synthesize other topologies with fault-tolerent capability.

ATS_PED16_0014 - Analysis of the Integrated SEPIC-Flyback Converter as a Single-Stage Single-Switch Power-Factor-Correction LED Driver

            This paper proposes a new isolated single-stage single-switch power-factor-correction (S4 PFC) driver for supplying light emitting diodes (LEDs) without electrolytic capacitor. In the proposed LED driver, the switch turns on under zero current switching (ZCS) condition. Also, it turns on at a voltage less than its nominal voltage stress and, therefore, the switch capacitive turn-on loss decreases too much extent. The leakage energy is absorbed, so there are no voltage spikes across the switch when the switch turns off. In this paper, operating principles of the proposed driver are discussed and design considerations are presented. Also, a laboratory prototype for supplying a 21W/30V LED module from 220Vrms/50Hz AC mains is implemented and experimental results are presented to verify the theoretical analysis.

ATS_PED16_0015 - Design and Implementation of a Novel Multilevel DC-AC Inverter

            In this paper, a novel multilevel DC-AC inverter is proposed. The proposed multilevel inverter generates seven levels AC output voltage with the appropriate gate signals design. Also, the low pass filter is used to reduce the total harmonic distortion of the sinusoidal output voltage. The switching losses and the voltage stress of power devices can be reduced in the proposed multi-level inverter. The operating principles of the proposed inverter and the voltage balancing method of input capacitors are discussed. Finally, a laboratory prototype multilevel inverter with 400 V input voltage and output 220 Vrms /2 kW is implemented. The multilevel inverter is controlled with sinusoidal pulse-width modulation (SPWM) by TMS320LF2407 digital signal processor (DSP). Experimental results show that the maximum efficiency is 96.9% and the full load efficiency is 94.6%.

ATS_PED16_0016 - BLDC Motor Driven Solar PV Array Fed Water Pumping System Employing Zeta Converter

            This paper proposes a simple, cost effective and efficient brushless DC (BLDC) motor drive for solar photovoltaic (SPV) array fed water pumping system. A zeta converter is utilized in order to extract the maximum available power from the SPV array. The proposed control algorithm eliminates phase current sensors and adapts a fundamental frequency switching of the voltage source inverter (VSI), thus avoiding the power losses due to high frequency switching. No additional control or circuitry is used for speed control of the BLDC motor. The speed is controlled through a variable DC link voltage of VSI. An appropriate control of zeta converter through the incremental conductance maximum power point tracking (INC-MPPT) algorithm offers soft starting of the BLDC motor. The proposed water pumping system is designed and modeled such that the performance is not affected under dynamic conditions. The suitability of proposed system at practical operating conditions is demonstrated through simulation results using MATLAB/ Simulink followed by an experimental validation.

ATS_PED16_0017 - Fault-tolerant Inverter for High-speed 3-Phase BLDC Drives in Aerospace Applications

            The fault tolerant control of BLDC motor is of great importance for its continuous operating capacity even under the faulty situation. Our proposed work consists of a fault tolerant topology composed of an additional phase leg which contains 6 Triacs act as fault Switching Circuit and a fault protective circuit for the high-speed low-inductance BLDC motor. This fault protective circuit consists of Mosfet gate that is driven by using PWM technique. Input DC voltage is given to Buck Boost converter and it working is if the voltage level less than the required voltage level converter act as boost converter and produce required output. If the Input voltage level is more converters reduces the voltage obtained from the converter. Buck Boost converter having switches (Mosfet) which used to isolate fault switches. The output obtained from fault protector circuit is inverted into 3-Phase DC Output. This 3-Phase Bridge consists of 6 Mosfet which is driven by Digital Pulses. Based on this pulses 3-Phase motor rotation happens either clockwise or anticlockwise direction. Speed of 3-Phase motor is controlled by Pulse Width Modulation and motor rotation varies based on percentage of duty cycle that we are giving to it. The method can achieve safe isolation and reconfiguration to avoid the secondary fault caused by direct switch of the redundant switch and the faulty switch after the fault diagnosis process.

ATS_PED16_0018 - Integrated DC-DC Converter Design for Electric Vehicle Powertrains

            In this paper, an integrated, reconfigurable DC-DC converter for plugin and hybrid Electric Vehicles (EV) is proposed. The converter integrates functionality for both EV powertrain and charging operation into a single unit. During charging, the proposed converter functions as a DAB converter, providing galvanic isolation. For powertrain operation, the converter functions as an interleaved boost converter. During light load powertrain operation, the efficiency of the converter can be further improved by employing the integrated DAB. The proposed integrated converter does not require any extra relays or contactors for charging and powertrain operation. By using such integration, the overall volume and weight of the power electronics circuits, passives and associated cooling system can be improved. In addition, the power flow efficiency from EV battery to the high voltage DC bus for the motor inverter can be improved. The experimental results of the prototype are presented to verify the functionality of the proposed converter.

ATS_PED16_0019 - High Step-Up/Step-Down Soft-Switching Bidirectional DC-DC Converter with Coupled-Inductor and Voltage Matching Control for Energy Storage Systems

            A soft-switching bidirectional DC-DC converter (BDC) with a coupled-inductor and a voltage doubler cell is proposed for high step-up/step-down voltage conversion applications. A dual-active half-bridge (DAHB) converter is integrated into a conventional buck-boost BDC to extend the voltage gain dramatically and decrease switch voltage stresses effectively. The coupled inductor operates not only as a filter inductor of the buck-boost BDC, but also as a transformer of the DAHB converter. The input voltage of the DAHB converter is shared with the output of the buck-boost BDC. So PWM control can be adopted to the buck-boost BDC to ensure that the voltage on the two sides of the DAHB converter is always matched. As a result, the circulating current and conduction losses can be lowered to improve efficiency. Phase-shift control is adopted to the DAHB converter to regulate the power flows of the proposed BDC. Moreover, zero-voltage-switching is achieved for all of the active switches to reduce the switching losses. The operational principles and characteristics of the proposed BDC are presented in detail. The analysis and performance have been fully validated experimentally on a 40-60V/400V 1kW hardware prototype.

ATS_PED16_0020 - On The Robust Control of Parallel-Cascade DC/DC Buck Converter

            This paper presents the design and simulation of robust control for DC/DC multi-converter based on terms like differential flatness and active disturbance rejection. The main goal of the control law is to obtain a regulated output voltage for each converter in cascade, and a balance of currents for parallel converters. The controller must actively reject the endogenous and exogenous disturbances, i.e. maintaining a robust output. The effectiveness of the proposed controller was verified by computer simulations using MATLAB/Simulink.

ATS_PED16_0021 - Single-Phase Grid Connected Motor Drive System with DC-link Shunt Compensator and Small DC-link Capacitor

            The single-phase diode rectifier system with small DC-link capacitor shows wide diode conduction time and it improves the grid current harmonics. By shaping the output power, the system meets the grid current harmonics regulation without any power factor corrector or grid filter inductor. However, the system has torque ripple and suffers efficiency degradation due to the insufficient DC-link voltage. To solve this problem, this paper proposes the DC-link shunt compensator (DSC) for small DC-link capacitor systems. DSC is located on DC-node parallel and operates as the voltage source, improving the system performances. This circuit helps the grid current-shaping control during grid-connection time, and reduces the flux-weakening current by supplying the energy to the motor during grid-disconnection time. This paper presents a power control method and the design guideline of DSC. The feasibility of DSC is verified by simulation and experimental results.

ATS_PED16_0022 - High Performance Solar MPPT Using Switching Ripple Identification Based on a Lock-In Amplifier

            Photovoltaic (PV) power converters and Maximum Power Point Tracking (MPPT) algorithms are required to ensure maximum energy transfer between the PV panel and the load. The requirements for the MPPT algorithms have increased over the years - the algorithms are required to be increasingly accurate, fast, and versatile, while reducing the intrusiveness on the overall performance of the PV panel and converter. The family of Hill-climbing algorithms such as Incremental Conductance (InCond) and Perturb and Observe (P&O) have gained popularity given their simplicity and accuracy, but they require the injection of a perturbation that changes the operating point even in steady-state and are prone to errors during changing environmental conditions. In recent literature, the use of the switching ripple has been proposed to replace the perturbation in the hill-climbing algorithms given its inherent presence in the system and speed. The constant work towards smaller and faster ripples presents challenges to the signal detection involved in this kind of algorithm. This paper develops and implements a new InCond MPPT technique based on switching ripple detection using a digital Lock-In Amplifier (LIA) to extract the amplitude of the oscillation ripple even in the presence of noise. The use of this advanced technique allows to push forward the reduction of the ripple in order to virtually eliminate the oscillation in steady-state maximizing the efficiency. The accurate detection allows for adaptive-step features for fast tracking of changing environmental conditions while keeping the efficiency at maximum during the steady-state. Detailed mathematical analysis of the proposed technique is provided. Overall, the use of the proposed LIA allows to push the reduction of the ripple even more while keeping accuracy and delivering superior performance. Simulations and experimental results are provided for the proposed technique and the InCond technique in order to validate the proposed approach.

ATS_PED16_0023 - Zero-sequence Current Suppression for Open-end Winding Induction Motor Drive with Resonant Controller

            Open-end winding topology with a common DC source has path for zero-sequence current. Zero-sequence current needs to be suppressed because it does not contribute to the drive torque but has harmful effects such as loss and torque ripple. Both inverter and motor have sources of the zero-sequence component. The zero-sequence source on the inverter side is the voltage error due to dead time in switching. The zero-sequence source on the motor side is the zero-sequence component of the back EMF which consists of the third harmonic. In this paper, the two zero-sequence sources are investigated both theoretically and experimentally for an open-end winding induction motor drive system, and a method to suppress the zero-sequence current suppression is proposed. The voltage error is compensated by feedforward to the reference voltage. The zero-sequence component of the back EMF is compensated by a proportional and resonant controller because its frequency is known while its amplitude and phase offset are unknown.

ATS_PED16_0024 - High Performance Model Predictive Technique for MPPT of Gird-tied Photovoltaic System Using Impedance-Source Inverter

            This paper presents a maximum power point tracking (MPPT) method using model predictive technique for a grid-tied photovoltaic harvesting energy system. A single-stage grid-tied Z-source inverter is used for extracting the maximum available power and feeding the power to the grid. The proposed technique predicts the future behavior of the photovoltaic side voltage and current by using a digital observer which estimates the parameters of the photovoltaic module. The proposed method features simultaneously reduction in oscillation around maximum power point (MPP) and fast convergence by adaptively changing the perturbation size using the predicted model of the system. The experimental results demonstrate fast tracking response under dynamic weather condition, small steady state error, small oscillation around MPP at steady state, and high MPPT efficacy for wide range of solar irradiance levels. Additionally, the grid side current total harmonics distortion (THD) meets the IEEE-519 standards.

ATS_PED16_0025 - New Equivalent Circuit of the IPM-type BLDC Motor for Calculation of Shaft Voltage by Considering Electric and Magnetic Fields

            Fast switching and common-mode voltage of the space vector pulse width modulation inverter output create several parasitic capacitances according to the geometry of the motor. The windings emit the electric and magnetic flux inside of the motor. They create capacitance links and electromotive force (EMF) in the shaft, respectively. Those capacitance links and EMF generate a current through two bearings of the shaft and directly reduces the bearing lifetime. In this study, we propose a new design of equivalent circuit taking into account all parasitic capacitor components.

ATS_PED16_0026 - Grid Current Shaping Method with DC-link Shunt Compensator for Three-Phase Diode Rectifier-Fed Motor Drive System

            This paper proposes grid current shaping method using DC-link shunt compensator for three-phase diode rectifier system without electrolytic DC-link capacitor. In the proposed method, the diode rectifier system can satisfy the grid regulation IEC61000-3-12 without any power factor correction circuit or heavy grid filter inductor. The proposed grid current shaping method can be realized by applying DSC with reduced-rating components and without electrolytic capacitor, which is connected parallel to the DC-link. Since DSC has no electrolytic capacitor, the system with DSC has high circuit reliability. Furthermore, DSC can enhance the system efficiency, especially in flux-weakening area, since the motor drive inverter recovers the reduced modulation index which has been spent in the additional control for small passive components. This paper presents the control method for DSC and analyzes the effect of proposed method. The feasibility of proposed method is verified by simulation and experimental results.

ATS_PED16_0027 - High Performance Predictive Control of Quasi Impedance Source Inverter

            The quasi-Z-source inverter (qZSI) has attracted much attention for motor drives and renewable energy applications due to its capability to boost or buck in a single converter stage. However, this capability is associated with different challenges related to the closed loop control of currents, control the DC capacitor voltage, produce three-phase AC output current with high dynamic performance and obtain continuous and low ripple input current. This paper presents a predictive control strategy for a three-phase qZSI that fulfills these requirements without adding any additional layers of control loops. The approach is to improve the overall performance of the converter with a switching strategy that reduces inverter switching losses. The proposed controller implements a discrete-time model of the qZSI to predict the future behavior of the circuit variables for each switching state, along with a set of multi-objective control variables all in one cost function. The quasi impedance network and the AC load are considered together when designing the controller in order to obtain stability of the impedance network with a step change in the output reference. A detailed comparative investigation between the proposed controller and the conventional PI controller is presented to prove the superiority of the proposed method over the conventional control method. Simulation and experimental results are presented.

ATS_PED16_0028 - Improved Fault-Tolerant Control for Brushless Permanent Magnet Motor Drives With Defective Hall Sensors

            Brushless permanent magnet motor drives based on Hall sensors have received significant attention in recent years. In this area, the faults of Hall sensors become a new concern and several fault-tolerant control (FTC) methods have been proposed. However, most of the state-of-the-art FTC methods require some time to reconstruct the correct Hall sensor signals, which results in significant transient currents and speed dip during fault diagnostic process (FDP). In this paper, a new and improved FTC scheme based on FDP and vector-tracking observer is proposed. A method to identify the duration of FDP is proposed based on the analysis of acceleration estimation and the fault diagnosis results. During FDP, the method defaults to an open-loop observer control, which removes the undesirable current/torque transient. After that, the close-loop observer is re-enabled and the motor operation is restored. The proposed FTC is demonstrated in detailed simulations and experimentally on 120° brushless dc motor drives and sinusoidal PM motor drives. For both types of drives, a significant improvement is achieved in steady state and transient operation with faults of up to two Hall sensors, which has not been possible with available alternative FTC approaches (unless a sensorless control is used).

ATS_PED16_0029 - Quasi-Resonant (QR) Controller with Adaptive Switching Frequency Reduction Scheme for Flyback Converter

            A quasi-resonant (QR) controller with an adaptive frequency reduction scheme has been developed for a flyback converter. While maintaining the valley switching, the QR controller reduces the switching frequency for lighter load by skipping some valleys to reduce the power loss and thereby achieve better light load efficiency. If the QR controller cannot detect any valley due to the damped oscillation of switch voltage, the valley switching is given up and the non-valley switching is employed to keep reducing the switching frequency for lighter load. The proposed QR controller has been implemented in a 0.35-μm 700-V BCDMOS process and applied to a 40-W flyback converter. The power efficiency of the flyback converter is improved by upto 3.0-% when the proposed QR controller is employed compared to the one employing the conventional QR controller.

ATS_PED16_0030 - Self-correction of Commutation Point for High-speed Sensorless BLDC Motor With Low Inductance and Nonideal Back EMF

            This paper presents a novel self-correction method of commutation point for high-speed sensorless brushless dc (BLDC) motors with low inductance and nonideal back electromotive force (EMF) in order to achieve low steady-state loss of magnetically suspended control moment gyro (MSCMG). The commutation point before correction is obtained by detecting the phase of EMF zero-crossing point and then delaying 30 electrical degrees. Since the speed variation is small between adjacent commutation points, the difference of the nonenergized phase’s terminal voltage between the beginning and the end of commutation is mainly related to the commutation error. A novel control method based on model-free adaptive control is proposed, and the delay degree is corrected by the controller in real time. Both the simulation and experimental results show that the proposed correction method can achieve ideal commutation effect within the entire operating speed range.

ATS_PED16_0031 - Performance Analysis of the Computational Implementation of a Simplified PV Model and MPPT Algorithm

            In many research centers around the world, had been researched models that accurately represent the PV modules operation. In this context, this paper presents a modeling of photovoltaic modules, which aims to simplify the simulation of photovoltaic systems in MATLAB®/Simulink. The model in question has not been very explored in the literature and, therefore, this paper has the purpose of evaluating its results connecting the model to a boost converter, being its main function the Maximum Power Point Tracking applying one of the most known methods, the P&O (Perturb & Observe). Furthermore, this converter elevates the voltage generated by the photovoltaic modules, in order to connect the PV array to the grid through an inverter. This paper also presents a good correlation between the theoretical and practical results from the proposed modeling and high efficiency of the implemented MPPT algorithm as well.

ATS_PED16_0032 - Model Predictive Control Scheme of Five-Leg AC-DC-AC Converter-Fed Induction Motor Drive

            AC-DC-AC converter-fed induction motor drive is mainly realized by back-to-back three-phase converters. However, fault in a single semiconductor switch will make it inoperative. To enable continued controllable operation in case of the faults occurring in the converter, the five-leg converter with a shared leg between the grid and load sides is a possible solution. However, this topology poses an inherent two-objective control problem because its grid and load sides should be controlled simultaneously. More importantly, the potential increase of the shared-leg current may destroy the converters. In this paper, a new control scheme based on the FCS-MPC combined with intrinsic characteristic of the five-leg converter is proposed for independent control of the rectifier and inverter subsystem with the shared-leg overcurrent constraint. The condition for independent control is analyzed. In order to give a complete evaluation of the proposed control scheme, the conventional PWM control scheme is conducted for comparison. Experimental results are provided to validate the effectiveness of the proposed scheme.

ARIHANT TECHNO SOLUTIONS

MATLAB IMAGE PROCESSING AND WIRELESS COMMUNICATION - 2016-2017

ATS_MAT16_001 - Dynamic Facial Expression Recognition with Atlas Construction and Sparse Representation

                   In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison.

ATS_MAT16_002 - Lossless Compression of JPEG Coded Photo Collections

                   The explosion of digital photos has posed a significant challenge to photo storage and transmission for both personal devices and cloud platforms. In this paper, we propose a novel lossless compression method to further reduce the size of a set of JPEG coded correlated images without any loss of information. The proposed method jointly removes inter/intra image redundancy in the feature, spatial, and frequency domains. For each collection, we first organize the images into a pseudo video by minimizing the global prediction cost in the feature domain. We then present a hybrid disparity compensation method to better exploit both the global and local correlations among the images in the spatial domain. Furthermore, the redundancy between each compensated signal and the corresponding target image is adaptively reduced in the frequency domain. Experimental results demonstrate the effectiveness of the proposed lossless compression method. Compared with the JPEG coded image collections, our method achieves average bit savings of more than 31%.

ATS_MAT16_003 - Pixel modeling using histograms based on fuzzy partitions for dynamic background subtraction

                   We propose a novel pixel-modeling approach for background subtraction using histograms based on strong uniform fuzzy partitions. In the proposed method, the temporal distribution of pixel values is represented by a histogram based on a triangular partition. The threshold for background segmentation is set adaptively according to the shape of the histogram. Histogram accumulation is controlled adaptively by a fuzzy controller under a supervised learning framework. Benefiting from the adaptive scheme, with no parameter tuning, the proposed algorithm functions well across a wide spectrum of challenging environments. The performance of the proposed method is evaluated against more than 20 state-of-the-art methods in complex outdoor environments, particularly in those consisting of highly dynamic backgrounds and camouflaged foregrounds. Experimental results confirm that the proposed method performs effectively in terms of both the true positive rate and the noise suppression ability. Further, it outperforms other state-of-the-art methods by a significant margin.

ATS_MAT16_004 - Layer-Based Approach for Image Pair Fusion

                   Recently, image pairs, such as noisy and blurred images or infrared and noisy images, have been considered as a solution to provide high-quality photographs under low lighting conditions. In this paper, a new method for decomposing the image pairs into two layers, i.e., the base layer and the detail layer, is proposed for image pair fusion. In the case of infrared and noisy images, simple naive fusion leads to unsatisfactory results due to the discrepancies in brightness and image structures between the image pair. To address this problem, a local contrast-preserving conversion method is first proposed to create a new base layer of the infrared image, which can have visual appearance similar to another base layer, such as the denoised noisy image. Then, a new way of designing three types of detail layers from the given noisy and infrared images is presented. To estimate the noise-free and unknown detail layer from the three designed detail layers, the optimization framework is modeled with residual-based sparsity and patch redundancy priors. To better suppress the noise, an iterative approach that updates the detail layer of the noisy image is adopted via a feedback loop. This proposed layer-based method can also be applied to fuse another noisy and blurred image pair. The experimental results show that the proposed method is effective for solving the image pair fusion problem.

ATS_MAT16_005 - Adaptive Pairing Reversible Watermarking

                   This letter revisits the pairwise reversible watermarking scheme of Ou et al., 2013. An adaptive pixel pairing that considers only pixels with similar prediction errors is introduced. This adaptive approach provides an increased number of pixel pairs where both pixels are embedded and decreases the number of shifted pixels. The adaptive pairwise reversible watermarking outperforms the state-of-the-art low embedding bit-rate schemes proposed so far.

ATS_MAT16_006 - Adaptive Part-Level Model Knowledge Transfer for Gender Classification 

                   In this letter, we propose an adaptive part-level model knowledge transfer approach for gender classification of facial images based on Fisher vector (FV). Specifically, we first decompose the whole face image into several parts and compute the dense FVs on each face part. An adaptive transfer learning model is then proposed to reduce the discrepancies between the training data and the testing data for enhancing classification performance. Compared to the existing gender classification methods, the proposed approach is more adaptive to the testing data, which is quite beneficial to the performance improvement. Extensive experiments on several public domain face data sets clearly demonstrate the effectiveness of the proposed approach.

ATS_MAT16_007 - Patch-Based Video Denoising With Optical Flow Estimation

                   A novel image sequence denoising algorithm is presented. The proposed approach takes advantage of the selfsimilarity and redundancy of adjacent frames. The algorithm is inspired by fusion algorithms, and as the number of frames increases, it tends to a pure temporal average. The use of motion compensation by regularized optical flow methods permits robust patch comparison in a spatiotemporal volume. The use of principal component analysis ensures the correct preservation of fine texture and details. An extensive comparison with the state-of-the-art methods illustrates the superior performance of the proposed approach, with improved texture and detail reconstruction.

ATS_MAT16_008 - Fusion of Quantitative Image and Genomic Biomarkers to Improve Prognosis Assessment of Early Stage Lung Cancer Patients

                   This study aims to develop a new quantitative image feature analysis scheme and investigate its role along with 2 genomic biomarkers namely, protein expression of the excision repair cross-complementing 1 (ERCC1) genes and a regulatory subunit of ribonucleotide reductase (RRM1), in predicting cancer recurrence risk of Stage I non-small-cell lung cancer (NSCLC) patients after surgery. Methods: By using chest computed tomography images, we developed a computer-aided detection scheme to segment lung tumors and computed tumor-related image features. After feature selection, we trained a Naïve Bayesian network based classifier using 8 image features and a Multilayer Perceptron classifier using 2 genomic biomarkers to predict cancer recurrence risk, respectively. Two classifiers were trained and tested using a dataset with 79 Stage I NSCLC cases, a synthetic minority oversampling technique and a leave-one-case-out validation method. A fusion method was also applied to combine prediction scores of two classifiers. Results: AUC (areas under ROC curves) values are 0.78±0.06 and 0.68±0.07 when using the image feature and genomic biomarker based classifiers, respectively. AUC value significantly increased to 0.84±0.05 (p<0.05) when fusion of two classifier-generated prediction scores using an equal weighting factor. Conclusion: A quantitative image feature based classifier yielded significantly higher discriminatory power than a genomic biomarker based classifier in predicting cancer recurrence risk. Fusion of prediction scores generated by the two classifiers further improved prediction performance. Significance: We demonstrated a new approach that has potential to assist clinicians in more effectively managing Stage I NSCLC patients to reduce cancer recurrence risk.

ATS_MAT16_009 - Multivideo Object Cosegmentation for Irrelevant Frames Involved Videos

                   Even though there have been a large amount of previous work on video segmentation techniques, it is still a challenging task to extract the video objects accurately without interactions, especially for those videos which contain irrelevant frames (frames containing no common targets). In this essay, a novel multivideo object cosegmentation method is raised to cosegment common or similar objects of relevant frames in different videos, which includes three steps: 1) object proposal generation and clustering within each video; 2) weighted graph construction and common objects selection; and 3) irrelevant frames detection and pixel-level segmentation refinement. We apply our method on challenging datasets and exhaustive comparison experiments demonstrate the effectiveness of the proposed method.

ATS_MAT16_010 - Multi-Viewpoint Panorama Construction with Wide-Baseline Images

                   We present a novel image stitching approach, which can produce visually plausible panoramic images with input taken from different viewpoints. Unlike previous methods, our approach allows wide baselines between images and non-planar scene structures. Instead of 3D reconstruction, we design a mesh based framework to optimize alignment and regularity in 2D. By solving a global objective function consisting of alignment and a set of prior constraints, we construct panoramic images, which are locally as perspective as possible and yet nearly orthogonal in the global view. We improve composition and achieve good performance on misaligned area. Experimental results on challenging data demonstrate the effectiveness of the proposed method.

ATS_MAT16_011 - A Security-Enhanced Alignment-Free Fuzzy Vault-Based Fingerprint Cryptosystem Using Pair-Polar Minutiae Structures

Alignment-free fingerprint cryptosystems perform matching using relative information between minutiae, e.g., local minutiae structures, is promising, because it can avoid the recognition errors and information leakage caused by template alignment/registration. However, as most local minutiae structures only contain relative information of a few minutiae in a local region, they are less discriminative than the global minutiae pattern. Besides, the similarity measures for trivially/coarsely quantized features in the existing work cannot provide a robust way to deal with nonlinear distortions, a common form of intra-class variation. As a result, the recognition accuracy of current alignment-free fingerprint cryptosystems is unsatisfying. In this paper, we propose an alignment-free fuzzy vault-based fingerprint cryptosystem using highly discriminative pair-polar (P-P) minutiae structures. The fine quantization used in our system can largely retain information about a fingerprint template and enables the direct use of a traditional, well-established minutiae matcher. In terms of template/key protection, the proposed system fuses cancelable biometrics and biocryptography. Transforming the P-P minutiae structures before encoding destroys the correlations between them, and can provide privacy-enhancing features, such as revocability and protection against cross-matching by setting distinct transformation seeds for different applications. The comparison with other minutiae-based fingerprint cryptosystems shows that the proposed system performs favorably on selected publicly available databases and has strong security.

ATS_MAT16_012 - Microwave Unmixing With Video Segmentation for Inferring Broadleaf and Needleleaf Brightness Temperatures and Abundances From Mixed Forest Observations

Passive microwave sensors have better capability of penetrating forest layers to obtain more information from forest canopy and ground surface. For forest management, it is useful to study passive microwave signals from forests. Passive microwave sensors can detect signals from needleleaf, broadleaf, and mixed forests. The observed brightness temperature of a mixed forest can be approximated by a linear combination of the needleleaf and broadleaf brightness temperatures weighted by their respective abundances. For a mixed forest observed by an N-band microwave radiometer with horizontal and vertical polarizations, there are 2 N observed brightness temperatures. It is desirable to infer 4 N + 2 unknowns: 2 N broadleaf brightness temperatures, 2 N needleleaf brightness temperatures, 1 broadleaf abundance, and 1 needleleaf abundance. This is a challenging underdetermined problem. In this paper, we devise a novel method that combines microwave unmixing with video segmentation for inferring broadleaf and needleleaf brightness temperatures and abundances from mixed forests. We propose an improved Otsu method for video segmentation to infer broadleaf and needleleaf abundances. The brightness temperatures of needleleaf and broadleaf trees can then be solved by the nonnegative least squares solution. For our mixed forest unmixing problem, it turns out that the ordinary least squares solution yields the desired positive brightness temperatures. The experimental results demonstrate that the proposed method is able to unmix broadleaf and needleleaf brightness temperatures and abundances well. The absolute differences between the reconstructed and observed brightness temperatures of the mixed forest are well within 1 K.

ATS_MAT16_013 - 2D Orthogonal Locality Preserving Projection for Image Denoising

Sparse representations using transform-domain techniques are widely used for better interpretation of the raw data. Orthogonal locality preserving projection (OLPP) is a linear technique that tries to preserve local structure of data in the transform domain as well. Vectorized nature of OLPP requires high-dimensional data to be converted to vector format, hence may lose spatial neighborhood information of raw data. On the other hand, processing 2D data directly, not only preserves spatial information, but also improves the computational efficiency considerably. The 2D OLPP is expected to learn the transformation from 2D data itself. This paper derives mathematical foundation for 2D OLPP. The proposed technique is used for image denoising task. Recent state-of-the-art approaches for image denoising work on two major hypotheses, i.e., non-local self-similarity and sparse linear approximations of the data. Locality preserving nature of the proposed approach automatically takes care of self-similarity present in the image while inferring sparse basis. A global basis is adequate for the entire image. The proposed approach outperforms several state-of-the-art image denoising approaches for gray-scale, color, and texture images.

ATS_MAT16_014 - Exploring the Usefulness of Light Field Cameras for Biometrics : An Empirical Study on Face and Iris Recognition

A light field sensor can provide useful information in terms of multiple depth (or focus) images, holding additional information that is quite useful for biometric applications. In this paper, we examine the applicability of a light field camera for biometric applications by considering two prominently used biometric characteristics: 1) face and 2) iris. To this extent, we employed a Lytro light field camera to construct two new and relatively large scale databases, for both face and iris biometrics. We then explore the additional information available from different depth images, which are rendered by light field camera, in two different manners: 1) by selecting the best focus image from the set of depth images and 2) combining all the depth images using super-resolution schemes to exploit the supplementary information available within the set elements. Extensive evaluations are carried out on our newly constructed database, demonstrating the significance of using additional information rendered by a light field camera to improve the overall performance of the biometric system.

ATS_MAT16_015 - Spectral–Spatial Adaptive Sparse Representation for Hyperspectral Image Denoising

In this paper, a novel spectral-spatial adaptive sparse representation (SSASR) method is proposed for hyperspectral image (HSI) denoising. The proposed SSASR method aims at improving noise-free estimation for noisy HSI by making full use of highly correlated spectral information and highly similar spatial information via sparse representation, which consists of the following three steps. First, according to spectral correlation across bands, the HSI is partitioned into several nonoverlapping band subsets. Each band subset contains multiple continuous bands with highly similar spectral characteristics. Then, within each band subset, shape-adaptive local regions consisting of spatially similar pixels are searched in spatial domain. This way, spectral-spatial similar pixels can be grouped. Finally, the highly correlated and similar spectral-spatial information in each group is effectively used via the joint sparse coding, in order to generate better noise-free estimation. The proposed SSASR method is evaluated by different objective metrics in both real and simulated experiments. The numerical and visual comparison results demonstrate the effectiveness and superiority of the proposed method.

ATS_MAT16_016 - Robust Sclera Recognition System With Novel Sclera Segmentation and Validation Techniques

Sclera blood veins have been investigated recently as a biometric trait which can be used in a recognition system. The sclera is the white and opaque outer protective part of the eye. This part of the eye has visible blood veins which are randomly distributed. This feature makes these blood veins a promising factor for eye recognition. The sclera has an advantage in that it can be captured using a visible-wavelength camera. Therefore, applications which may involve the sclera are wide ranging. The contribution of this paper is the design of a robust sclera recognition system with high accuracy. The system comprises of new sclera segmentation and occluded eye detection methods. We also propose an efficient method for vessel enhancement, extraction, and binarization. In the feature extraction and matching process stages, we additionally develop an efficient method, that is, orientation, scale, illumination, and deformation invariant. The obtained results using UBIRIS.v1 and UTIRIS databases show an advantage in terms of segmentation accuracy and computational complexity compared with state-of-the-art methods due to Thomas, Oh, Zhou, and Das.

ATS_MAT16_017 - Enhancing Sketch-Based Image Retrieval by Re-Ranking and Relevance Feedback

A sketch-based image retrieval often needs to optimize the tradeoff between efficiency and precision. Index structures are typically applied to large-scale databases to realize efficient retrievals. However, the performance can be affected by quantization errors. Moreover, the ambiguousness of user-provided examples may also degrade the performance, when compared with traditional image retrieval methods. Sketch-based image retrieval systems that preserve the index structure are challenging. In this paper, we propose an effective sketch-based image retrieval approach with re-ranking and relevance feedback schemes. Our approach makes full use of the semantics in query sketches and the top ranked images of the initial results. We also apply relevance feedback to find more relevant images for the input query sketch. The integration of the two schemes results in mutual benefits and improves the performance of the sketch-based image retrieval.

ATS_MAT16_018 - Detection of Moving Objects Using Fuzzy Color Difference Histogram Based Background Subtraction

Detection of moving objects in the presence of complex scenes such as dynamic background (e.g, swaying vegetation, ripples in water, spouting fountain), illumination variation, and camouflage is a very challenging task. In this context, we propose a robust background subtraction technique with three contributions. First, we present the use of color difference histogram (CDH) in the background subtraction algorithm. This is done by measuring the color difference between a pixel and its neighbors in a small local neighborhood. The use of CDH reduces the number of false errors due to the non-stationary background, illumination variation and camouflage. Secondly, the color difference is fuzzified with a Gaussian membership function. Finally, a novel fuzzy color difference histogram (FCDH) is proposed by using fuzzy c-means (FCM) clustering and exploiting the CDH. The use of FCM clustering algorithm in CDH reduces the large dimensionality of the histogram bins in the computation and also lessens the effect of intensity variation generated due to the fake motion or change in illumination of the background. The proposed algorithm is tested with various complex scenes of some benchmark publicly available video sequences. It exhibits better performance over the state-of-the-art background subtraction techniques available in the literature in terms of classification accuracy metrics like MCC and PCC.

ATS_MAT16_019 - A Decomposition Framework for Image Denoising Algorithms

In this paper, we consider an image decomposition model that provides a novel framework for image denoising. The model computes the components of the image to be processed in a moving frame that encodes its local geometry (directions of gradients and level lines). Then, the strategy we develop is to denoise the components of the image in the moving frame in order to preserve its local geometry, which would have been more affected if processing the image directly. Experiments on a whole image database tested with several denoising methods show that this framework can provide better results than denoising the image directly, both in terms of Peak signal-to-noise ratio and Structural similarity index metrics.

ATS_MAT16_020 - Distance-Based Encryption: How to Embed Fuzziness in Biometric-Based Encryption

We introduce a new encryption notion called distance-based encryption (DBE) to apply biometrics in identity-based encryption. In this notion, a ciphertext encrypted with a vector and a threshold value can be decrypted with a private key of another vector, if and only if the distance between these two vectors is less than or equal to the threshold value. The adopted distance measurement is called Mahalanobis distance, which is a generalization of Euclidean distance. This novel distance is a useful recognition approach in the pattern recognition and image processing community. The primary application of this new encryption notion is to incorporate biometric identities, such as face, as the public identity in an identity-based encryption. In such an application, usually the input biometric identity associated with a private key will not be exactly the same as the input biometric identity in the encryption phase, even though they are from the same user. The introduced DBE addresses this problem well as the decryption condition does not require identities to be identical but having small distance. The closest encryption notion to DBE is the fuzzy identity-based encryption, but it measures biometric identities using a different distance called an overlap distance (a variant of Hamming distance) that is not widely accepted by the pattern recognition community, due to its long binary representations. In this paper, we study this new encryption notion and its constructions. We show how to generically and efficiently construct such a DBE from an inner product encryption (IPE) with reasonable size of private keys and ciphertexts. We also propose a new IPE scheme with the shortest private key to build DBE, namely, the need for a short private key. Finally, we study the encryption efficiency of DBE by splitting our IPE encryption algorithm into offline and online algorithms.

ATS_MAT16_021 - Scalable Feature Matching by Dual Cascaded Scalar Quantization for Image Retrieval

In this paper, we investigate the problem of scalable visual feature matching in large-scale image search and propose a novel cascaded scalar quantization scheme in dual resolution. We formulate the visual feature matching as a range-based neighbor search problem and approach it by identifying hyper-cubes with a dual-resolution scalar quantization strategy. Specifically, for each dimension of the PCA-transformed feature, scalar quantization is performed at both coarse and fine resolutions. The scalar quantization results at the coarse resolution are cascaded over multiple dimensions to index an image database. The scalar quantization results over multiple dimensions at the fine resolution are concatenated into a binary super-vector and stored into the index list for efficient verification. The proposed cascaded scalar quantization (CSQ) method is free of the costly visual codebook training and thus is independent of any image descriptor training set. The index structure of the CSQ is flexible enough to accommodate new image features and scalable to index large-scale image database. We evaluate our approach on the public benchmark datasets for large-scale image retrieval. Experimental results demonstrate the competitive retrieval performance of the proposed method compared with several recent retrieval algorithms on feature quantization.

ATS_MAT16_022 - ACE–An Effective Anti-forensic Contrast Enhancement Technique

Detecting Contrast Enhancement (CE) in images and anti-forensic approaches against such detectors have gained much attention in multimedia forensics lately. Several contrast enhancement detectors analyze the first order statistics such as gray-level histogram of images to determine whether an image is CE or not. In order to counter these detectors various anti-forensic techniques have been proposed. This led to a technique that utilized second order statistics of images for CE detection. In this letter, we propose an effective anti-forensic approach that performs CE without significant distortion in both the first and second order statistics of the enhanced image. We formulate an optimization problem using a variant of the well known Total Variation (TV) norm image restoration formulation. Experiments show that the algorithm effectively overcomes the first and second order statistics based detectors without loss in quality of the enhanced image.

ATS_MAT16_023 - Visualization of Tumor Response to Neoadjuvant Therapy for Rectal Carcinoma by Nonlinear Optical Imaging

The continuing development of nonlinear optical imaging techniques has opened many new windows in biological exploration. In this study, a nonlinear optical microscopy-multiphoton microscopy (MPM) was expanded to detect tumor response in rectal carcinoma after neoadjuvant therapy; especially normal tissue, pre- and post-therapeutic cancerous tissues were investigated in order to present more detailed information and make comparison. It was found that the MPM has ability not only to directly visualize histopathologic changes in rectal carcinoma, including stromal fibrosis, colloid response, residual tumors, blood vessel hyperplasia, and inflammatory reaction, which had been proven to have important influence on estimation of the prognosis and the effect of neoadjuvant treatment, but also to provide quantitative optical biomarkers including the intensity ratio of SHG over TPEF and collagen orientation index. These results show that the MPM will become a useful tool for clinicians to determine whether neoadjuvant therapy is effective or treatment strategy is approximate, and this study may provide the groundwork for further exploration into the application of MPM in a clinical setting.

ATS_MAT16_024 - Robust Edge-Stop Functions for Edge-Based Active Contour Models in Medical Image Segmentation

Edge-based active contour models are effective in segmenting images with intensity inhomogeneity but often fail when applied to images containing poorly defined boundaries, such as  in medical images. Traditional edge-stop functions (ESFs) utilize only gradient information, which fails to stop contour evolution at such boundaries because of the small gradient magnitudes. To address this problem, we propose a framework to construct a group of ESFs for edge-based active contour models to segment objects with poorly defined boundaries. In our framework, which incorporates gradient information as well as probability scores from a standard classifier, the ESF can be constructed from any classification algorithm and applied to any edge-based model using a level set method. Experiments on medical images using the distance regularized level set for edge-based active contour models as well as the k-nearest neighbors and the support vector machine confirm the effectiveness of the proposed approach.

ATS_MAT16_025 - A Combined KFDA Method and GUI Realized for Face Recognition

Traditional face recognition methods such as Principal Components Analysis(PCA), Independent Component Analysis(ICA) and Linear Discriminant Analysis(LDA) are linear discriminant methods, but in the real situation, a lot of problems can't be linear discriminated; therefore, researchers proposed face recognition method based on kernel techniques which can transform the nonlinear problem of inputting space into the linear problem of high dimensional space. In this paper, we propose a recognition method based on kernel function which combines kernel Fisher Discriminant Analysis(KFDA) with kernel Principle Components Analysis(KPCA) and use typical ORL(Olivetti Research Laboratory) face database as our experimental database. There are four key steps: constructing feature subspace, image projection, feature extraction and image recognition. We found that the recognition accuracy has been greatly improved by using nonlinear identification method and combined feature extraction methods. We use MATLAB software as the platform, and use the GUI to demonstrate the process of face recognition in order to achieving human-computer interaction and making the process and result more intuitive.

ATS_MAT16_026 - A Cost-Effective Minutiae Disk Code For Fingerprint Recognition And Its Implementation

Fingerprint is one of the unique biometric features for the application of identity security. Minutiae cylinder code (MCC) constructs a cylinder for each minutia to record the contribution of the neighbor minutiae, which has great performance on fingerprint recognition. However, the computation time of the MCC is high. Therefore, we proposed a new disk structure to encode the local structure for each minutia. The proposed minutiae disk code (MDC) clearly illustrates the distribution of the neighbor minutiae and encodes the neighbor minutiae more efficiently by having 280.08× speed faster than the MCC encoding part on Matlab platform. The proposed MDC approach has 96.81% recognition rate on FVC2000 and FVC2002 datasets. The hardware implementation can achieve the operating frequency at 111MHz, which can process 1234 fingerprint images per second with the image size of 255 χ 255 and the maximum of 64 minutiae, under TSMC 90nm CMOS technology. The hardware implementation has 141.27× speed faster than the MCC method.

ATS_MAT16_027 - A Hands-on Application-Based Tool for STEM Students to Understand Differentiation

The main goal of this project is to illustrate to college students in science, technology, engineering, and mathematics (STEM) fields some fundamental concepts in calculus. A high-level technical computing language - MATLAB, is the core platform used in the construction of this project. A graphical user interface (GUI) is designed to interactively explain the intuition behind a key mathematical concept: differentiation. The GUI demonstrates how a derivative operation (as a form of kernel) can be applied on one-dimensional (1D) and two-dimensional (2D) images (as a form of vector). The user can interactively select from a set of predetermined operations and images in order to show how the selected kernel operates on the corresponding image. Such interactive tools in calculus courses are of great importance and need, especially for STEM students who seek an intuitive and visual understanding of mathematical notions that are often presented to them as abstract concepts. In addition to students, instructors can greatly benefit from using such tools to elucidate the use of fundamental concepts in mathematics in a real world context.

ATS_MAT16_028 - Rotation Invariant Texture Description Using Symmetric Dense Microblock Difference

This letter is devoted to the problem of rotation invariant texture classification. Novel rotation invariant feature, symmetric dense microblock difference (SDMD), is proposed which captures the information at different orientations and scales.  N -fold symmetry is introduced in the feature design configuration, while retaining the random structure that provides discriminative power. The symmetry is utilized to achieve a rotation invariance. The SDMD is extracted using an image pyramid and encoded by the Fisher vector approach resulting in a descriptor which captures variations at different resolutions without increasing the dimensionality. The proposed image representation is combined with the linear SVM classifier. Extensive experiments are conducted on four texture data sets [Brodatz, UMD, UIUC, and Flickr material data set (FMD)] using standard protocols. The results demonstrate that our approach outperforms the state of the art in texture classification. The MATLAB code is made available.11

ATS_MAT16_029 - A Novel Image Quality Assessment With Globally and Locally Consilient Visual Quality Perception

Computational models for image quality assessment (IQA) have been developed by exploring effective features that are consistent with the characteristics of a human visual system (HVS) for visual quality perception. In this paper, we first reveal that many existing features used in computational IQA methods can hardly characterize visual quality perception for local image characteristics and various distortion types. To solve this problem, we propose a new IQA method, called the structural contrast-quality index (SC-QI), by adopting a structural contrast index (SCI), which can well characterize local and global visual quality perceptions for various image characteristics with structural-distortion types. In addition to SCI, we devise some other perceptually important features for our SC-QI that can effectively reflect the characteristics of HVS for contrast sensitivity and chrominance component variation. Furthermore, we develop a modified SC-QI, called structural contrast distortion metric (SC-DM), which inherits desirable mathematical properties of valid distance metricability and quasi-convexity. So, it can effectively be used as a distance metric for image quality optimization problems. Extensive experimental results show that both SC-QI and SC-DM can very well characterize the HVS's properties of visual quality perception for local image characteristics and various distortion types, which is a distinctive merit of our methods compared with other IQA methods. As a result, both SC-QI and SC-DM have better performances with a strong consilience of global and local visual quality perception as well as with much lower computation complexity, compared with the state-of-the-art IQA methods. The MATLAB source codes of the proposed SC-QI and SC-DM are publicly available online at https://sites.google.com/site/sunghobaecv/iqa.

ATS_MAT16_030 - A DCT-based Total JND Profile for Spatio-Temporal and Foveated Masking Effects

In image and video processing fields, DCT-based just noticeable difference (JND) profiles have effectively been utilized to remove perceptual redundancies in pictures for compression. In this paper, we solve two problems that are often intrinsic to the conventional DCT-based JND profiles: (i) no foveated masking (FM) JND model has been incorporated in modeling the DCT-based JND profiles; and (ii) the conventional temporal masking (TM) JND models assume that all moving objects in frames can be well tracked by the eyes and that they are projected on the fovea regions of the eyes, which is not a realistic assumption and may result in poor estimation of JND values for untracked moving objects (or image regions). To solve these two problems, we first propose a generalized JND model for joint effects between TM and FM effects. With this model, called the temporal-foveated masking (TFM) JND model, JND thresholds for any tracked/untracked and moving/still image regions can be elaborately estimated. Finally, the TFM-JND model is incorporated into a total DCT-based JND profile with a spatial contrast sensitivity function, luminance masking, and contrast masking JND models. In addition, we propose a JND adjustment method for our total JND profile to avoid overestimation of JND values for image blocks of fixed sizes with various image characteristics. To validate the effectiveness of the total JND profile, an experiment involving a subjective distortionvisibility assessment has been conducted. The experiment results show that the proposed total DCT-based JND profile yields significant performance improvement with much higher capability of distortion concealment (average 5.6 dB lower PSNR) compared to state-of-the-art JND profiles. The MATLAB source code of the proposed total DCT-based JND profile is publicly available online at https://sites.google.com/site/sunghobaecv/jnd

ATS_MAT16_031 - PiCode: a New Picture-Embedding 2D Barcode

Nowadays, 2D barcodes have been widely used as an interface to connect potential customers and advertisement contents. However, the appearance of a conventional 2D barcode pattern is often too obtrusive for integrating into an aesthetically designed advertisement. Besides, no human readable information is provided before the barcode is successfully decoded. This paper proposes a new picture-embedding 2D barcode, called PiCode, which mitigates these two limitations by equipping a scannable 2D barcode with a picturesque appearance. PiCode is designed with careful considerations on both the perceptual quality of the embedded image and the decoding robustness of the encoded message. Comparisons with the existing beautified 2D barcodes show that PiCode achieves one of the best perceptual qualities for the embedded image, and maintains a better tradeoff between image quality and decoding robustness in various application conditions. PiCode has been implemented in the MATLAB on a PC and some key building blocks have also been ported to Android and iOS platforms. Its practicality for real-world applications has been successfully demonstrated.

ATS_MAT16_032 - OCR Based Feature Extraction and Template Matching Algorithms for Qatari Number Plate

There are several algorithms and methods that could be applied to perform the character recognition stage of an automatic number plate recognition system; however, the constraints of having a high recognition rate and real-time processing should be taken into consideration. In this paper four algorithms applied to Qatari number plates are presented and compared. The proposed algorithms are based on feature extraction (vector crossing, zoning, combined zoning and vector crossing) and template matching techniques. All four proposed algorithms have been implemented and tested using MATLAB. A total of 2790 Qatari binary character images were used to test the algorithms. Template matching based algorithm showed the highest recognition rate of 99.5% with an average time of 1.95 ms per character.

ATS_MAT16_033 - HD Qatari ANPR System

Recently, Automatic Number Plate Recognition (ANPR) systems have become widely used in safety, security, and commercial aspects. The whole ANPR system is based on three main stages: Number Plate Localization (NPL), Character Segmentation (CS), and Optical Character Recognition (OCR). In recent years, to provide better recognition rate, High Definition (HD) cameras have started to be used. However, most known techniques for standard definition are not suitable for real-time HD image processing due to the computationally intensive cost of localizing the number plate. In this paper, algorithms to implement the three main stages of a high definition ANPR system for Qatari number plates are presented. The algorithms have been tested using MATLAB and two databases as a proof of concept. Implementation results have shown that the system is able to process one HD image in 61 ms, with an accuracy of 98.0% in NPL, 99.75% per character in CS, and 99.5% in OCR.

ATS_MAT16_034 - Template Matching of Aerial Images using GPU

During the last decade, processor architectures have emerged with hundreds and thousands of high speed processing cores in a single chip. These cores can work in parallel to share a work load for faster execution. This paper presents performance evaluations on such multicore and many-core devices by mapping a computationally expensive correlation kernel of a template matching process using various programming models. The work builds a base performance case by a sequential mapping of the algorithm on an Intel processor. In the second step, the performance of the algorithm is enhanced by parallel mapping of the kernel on a shared memory multicore machine using OpenMP programming model. Finally, the Normalized Cross-Correlation (NCC) kernel is scaled to map on a many-core K20 GPU using CUDA programming model. In all steps, the correctness of the implementation of algorithm is taken care by comparing computed data with reference results from a high level implementation in MATLAB. The performance results are presented with various optimization techniques for MATLAB, Sequential, OpenMP and CUDA based implementations. The results show that GPU based implementation achieves 32x and 5x speed-ups respectively to the base case and multicore implementations respectively. Moreover, using inter-block sub-sampling on an 8-bit 4000×4000 reference gray-scale image achieves the execution time upto 2.8sec with an error growth less than 20% for the selected templates of size 96×96.

ATS_MAT16_035 - Analysis of Adaptive Filter and ICA for Noise Cancellation from a Video Frame

Noise cancellation algorithms have been frequently applied in many fields including image/video processing. Adaptive noise cancellation algorithms exploit the correlation property of noise and remove the noise from the input signal more effectively than non-adaptive algorithms. In this paper different noise cancellation techniques are applied to de-noise a video frame. Three different variants of gradient based adaptive filtering algorithms and independent component analysis (ICA) procedure are implemented and compared on the basis of signal to noise ratio (SNR) and computational time. The common algorithms used in adaptive filters are least mean square (LMS), normalized least means square (NLMS), and recursive least mean square (RLS). The simulation results demonstrates that NLMS algorithm is computationally efficient but cannot handle impulsive noise whereas LMS and RLS can perform better for long duration noise signals. The comparative analysis of adaptive filtering algorithms and ICA shows that ICA can perform better then all three iterative gradient based algorithms because of its non-iterative nature. For testing and simulations, three variants of white Gaussian noise (WGN) are used to corrupt the video frame.

ATS_MAT16_036 - Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval

Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes. However, they face difficulties in coping with large training data sets. With the increasing amount of optical remote sensing data made available for analysis and the possibility of using a large amount of simulated data from radiative transfer models (RTMs) to train kernel MLRAs, efficient data reduction techniques will need to be implemented. Active learning (AL) methods enable to select the most informative samples in a data set. This letter introduces six AL methods for achieving optimized biophysical variable estimation with a manageable training data set, and their implementation into a Matlab-based MLRA toolbox for semiautomatic use. The AL methods were analyzed on their efficiency of improving the estimation accuracy of the leaf area index and chlorophyll content based on PROSAIL simulations. Each of the implemented methods outperformed random sampling, improving retrieval accuracy with lower sampling rates. Practically, AL methods open opportunities to feed advanced MLRAs with RTM-generated training data for the development of operational retrieval models.

ATS_MAT16_037 - Development of a Brain-Computer Interface Based on Visual Stimuli for the Movement of a Robot Joints

This paper presents a brain computer interface (BCI) to control a robotic arm by brain signals from visual stimuli. The following signal processing steps were established; acquisition of brain signals by electroencephalography (EEG) electrodes; noise reduction; extraction of signal characteristics and signal classification. Reliable brain signals were obtained by the use of the Emotiv EPOC® commercial hardware. The OpenViBE® commercial software was used to program the signal processing algorithms. By using Matlab® together with an Arduino® electronic board, two servo motors were controlled to drive two joints of a 5 degrees-of-freedom robot commanded by P300-type evoked potential brain signals from visual stimulation when a subject concentrates on particular images from an image matrix displayed in the computer screen. The experiments were conducted with and without auditive and visual noise (artifacts) to find out the noise influence in the signal classification outcome. The obtained experimental results presented an efficiency in the identification stage up to 100% with and without hearing noise conditions. However, under visual noise conditions a maximum efficiency of 50% was reached. The experiments for the servomotors control were carried out without noise, reaching an efficiency of 100% in the identification stage.