Active Power Decoupling with Reduced Converter Stress for Single-Phase Power Conversion and Interfacing

Single-phase DC/AC power electronic converters suffer from pulsating power at double the line frequency. The commonest practice to handle the issue is to provide a huge electrolytic capacitor for smoothening out the ripple. But, the electrolytic capacitors having short end of lifetimes limit the overall lifetime of the converter. Another way of handling the ripple power is by active power decoupling (APD) using the storage devices and a set of semiconductor switches. Here, a novel topology has been proposed implementing APD. The topology claims the benefit of 1) reduced stress on converter switches 2) using smaller capacitance value thus alleviating use of electrolytic capacitor in turn improving the lifetime of the converter. The circuit consists of a third leg, a storage capacitor and a storage inductor. The analysis and the simulation results are shown to prove the effectiveness of the topology.

QoS Aware Trust Metric based Framework for Wireless Sensor Networks

Wireless Sensor Networks have gained popularity due to their real time applications and low-cost nature. These networks provide solutions to scenarios that are critical, complicated and sensitive like military fields, habitat monitoring, and disaster management. The nodes in wireless sensor networks are highly resource constrained. Routing protocols are designed to make efficient utilization of the available resources in communicating a message from source to destination. In addition to the resource management, the trustworthiness of neighboring nodes or forwarding nodes and the energy level of the nodes to keep the network alive for longer duration is to be considered. This paper proposes a QoS Aware Trust Metric based Framework for Wireless Sensor Networks. The proposed framework safeguards a wireless sensor network from intruders by considering the trustworthiness of the forwarder node at every stage of multi-hop routing. Increases network lifetime by considering the energy level of the node, prevents the adversary from tracing the route from source to destination by providing path variation. The framework is built on NS2 Simulator. Experimental results show that the framework provides energy balance through establishment of trustworthy paths from the source to the destination. (C) 2015 The Authors. Published by Elsevier B.V.

Generalized Partial Response Equalization and Data-Dependent Noise Predictive Signal Detection Over Media Models for TDMR

Two-dimensional magnetic recording (2-D TDMR) is an emerging technology that aims to achieve areal densities as high as 10 Tb/in(2) using sophisticated 2-D signal-processing algorithms. High areal densities are achieved by reducing the size of a bit to the order of the size of magnetic grains, resulting in severe 2-D intersymbol interference (ISI). Jitter noise due to irregular grain positions on the magnetic medium is more pronounced at these areal densities. Therefore, a viable read-channel architecture for TDMR requires 2-D signal-detection algorithms that can mitigate 2-D ISI and combat noise comprising jitter and electronic components. Partial response maximum likelihood (PRML) detection scheme allows controlled ISI as seen by the detector. With the controlled and reduced span of 2-D ISI, the PRML scheme overcomes practical difficulties such as Nyquist rate signaling required for full response 2-D equalization. As in the case of 1-D magnetic recording, jitter noise can be handled using a data-dependent noise-prediction (DDNP) filter bank within a 2-D signal-detection engine. The contributions of this paper are threefold: 1) we empirically study the jitter noise characteristics in TDMR as a function of grain density using a Voronoi-based granular media model; 2) we develop a 2-D DDNP algorithm to handle the media noise seen in TDMR; and 3) we also develop techniques to design 2-D separable and nonseparable targets for generalized partial response equalization for TDMR. This can be used along with a 2-D signal-detection algorithm. The DDNP algorithm is observed to give a 2.5 dB gain in SNR over uncoded data compared with the noise predictive maximum likelihood detection for the same choice of channel model parameters to achieve a channel bit density of 1.3 Tb/in(2) with media grain center-to-center distance of 10 nm. The DDNP algorithm is observed to give similar to 10% gain in areal density near 5 grains/bit. The proposed signal-processing framework can broadly scale to various TDMR realizations and areal density points.

An Open-End winding IM drive with Multilevel 12-sided Polygonal Vectors with Symmetric Triangles

Multilevel inverters with hexagonal voltage space vector structures have improved performance of induction motor drives compared to that of the two level inverters. Further reduction in the torque ripple on the motor shaft is possible by using multilevel dodecagonal (12-sided polygon) voltage space vector structures. The advantages of dodecagonal voltage space vector based PWM techniques are the complete elimination of fifth and seventh harmonics in phase voltages for the full modulation range and the extension of linear modulation range. This paper proposes an inverter circuit topology capable of generating multilevel dodecagonal voltage space vectors with symmetric triangles, by cascading two asymmetric three level inverters with isolated H-Bridges. This is made possible by proper selection of DC link voltages and the selection of resultant switching states for the inverters. In this paper, a simple PWM timing calculation method is proposed. Experimental results have also been presented in this paper to validate the proposed concept.

A Voltage Space Vector Diagram Formed by Nineteen Concentric Dodecagons for Medium-Voltage Induction Motor Drive

In this paper, a multilevel dodecagonal voltage space vector structure with nineteen concentric dodecagons is proposed for the first time. This space vector structure is achieved by cascading two sets of asymmetric three-level inverters with isolated H-bridges on either side of an open-end winding induction motor. The dodecagonal structure is made possible by proper selection of dc link voltages and switching states of the inverters. The proposed scheme retains all the advantages of multilevel topologies as well as the advantages of dodecagonal voltage space vector structure. In addition to that, a generic and simple method for calculation of pulsewidth modulation timings using only sampled reference values (v(alpha) and v(beta)) is proposed. This enables the scheme to be used for any closed-loop application such as vector control. In addition, a new method of switching technique is proposed, which ensures minimum switching while eliminating the fifth-and seventh-order harmonics and suppressing the eleventh and thirteenth harmonics, eliminating the need for bulky filters. The motor phase voltage is a 24-stepped wave-form for the entire modulation range thereby reducing the number of switchings of the individual inverter modules. Experimental results for steady-state operation, transient operation, including start-up have been presented and the results of fast Fourier transform analysis is also presented for validating the proposed concept.

QEP Based Solution for Power Allocation in Amplify and Forward Networks

The problem of cooperative beamforming for maximizing the achievable data rate of an energy constrained two-hop amplify-and-forward (AF) network is considered. Assuming perfect channel state information (CSI) of all the nodes, we evaluate the optimal scaling factor for the relay nodes. Along with individual power constraint on each of the relay nodes, we consider a weighted sum power constraint. The proposed iterative algorithm initially solves a set of relaxed problems with weighted sum power constraint and then updates the solution to accommodate individual constraints. These relaxed problems in turn are solved using a sequence of Quadratic Eigenvalue Problems (QEP). The key contribution of this letter is the generalization of cooperative beamforming to incorporate both the individual and weighted sum constraint. Furthermore, we have proposed a novel algorithm based on Quadratic Eigenvalue Problem (QEP) and discussed its convergence.

Active Power Decoupling with Reduced Converter Stress for Single-Phase Power Conversion and Interfacing

Single-phase DC/AC power electronic converters suffer from pulsating power at double the line frequency. The commonest practice to handle the issue is to provide a huge electrolytic capacitor for smoothening out the ripple. But, the electrolytic capacitors having short end of lifetimes limit the overall lifetime of the converter. Another way of handling the ripple power is by active power decoupling (APD) using the storage devices and a set of semiconductor switches. Here, a novel topology has been proposed implementing APD. The topology claims the benefit of 1) reduced stress on converter switches 2) using smaller capacitance value thus alleviating use of electrolytic capacitor in turn improving the lifetime of the converter. The circuit consists of a third leg, a storage capacitor and a storage inductor. The analysis and the simulation results are shown to prove the effectiveness of the topology.

Hybrid Working Set Algorithm for SVM Learning With a Kernel Coprocessor on FPGA

Support vector machines (SVM) are a popular class of supervised models in machine learning. The associated compute intensive learning algorithm limits their use in real-time applications. This paper presents a fully scalable architecture of a coprocessor, which can compute multiple rows of the kernel matrix in parallel. Further, we propose an extended variant of the popular decomposition technique, sequential minimal optimization, which we call hybrid working set (HWS) algorithm, to effectively utilize the benefits of cached kernel columns and the parallel computational power of the coprocessor. The coprocessor is implemented on Xilinx Virtex 7 field-programmable gate array-based VC707 board and achieves a speedup of upto 25x for kernel computation over single threaded computation on Intel Core i5. An application speedup of upto 15x over software implementation of LIBSVM and speedup of upto 23x over SVMLight is achieved using the HWS algorithm in unison with the coprocessor. The reduction in the number of iterations and sensitivity of the optimization time to variation in cache size using the HWS algorithm are also shown.

Part I: Physical Insights Into the Two-Stage Breakdown Characteristics of STI-Type Drain-Extended pMOS Device

In this paper, we study breakdown characteristics in shallow-trench isolation (STI)-type drain-extended MOSFETs (DeMOS) fabricated using a low-power 65-nm triple-well CMOS process with a thin gate oxide. Experimental data of p-type STI-DeMOS device showed distinct two-stage behavior in breakdown characteristics in both OFF-and ON-states, unlike the n-type device, causing a reduction in the breakdown voltage and safe operating area. The first-stage breakdown occurs due to punchthrough in the vertical structure formed by p-well, deep n-well, and p-substrate, whereas the second-stage breakdown occurs due to avalanche breakdown of lateral n-well/p-well junction. The breakdown characteristics are also compared with the STI-DeNMOS device structure. Using the experimental results and advanced TCAD simulations, a complete understanding of breakdown mechanisms is provided in this paper for STI-DeMOS devices in advanced CMOS processes.

Physical Insights on the Ambiguous Metal-Graphene Interface and Proposal for Improved Contact Resistance

The ambiguous behavior of metal-graphene interface has been addressed in this paper using density functional theory and nonequilibrium Green's function formalism. For the first time, the fundamental chemistry of metal-graphene interface, in particular role of sp-hybridized and sp(2)-hybridized carbon atoms, has been emphasized and discussed in detail in this paper. It was discovered that the sp-hybridized sites at the edge of a graphene monolayer contribute to 40% of current conduction when compared with sp(2)-hybridized atom sites in the graphene-metal overlap region. Moreover, we highlighted the insignificance of an additional metal layer, i.e., sandwiched contact, due to lacking sp-hybridized carbon sites. A fundamental way of defining the contact resistance, while keeping chemical bonding in mind, has been proposed. The bonding insight has been further used to propose the novel ways of interfacing metal with graphene, which results in a 40% reduction in contact resistance.

Query Click and Text Similarity Graph for Query Suggestions

Query suggestion is an important feature of the search engine with the explosive and diverse growth of web contents. Different kind of suggestions like query, image, movies, music and book etc. are used every day. Various types of data sources are used for the suggestions. If we model the data into various kinds of graphs then we can build a general method for any suggestions. In this paper, we have proposed a general method for query suggestion by combining two graphs: (1) query click graph which captures the relationship between queries frequently clicked on common URLs and (2) query text similarity graph which finds the similarity between two queries using Jaccard similarity. The proposed method provides literally as well as semantically relevant queries for users' need. Simulation results show that the proposed algorithm outperforms heat diffusion method by providing more number of relevant queries. It can be used for recommendation tasks like query, image, and product suggestion.

A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.

Kinematics and dynamics of Jansen leg mechanism: A bond graph approach

The Jansen mechanism is a one degree-of-freedom, planar, 12-link, leg mechanism that can be used in mobile robotic applications and in gait analysis. This paper presents the kinematics and dynamics of the Jansen leg mechanism. The forward kinematics, accomplished using circle intersection method, determines the trajectories of various points on the mechanism in the chassis (stationary link) reference frame. From the foot point trajectory, the step length is shown to vary linearly while step height varies non-linearly with change in crank radius. A dynamic model for the Jansen leg mechanism is proposed using bond graph approach with modulated multiport transformers. For given ground reaction force pattern and crank angular speed, this model helps determine the motor torque profile as well as the link and joint stresses. The model can therefore be used to rate the actuator torque and in design of the hardware and controller for such a system. The kinematics of the mechanism can also be obtained from this dynamic model. The proposed model is thus a useful tool for analysis and design of systems based on the Jansen leg mechanism. (C) 2015 Elsevier B.V. All rights reserved.

Secure Communication in Amplify-and-Forward Networks with Multiple Eavesdroppers: Decoding with SNR Thresholds

The problem of secure unicast communication over a two hop Amplify-and-Forward wireless relay network with multiple eavesdroppers is considered. Assuming that a receiver (destination or eavesdropper) can decode a message only if the received SNR is above a predefined threshold, we consider this problem in two scenarios. In the first scenario, we maximize the SNR at the legitimate destination, subject to the condition that the received SNR at each eavesdropper is below the target threshold. Due to the non-convex nature of the objective function and eavesdroppers' constraints, we transform variables and obtain a quadratically constrained quadratic program (QCQP) with convex constraints, which can be solved efficiently. When the constraints are not convex, we consider a semidefinite relaxation (SDR) to obtain computationally efficient approximate solution. In the second scenario, we minimize the total power consumed by all relay nodes, subject to the condition that the received SNR at the legitimate destination is above the threshold and at every eavesdropper, it is below the corresponding threshold. We propose a semidefinite relaxation of the problem in this scenario and also provide an analytical lower bound.

Peristaltic pump-based low range pressure sensor calibration system

Peristaltic pumps were normally used to pump liquids in several chemical and biological applications. In the present study, a peristaltic pump was used to pressurize the chamber (positive as well negative pressures) using atmospheric air. In the present paper, we discuss the development and performance study of an automatic pressurization system to calibrate low range (millibar) pressure sensors. The system includes a peristaltic pump, calibrated pressure sensor (master sensor), pressure chamber, and the control electronics. An in-house developed peristaltic pump was used to pressurize the chamber. A closed loop control system has been developed to detect and adjust the pressure leaks in the chamber. The complete system has been integrated into a portable product. The system performance has been studied for a step response and steady state errors. The system is portable, free from oil contaminants, and consumes less power compared to existing pressure calibration systems. The veracity of the system was verified by calibrating an unknown diaphragm based pressure sensor and the results obtained were satisfactory. (C) 2015 AIP Publishing LLC.