Henry Selvaraj

Bio: Henry Selvaraj is an academic researcher from University of Nevada, Las Vegas. The author has contributed to research in topics: Functional decomposition & Logic synthesis. The author has an hindex of 18, co-authored 116 publications receiving 1247 citations. Previous affiliations of Henry Selvaraj include Mepco Schlenk Engineering College & Monash University, Clayton campus.

Distributed Processing Applications for UAV/drones: A Survey

Abstract: Distributed Processing Systems are the ones that include multiple devices (which could be of many types, such as PC computers, mobile devices etc) that have computational and communication capabilities Their computational power is jointly used for collaborative processing of variety of tasks – and this processing is realized in distributed manner UAV - Unmanned Aerial Vehicles (also called drones) gain significant attention over recent years They have been employed to realize multiple tasks such as surveillance or environmental monitoring First implementations were based on single UAV, later the potential of multiple UAVs collaborating in a team was noticed Many applications were implemented in distributed manner, using multiple collaborative UAVs and the distributed processing systems principles In this paper we survey the applications implemented over cooperative teams of UAVs that operate as distributed processing systems

Brain MRI Slices Classification Using Least Squares Support Vector Machine

Abstract: This research paper proposes an intelligent classification technique to identify normal and abnormal slices of brain MRI data. The manual interpretation of tumor slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed which caters the need for classification of image slices after identifying abnormal MRI volume, for tumor identification. In this research work, advanced classification techniques based on Least Squares Support Vector Machines (LS-SVM) are proposed and applied to brain image slices classification using features derived from slices. This classifier using linear as well as nonlinear Radial Basis Function (RBF) kernels are compared with other classifiers like SVM with linear and nonlinear RBF kernels, RBF classifier, Multi Layer Perceptron (MLP) classifier and K-NN classifier. From this analysis, it is observed that the proposed method using LSSVM classifier outperformed all the other classifiers tested.

A General Approach to Boolean Function Decomposition and its Application in FPGABased Synthesis

Abstract: An effective logic synthesis procedure based on parallel and serial decomposition of a Boolean function is presented in this paper. The decomposition, carried out as the very first step of the .synthesis process, is based on an original representation of the function by a set of r-partitions over the set of minterms. Two different decomposition strategies, namely serial and parallel, are exploited by striking a balance between the two ideas. The presented procedure can be applied to completely or incompletely specified, single- or multiple-output functions and is suitable for different types of FPGAs including XILINX, ACTEL and ALGOTRONIX devices. The results of the benchmark experiments presented in the paper show that, in several cases, our method produces circuits of significantly reduced complexity compared to the solutions reported in the literature.

An application of functional decomposition in ROM-based FSM implementation in FPGA devices

Abstract: Modern FPLD devices have very complex structure. They combine PLA like structures, as well as FPGA and even memory-based structures. However lack of appropriate synthesis methods do not allow fully exploiting the possibilities the modern FPLDs offer. The paper presents a general method for the synthesis targeted to implementation of sequential circuits using embedded memory blocks. The method is based on the serial decomposition concept and relies on decomposing the memory block into two blocks: a combinational address modifier and a smaller memory block. An appropriately chosen decomposition strategy may allow reducing the required memory size at the cost of additional logic cells for address modifier implementation. This makes possible implementation of FSMs that exceed available memory by using embedded memory blocks and additional programmable logic.

A Survey of High Level Synthesis Languages, Tools, and Compilers for Reconfigurable High Performance Computing

Abstract: High Level Languages (HLLs) make programming easier and more efficient; therefore, powerful applications can be written, modified, and debugged easily. Nowadays, applications can be divided into parallel tasks and run on different processing elements, such as CPUs, GPUs, or FPGAs; for achieving higher performance. However, in the case of FPGAs, generating hardware modules automatically from high level representation is one of the major research activities in the last few years. Current research focuses on designing programming platforms that allow parallel applications to be run on different platforms, including FPGA. In this paper, a survey of HLLs, tools, and compilers used for translating high level representation to hardware description language is presented. Technical analysis of such tools and compilers is discussed as well.

Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint

Abstract: The days where swarms of unmanned aerial vehicles (UAVs) will occupy our skies are fast approaching due to the introduction of cost-efficient and reliable small aerial vehicles and the increasing demand for use of such vehicles in a plethora of civil applications. Governments and industry alike have been heavily investing in the development of UAVs. As such it is important to understand the characteristics of networks with UAVs to enable the incorporation of multiple, coordinated aerial vehicles into the air traffic in a reliable and safe manner. To this end, this survey reports the characteristics and requirements of UAV networks for envisioned civil applications over the period 2000–2015 from a communications and networking viewpoint. We survey and quantify quality-of-service requirements, network-relevant mission parameters, data requirements, and the minimum data to be transmitted over the network. Furthermore, we elaborate on general networking related requirements such as connectivity, adaptability, safety, privacy, security, and scalability. We also report experimental results from many projects and investigate the suitability of existing communication technologies for supporting reliable aerial networking.

Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges

Abstract: The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

Triangulating a simple polygon in linear time

Abstract: We give a deterministic algorithm for triangulating a simple polygon in linear time. The basic strategy is to build a coarse approximation of a triangulation in a bottom-up phase and then use the information computed along the way to refine the triangulation in a top-down phase. The main tools used are the polygon-cutting theorem, which provides us with a balancing scheme, and the planar separator theorem, whose role is essential in the discovery of new diagonals. Only elementary data structures are required by the algorithm. In particular, no dynamic search trees, of our algorithm.

Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm

Abstract: Computer-aided detection/diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. The objective of this paper is to review the recent published segmentation and classification techniques and their state-of-the-art for the human brain magnetic resonance images (MRI). The review reveals the CAD systems of human brain MRI images are still an open problem. In the light of this review we proposed a hybrid intelligent machine learning technique for computer-aided detection system for automatic detection of brain tumor through magnetic resonance images. The proposed technique is based on the following computational methods; the feedback pulse-coupled neural network for image segmentation, the discrete wavelet transform for features extraction, the principal component analysis for reducing the dimensionality of the wavelet coefficients, and the feed forward back-propagation neural network to classify inputs into normal or abnormal. The experiments were carried out on 101 images consisting of 14 normal and 87 abnormal (malignant and benign tumors) from a real human brain MRI dataset. The classification accuracy on both training and test images is 99% which was significantly good. Moreover, the proposed technique demonstrates its effectiveness compared with the other machine learning recently published techniques. The results revealed that the proposed hybrid approach is accurate and fast and robust. Finally, possible future directions are suggested.

A region-based fuzzy feature matching approach to content-based image retrieval

Abstract: This paper proposes a fuzzy logic approach, UFM (unified feature matching), for region-based image retrieval. In our retrieval system, an image is represented by a set of segmented regions, each of which is characterized by a fuzzy feature (fuzzy set) reflecting color, texture, and shape properties. As a result, an image is associated with a family of fuzzy features corresponding to regions. Fuzzy features naturally characterize the gradual transition between regions (blurry boundaries) within an image and incorporate the segmentation-related uncertainties into the retrieval algorithm. The resemblance of two images is then defined as the overall similarity between two families of fuzzy features and quantified by a similarity measure, UFM measure, which integrates properties of all the regions in the images. Compared with similarity measures based on individual regions and on all regions with crisp-valued feature representations, the UFM measure greatly reduces the influence of inaccurate segmentation and provides a very intuitive quantification. The UFM has been implemented as a part of our experimental SIMPLIcity image retrieval system. The performance of the system is illustrated using examples from an image database of about 60,000 general-purpose images.