Miracle-dz/UniFAR-FTUs huggingface dataset:
Inspection of Partial Bitstreams for FPGAs Using Artificial Neural Networks

Incorporating FPGAs in embedded designs, both for research and industry related applications, is getting increasingly common. Due to the inherent capability of an FPGA to reconfigure itself during run-time, entirely or partially, it has become a very cost effective and time efficient solution for end-users with ever-changing needs for their embedded and custom hardware designs. This capability allowing dynamic reconfiguration of FPGAs, unfortunately also poses a threat to hardware security in terms of malicious bitstream manipulation that can include attacks through intended hardware changes by insertion of hardware trojans, spy-wares or even energy thirsty hardware modules which eventually have adverse effects on energy critical applications. In this paper, we introduce a novel approach to tackle this problem using machine learning techniques for FPGA bitstream analysis. By making use of different Neural Networks, we present how it paves a way to analyze partial FPGA bistreams to trace a certain module, or to find inconsistencies which can be malicious to the target hardware. In contrast to traditional methods to inspect bitstreams, our method saves a significant amount of time.
Electromagnetic Fields Produced by an Undersea Source Using Discrete Complex Imaging Method

This paper presents the procedure for evaluation of the electromagnetic fields produced by an undersea long dipole source. A two-layered model consisting of seawater and seabed is adopted. First, the magnetic vector potentials are expressed as Sommerfeld-type integrals. Then, the integrands are fitted using the discrete complex image method (DCIM), which permits to the electromagnetic fields to be calculated in closed-form expressions. Finally, numerical results of electromagnetic fields on the sea floor along a prescribed measurement line are shown.
An Anthropomorphic Soft Exosuit for Hand Rehabilitation

Functional impairment of the hand, for example after a stroke, can be partially improved by intensive training. This is currently done by physiotherapy and the optimal intensity of hand rehabilitation programs is usually not reached due to a lack in human resources (high costs) and patients fatigue. In this work a cost-effective soft exosuit to support the hand’s grasping function is presented. The system is based on tendon-like wires and all fingers except the little finger are actuated. Each of the remaining four fingers is bidirectionally controlled by an electrical motor. This allows a variety of gripping situations, e.g. a power or precision grip. Our prototype weighs 435g, including the battery and can be worn on the upper arm. The force applicable for a power grip exceeds 20N with a maximum gripping frequency of 4Hz. Furthermore, a force control is implemented, giving the wearer the opportunity to grab sensitive objects. All components used are available in different sizes, allowing a quick and individual preparation per patient. Therefore, our prototype can be used for rehabilitation while doing activities of daily living (ADL) starting on the day of the injury.
Adversarial Nuclei Segmentation on H&E Stained Histopathology Images

Computer aided methods in pathology are advancing rapidly. Problems like segmentation, classification and detection of pathology images are solved with machine learning and image processing techniques. State-of-the-art methods in nuclei segmentation problem include supervised deep learning techniques. However, labeling process of pathology images is an expensive and time consuming process. In this work, nuclei segmentation problem is formulated as image-to-image translation problem and using Cycle-Consistent Generative Adversarial Networks, an unsupervised segmentation scheme is proposed for hematoxylin&eosin stained histopathology data.
Image Hashing based on Shape Context and Speeded Up Robust Features (SURF)

Image Authentication is one of the key issue in today’s age of multimedia technology. With the availability of many hacking techniques and picture editing tools like Photoshop, image security is a major research field. Local feature points are elaborately used in resolving many problems, i.e., object detection, robust matching etc., but its use in the field of image hashing is not explored. In this work, we propose image hashing based on shape-context via Speeded Up Robust Features (SURF). The work is motivated by SIFT-based approach. Our contributions are: 1) SURF based algorithm is several times faster than SIFT-based algorithm. 2) It is more robust against different type of image modifications than SIFT. 3) We have incorporated rotation invariance property. Rotation is a difficult content preserving operation to model. The experiments are carried out on 1000 images and have shown that the proposed image hashing technique is robust against content-preserving operations. The region of convergence shows the efficacy of the proposed method compared to many state-of-the-art methods.
Test Case Prioritization based on Historical Failure Patterns using ABC and GA

Regression testing is defined as type of software testing carried out to ensure that no new bugs are introduced due to modifications to existing code or addition of new features. Prioritization techniques order the set of test cases for improved & effective testing. Test Case Prioritization (TCP) in corrective regression testing is an indispensable arsenal to help discover faults faster during the initial phase of testing. Many techniques have been proposed for Test Case Prioritization (TCP) based on requirement correlation, test coverage, information retrieval which are dependent on data which is not easily available. Our approach considers the historical execution of the regression cycles through use of Artificial Bee Colony Optimization (Swarm Intelligence) & Genetic Algorithm for fault detection with improved results.
Volume Visualization and Compositing on Large-Scale Displays Using Handheld Touchscreen Interaction

Volume Visualization and Compositing on Large-Scale Displays Using Handheld Touchscreen Interaction Cristhopper Jacobo Armenta Gastelum Advances in the physical sciences have progressively delivered ever increasing, already extremely large data sets to be analyzed. High performance volume rendering has become critical to the scientists for a better understanding of the massive amounts of data to be visualized. Cluster based rendering systems have become the base line to achieve the power and flexibility required to perform such task. Furthermore, display arrays have become the most suitable solution to display these data sets at their natural size and resolution which can be critical for human perception and evaluation. The work in this thesis aims at improving the scalability and usability of volume rendering systems that target visualization on display arrays. The first part deals with improving the performance by introducing the implementations of two parallel compositing algorithms for volume rendering: direct send and binary swap. The High quality Volume Rendering (HVR) framework has been extended to accommodate parallel compositing where previously only serial compositing was possible. The preliminary results show improvements in the compositing times for direct send even
Design of Interior Permanent-Magnet Machines with Novel Semi-Overlapping Windings

In this paper, a novel winding topology, comprising of semi-overlapped very short-end windings with significantly low magneto-motor force (MMF) harmonics, has been proposed. The key design rules, basic properties, advantages, and disadvantages of such a winding have been explained in detail. In order to reveal the key performance characteristics of the proposed winding topology, an interior permanent-magnet (IPM) machine with proposed winding structure is designed and the obtained results have been compared with other IPM machines having conventional distributed and concentrated fractional windings. It has been shown that the proposed winding topology promises significant advantages; such as improved efficiency with substantially reduced total axial length and low eddy current losses over conventional winding topologies.
Convolutional Neural Network for Pornographic Images Classification

The pornographic content that spreads across the internet can potentially harm underage users. Consequently, pornographic images detection has become a popular topic for many researchers. In previous studies on image content detection, features were selected by an expert. In some cases, the features cannot represent a significant attribute or characteristic. Convolutional Neural Networks are among the deep learning approaches that have been applied to tackle this problem. In this research, a modification on the last layer is proposed to fit the pornographic detection. According to the final results, the pornographic image classification accuracy reached 93.8%.
Decoding covert speech for intuitive control of brain-computer interfaces based on single-trial EEG: a feasibility study

For individuals with severe motor deficiencies, controlling external devices such as robotic arms or wheelchairs can be challenging, as many devices require some degree of motor control to be operated, e.g. when controlled using a joystick. A brain-computer interface (BCI) relies only on signals from the brain and may be used as a controller instead of muscles. Motor imagery (MI) has been used in many studies as a control signal for BCIs. However, MI may not be suitable for all control purposes, and several people cannot obtain BCI control with MI. In this study, the aim was to investigate the feasibility of decoding covert speech from single-trial EEG and compare and combine it with MI. In seven healthy subjects, EEG was recorded with twenty-five channels during six different actions: Speaking three words (both covert and overt speech), two arm movements (both motor imagery and execution), and one idle class. Temporal and spectral features were derived from the epochs and classified with a random forest classifier. The average classification accuracy was $67 \pm 9$ % and $75\pm 7$ % for covert and overt speech, respectively; this was 5–10 % lower than the movement classification. The performance of the combined movement-speech decoder was $61 \pm 9$ % and $67\pm 7$ % (covert and overt), but it is possible to have more classes available for control. The possibility of using covert speech for controlling a BCI was outlined; this is a step towards a multimodal BCI system for improved usability.
Adaptive Power System Stabilizer Design For Interconnected Power Systems

A power system is a nonlinear dynamic system that operates under an ever-changing set of parameters. With modern dynamic power systems maintaining plant stability is crucial. In this paper, Lyapunov theory, combined with Model Reference Adaptive Controller (MRAC) technique, has been used to design a Power System Stabilizer (PSS). The proposed controller is tested on the Kundur Two Area system operating under a variety of loading conditions. The results show improvement in the transient stability of the system whit applying the new controller to the Kundur Two Area system.
Análise de escalabilidade de aplicações Hadoop/Mapreduce por meio de simulação

Durante os ultimos anos, houve um significativo crescimento na quantidade de dados processados diariamente por companhias, universidades e outras instituicoes. Mapreduce e um modelo de programacao e um framework para a execucao de aplicacoes que manipulam grandes volumes de dados em maquinas compostas por milhares de processadores ou nucleos. Atualmente, o Hadoop e a implementacao como software livre de Mapreduce mais largamente adotada. Embora existam relatos na literatura sobre o uso de aplicacoes Mapreduce em plataformas com cerca de quatro mil nucleos processando dados da ordem de dezenas de petabytes, o estudo dos limites de escalabilidade nao foi esgotado e muito ainda resta a ser estudado. Um dos principais desafios no estudo de escalabilidade de aplicacoes Mapreduce e o grande numero de parâmetros de configuracao da aplicacao e do ambiente Hadoop. Na literatura ha relatos que mencionam mais de 190 parâmetros de configuracao, sendo que 25 podem afetar de maneira significativa o desempenho da aplicacao. Este trabalho contem um estudo sobre a escalabilidade de aplicacoes Mapreduce executadas na plataforma Hadoop. Devido ao numero limitado de processadores disponiveis, adotou-se uma abordagem que combina experimentacao e simulacao. A experimentacao foi realizada em um cluster local de 32 nos (com 64 processadores), e para a simulacao empregou-se o simulador MRSG (MapReduce Over SimGrid). Como principais resultados, foram identificados os parâmetros de maior impacto no desempenho e na escalabilidade das aplicacoes. Esse resultado foi obtido por meio de simulacao. Alem disso, apresentou-se um metodo para a calibracao do simulador MRSG, em funcao de uma aplicacao representativa escolhida como benchmark. Com o simulador calibrado, avaliou-se a escalabilidade de uma aplicacao bem otimizada. O simulador calibrado permitiu obter uma predicao sobre a escalabilidade da aplicacao para uma plataforma com ate 10 mil nos.
Dual-Band Dual-Polarized Slot-Coupled Microstrip Antenna for Duplex Applications

A dual-band dual-polarized slot-coupled microstrip antenna is proposed in this paper. Good isolation between the two input ports and low cross-polarization radiation level are achieved, which are required for duplex applications. The element is designed for operation at 4.1 and 6.6 GHz. By loading short pins, the proposed design exhibits considerable improvement in terms of the gain at higher resonant frequency, cross-polarization level, and impedance bandwidth, compared to the original design. Each design has been fabricated and experimented.
Overview and Comparative Performance Analysis of Various Full Adder Cells in 90 nm Technology

Full Adders considered as the major component in Arithmetic Logic Unit of digital signal processing chips and microprocessors gained much interest among researchers over the years. As a result, various adder cells have been developed by employing various logic styles. Therefore, it is necessary to have a complete overview of the various sorts of full adders to Figure out their performance level. This paper presents an overview and comparative analysis of different sorts of full adder cells. Analysis and simulation on Complementary Pass Logic, Complementary CMOS, Transmission Gate, Transmission Function, 24 Transistor, 14 Transistor, 10 Transistor, Swing Restored Complementary Pass Logic, Hybrid Pass Static CMOS, Double Pass Transistor and some Hybrid full adder cells have been conducted using Cadence Virtuoso Tools in 90nm technology. It was observed that Complementary Pass Logic adder cell had the least delay and 14-Transistor adder cell displayed the worst delay. Hybrid Pass Static CMOS full adder had the least power consumption whereas 24 Transistor full adder consumed highest power. Highest power-delay product was displayed by Double Pass Transistor Logic adder cell.
Assistive Exoskeleton Control with User-Tuned Multi-Objective Optimization

Assistive exoskeletons that utilize trajectory following control have been shown to produce stable gait for users. These however, do not allow intuitive tuning to customize gait to users’ preferences. When persons walk on their own, they balance a variety of needs such as speed, comfort, and energy. Providing user tuning by optimizing between different gait performance measures gives an intuitive flexibility. We have shown the optimization between natural walking and gait energy produces stable bipedal gait through simulation in a virtual constraint framework. This verification shows validity of the methodology and framework for improving tuning and customization of assistive exoskeletons.
Seeded One Time Pad for Security of Medical Images in Health Information

The main motivation behind this paper is to provide a secure method to transfer critical medical information over different channels so that there is no scope of interference with the medical information stored in the image. This paper involves developing a secure method for robust and well-authenticated transfer of the sensitive medical image from a sender to a receiver. The method involves encryption of the sensitive portion of the image in the same image in non-salient pixel locations and secure transfer of images. Any attack on the image must be detected and original data must be recovered with high probability. This ensures that even in case of attack the medical image can be used. A seeded one time pad is used to achieve the desired encryption and recovery.
A Survey on Cloud and Internet of Things Based Healthcare Diagnosis

At present, the Internet of Things and healthcare related services are provided using online applications. The applications developed are further used in building strong healthcare service that is made available to everyone in critical and prior circumstances. These applications use IoT devices to collect real-time data regarding any person’s condition and also refer to the already existing records for further reference. The data collected are stored in a huge database, which contains the details from each healthcare environment. Cloud computing technology is used to secure and store the health relevant data. Cloud and IoT technology are combined to provide a full-fledged detail about healthcare issues and secure them. Recently, the healthcare related diagnosis are also added inorder to bring in a more developed healthcare and user friendly application. The diagnosis procedure is used to predict the severity of any disease and store it in the database. This paper, surveys on the classification technique in Cloud and IoT based health monitoring and diagnosis approach from the healthcare data acquired from any repository or by using sensors that collect real-time data and store them in tables for reference to the severity of the disease by any professional.
Texture Discrimination using a Soft Biomimetic Finger for Prosthetic Applications

Soft robotic fingers have shown great potential for use in prostheses due to their inherent compliant, light, and dexterous nature. Recent advancements in sensor technology for soft robotic systems showcase their ability to perceive and respond to static cues. However, most of the soft fingers for use in prosthetic applications are not equipped with sensors which have the ability to perceive texture like humans can. In this work, we present a dexterous, soft, biomimetic solution which is capable of discrimination of textures. We fabricated a soft finger with two individually controllable degrees of freedom with a tactile sensor embedded at the fingertip. The output of the tac- tile sensor, as texture plates were palpated, was converted into spikes, mimicking the behavior of a biological mechanoreceptor. We explored the spatial properties of the textures captured in the form of spiking patterns by generating spatial event plots and analyzing the similarity between spike trains generated for each texture. Unique features representative of the different textures were then extracted from the spikes and input to a classifier. The textures were successfully classified with an accuracy of 94% when palpating at a rate of 42 mm/s. This work demonstrates the potential of providing amputees with a soft finger with sensing capabilities, which could potentially help discriminate between different objects and surfaces during activities of daily living (ADL) through palpation.
Identification of Spring Coefficient for Heel Rocker Function Support Based on Estimated Dorsiflexion Torque

In previous research, we have developed a high-dorsiflexion assistive robotic technology aiming for gait rehabilitation targeting on ankle dorsiflexion movement. A McKibben-type artificial muscle is applied to provide large dorsiflexion force while adding little weight to the device. This ensures the foot clearance before initial stance phase in gait. Meanwhile, a tension spring is deployed in series with the artificial muscle to support heel rocker function in loading response phase. Suitable spring coefficient for each individual differs according to ankle’s dorsiflexion torque in loading response. An unsuitable spring would lead to knee deviation in this phase. In this study, we derived an identification equation to determine a suitable spring coefficient for individuals based on estimation of dorsiflexion torque required to support. An evaluation test on healthy objects was conducted, which shows no negative effects on participants’ knee angles with the identified spring coefficient.
Preliminary Design of an Active Stabilization Assistive Eating Device for People Living with Movement Disorders

This paper presents the development of a new active assistive eating device, which aims to stabilize the movements of people living with movement disorders, such as spasticity and ataxia. Many people living with upper-body incapacities are unable to eat on their own, due to movement disorders (ex. tremors, spastic motions, lack of muscular tone), resulting from various ailments like Cerebral palsy, Parkinson’s disease, Dystonia, Multiple sclerosis, strokes, and Muscular dystrophy). Our past work focused on the development of a purely mechanical device, which involved damping of the system via passive mechanical dampers. This paper extends said work by using active stabilization of user movements. The active assistance enables the design of intelligent algorithms that can assist human movements more efficiently. This active version has the benefits of being easily adjustable; the level of damping can be adjusted in real-time, depending on the user movement; different control modes are offered, and the guiding of user movements is also allowed. Firstly, the mechanical design of the device is presented, followed by the damping arrangement, the electronic design, the control algorithms and finally, the preliminary experiments are mentioned.
Graphene film based Inverted-F Antenna for 5G Mobile Communications

This paper presents an inverted-F antenna based on graphene film for 5G mobile communications. The graphene film has a high conductivity of 1.1×106 S/m. The graphene film based inverted-F antenna with the resonant frequency of 3.48 GHz has excellent performance, which the |S11| is -43.2 dB and the bandwidth is from 3.08 GHz to 4.65 GHz.
Legal Clause Extraction From Contract Using Machine Learning with Heuristics Improvement

In this paper we are going to motivate and propose a novel task: Legal Clause extraction from a contract document. For clause extraction, we perform paragraph segmentation using paragraph boundary detection. In this paper, we have discussed conventional paragraph detection approaches and then describe the machine learning based approach. We detect paragraph boundary using sequential classification algorithm. We also added varieties of text feature which help to improve the accuracy of paragraph boundary detection. To improve the accuracy of the model we have added false positive samples and create a revised model. We introduce the legal clause extraction system that performs the task with high accuracy.
Trajectory tracking of underwater vehicle based on Back-Stepping neural network adaptive robust sliding mode control

Complex marine environment and modeling uncertainties have a great impact on the precise control of the marine vehicles. A novel adaptive sliding mode controller, namely BRS-controller, is proposed based on back-stepping and RBF neural network. The back-stepping technique is employed to reduce the order of the underwater vehicle system by introducing the virtual speed to the system kinematic model. An adaptive robust sliding mode rule is presented based on the system dynamic model to improve the system’s robust performance. In addition, the RBF neural network theory is used to approximate the modeling uncertainties of the underwater vehicle system. The stability of the system is analyzed based on Lyapunov theory. The numerical simulation results demonstrate the effectiveness and robustness of the proposed BRS-Controller.