Miracle-dz/UniFAR-FTUs huggingface dataset:
Signal Processing Algorithm for Active Aperture Synthesis Systems

The real-time radar imaging of land cover over large areas is performed from aerospace carriers via imaging equipment operating in the optical, infrared and radio wavebands. The latter waveband allows to carry out the surveys regardless of weather and time of day. However, since radio waves are significantly longer (in comparison with optical and infrared ones), to achieve high-resolution images in spatial coordinates it is necessary to use antennas dozens, hundreds or even thousands of meters long. Naturally, it is rather difficult – and in most cases simply impossible – to implement this large antenna aperture. Therefore, since the middle of the XX century, specially synthesized systems consisting one or several antennas, which can be repositioned according to a given law, has been widely used. Signals recorded in different areas of space are accumulated and then processed altogether. This creates the effect of using large antennas. In this case, there are two main methods of signal processing implemented, respectively, in active radar systems with aperture synthesis and passive aperture synthesis systems. Traditionally, the viewing areas of these systems are correlated – the active systems form images within the range of angles ±(15°-50°) from the nadir, and passive systems within ±15° from the nadir. The physical essence of the images obtained at the outputs of these systems is also different complicating the image formation within the entire region of ± 50° from the nadir. This report combines the features of signal processing in active and passive systems with the aperture synthesis proposing a system of active aperture synthesis. This allows to obtain radar images of high resolution in spatial coordinates within the monitored area that cannot be viewed by active radars, and the physical essence of the images themselves will be close to the essence of the images obtained by active radar systems with synthesized aperture.
On Analysis and Discussion of Various Performance Parameters of Omega and Advance Omega Interconnection Network

High performance computing is the challenging research area in the field of computer science and engineering. To speed up the computation process and to attain the high performance; parallel computing is one of the best solutions. Interconnection networks (INs) work as a backbone in improving the parallelism. A variety of INs are developed by various researchers. Multi-stage interconnection networks (MINs) are the subcategory of INs. The reason of popularity of MINs is due to its cost effectiveness, high performance, and fault tolerability. This paper is analyzing the performances of two popular MINs named as omega and advance omega network. The simulation results show that advance omega network is better than omega network in terms of throughput, transmission time, processor utilization, and processing power.
User Behavior Profiling using Ensemble Approach for Insider Threat Detection

The greatest threat towards securing the organization and its assets are no longer the attackers attacking beyond the network walls of the organization but the insiders present within the organization with malicious intent. Existing approaches helps to monitor, detect and prevent any malicious activities within an organization’s network while ignoring the human behavior impact on security. In this paper we have focused on user behavior profiling approach to monitor and analyze user behavior action sequence to detect insider threats. We present an ensemble hybrid machine learning approach using Multi State Long Short Term Memory (MSLSTM) and Convolution Neural Networks (CNN) based time series anomaly detection to detect the additive outliers in the behavior patterns based on their spatial-temporal behavior features. We find that using Multistate LSTM is better than basic single state LSTM. The proposed method with Multistate LSTM can successfully detect the insider threats providing the AUC of 0.9042 on train data and AUC of 0.9047 on test data when trained with publically available dataset for insider threats.
Generating SAR Images Based on Neural Network

Compared with measurement, electromagnetic simulation can greatly reduce time and funding cost in SAR imaging. But there are still many differences between simulated and measured SAR images since the simulation is hard to take stochastic environments into account. In this paper, a cycle generative adversarial neural network, which can generate SAR images by learning the mapping between simulated SAR images and measured SAR images (MSTAR datasets), is constructed. The generated SAR images can be purely similar with measured SAR images.
Slot Loading Effect on the Impedance and Radiation Performance of the TM03-Mode High-Gain Square Patch Antenna

In this paper, the slot loading effect on the TM03mode high-gain square patch antenna is investigated in terms of its impedance and radiation performance. Three different slot configurations along the central line of the patch can all reduce sidelobes in the E-plane. However, the H-plane beamwidth, impedance bandwidth and radiation Q factor in these cases are significantly different. Initially, the central slot loading can help to achieve broader H-plane beamwidth, lower Q factor and the wider impedance bandwidth. In contrast, narrower H-plane beamwidth, higher Q factor and the narrower impedance bandwidth will be obtained by etching the slots at two edges. Consequently, more balanced performance can be obtained by loading both the central and sided slots.
Abnormal Cervical Cell Detection using HoG Descriptor and SVM Classifier

Cervical cancer is preventable and treatable if it can be detected at early stage by using simple Papanicolaou (PAP) test. Unfortunately, the incident rate in developing countries is still high due to lack of national PAP test policy and limited number of trained cytotechnologists and cytopathologists. Therefore, using image processing system to analysis PAP screening image can be an effective solution. We proposed a machine learning based system for abnormal cell detection as well as cell type classification. The proposed system relies on HoG feature extraction method and SVM classifier. The experiment results conducted on Harlev dataset have shown a recognition rate of 94.70% for normal and abnormal cell classification when considering that nucleus in each cell image is perfectly detected. When considering a real scenario application by using our proposed nucleus detection method, our system archives 88.83% of recognition rate.
Intrusion Detection on Critical Smart Grid Infrastructure

Traditional power grid, regarding the problems which it faces with, is going toward Smart grid, and this will be conducive to enhancing the efficiency of the system. However, Smart grid is dependent on a huge amount of data which are transmitted between different parts of the system and central controller through an extensive communication network in order to control the system. This information transmission makes Smart grid vulnerable to cyber attacks that can give rise to catastrophic consequences. In this paper, we employ an algorithm to detect and estimate attacks on smart grids. In this algorithm due to the stochastic nature of the power system, we design a Kalman filter to estimate state variables and the output of the system and use a $\chi^{2}$-detector to detect attack occurrence. The efficiency of this algorithm for smart grid is evaluated under Scaling, Ramp, Random and DOS attacks by numerical simulation.
Hybrid Microgrid Considering PV Generator and Energy Storage Systems

A laboratory building connected to a hybrid microgrid operates either in interconnected or islanded mode. This microgrid contains a PV generator and storage system. Energy production should be scheduled to provide the load of the building properly to adjust optimally the generation exchange within the microgrid according to a defined methodology. In this work, the problem of decreasing the cost of energy bills is addressed under different constraints and user decisions. An optimization model reducing the power costs of the utility grid is proposed. The ESS performances are achieved according to the peak demand to minimize the using of the grid. The proposed methodology schedules the charging and discharging of the ESS taking account the state-of-charge (SoC) limits. A control technique is used for optimal shifting of the power from the hybrid system. Simulation is executed using GAMS, the obtained results validate that the suggested hybrid system model can decrease the operation cost.
Implementation of Machine Learning Techniques in Software Reliability: A framework

In this paper review of existing literature in the field of software reliability models based on machine learning techniques presented. Software reliability is very useful tool in determining the software quality. By using machine learning techniques for getting unhidden parameters affecting software fault prediction for exploring various parameters leading to obsoleteness of software by presenting category of papers of software reliability, software fault prediction, software trustworthiness, software reusability, using machine learning techniques based on statistical inferences which could predict useful pattern on hidden data of faulty software database of empirical datasets related to software testing. After studying plenary relevant papers on faults generated during fault removal, faults already present, we proposed a novel approach based on identifying most relevant parameter affecting the software reliability using Machine Learning Techniques.
ClPy: A NumPy-Compatible Library Accelerated with OpenCL

We developed ClPy, a Python library that supports OpenCL with a simple NumPy-like interface, and an extension of Chainer machine learning framework for OpenCL support. OpenCL emerged as a parallel computing standard with the goal of supporting a wide range of accelerators including GPUs (NVIDIA and others), FPGAs, DSPs, and CPUs. In contrast, many machine learning frameworks including Chainer have been built on top of CUDA, a predominant API for programming NVIDIA GPUs. As such, they cannot leverage other devices including non-NVIDIA GPUs and FPGAs. To facilitate developing cross-platform machine learning frameworks, ClPy is designed with an interface compatible with CuPy (CUDA Python), which itself has a NumPy-compatible interface and is used in Chainer to support both CPUs and NVIDIA GPUs. ClPy extends Chainer to any platform supporting OpenCL and can potentially do the same for other machine learning frameworks. This paper describes the design and implementation of ClPy and demonstrates it achieves reasonable performance on several machine learning applications. Our experiments show that the overhead of ClPy itself and serious performance degradation was caused by the lack of GPU-accelerated libraries of OpenCL including BLAS.
Capillary Force Gripper for Complex-Shaped Micro-Objects With Fast Droplet Forming by On–Off Control of a Piston Slider

In this letter, we describe the newly proposed capillary force gripper that combines fast water refilling via the capillary phenomenon and fast droplet forming via the on–off control of a piston slider. The capillary force is suitable for capturing and releasing heterogeneous and complex-shaped micro-objects because it is one of the most dominant forces in the microscopic world and acts on any shaped objects by the water flexible deformation. However, a water droplet easily evaporates and loses during every pick-and-place operations. To solve this problem, we developed a gripper that can quickly form droplets with simple control. In the experiments, we confirm that the gripper can generate a sufficient capillary force to grip a 1-mm3 micro-object for over 100 s. Furthermore, we realize the automatic pick-and-place of cube, cone, and semicylinder samples. We compare the positioning errors among them to discuss the feasibility, future prospects, and applications in electronics and MEMS fields.
Method for Muscle Tone Monitoring During Robot-Assisted Therapy of Hand Function: A Proof of Concept

Robot-assisted rehabilitation of hand function is becoming an established approach to complement conventional therapy after stroke, particularly in view of its possible unsupervised use to promote an increase in therapy dose. Given their intensive therapy regime, robots may promote a temporary increase in hand muscle tone and spasticity, which may cause pain and negatively affect recovery. To integrate hand muscle tone monitoring into an assessment-driven robot-assisted therapy concept, an online assessment of muscle tone is proposed and incorporated into an exercise. The exercise was preliminarily tested in a pilot study with five chronic stroke survivors (non-spastic at rest) and five healthy participants to identify the range of potential physiological muscle tone change that can happen also in a non-spastic population during a single exercise session. In both groups, the muscle tone level during hand opening was higher in fast 20 mm ramp-and-hold perturbations (150 ms) compared to slow (250 ms) perturbations, and corresponded to a force change of approximately 4-5 N. Despite not being statistically significantly different, in the stroke group the force change (and the speed dependency) increased with exercise time. This information could serve as a basis to develop strategies to continuously adapt the difficulty and activity level required in robot-assisted rehabilitation and to monitor or even control the muscle tone evolution over time
A Survey of Localization in Wireless Sensor Network Using Optimization Techniques

The objective of this paper is to review the different existing optimization techniques that are used to overcome the localization issue in the wireless sensor network. Wireless sensor networks made up of devices that are placed in huge quantity over a large geographical region to perform several tasks like sensing the data and communicate with other devices. Most of the applications of wireless sensor networks like forest fire detection required the exact position of the sensing element. The aim of the localization process is to allocate the coordinates to the every node with unidentified location in the sensing area. In this paper various optimization techniques for localizing the nodes like particle swarm optimization, bees optimization algorithm, bat algorithm, cuckoo optimization and butterfly optimization algorithm etc. are reviewed. The detail comparison of these techniques in terms of localization error, computation time and amount of localized nodes is also presented in this paper.
Towards making Morphing Attack Detection robust using hybrid Scale-Space Colour Texture Features

The widespread use of face recognition algorithms, especially in Automatic Border Control (ABC) systems has raised concerns due to potential attacks. Face morphing combines more than one face images to generate a single image that can be used in the passport enrolment procedure. Such morphed passports have proven to be a significant threat to national security, as two or more individuals that contributed to the morphed reference image can use that single travel document. In this work, we present a novel method based on hybrid colour features to automatically detect morphed face images. The proposed method is based on exploring multiple colour spaces and scale-spaces using a Laplacian pyramid to extract robust features. The texture features corresponding to each scale-space in different color spaces are extracted with Local Binary Patterns (LBP) and classified using a Spectral Regression Kernel Discriminant Analysis (SRKDA) classifier. The scores are further fused using sum rule to detect the morphed face images. Experiments are carried out on a large-scale morphed face image database consisting of printed and scanned images to reflect the real-life passport issuance scenario. The evaluation database consists of images comprised of 1270 bona fide face images and 2515 morphed face images. The performance of the proposed method is compared with seven different deep learning and seven different non-deep learning methods, which has indicated the best performance of the proposed scheme with Bona fide Presentation Classification Error (BPCER) = 0.86% @ Attack Presentation Classification Error Rate (APCER) = 10% and BPCER = 7.59% @ APCER = 5%. The obtained results indicate the robustness in detecting morphing attacks as compared to earlier works.
Test Vector Reordering by using Hybrid Genetic Algorithm-Simulated Annealing for Lower Switching Activity

Test power dissipation is way higher than normal mode power dissipation due to extensive switching activity, which affects the reliability of the device. Switching activity can be reduced by reordering of test vectors without modification in the circuit under test. In this work, a genetic algorithm with simulated annealing is proposed for reordering of test patterns and thereby finding optimum orders of test vectors to reduce overall switching activity at the time of application of test vectors. Hamming distance among consecutive test patterns is minimized in this work for reordering the sequence. Experimental results demonstrate a significant improvement of 16.5% in the reduction of switching activity on ISCAS’89 benchmark circuits.
Comparision Study on Various Face Detection Techniques

Nowadays research has been developed by many security facilities such as fingerprint, palm biometric and different biometrics. This information contains about comparisons of face detection techniques, which are very useful for identifying the people in any situation. The face biometrics is more secured because of its unique characteristics which gives advantages over other biometrics i.e. palm and fingerprint. In this paper, we discussed various existing techniques based on their accuracy on different kind of databases.
Kullback-Leibler Divergence based Marker Detection in Augmented Reality

A marker is a pre-defined pattern that is scanned for in augmented reality videos. Its presence when detected would provide the exact location for the placement of the 3D object in the video. The accurate detection of markers is based on efficient matching strategies, correlation being the most common measure. In this experimental study, we investigate the utility of the Kullback-Leibler (KL) divergence for marker matching and detection. This requires the marker intensities to be normalized to a probability distribution since the KL divergence measures the distance between two probability distributions.
Adaptive fault-tolerant control for a class of remotely operated vehicles under thruster redundancy

For a class of remotely operated vehicles (ROVs) with redundant thrusters, an adaptive fault-tolerant controller was addressed in this paper. Considering three categories of thruster faults, a fault model was established, and an adaptive controller based on radial basis function (RBF) neural networks sliding mode method was designed. Due to the ROVs are thruster redundancy, a thrust allocation method with the principle of yaw moment priority was proposed. It was proved that the trajectory tracking errors of in the inertial coordinate were uniformly ultimately bounded based on Lyapunov stability theory. Finally, based on a ROV mathematical model, the simulations were carried out in Matlab environment to show the effectiveness of the proposed controller.
Design of Cortical Bone Drilling Temperature Measurement Instrument in Virtual reality system

Cortical bone drilling technique has been widely used in orthopedic surgery. This paper designed a new virtual instrument of monitoring cortical bone drilling temperature based on LabVIEW, which selected K type thermocouple as the temperature sensor, and AD595 as signal amplification components. The data collection technology, sensor technology and computer technology was used in the new virtual instrument. A single variable experiment was carried out to test the cortical bone drilling by the new virtual instrument. The experiment results show that the virtual instrument measured the specified period of time drilling temperature accurately. Through the experimental analysis, the optimal spindle speed and feed speed could be definite. This would provide theoretical guidance for practical clinical medical.
Job Stress-Performance in Academia: Role of Emotional Intelligence & Social Support

The phenomenon of job stress has been widely researched and its impact on performance, satisfaction, motivation, and other related aspects has been well established in various professions. The teaching profession too is no longer an exception. The aim of the present study is to examine the relationship between job stress and job performance among teaching faculty in higher education sector in New Delhi/NCR region and whether this relationship is moderated by emotional intelligence and social support at these work places. The study was conducted among 180 higher education teaching faculties working at various universities in New Delhi/NCR region. A multiple hierarchical regression was performed, and results confirmed about the existence of significant negative relationship between these two factors, establishing about the increased stress at work resulting in decreased teacher performance. However, emotional intelligence and presence of social support at workplace were found to mitigate the effects of stress indicating that these elements act as significant moderators. The findings of the study have deep implications for higher education system practices which contribute to stress on the teaching community. The results of the study suggest that job stress significantly impacts job performance in the field of academia as well, but these deleterious effects of job stress could be buffered through emotional intelligence and adequate social support system at work.
Design and Simulation of Autonomous Fire Fighting Robots for Forests

Forest fires are a ferocious phenomenon. They require quick action to prevent any damage from happening. Robots can act as great first responders in this regard. In this paper a model of a robot for forest firefighting purposes is presented. The model is tested against static stress loading. The design and simulation for the same has been done in Autodesk’s Fusion 360.
FIFO-Based Hardware Sorters for High Bandwidth Memory

The main contribution of this paper is to show efficient FIFO-based hardware sorters that sort n elements with w bits each stored in a high bandwidth memory with modest access latency. We assume that each address of the high bandwidth memory can store p elements of w bits each, which can be read or written at the same time. The access latency l of the high bandwidth memory is assumed to take l clock cycles to access p elements in a specified address. Furthermore, burst mode is supported and k (≥ 1) consecutive addresses can be accessed in k+l-1 clock cycles in a pipeline fashion. However, if k addresses are not consecutive, kl clock cycles are necessary to access all of them. Clearly, all n elements arranged n/p addresses can be duplicated in 2(n/p+l-1) clock cycles. We present two types of hardware sorters that sort n=rc elements stored in an r×c matrix of the high bandwidth memory. We first develop Three-Pass-Sort and Four-Pass-Sort that sort an r×c matrix by reading from and witting in it three times and four times, respectively. We implement these two algorithms using FIFO-based mergers that can be configured as pairwise mode and sliding mode. Our hardware sorter based on Three-Pass-Sort runs in 6n/p+3c^2/p^2l+O(c/p(l+log r)+r) clock cycles using a circuit of size O(rwp) provided that r≥c^2. Also, our hardware sorter based on Four-Pass-Sort runs in 8n/p+2c^2l+O(cl+log r+p) clock cycles using a circuit of size O(rw).