Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Deep Convulational Neural Network in Medical Image Classification

The convolution networks make a great success in image classification since AlexNet proposed for classification in ImageNet challenge. This End-to-End training method makes the researcher focus more on how to build an efficient network. The user focuses more on how to use convolution networks in their application. After AlexNet, there are much ConvNet architecture proposed, such as VGGNet and ResNet. These ConvNet architectures are widely used in many actual real applications. Different types of techniques for image classification and many other medical applications focused on Convolutional Neural Networks(CNN) are addressed in this paper. So this paper will help us in selecting an appropriate classification technique among all the available methods.
Non-Cooperative Bistatic Sonar for Underwater Environmental Monitoring: a Model-based Approach

The paper addresses the problem of target identification in non-cooperative bistatic sonar systems and reports some preliminary simulative results obtained by applying a matched channel response target estimation algorithm. In particular, the paper describes the processing chain and reports a sensitivity analysis of the processing chain with respect to the length in time of the source chirp signal and to Doppler effects due to relative source-receiver motion.
Comparison two wake models based on full geometry turbine and actuator disc theory and study the effect of blockage

Two numerical wake model were established, one based on full geometry turbine and one based on actuator disc theory. The Actuator disc momentum theory assumes turbine as a constant sink of momentum in numerical domain. Results of numerical models were compared with experimental results, the experimental data is in good agreement with the simulation data. The difference between two models were analyzed in detail. The the difference between simulation results of actuator disc and test results are slightly larger in near wake region, the difference gradually decreased as the axial distance increased. The lack of the simulation of rotation of the model may lead this difference. The effect to wake recovery of blockage ratio was studied using numerical model based on actuator disc theory, 11 cases were studied, the results showed that the blockage ratio can heavily effect the recovery speed of turbine wake. Along the centerline, the degree of wake recovery decreased as the blockage ratio increased before X/D=10.8, and the degree of wake recovery increased as the blockage ratio increased after X/D=10.8.
OA Verilog AMS Consistency Validation in logical Library with Different Technological Standards from 16 to 40 nm

Logic element libraries are widely used in the design of modern digital blocks. One type of design data provided is OA Verilog AMS. It simulates the behavior of blocks and engineering systems for analog and mixed signals. The problem of developing a unit test for checking this data in the conditions of using libraries with different technological standards is investigated. Studied and compared with similar verification in integration tests. As a result, the advantages and disadvantages of the developed test are shown. The specifics of working on different technology libraries, as well as the advantages and disadvantages in comparison with the implementation of verification through the block of integration tests.
Optimization of Chemical and Electrochemical Methods of Metal Deposition on Thermoelectric Materials

Methods of obtaining contacts to thermoelectric materials based on Bi2Te2.8Se0. 2 and Bi0.5Sb1. 5Te3 by chemical and electrochemical deposition of nickel, up to 20 μm thick, have been optimized. Using a galvanostatic mode in an electrolyte containing: NiSO4×7H2O; NaCl; H3BO3, the rest is water, at a current density of 1 A/dm<sup>2</sup>, nickel contacts with an adhesion strength of 8.70 to 10.66 MPa were obtained. The maximum adhesion strength was obtained for contact layers with a thickness of 8 microns formed by chemical deposition of Ni on the legs of thermoelements with a pre-deposited Ni layer 300 nm thick. Chemical deposition of Ni was carried out in a borohydride electrolyte containing: NiSO<inf>4</inf>×7H<inf>2</inf>O; KNaC<inf>4</inf>H<inf>4</inf>O<inf>6</inf>×4H<inf>2</inf>O; NaOH; C<inf>2</inf>H<inf>5</inf>NO<inf>2</inf>; K<inf>2</inf>Cr<inf>2</inf>O<inf>7</inf>; NaBH<inf>4</inf> at a temperature of 80-90°C. The adhesion strength of the obtained nickel contacts is 15.87-16.56 MPa. The specific contact resistance for optimized contact formation methods ranges from 1.0 to 2.0*10<sup>−9</sup> Ohm*m<sup>2</sup>.
Automated Timetabling System for University Course

Generating timetable for university with many branches, different years, and multiple batches is a tiresome job. It takes a lot of time and is required for every semester that is of six months. Thus, a lot of time and manpower is taken up by this process. In some cases, the manual process of creating timetables is too cumbersome. Creating temporary timetables when a faculty is on leave is impractical. In this paper, we have created an algorithm to generate timetables which can save a lot of time and pressure on the person doing this job manually. A software-based approach is better, as a computer can process data and churn out results at a much higher rate with greater accuracy. Also, it can be coded and optimized as per requirements set by different universities and the base code will remain the same.
Novel Single-Channel EEG Features Correlate with Working Memory Load

Working Memory (WM) load is a cognitive feature, which is highly correlated with mental effort. Theta power, among other neurological biomarkers, shows increased activity with increasing WM load. These correspondences frequently break down in individuals with cognitive decline, making WM load biomarkers a potentially valuable tool for early detection of cognitive impairment. However, such studies used multiple-channel EEG headsets, which are not easy to use and accessible. Here we used a single-channel EEG set (Aurora by Neurosteer®) and evaluated the ability of novel features, previously extracted on a different dataset, to serve as WM load biomarkers. In a single experiment, fourteen participants underscored the widely used n-back task while their brain activity was recorded with the mobile EEG device. Frontal theta power, as well as two novel features, significantly correlated with the levels of WM load. However, the two novel features exhibited higher sensitivity to lower WM load changes. These more sensitive biomarkers of WM load are a promising tool for mass screening of mild cognitive impairment.
The Rehabilitation and Assessment in Virtual Reality Game for the patient with Cognitive impairment

Nowadays, traditionally cognitive rehabilitation focuses on the social activities and psychological counseling. It requires lots of human resources but gets few useful information from patients; however, we want to solve this problem with the virtual reality technology. By using the environmental simulation, it can record the real-time behavioral data and train memory at the same time. In order to avoid the monotonous rehabilitation that will cause psychological burden, we developed this as a game system. In this system, there are four kinds of games: Water Maze, Mental Rotation, Ghost Catching, Recycling and Trash. Each game has instant feedback like sound effects and device vibrations to let patients know what they have done. Furthermore, the doctor can understand the condition of patients through the monitor. To reduce patient's stress and maintaining the rehabilitation are the most important mission in medical profession. We believe that our game system can achieve it.
On the localization of artificial submarine groundwater discharge sites using a low-cost multi-sensor-platform

In this paper, a low-cost, multi-sensor platform designed to investigate the spatial distribution of water quality properties is presented. The platform is based on an off-the-shelf underwater vehicle and various environmental sensors. Its intended application is the detection and mapping of submarine groundwater discharge (SGD), but it could be used for other related applications. In this study, an artificial submarine groundwater discharge (ASGD) was used to test the platform. Before the ASGD was installed, the study area was thoroughly surveyed using the platform developed to establish a baseline. After installation of the ASGD, the area was again thoroughly surveyed with the platform. The spatial and temporal variation in the data collected was used to evaluate the platform and sensors performances, and the usefulness of the different environmental parameters in mapping SGD. Although the experiments performed showed an influence of the ASGD on the measurements, this influence was smaller than expected. Different possible causes for this are discussed.
Estimation of underwater horizontal visibility for divers and ROVs

An approximate understanding of horizontal visibility is essential for divers and operators of underwater Remote Operated Vehicles (ROVs). Recently there have been a substantial number of underwater viewing systems which rely on either passive or active imaging and thus require good visibility. In this paper, a practical estimate of the horizontal visibility range for an object is developed using a Single photon avalanche diode (SPAD) camera and Secchi disk (SD). Also, a modified version of the visibility equation is given. Both of these results are from analyzing measurements from a series of indoor and outdoor measurements in sea water. We observe that the horizontal visibility can vary from 0.8x to 1.24x the SD depth values. Thus with a single SD depth measurement, horizontal visibility can be estimated. This can then guide the decision about the appropriate camera mode that can be used in the operation of a ROV at specific location (e.g. seas, lakes, rivers).
A Performance-Stable NUMA Management Scheme for Linux-Based HPC Systems

Linux is becoming the de-facto standard operating system for today’s high-performance computing (HPC) systems because it can satisfy the demands of many HPC systems for rich operating system (OS) features. However, owing to features intended for the general-purpose OS, Linux has many OS noise sources such as page faults or thread migrations that can result in the unstable performance of HPC application. Furthermore, in the case of the non-uniform memory access (NUMA) architecture, which has different memory access latencies to local and remote memory nodes, the performance stability of the application can be more exacerbated by the OS noise. In this paper, we address the OS noise caused by Linux in the NUMA architecture and propose a novel performance-stable NUMA management scheme called Stable-NUMA. Stable-NUMA comprises three techniques for improving performance stability: two-level thread clustering, state-based page placement, and selective page profiling. Our proposed Stable-NUMA scheme significantly alleviates OS noise and enhances the local memory access ratio of the NUMA system as compared to Linux. We implemented Stable-NUMA in Linux and experimented with various HPC workloads. The evaluation results demonstrated that Stable-NUMA outperforms Linux with and without its NUMA-aware feature by up to 25% in terms of average performance and 73% in terms of performance stability.
Automatic Generation of Preview Images Based on Video Sequence Analysis Using Computer Vision

The article is devoted to the problems of video analysis, selecting keyframes from it, searching for the most attractive thumbnails in it, as well as analyzing existing approaches to solving these problems. The article offers a new method for selecting attractive frames based on selecting frames with the largest number of attractive objects. This results in a frame that is visually appealing and relevant to the video. The solution also considers the selection of the most attractive thumbnails based on the analysis of various visual qualities and aesthetic indicators of video frames, and then performs cluster analysis to ensure that the resulting images match the video content. The research also considers the method of selecting the frames with the greatest activity through the emotional analysis of people in the video. If necessary, a style is applied to the final image to get a more interesting result.
Passive Localization Based on Spectral Estimation Methods Using Sources of Opportunity

Passive localization methods based on Double-Correlation Function (DCF) can localize an unknown source by matching the signal-correlated structures extracted from correlation function of source signal received at two sensors, as the structures are dependent on the position of the source and the waveguide. It is similar to classical matched field processing, but differs in that data-based measured replicas are used instead of modeled replicas, and the signal-correlated structures are matched rather than acoustic field. This paper presents an extension of this method from time domain to frequency domain, by utilizing generalized cross-correlation spectral estimation methods to normalize the cross-correlation function. The analysis based on air experiment shows that the proposed algorithm has significant improvements against interference and on the estimation accuracy.
Comparison of Integral Reconstruction Methods in SPECT Based on Numerical Simulation of Detector’s Count by the Monte Carlo Method Using the Geant4 System

Scattering of radiation make the largest contribution to data distortion in SPECT. There is no algorithm to fully take into account the influence of scattering. This is due to the fact that emission tomography is based on the radiation transfer equation, which does not have an exact analytical solution in the general case. In this regard, various approximations are used. To study the accuracy of the reconstruction in various approximations, it is possible to use the numerical simulation of the detector data by the Monte Carlo method using the Geant4 system. The comparison was made between the traditional reconstruction method and the reconstruction method based on approximation of the "scattering straight back", which takes into the account the influence of scattering in the tomogram reconstruction. The numerical simulation was carried out for a heterogeneous scattering medium. The results obtained show the superiority of the proposed method over the traditional one.
Analyzing the Application of SMOTE on Machine Learning Classifiers

Data mining functionalities are used in almost every sector including education. Due to continuous evolving information technology and decreasing cost of hardware, every educational institute has an urge to keep the students as much as possible data, in their databases to mine unknown insights. These unknown insights make institutes more proactive and counter reactive and helps them to induce more quality to their teaching and learning processes. In this paper, supervised machine learning techniques are used to predict the student performance using classification. Rapid miner 9.6, leading data science platform, is used for comparative analysis of predictions made by k nearest neighbor, naïve bayes, chi square automatic interaction detection decision tree, and random forest using synthetic minority over-sampling technique.
Learning to Find Bugs and Code Quality Problems - What Worked and What not?

The recent growth of open source repositories and deep learning models brought big promises for the next generation of programming tools that can automate or significantly improve the software development process. Yet, such tools are still rare and the machine learning components in them are not always apparent to their users. The current most useful techniques in machine learning for code are also not coming from the organizations such as Microsoft, Google, DeepMind, Facebook, OpenAI or nVidia that invested the most in deep neural techniques such as huge neural networks. This probably means that either many of these coding problems are significantly different from other hot topics in deep learning such as image processing or that it is much more difficult to collect datasets that would result in similarly successful tools. In this work, we study the results in the literature on the topic and discuss ways to address these shortcomings.
High-Resolution Underwater 3D Acoustical Imaging via Deconvolved Conventional Beamforming

Conventional beamforming is widely used in underwater three-dimensional (3D) acoustical imaging. However, the imaging quality of the conventional beamforming is limited, because of the limited capability of angular resolution and high sidelobe levels of conventional beamforming. This paper proposes to apply a deconvolution method based on the Fourier-based nonnegative least-squares algorithm to underwater 3D imaging, to improve the imaging quality. Experiment shows that the deconvolution method improves the angular resolution by two times and decreases sidelobe levels by more than 10dB for the 3D imaging sonar, compared to the conventional beamforming.
Comparative Analysis of Heuristic-based Energy Efficient Protocol for M2M Communications in Green IoT

There is a huge development in the Internet of Things (IoT) that causes massive deployment of sensors and thus automatic reconfigurable energy-efficient protocol for Green IoT is required. Machine-to-Machine (M2M) interactions are one of the core features of IoT. The M2M means the exchange of data among two or more entities, objects or machines, which do not require human interaction. To tackle the sustainability constraints of present IoT models, few developing models are thought to have been promised with new models. However, the fully usage of these models from communication, computing, and data processing for enhancing the energy efficiency of IoT still faces several basic defects. This paper tactics to plan for the comparative analysis on diverse heuristic approaches incorporated with Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol for energy-efficient M2M communication in Green IoT. The minimization of energy consumption by nodes in Green IoT is the main objective of the model. The algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Dargonfly Algorithm (DA), and Deer Hunting Optimization Algorithm (DHOA) is adopted making the comparison part in energy-efficient Green IoT. This energy-efficient model maximizes the network lifetime finally.
High-linearity Radiation-hardened Segmented 12-bit Digital-to-analog Converter Using Binary-weighted Switches

An experimental sample of a 12-bit digital-to-analog converter with high linearity has been developed. A new method for compensating the resistance of switches in an R-2R DAC is proposed. It is based on equalizing the currents of each branch. Segmentation and the use of binary-weighted switches were used to improve the values of integral and differential nonlinearities.
A Novel Approach for Service Function Chain Embedding in Cloud Datacenter Networks

Network function virtualization (NFV) and software defined networking (SDN) have recently introduced together, which show great potential in openness, innovation, flexibility and scalability of the networks. Embedding SDN/NFV-enabled service function chain (SFC) has been considered as an efficient way to achieve the end-to-end communication. Considering the multiple users’ delay diversities oriented to future networks, from the perspective of network operators, determining the placement of virtual network functions (VNFs) and routing the service paths is a challenging problem. However, the traditional solutions (e.g., exact, heuristic, meta-heuristic, game, etc.) based on the SFC dynamic embedding problem (SFC-DMP) follow the strong assumptions as usual, which might limit their applications in dynamic and complex network scenarios. To address this narrow, we are motivated to present a novel approach for handling SFC-DMP in the cloud datacenter networks that simultaneously combines the quantum genetic algorithm (QGA) and fuzzy logic. Simulation results show the efficient performance of our proposal.
Impact of Awareness Control on V2V-Based Overtaking Application in Autonomous Driving

In autonomous driving, IEEE 802.11p-based vehicle-to-vehicle (V2V) communication is considered for overcoming the intrinsic limitations of sensors and supporting safety applications. In this letter, we evaluate the effectiveness of relevant awareness control approaches, such as ETSI DMG, IVTRC, and POSACC, to support the V2V-based overtaking application in autonomous driving. For this, we assess the incident detection capability of the overtaking application when it is running with messages gathered from these approaches, considering packet losses due to channel fading. Simulations show that POSACC is more effective than the remaining approaches for detecting unsafe overtaking maneuvers in different operating conditions.
A Weakly-Coupled Few-Mode Optical Fiber With a Graded Concave High-Index-Ring

This paper proposes a novel refractive index profile design based on few-mode fibers (FMFs) which can support 4 linear polarization (LP) modes. We first present a FMF whose core is dually assisted by a nano-hole (NH) and a high-index-ring (HIR), and then substitute the dual assistance by an innovative graded concave HIR (GC-HIR) assisted structure. Using the finite element method (FEM), the parameters of the NH-HIR and GC-HIR optical fiber are adjusted to investigate their respective minimum effective refractive index difference (minΔneff), which is 2.012 × 10−3 for the former, and 2.532 × 10−3 for the latter. Both optical fibers have a significant improvement on the crosstalk suppression effect, and the GC-HIR optical fiber is even better. The proposed GC-HIR FMF with special refractive index profile has potential application prospects in mode division multiplexing (MDM) optical fiber communication system, and can provide theoretical foundation for the design and analysis of subsequent optical fibers and key components.
Text-mining in Terms of Methodology and Development

Text-mining is gaining popularity and has attained the status of cross-field technology, which brings together soft and hard sciences. This happens by virtue of its applicability in various fields, for instance, Linguistics, for language units’ identification and categorization, History, for ancient texts reconstruction, Sociology, for sentiment analysis, and others. In this paper, we make a deep analysis of the methodological background of text-mining. We prove that text-mining has accumulated several methodologies, which make it flexible for various purposes and fuzzy enough to bring into proximity of human-like text comprehension and knowledge extraction. We also make a retrospective journey into the history of text-mining, providing the description of technical means that underpin its appearance and the methods which draw it nearer natural cognitive processes. The abovesaid makes the study unique, for no papers focused on the methodologic and historical background of this rapidly developing technology are available.