Miracle-dz/UniFAR-FTUs huggingface dataset:
Beam Squint Mitigation Using Digital Beam Formers for Wideband Radar Applications

In modern phased array radars, digital beamformers are used for beam steering in transmit and receive operations. Most of the conventional radars operate in narrowband applications with phase shift beamformers. The high-resolution imaging radars operating with wide bandwidths suffer from beam squint with phase shift beamformers. In this work, a methodology is proposed to address this effect through suitable corrections in the frequency domain. The focus is given for receive beam formation with squint correction using Linearly Frequency Modulated waveforms. The paper includes the analysis of beam squint with various parameters like bandwidth and steering angle. This is supported with results and associated discussions obtained at different stages of analysis.
The use of polycyclic aromatic in the assessment of marine life and the impact on petroleum pollution in the Suez Gulf, Egypt

Abstract This study represents the concentrations of 16 PAHs in a commercial aquatic species Sauridaund squamis; Euthynnus affinis; Rhabdosargus haffara; Argyrops Spinifer; Nemipterus Japonicus, Oreochromis Niloticus; Trachurus Indicus; Peneus Japonicas; Scomber Japonicus; and Pomadasys Stridens, which were collected from the coastal area of the Suez Gulf waters. The analyses of polycyclic aromatic hydrocarbons (PAHs) were determined using HPLC to identify the sources of PAHs. The results revealed that low molecular weight of PAHs were more than high molecular weight in aquatic species, Benzo (a)Anthracene/(Benzo (a) Anthracene + Chrysene) BaA/(BaA + Chr), Indeno (1,2,3-cd) pyrene/(Indeno (1,2,3-cd) pyrene + Benzo (ghi) perylene) IP/(IP + BP) and Fluoranthene/Pyrene (Fl/Pyr), they reflected that the PAHs sources in aquatic species are petrogenic as a main sources, pyrogenic sources. The study area was generally contaminated with hydrocarbons and continuous consumption of food from this area may pose public health hazards.
Modeling and Simulation of Communication Systems in OPNET

Abstract : This research aims to present accurate computer models of a communication link and a Super High Frequency (SHF) radio communication system. Network Warfare Simulation (NETWARS) is a J-6 initiative aimed at modeling all communication traffic in the Department of Defense (DoD) for testing and analysis of specific real world scenarios. The AN/TSC-94 is a SHF radio system with satellite communication capabilities. The AN/TSC-94 incorporates a Direct Sequence Spread Spectrum (DSSS) radio link for certain Anti-Jam (AJ) features. A DSSS spreads' signal power over a large bandwidth, reducing power previously concentrated within the original system bandwidth. The simulations were performed using OPNET. Simulation results show DSSS lowered Bit Error Rate (BER) over links not using spread spectrum. Results show that in the presence of multiple jamming forms, the DSSS link performed without bit errors while the normal (non-DSSS) link was disrupted by the jammer, experiencing BER's of up to 0.43. The AN/TSC-94 was able to defeat the jammer using the DSSS link. By performing in normal mode during unjammed scenarios, and switching to AJ mode in the presence of a hostile transmitter, the AN/TSC-94 demonstrated its ability to successfully communicate in multiple access and hostile environments.
A Temporal Neural Network Model for Probabilistic Multi-Period Forecasting of Distributed Energy Resources

Probabilistic forecasts of electrical loads and photovoltaic generation provide a family of methods able to incorporate uncertainty estimations in predictions. This paper aims to extend the literature on these methods by proposing a novel deep-learning model based on a mixture of convolutional neural networks, transformer models and dynamic Bayesian networks. Further, the paper also illustrates how to utilize Stochastic Variational Inference for training output distributions that allow time series sampling, a possibility not given for most state-of-the-art methods which do not use distributions. On top of this, the model also proposes an encoder-decoder topology that uses matrix transposes in order to both train on the sequential and the feature dimension. The performance of the work is illustrated on both load and generation time series obtained from a site representative of distributed energy resources in Norway and compared to state-of-the-art methods such as long-short-term memory. With a single-minute prediction resolution and a single-second computation time for an update with a batch size of 100 and a horizon of 24 hours, the model promises performance capable of real-time application. In summary, this paper provides a novel model that allows generating future scenarios for time series of distributed energy resources in real-time, which can be used to generate profiles for control problems under uncertainty.
Computer-simulated explosion of poly-silicide links in laser-programmable redundancy for VLSI memory repair

A computer model has been used to describe the temperature evolution of link targets as they are typically processed in laser programmable redundancy. Three such infrared laser simulations are described: (1) short-pulse polysilicon targets, (2) short-pulse TaSi/sub 2/-polysilicon targets, and (3) long-pulse TaSi/sub 2/-polysilicon targets. Reliability issues involved with each of these three laser target systems are discussed. Good correlation has been noted between the model results and two experiments involving ambient wafer temperature and long-pulse laser processing. The poly-silicide target is shown to present short-pulse infrared laser processing difficulties not encountered in a solid polysilicon link. These are caused by the strong optical absorption of the TaSi/sub 2/, the subsequent screening of deeper target material, and the high melting and vaporization temperatures of this silicide. The transparent top dielectric and the optical properties of solid and liquid silicon play important roles in the suggested reliability of the solid polysilicon link. Although the use of a long-pulse laser on poly-silicide targets appears to overcome some of the above-mentioned difficulties, subsequent reliability and performance degradation casts doubt on this technique for high-volume manufacturing. >
Cattle Disease Detecting IoT Thermographic System

The food industry is facing enormous challenges in order to ensure sufficient amounts of food for the expanding population, while meeting and fulfilling rapidly changing quality demands. With cattle being one of the essential food-supplying animal breeds, imposed are strict requirements towards their wellbeing support and monitoring. Within this work a novel developed IoT based thermographic system for cattle disease detection is presented, which allows scalability and direct farming applicability. Emphasis is additionally pointed towards methods for parasitic effects' cancellation.
A Fuzzing Method for Embedded Software

Fuzz testing is an important means of vulnerability discovery, but now fuzz testing technology is most for application software on traditional computer platforms, which can be not used on embedded software. We propose a fuzzing method for embedded software, which can solve the two technical difficulties: abnormal monitoring and automation. Also, we independent research and develop embedded fuzzy testing tool called Efuzzer. Experiments prove that the tool has very good practicality and is effective for discover buffer overflow vulnerabilities which can cause a system crash. Now the tool is tried in several software testing institutions, a few vulnerabilities were discovered.
An Alternate Switch Selection for Fault Tolerant Load Administration and VM Migration in Fog Enabled Cloud Datacenter

Users of Cloud-fog can access elastic clusters of available Virtual Machines (VM) for their data processing needs. Consumption of lesser hardware by the individuals in additional to the VM technologies was attained by means of introducing Fog-IoT set-up and Cloud-resources employment. A better recapturing of failure renovation services requires VM based infrastructure. Service accuracy for the provider's Virtual Domain Controller (DC) can be achieved through dedicated routing resolution. In case of node failure, it is very difficult to decide which and how-many VM's should be chosen for migration to keeps up the accuracy assurance at the failed node. The selected VM can restrict the number of VM's should be migrated. For decreasing the given cloud-fog resources loads, choosing a single or multiple promising VMs to be migrated may create an issue. An Alternate switch Identification and Fault Tolerant Load Administration (AsI_FTLA) for implementing the Cloud-fog data center infrastructure while VM migration via improved recovery method of Virtual Network (VN) is proposed in this work. A model of linear-integer programming is imposed to study the path traffic that examines every associated numerical factor for selecting the most favorable VM through the best route. The new VM migration is then established by alternative switch identification algorithm and routing is achieved. CloudSim was employed to study the performance of proposed AsI_FTLA system. Simulation results states, there is a considerable improvements in average resource and storage utilization along with throughput, and reduction in total execution time against existing strategies.
Generative Adversarial Networks (GAN) for Arabic Calligraphy

Arabic calligraphy is one of the most aesthetic art forms in the world due to its variety and long history. However, generating calligraphic style is mainly done by human expert calligrapher (also known as Khattat) and has not been carried out by machine learning techniques. Generative adversarial networks (GAN) are deep learning tools that achieved outstanding results in the field of style transfer and generation. In this paper, various GAN architectures were investigated such as CycleGAN, Pix2pix, and deep convolutional generative adversarial networks (DCGAN) within Arabic calligraphy in two aspects: generation and style transfer. The results show that CycleGAN can transfer skeleton letters to both Naskh and Thulth styles, Pix2Pix can denoise the calligraphy papers, and DCGAN can generate realistic Arabic calligraphy letters. The proposed approaches are applicable for other calligraphy styles besides Naskh and Thulth. Finally, the models are evaluated qualitatively using a preference judgment technique survey.
Investigating Effective Brain Networks of Action Observation Tasks from Different Visual Perspectives based on Generalized Partial Directed Coherence: An fMRI Study

Several studies have been provided the principal activated regions and locations during action observation. However, it remains unclear that the dynamic link and information flows in different visual perspectives during the action observation tasks. The aim of this study was investigating the effective connectivity of different visual perspectives during action observation tasks and classify the perspectives. Eleven regions were selected as the regions of interest (ROI) by meta-analysis using the term of "action observation" for this study. We employed weighted and directed (WD) brain networks by Generalized partial directed coherence (GPDC) on the fMRI BOLD time series of action observation tasks from different visual perspectives. Afterward, the paired sample t-test was implemented to find the alteration of brain networks between the two perspectives. According to the results, global efficiency, local efficiency and cluster coefficient were statistically significantly different between first-person perspective (1PP) and third-person perspective (3PP) in Supplementary motor cortex (SMA) and Right inferior parietal lobe (RIPL) and Left inferior parietal lobe (LIPL). These three regions which we considered may provide the additional support for the visuospatial transformation from 3PP to 1PP. WD brain network topological measures of SMA, RIPL and LIPL were also selected to classify the 1PP and 3PP using support vector machine (SVM) with the best accuracy close to 90%.
Extended Describing Function Modeling and Closed-Loop Control of LLC Converter in Battery Charging Applications

The variable switching frequency control of LLC converters makes the modeling and compensator design rather a difficult task. In this paper, an extended describing function modeling that can represent the beat frequency successfully has been examined in detail. The small signal model of the converter dynamics has been extracted for output current and voltage. Two compensators have been designed to close the loop for constant-current and constant-voltage operations considering main operating regions. The performances of the designed compensators have been verified under different load conditions. It has been shown that the designed controller successfully tracks the reference output current and voltage appropriately by adjusting the switching frequency.
Meta Distribution-optimal Base Station Deployment for Finite-Area Mobile Networks

User mobility in cellular networks deployed in finite areas, results in the non-uniform spatial distribution of the mobile users (MUs), necessitating the effective deployment of base stations (BSs) in order to enhance the network connectivity. In this paper, we analyze the optimal non-uniform distribution of the BSs that provides ultra-reliable connectivity in the context of finite-area cellular networks, where the MUs employ the random waypoint (RWP) model. By using stochastic geometry tools, we first establish an analytical and tractable mathematical framework to investigate the uplink success probability in the context of non-uniform cellular networks. Furthermore, analytical expressions for the moments of the conditional success probability are derived, and a simple approximation of the meta-distribution (MD) is calculated, leveraging the moment-matching method with the Beta-distribution. Finally, we exploit the MD as a criterion for defining the optimal BS distribution, conditioned on the user’s mobility. Our results reveal that the developed framework provides guidance in the design of cellular networks to efficiently determine the optimal network spatial deployment that offer ultra-reliable connectivity based on the mobility of MUs.
A Novel Characterization Method of Jimusaer Shale Oil Reservoir Based on Quantitative Digital Cutting Analysis

The resource of shale oil reservoir in Jimusaer of eastern Junggar basin is about 2 billion tons, which is the key block of exploration and development in China. However, due to the ultra-tight formation, there are various shortcomings in the traditional coring and core analysis, such as high cost of shale coring, long time of laboratory measurements, etc. Also, the laboratory measurements are insufficient to describe the reservoir characteristics at the pore-scale. This paper proposes a shale oil reservoir characterization method based on the quantitative digital cutting analysis. In this study, the cuttings of J174, a typical well in Lucaogou shale oil reservoir of Jimusaer, were selected. Combined with the SEM digital quantitative analysis and J-function method, the minimum pore throat size for mobile shale oil was evaluated, which agrees well with the NMR logging.
Can ISPs Help Mitigate IoT Malware? A Longitudinal Study of Broadband ISP Security Efforts

For the mitigation of compromised Internet of Things (IoT) devices we rely on Internet Service Providers (ISPs) and their users. Given that devices are in the hands of their subscribers, what can ISPs realistically do? This study examines the effects of ISP countermeasures on infections caused by variants of the notorious Mirai family of IoT malware, still among the dominant families. We collect and analyze more than 4 years of longitudinal darknet data tracking Mirai-like infections in conjunction with threat intelligence data on various other IoT and non-IoT botnets across the globe from January 2016 to May 2020. We measure the effect of two ISP countermeasures on Mirai variant infection numbers: (i) reducing the attack surface (i.e., closing ports that are used by the malware for propagation) and (ii) ISPs increasing their general network hygiene and malware removal efforts (as observed by proxy of the remediation of infections of other families of IoT and non-IoT malware and reductions in the number of DDoS amplifiers in their networks). We map our infection data to 342 broadband providers that have the bulk of the broadband market share in their respective 83 countries. We find that the number of infections correlates strongly with the number of ISP subscribers ($R^{2}=0.55$). Yet, infection numbers can still vary by three orders of magnitude even for ISPs with comparable subscriber numbers. We observe that many ISPs, together with their subscribers, have reduced their attack surface for IoT compromise by blocking traffic to commonly-exploited infection vectors such as Telnet and FTP. We statistically estimate the impact of these reductions on infection levels and, counter-intuitively, find no significant impact. In contrast, we do find a significant impact for improving general network hygiene and best malware mitigation practices. ISPs that were more successful in reducing DDoS amplifiers and non-Mirai malware infections in their networks also end up with significantly lower Mirai infection rates. In other words, rather than investing in IoT-specific countermeasures like reducing the attack surface, our findings suggest that ISPs might be better off investing in general security efforts to improve network hygiene and clean up abuse.
Automatic Classification of Sleep Stages using EEG Sub-bands based Time-spectral Features

Sleep scoring is proved of having major impact on treating various sleep oriented disorders. But achieving this task manually is very time consuming and long process. Hence, an efficient computer based system is required to carry out the epoch based multi-class sleep stages classification. Among all the polysomnography (PSG) signals, Electroencephalogram (EEG) provides valuable information for sleep related analysis by sensing and monitoring the brain functions. Hence in this study, an effective computer-assisted technique is proposed for classifying various sleep stages. Firstly, the input signal is segmented into 30 seconds epochs as per the Rechtschaffen and Kales criteria (1968). From the six EEG sub-bands, five features such as Normalized power, Movement, Mean Absolute Deviation, Inter-quartile range and Fourier Synchrosqueezed transform are extracted. The feature vector is subjected to 10-fold cross-validation for 2-class to 6-class classification. The results are obtained after computing accuracy, sensitivity, specificity and Cohen Kappa's statistics using SVM classifier. The highest accuracy of 98.4%, 95.8%, 94.3%, 93.4% and 92.5% is achieved for 2-class to 6-class classification respectively. Also, subject specific results are computed for 5-class problem for which F1 score is evaluated for each stage. This proposed method offers improved results as compared with other previous studies.
Smart Optimization of Machine Systems Using Hierarchical Genotype Representations

Design problems for machine products are generally hierarchically expressed. With conventional product optimization methods, however, it is difficult to concurrently optimize all design variables of portions within such hierarchical structures. This paper proposes a design optimization method using genetic algorithms containing hierarchical genotype representations, so that the hierarchical structures of machine system designs are exactly expressed through genotype coding, and optimization can be concurrently conducted for all of the hierarchical structures. Crossover and mutation operations for manipulating the hierarchical genotype representations are also developed. The proposed method is applied to a machine-tool structural design and a 2 DOF robot arm design to demonstrate its effectiveness.
Linker Energy Optimizations in Microcontrollers Using the GCC Toolchain: A Case Study

The following paper discusses in detail the remapping of executable code from Flash to SRAM in order to achieve an energy efficient firmware in microcontrollers. The used architecture is ARM Cortex-M4F and therefore the conclusions made here are applicable to other Harvard machines as well. A selected set of five benchmarks from the BEEBS package have been evaluated before and after the code relocation. Some of the optimizations are successful, others are not, but all the projects exhibited a lower power consumption than that of the original programs. This may serve as a guide to compiler architects that wish to implement energy optimization rules, which currently do not exist in the GCC toolchain.
Modelling and Virtual Testing of a Wound Rotor Synchronous Machine for Electrical Vehicles Propulsion System

This paper presents a dq flux-linkage modeling approach and the virtual testing of a wound rotor synchronous machine that assures the propulsion of a battery electrical vehicle. The motor model, organised using Energetic Macroscopic Representation, is constructed based on a series of electromagnetic finite-element analysis defined by various armature and rotor excitation currents. The performances of the dq-model, together with its control and electronic units, are evaluated through virtual tests. Furthermore, the model accuracy is validated by comparing the simulation results with the experimental data obtained from different tests performed on a Renault ZOE Q90 car. Two test scenarios, an urban test and a highway one, are chosen in order to evaluate the precision and the reliability of the developed model under normal and maximum electrical vehicle speed conditions. The combination of dq-modeling and virtual testing represents a way to rapidly develop and validate high performance system models and accelerate time to market.
Characterising the Performance of AODV for Various Mobility Scenarios

Mobile ad-hoc network (MANET) is an infrastructure-less, rapidly deployable and self-organizing distributed network with mobile autonomous terminals. Due to these characteristics, MANETs have always been important for defence applications especially for countries like India having boundaries and regions with large geographical diversity. Mobility is one of the defining features of MANET and has a major impact on the performance of routing protocols. In the present work, the performance of the AODV routing protocol is characterised for various mobility scenarios including random movement-based, controlled movement-based and realistic mobility models. A rigorous mobility and scalability analysis of AODV has been presented in graphical form considering transmission delay, number of received packet, control overhead, normalized routing overhead, packet delivery ratio and throughput as the performance measure. Network Simulator 2.35 is chosen as the primary simulator along with BonnMotion as well as the MOVE tool, for the generation of various mobility scenarios.
Polarization Induced Multiferroic Bismuth Ferrite Nanostructures: Investigation of Dielectric and Magnetic Properties

Spherical-shaped multiferroic BiFeO3 were synthesized successfully by hydrothermal method having diameter of tens of nanometre with good crystallinity. The structural and micro-structural studies were done by X-Ray diffraction, Raman analysis and high-resolution transmission electron microscope. Frequency dependent dielectric measurements were done to study the change in dielectric permittivity of different samples of BFO. It was found out that the value of dielectric permittivity nearly doubles for the poled and the calcined samples as compared to the unpoled and the as-synthesized samples, respectively. Magnetic characterisation showed the temperature dependence & particle size dependence of coercivity. It also revealed the anti-ferromagnetic nature at room temperature and weak ferromagnetic nature at 10 K. The results showed that the poled nanoparticles might be useful in designing the energy storage devices coupled with magnetic functionalities like microcapacitors.