Miracle-dz/UniFAR-FTUs huggingface dataset:
Experience Report on an Undergraduate Project in Distributed Systems

We describe our experience in building an open-ended, loosely-defined distributed systems project in a terminal-year course. The class had forty students organized into eight groups, producing eight projects. The direct objective of the project was to build a distributed application incorporating forms for synchronization, replication, consistency, and fault tolerance. The indirect objectives were to explore in more details concepts and algorithms studied in the lectures and to learn and utilize new concepts and algorithm not covered in the lectures. We briefly describe each project and reflect on this experience. This paper shows with a dominant student voice that the students took on the challenge to higher levels, raising their own expectation, bringing out their creativity to its best, tackling domain-specific research, and demonstrating their resourcefulness with available technologies.
A Supervisory Control Strategy for Battery-Ultra Capacitor Energy Storage Management

Battery electric vehicles have several challenges like power density, continuity of supply, life time etc. To solve this problem another power source is connected with the battery. In this paper a battery-ultra capacitor (UC) hybrid energy storage system (HESS) is proposed. This combinational arrangement gives better power and energy density. In this case UC supports both acceleration and regeneration periods of electric vehicles (EV). This paper presents a supervisory controller for HESS including a bidirectional three port buck-boost converter and a brushless dc (BLDC) motor. The proposed control strategy regulates power level of battery and state of charge (SOC) of UC with the help of a conventional proportional integral controller (PI) and fuzzy logic controller (FLC) and are evaluated with simulations in MATLAB®. Simulation results have confirmed that the controller effectively splits the load demand on battery and UC.
Arbitrary wideband open-loop active load-pull measurement using Unequally Spaced Multi-Tone stimulus

This paper presents an implementation of active load-pull measurements. This set-up uses the innovative Unequally Spaced Multi-Tone (USMT) wideband test signal in order to assess the in-band linearity degradation of non-linear microwave power devices. The proposed test bench is suitable for both on-wafer and connectorized devices characterization.A proof of concept of the arbitrary wide open-loop active load-pull measurement using 8-tones USMT has been detailed, using 90 MHz bandwidth with a VSWR of 10:1 on an in-house power amplifier. Load tuning is achieved by reinjecting both the carrier tones and third order intermodulation tones at the output of the amplifier.
Broadband 220 GHz network analysis: structures and performance

As mm-wave applications continue to increase in frequency and, simultaneously, the broadband measurement needs for the underlying device and subsystem development work spread, continuous network analyzer coverage beyond 200 GHz can be helpful. One such system uses an extension of an existing multiplied source multiplexing scheme and a modified shaped-LO converter structure to achieve power leveling over more than a 40 dB range from 70 kHz to 226 GHz, high receiver linearity, a noise floor below -100 dBm across the band, and $\sim \lt 0.1$ dB thru transmission measurement stability over 24 hours.
Bluetooth Low Energy Mesh Networks: A Standards Perspective

Bluetooth Low Energy (BLE) mesh networking is an emerging technology domain that promises an important role in the Internet of Things. Significant market opportunities for BLE mesh networking have motivated the recent development of two different BLE mesh networking standards, Bluetooth Mesh and 6BLEMesh, produced by the Bluetooth SIG and IETF, respectively. These two standards follow different technical approaches. In this article, we present the main features of Bluetooth Mesh and 6BLEMesh, and investigate their performance characteristics and trade-offs.
A Design of Stabilized Sensor-Error-Tolerant V/f Control in iPMSMs for EV Traction

This paper analyzes the characteristics of interior permanent-magnet synchronous machines using the voltage-frequency control by deriving a generic small-signal model and considering the electric dynamics and nonlinear parameters. Furthermore, a novel closed-loop voltage-frequency control algorithm is developed and demonstrated. By applying this algorithm, it is intended to switch the 3-phase interior permanent-magnet synchronous machines from the field oriented control mode to the voltage-frequency control mode stably on purpose of sensor error diagnosis and compensation in real time.
A Food Recognition and Tracking System for Diabetics in the Middle East

The concerns for a healthier diet are increasing day by day, especially in diabetics wherein the aim of healthier diet can only be achieved by keeping a track of daily food intake and glucose-level. As a consequence, there is an ever-increasing need of automatic tools able to help diabetics to manage their diet and also help physicians to better analyze the effects of various types of food on the glucose-level of diabetics. In this paper, we propose an intelligent food recognition and tracking system for diabetics, which is potentially an essential part of a mobile application that we propose to couple food intake with the blood glucose-level using glucose measuring sensors. Being an essential component of the application, for food recognition we rely on several feature extraction and classification techniques individually and jointly utilized using an early and two different late fusion techniques, namely (i) Particle Swarm Optimization (PSO) based fusion and (iii) simple averaging. Moreover, we also evaluate the performance of several deep features. In addition, we collect a large-scale dataset containing images from several types of local Middle-Eastern food, which is intended to become a powerful support tool for future research in the domain.
Design and Optimization of a Multi-DOF Hand Exoskeleton for Haptic Applications

This paper describes the design and kinematic optimization of a novel, underactuated, linkage driven exoskeleton mechanism to provide haptic force feedback to the index and thumb fingers. Existing exoskeletons are either not compatible with the human hand kinematic chain or so heavy that they cannot be compatibly installed on a human hand, which in turn affects the natural motion of the hand. In order to improve functionalities, the design of a novel (HEXON11Hand Exoskeleton of New technologies research center) based on a multi-criteria optimization is proposed to simultaneously maximize applied force to the finger and workspace of the attached exoskeleton. The optimization procedure consists of both the Perpendicular Impact Force (PIF) and the Global Isotropy Index (GIl), considering worst-case collision avoidance. Finally, the exoskeleton mechanism functionalities within the achieved link length through the optimization procedure are validated, and design is proposed for further fabrication.
The effect of local bone density on stability of dental implants: a finite element analysis

Finding the ideal replacement for lost teeth has been a key area of interest in restorative dentistry. Despite the advances in dental implantology, failure of the dental implant are still frequent. The relationship between local bone properties and the implant design is arguably have an impact on the implant stability. This study aimed at computationally assessing the suitability of implant materials for various locations in the maxilla and mandible taking into account the variation in cortical and cancellous bone densities. In particular, we examined Titanium and Zirconia as two potential materials for anterior (incisor) and posterior (molar) implants in the maxilla and mandible utilizing finite element analysis. A geometrical model representing the implant and the paraimplant bone was developed. Different bone locations were defined by varying bone material parameters based on the local bone densities. Stress induced in the implant and the cancellous bone were examined under 114. 6N compressive load. The highest stress in the cancellous bone was predicted when Titanium implant was used at the maxilla incisor location (4.01 MPa). Unlike Titanium implant, no change in predicted stress at the four locations was observed when Zirconia implant was used. In general, Zirconia implant resulted in a slightly higher stress in the cancellous bone compared to Titanium implant with one exception; at the incisor location of the maxilla where Titanium induced a slightly higher stress. The study showed that different implant materials can induce distinct mechanical stresses in the cancellous bone. Such impact motivates the need to carefully assess the properties of local cancellous bone when selecting the implant material.
A Computation Resource Friendly Convolutional Neural Network Engine For EEG-based Emotion Recognition

EEG-based Emotion recognition is a crucial link in Human-Computer Interaction (HCI) application. Nowadays, Convolutional Neural Network (CNN) and its related CNN-hybrid approaches have achieved the state-of-art accuracy in this field. However, most of these existing techniques employ large-scale neural networks which cause performance bottleneck in portable systems. Moreover, traditional convolution kernel confuses EEG multiple frequency bands information, which is critical for investigating emotion status. To improve these issues, firstly, we extract power spectral features from four frequency bands (θ,α,β,γ) and transform obtained features into cortex-like frames while preserving spatial information of electrodes position, so that the multi-channel, multi-frequency bands and time series EEG signals can be efficiently represented. Then, we design a shallow depthwise parallel CNN inspired by Mobilenet technique to learn spatial representation from labeled frames. Segment-level emotion recognition experiments are implemented to verify the proposed architecture with DEAP database. Our approach achieves the competitive accuracy of 84.07% and 82.95% on arousal and valence respectively. Besides, the experimental results prove the computation-effectiveness of the proposed method. Compared with the state-of-art approach, our approach saves 69.23% GPU memory and reduces 30% GPU peak utilization with only 6.5% accuracy drop. Therefore, our method shows extensive application prospects for EEG-based emotion recognition on resource-limited devices.
Generalized Variable Interleaving Technique for Parallel Connected DC-DC Converters

This paper deals with a generalized interleaving technique applied to parallel connected DC-DC converters. When the operating conditions for each of the converters (like input voltages, power sharing etc.) are different, the fixed interleaving technique with equal amount of phase shift among the carriers become inefficient due to improper ripple cancellation in the combined current. A variable interleaving technique with generalized approach is proposed in this work. The algorithm will seamlessly work for ‘n’ number of converters in parallel to optimize their combined ripple current. The analysis and results are presented for four and five parallel converters to demonstrate the effectiveness of the proposal.
DeBNet: Multilayer Deep Network for Liveness Detection in Face Recognition System

As the uniqueness property of face, face recognition is widely used in area which needs security. But it has some spoof attack by the unauthorized person to gain access of the authorized person. These attacks are the big challenge for current time. As the deep learning approaches gives the promising results in image processing so, in this study, a liveness detection technique for face recognition is proposed which gives robust and accurate results for different percentage of training set of images by using multilayer deep network DBN. The proposed DeBNet is used for extracting deep features and classification of real and spoof faces. Photograph imposter NUAA data set of images is used in this study.
Internet of Things Connectivity-based Smart Grids in Morocco: Proof of Concept and Guide to Massive Deployments

The Smart Grid (SG) is a promising solution solving the energy crisis issues and the mismatch between energy offer and demand. This can be achieved through the integration of reliable, cost-effective and secure data communication networks to manage the complex power systems efficiently. This paper presents an advanced study of the SG platform and its implementation in the Moroccan environment, based on Internet of Things (IoT) connectivity. In our study, we have employed LoRa (Long Range) communications, as an emerging technology exploiting the unlicensed frequency bands to provide a long-range coverage to devices connected to the grid. We first described our developed solution, which consists of a robust platform that gives customers and utilities an updated view and control of various grid events (energy and water consumption, temperature and humidity levels,…). Extensive indoor and outdoor experiments have been conducted to assess the performance of our proposed SG solution. The obtained measurements confirmed the effectiveness and the scalability of our designed framework, which is expected to be a fundamental part of a near-future large-scale smart metering deployment in Morocco.
Industrial Internet of Things: Device Management Architecture Proposal

The advent of Industry 4.0 along with the subsequent Industrial Internet of Things (IIoT) paradigm have exponentially increased the need for sensing and data gathering from industrial processes using diverse technologies. Although these heterogeneous technologies enable data collection from different sources, presenting multiple formats and diverse connectivity conditions, they also generate a complex environment due to the proliferation of devices with variated processing capacities, operating systems and communication protocols. This scenario can potentially create a management challenge that could evolve into utter chaos if not properly addressed. This Study aims to delineate the fundamental requirements for device management in industrial scenarios and propose an agnostic, federated management architecture that targets any IIoT devices, independent of technology specificities or communication mechanisms.
Applicability Domains of Machine Learning in Next Generation Radio Access Networks

The Radio Access Network (RAN) is envisaged to undergo a significant transformation in the context of 5G and beyond mobile communications systems. One of the driving forces behind this transformation is the applicability of Machine Learning (ML) techniques. Taking as a reference the high-level architecture for a next generation RAN proposed by the Open RAN Alliance, this paper identifies the applicability domains where ML techniques can play a relevant role. For each domain, namely radio physical layer processing, Medium Access Control (MAC) scheduling, near-real time Radio Resource Management (RRM), RAN data analytics and RAN operational automation, the paper discusses the specific functionalities that can benefit from the application of ML and analyses the key considerations and challenges that need to be addressed when developing ML-based solutions, given the particular characteristics of the mobile radio environment.
Series Parallel Cascaded H-Bridge Inverter Based on A Simlpe Staircase Modulation

This paper presents the main types of a series-parallel cascaded H-bridge inverter. It consists of H-bridge cells, and an inverter which output is a multilevel voltage by adding the dc voltage sources in series and parallel. A simple staircase modulation is used to obtain on-off of transistors used in series-parallel H-bridge multilevel inverter. The modulation technique used in this paper can calculate the independent switching angles more simply than the traditional method, and can be applied to all kinds of multilevel inverters. The proposed inverter topology results from implementation of optimum switching angles in low total harmonic distortion values in the phase and line voltage waveforms. The circuit configuration, theoretical operation, is also discussed in this paper. PSIM, PROTEUS-V8 based simulation results are obtained, which are consistent with the experimental results.
Implementation of Distributed Energy Resource of Photovoltaic Systems with LSTM Neural Network System

The use of solar power is rapidly increasing in today’s power grid, because of the rapid depletion of non-renewable energy sources such as fossil fuels. This new source of power is coming up as independent units of solar panels all around the globe. However, the problem with solar power is its seasonal nature, due to which a fixed output can’t be expected. Hence in the process of moving towards solar power it is important to be able to forecast the amount of power that will be produced by a certain photovoltaic plant in the next time horizon. The proposed technique is an attempt to implement a system that constantly monitors this power forecast obtained for multiple distributed energy sources, through means of neural network models and web-based applications and gives access to such predictions at the consumer level.
Harmonic Analysis of LED Biodynamic Lighting Luminaires in Buildings

In order to improve energy efficiency in buildings power quality problems caused by nonlinear loads must be taken into account. From the point of view of energy consumption in buildings, harmonics generated by nonlinear loads plays a particularly important role in increasing energy consumption. This work deals with the analysis of LED biodynamic luminaires in buildings, take into consideration the issue of power quality. This paper brings a case study addressing harmonic disturbances generated by LED biodynamic luminaire functioning using a driver. An experimental setup is carried out to measure the electrical parameters of power quality in terms of harmonics, voltage, power and current waveforms. The overall functioning of the system should be ensured by comparing experimental results of harmonic distortion with the limit value of IEC 61000-3-2 standard.
Consensus Algorithm based Two-Level Control Design for a DC Microgrid

This paper presents two-level control of DC microgrid for proper voltage regulation and equal current sharing across the load. DC microgrid is emerging as an alternate solution for rural electrification but it needs a robust and stable control system for good quality power delivery and proper energy management. In this paper, four microgrids are joined together to form a cluster and jointly all four microgrids deliver equal current to a common load. Droop control is implemented as a local level control for the regularization of voltage and current sharing. In order to mitigate the deviations created by primary level, consensus algorithm is used for generating a universal voltage reference for all droop controllers. It has been found that in a two level control structure there is lesser voltage deviation and proper current sharing among the units as compared to only droop control.
Rectal seed bezoar due to sunflower seed: a case report and review of the literature

Seed bezoars are a subcategory of phytobezoars, caused by consumption of indigestible vegetable or fruit seeds. We present the case of a 64-year-old male patient, who presented at the Emergency Department, complaining of constipation, tenesmus and rectal pain. History and digital examination revealed a rectal seed bezoar due to sunflower seeds, impacted in the lower rectum. The patient underwent manual disimpaction under general anaesthesia, after conservative measures failed. Seed bezoars represent a different pathophysiological process compared to fibre bezoars. They are usually found in the rectum of patients without predisposing factors, causing constipation and anorectal pain. History taking and digital rectal examination are the cornerstones of diagnosis, with manual disimpaction under general anaesthesia being the procedure of choice.
Analysis & Evaluation of Image filtering Noise reduction technique for Microscopic Images

Image processing in the field of microscopy is gaining popularity with the use of advanced techniques used for accurate classification of cells. The abnormalities in the image can be detected accurately after the image has been processed using digital image processing techniques. Preprocessing is an important step in which the noise and other undesirable content will be removed. Preprocessing is essential because the noise will cause inaccuracy in the image processing techniques. Filtering the image to denoise is the first step in preprocessing. The accuracy of denoising using filters determines the quality of the entire image processing cycle. This paper proposes filters to denoise the microscopic images. In this paper, two filters - Wiener and Median filters are compared for accuracy in denoising the image in the preprocessing stage of the cell classification. The Wiener filter and the Median filter were implemented and compared for Peak Signal to Noise Ratio (PSNR) which can be used for better image classification in the later stages. The proposed method was tested using 35 real time images which has Gaussian noise. The two filters perform for the collected dataset whereas the median filter gives highest PSNR outperforming the Wiener filter.
Plans and Challenges of the Renewable Energy in Sudan

Electrical energy is the basis of modern economic and social development for any country. Sudan is a large country with a population of about 40.7 million and used to be the largest country in Africa before split of South Sudan in 2011. Electricity generation in Sudan depends on hydro and thermal power. Average electricity demand increases by 11.5% annually and the consumption per capita is 376.6 kwh/year. Like many developing countries, Sudan suffers from a shortage of electricity generation and network expansion and about 35% of the population has access to supply. It has become necessary to exploit other energy resources, especially solar, to complement the other limited generation. Considering the abundance of solar energy resources in the country, solar power is becoming so competitive against conventional energy in Sudan especially for the remote areas. Wind generation is also part of the future generation’s map with solar, to support hydro, thermal generation. This paper presents the plans and challenges of Renewable Energy Systems (RES) in Sudan with focus solar energy generations and some applications.