Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Performance Analysis of Memory Behavior in a Task-Based Runtime on Hybrid Platforms

Programming parallel applications for heterogeneous HPC platforms is much more straightforward when using the task-based programming paradigm. The simplicity exists because a runtime takes care of many activities usually carried out by the application developer, such as task mapping, load balancing, and memory management operations. In this paper, we present a visualization-based performance analysis methodology to investigate the CPU-GPU-Disk memory management of the StarPU runtime, a popular task-based middleware for HPC applications. We detail the design of novel graphical strategies that were fundamental to recognize performance problems in four study cases. We first identify poor management of data handles when GPU memory is saturated, leading to low application performance. Our experiments using the dense tiled-based Cholesky factorization show that our fix leads to performance gains of 66% and better scalability for larger input sizes. In the other three cases, we study scenarios where the main memory is insufficient to store all the application's data, forcing the runtime to store data out-of-core. Using our methodology, we pin-point different behavior among schedulers and how we have identified a crucial problem in the application code regarding initial block placement, which leads to poor performance.
Performance-Based Design of Induction Motor Drive for Single-Stage PV Array Fed Water Pumping

This paper presents an improved designed induction motor, used for photovoltaic (PV) array fed water pumping system by modifying the rotor and stator slots. The overall system is designed without a mechanical sensor to reduce both cost and complexity with the simultaneous assurance of optimum power utilization of a PV array. The proposed system consists of an induction motor operated water pump, controlled by field-oriented control with artificial neural network current control technique. The maximum power point tracking as well as dc-link voltage, is regulated by three-phase voltage source inverter. The estimation of motor speed eliminates the use of a mechanical sensor and makes the system cheaper and robust. A new robust speed adaptive algorithm is presented, which is less dependent on parameters. A detailed study of various factors affecting the efficiency of the motor is given to improve the behavior of the induction motor drive for water pumping. The designed motor is tested on a developed prototype in the laboratory and its suitability is judged through various tests under steady-state and dynamic conditions of insolation variations.
The Monitor Strategies of Lens Flare for Nikon ArF and KrF Scanner Machines: Simulated Results and Discussion

Flare is a problem that has been known for some time on 248nm KrF scanner but has grown in importance on 193nm ArF systems. This paper discusses the strategy of monitoring the level of optic system flare in lithographic scanner KrF and ArF machines in the lithography process. Based on the optical simulated result, it is recommended to employ both the dose-to-clear Kirk method for large square patterns and the critical dimension uniformity (CDU) monitor method for the dense line/space patterns to tracking changes of project lens flare in lithographic scanner tools. The impact factor of flare in monitor process, such as the mask transmission rate, the illumination condition and the monitor pattern type selection, are also discussed in this paper.
Cost-Based Droop Scheme for Converters in Interconnected Hybrid Microgrids

Hybrid microgrids are the future of the power system as they bring the advantages of ac and dc networks under a common roof. As the power system continues to expand, hybrid microgrids will be connected together for increased reliability. One of the main concerns of such an arrangement is economical power sharing among multiple grids. This paper aims to address this issue by proposing a cost-based droop scheme for interlinking converters (ILCs). At the basic level, a novel voltage-frequency droop is presented which manages bidirectional power flow among the grids. Any change in ac grid frequency is translated into a dc bus voltage variation which allows active power flow from an underloaded to the overloaded grid. The conventional secondary control within the individual ac and dc grids is used to eliminate the inherent frequency and dc voltage deviations, restoring them to the nominal values. Economical power sharing among multiple grids is realized by embedding the incremental costs into the voltage-frequency droop and minimizing the total active power generation cost based on equal incremental cost principle. Different operational modes are discussed for interconnected grids, and simulations performed in power system computer-aided design (PSCAD) are used to confirm the validity of the scheme for each mode.
Multi-Objective Energy Management Approach Considering Energy Storages in Distribution Networks with Respect to Voltage Security

The presence of electrical energy storage (EES) units in distribution systems has potentials to improve the network profiles (e.g. bus voltage and branch current profiles) as well as to reduce operational cost and power losses. This paper presents a novel approach to determine the optimal charging/discharging schedule of EES units in distribution systems by employing multi-objective optimization methods, aiming at reducing operation cost and enhancing radial distribution networks security. In this regard, a voltage stability index (VSI) to improve the radial network security is presented as a separate objective function. In order to assess effectiveness and applicability of the proposed method, it is applied to a 33-bus IEEE standard distribution test system and then the obtained results are compared with existing methodologies.
Radio Map Building with IEEE 802.15.4 for Indoor Localization Applications

Automated radio map building for Radio Signal Strength (RSS) based fingerprinting localization is implemented in this paper. Fingerprinting technique is composed of an offline phase for collecting geotagged fingerprint data called a radio map and an online phase for location estimation by comparing an unknown object’s location with the radio map. Typically, radio map is created manually by moving and pin-pointing a location while scanning a network for RSS values within a pre-specified pattern. This cumbersome method is very time consuming and results in inaccuracies in the radio map. This paper presents an automated system to build a radio map of indoor environment by using a self-directed car. The car can move, measure 2D distances, collect RSS values and forward the data to a server via a gateway which calculates the car location coordinates. To test and verify the system, an experiment is conducted in our departmental office spaces. The coordinate estimation accuracy and RSS mapping consistency produced by the proposed system make it possible to build a more accurate and realistic radio map for indoor environments in an automated fashion with decreased possibility of human errors.
Generalized Segmented Bit-Flipping Scheme for Successive Cancellation Decoding of Polar Codes With Cyclic Redundancy Check

As a new flavor for the successive cancellation decoding of polar codes, bit-flipping technology can be used to improve the frame error rate performance of polar codes at moderate code lengths. In this paper, a generalized segmented scheme of bit-flipping technology is taken into full consideration, in which several segmented decoding patterns are described. First, the basic ones considering single-error-correcting and multiple-error-correcting are described, where a fully protected CRC is used. Second, with a constructed critical set for the segmented scheme, the improved version of the above basic patterns is fully discussed. Unfrozen bits corresponding to the critical set called critical information bits are divided into several parts and protected by cyclic redundancy check codes. Third, for a better performance of the segmented scheme, a segmented strategy indicating the positions of segments is effectively designed by analyzing the effect of positions of cyclic redundancy check and the error probability of each bit. At last, the frame error rate performance and the average computational complexity of the segmented decoding patterns are analyzed at matching average computational complexity or under equivalent frame error rate for different rates of polar codes. By proper design, the simulation results in additive white Gaussian noise channel demonstrated that the segmented decoding patterns can efficiently improve the error-correction performance of the flip-based scheme at different rates of polar codes while keeping a lower complexity.
Review of SiC MOSFET Drive Circuit

SiC MOSFET (Silicon Carbide Metal-Oxide-Semiconductor Field-Effect Transistor) has received extensive attention from researchers for its superior characteristics. Profiting from its material advantages, SiC MOSFETs possess lower switching losses, lower on-resistance and excellent thermal performance. There is a large amount of literature on the research of SiC MOSFET drive circuits, but there is still a lack of systematic summary of SiC MOSFET drive circuit and its applications. This paper summarizes the key features of SiC MOSFETs need to be focused on in practical applications. In addition, the different SiC MOSFET driver circuits are summarized and reviewed. Finally, how to choose the appropriate SiC MOSFET driver circuit and future research directions is pointed out.
MQTT Based Monitoring System for Urban Farmers Using ESP32 and Raspberry Pi

With rapidly developing technology and the Internet of Things taking the world by storm, easy-to-use and low-cost solutions are being provided to almost any and every application in the world. In a similar way, technological solutions are also now being applied to urban farming, a practice which has gained momentum especially in large cities, to help citizens grow vegetables and the like in their own homes or establishments. Urban farming is now more important than ever in the context of the rapid urbanization and development of smart cities all over the world. It is important that residents in urban areas grow their vegetables, and it is important that they do it the right way. Only then will it be possible, in the future, to sustain ourselves in large cities and places which are not quite accessible by villages where farming is done on a large scale. In this paper, we propose a process of growing and taking care of the plants, so that maximum efficiency is guaranteed in the growth process and also information is generated and shared by the whole community. This information will help us crunch the data and arrive at better decisions. The solution works on sensors monitoring the temperature, humidity, ambient light intensity and soil moisture levels pertaining to a certain plant. These sensors are connected to a device which makes understandable data out of these sensor outputs. This then communicates with a central device which collects information from all such sensors, and displays this on a locally hosted webpage. The content is also shown on a dashboard for comfortable visualization and understanding. It also stores a database which can be referred for future analytics. All the devices used are low-cost and consume low-power, which is ideally suited for IoT applications which focus more on communicating effectively at low costs.
Tunisian agro-food wastes recovery by pyrolysis: Thermogravimetric analysis and kinetic study

In keeping with the hierarchy of waste management methods, thermal treatment makes it possible to recover energy from wastes that could not be processed by recycling or material recovering. As far as agricultural wastes are concerned, this biomass has for a long time been covering the domestic energy needs of rural and urban areas, thus reducing the gas and oil import costs. In the context of climate change, biomass is neutral with respect to the carbon cycle, allowing it to be a reliable alternative to fossil fuels. In addition, bioenergy can be stored, which is a key advantage over other renewable energies and therefore allows on-demand energy production. However, the extreme diversity of resources, the wide variety of conversion technologies and the disparity of energy sectors give bioenergy a certain complexity unless technical and economic issues are overcome. In this perspective, the present study focused to identify, through the Tunisian territory, the biomass deposits, to map and evaluate their energy potential. This preliminary phase made it possible to conclude that although Tunisia is among the worldwide leading producers of dates and derivatives, olive oil, wine and concentrated tomatoes, the value chains for their by-products remain vulnerable. This paper is then devoted to the recovery of these wastes for the dual purpose of minimizing their increasing quantities on the one hand and to obtain the energy necessary to operate the process of pyrolysis at the lowest cost, while monitoring their gaseous and particulate emissions on the other hand. To carry out this work, the survey begins with proximate and elemental analyses (ash content, moisture content, calorific value, etc.). The thermal degradation of the different materials was carried out under an inert atmosphere in thermogravimetric analysis conditions. Kinetic models were tested to extract the kinetic parameters required for the development of industrial plants using these different materials as fuels.
Mismatch optimization for analog circuits using the DesignAssistant

This paper presents a new mismatch optimization feature of the DesignAssistant (DA), which is a part of the ALADIN (Automatic Layout Design Aid for Analog Integrated Circuits) program package. This interactive tool is integrated into a commercial design framework and supports analog designers during circuit design. The matching of two circuit elements can be calculated numerically in the design environment using the standard deviation of electrical parameters and simplified device models. The matching of the devices is estimated in accordance with their orientation and their surrounding layout. These features ease the design and improve the quality and reliability of analog integrated circuits.
Individual Factors As Antecedents of Mobile Payment Usage

The aim of this research was to discover the stances of individual elements as antecedents of mobile payment usage. Data was gathered by distributing a questionnaire, which in latter steps was analyzed quantitatively. This research collected 90 samples, of whom represented users of a mobile payment service in Indonesia. The collected dataset was statistically analyzed, by employing partial least square structural equational modelling (PLS-SEM), aided with SmartPLS3.0. The results showed that two types of individual factors, namely individual difference and behavioral belief played significant roles in shaping users’ intention to use mobile payments. Individual differences, consisting of mobile payment knowledge and compatibility significantly influenced perceived ease of use. Behavioral belief, such as trust, was shown to significantly influenced perceived usefulness. Finally, perceived ease of use and perceived usefulness concertedly affected mobile payment users’ intention to use.
Multihop Gateway-to-Gateway Communication Protocol for LoRa Networks

The long range wide area network (LoRaWAN) forms a star topology where end device (ED) sends data to network server (NS) through gateway (GW). It requires a GW to be connected to the NS through an IP network as the backhaul link. However, when a LoRaWAN is deployed in a remote area, such connection may not be possible. Thus, it is important that the GW can reach the NS through multihop GW-to-GW (G2G) communication using LoRa technology. To realize it, we propose a multihop G2G communication protocol for LoRa networks. The proposed protocol includes both routing protocol and channel access scheme. A packet aggregation feature is also added for improving the transmission efficiency of GW. The simulation results demonstrate that the proposed protocol successfully enables multihop G2G communication for LoRa networks with reasonably good performance.
Maintenance Decision-Making Support for Textile Machines: A Knowledge-Based Approach Using Fuzzy Logic and Vibration Monitoring

A condition-based maintenance approach may be used for planning the maintenance activities of textile machines with a satisfactory performance by developing maintenance decision-making support based on fuzzy logic and vibration monitoring. Since textile machines are systems with moving parts operating at relatively high-speed, vibration monitoring was used to indicate their failure development. At the same time, the characterization of the degradation phenomenon of textile machines involves some degree of uncertainty and vagueness. Within this context, a knowledge-based approach that employed fuzzy logic and vibration monitoring was developed. Deterioration symptoms do announce future failures of industrial machines, therefore building a maintenance decision-making support for scheduling maintenance actions of textile machines based on the estimation of their condition becomes a resourceful way to prevent their further deterioration.
COCME: Content-Oriented Caching on the Mobile Edge for Wireless Communications

With the rapid development of mobile networks and mobile communications, various content can be conveniently accessed. Recently, some novel caching-based approaches have been proposed to enhance the QoE in wireless communications, but it is still challenging to develop advanced content delivery in wireless communications due to limitations of conventional D2D communications and caching strategies. To address these challenges, this article proposes content-oriented caching on the mobile edge for wireless communications. Specifically, it designs novel mobile edge caching based on popular content recommendations. Sufficient experiments demonstrate that the proposed approaches can effectively decrease the traffic load by using an acceptable volume of storage resources on mobile edges.
A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.
Dictionary Learning Using EMD and Hilbert Transform for Sparse Modeling of Environmental Sounds

This paper presents a new dictionary learning method for reconstruction tasks. Based on Empirical Mode Decomposition(EMD) and Hilbert Transformation, the dictionary is learnt, once and for all, from data having a complex structure and belonging to the same manifold. Searching sparse representation of signals over the learned dictionary is achieved by solving a minimum norm least square error problem. Experiments on audio patterns demonstrate that it is effective. The dictionary is data adaptive and allows having signal sparse models which are well suited to restoration tasks. It has a good reconstructive sparse over complete encoding and is largely competitive against KSVD and MOD algorithms. Our approach can be of much interest in off-line dictionary learning applications.
Enhancing Robustness through Mechanical Cognitivization

The common approach for training robots is to expose them to different environmental scenarios, training their controllers to have the best possible commands when untrained scenarios are encountered. When humans train they do the same. They try new manipulations by performing within different environments. However, humans training (and in fact development from infancy to maturity) also includes a type of training which, although claimed to improve cognitive capabilities, has not, to date, been adopted for the training of robots. This type of training involves the restriction of manipulation capabilities while performing different tasks, e.g., climbing with just one hand. Recently a research that facilitates functions instead of a mechanical systems that aims at exploring the invigorating idea that such training, would enhance the robustness of robots, has been published. This type of training has been termed as Mechanical Cognitivization. In the current paper, the preliminary published results are detailed and more elaborated examples are given. Specifically, it is shown that the Mechanical Cognitivization based training improves the performances when performing within untrained environments and when malfunctions occur. The advantages of the suggested training are highlighted through facilitating a comparison between two schemes that include a common neural net (with no training of restricted modes) and the recently introduced Mechanical Cognitivization based neural net for which the training includes training of restricted modes. The results highlight the advantages of Mechanical Cognitivization based training in enhancing robustness. Keywords—Cognitive robotics, developmental robotics, evolu-
Advanced Control Strategies for Back-to-Back Power Converter PMSG Wind Turbine Systems

With the increasing emphasis on environmental protection, renewable energy, especially wind power, accounts for an increasing proportion of power generation. For its higher reliability and efficiency, permanent magnet synchronous generator becomes a promising alternative. Control techniques play a very essential role in wind turbine systems. By now, many advanced control schemes have been discussed and employed to permanent magnet synchronous generator wind turbine systems. In this paper, four popular advanced control schemes, namely, field oriented control (FOC) and voltage oriented control (VOC), direct torque control (DTC) and direct power control (DPC), deadbeat control (DBC) and model predictive control (MPC), are presented. Their theories and performances are investigated and compared, which are confirmed with experimental data obtained at a lab constructed test bench.