Miracle-dz/UniFAR-FTUs huggingface dataset:
Scheduling strategies and throughput optimization for the Uplink for IEEE 802.11ax and IEEE 802.11ac based networks

The new IEEE 802.11 standard, IEEE 802.11ax, has the challenging goal of serving more Uplink (UL) traffic and users as compared with his predecessor IEEE 802.11ac, en- abling consistent and reliable streams of data (average throughput) per station. In this paper we explore several new IEEE 802.11ax UL scheduling mechanisms and compare between the maximum throughputs of unidirectional UDP Multi Users (MU) triadic. The evaluation is conducted based on Multiple-Input-Multiple-Output (MIMO) and Orthogonal Frequency Division Multiple Acceess (OFDMA) transmission multiplexing format in IEEE 802.11ax vs. the CSMA/CA MAC in IEEE 802.11ac in the Single User (SU) and MU modes for 1, 4, 8, 16, 32 and 64 stations scenario in reliable and unreliable channels. The comparison is conducted as a function of the Modulation and Coding Schemes (MCS) in use. In IEEE 802.11ax we consider two new flavors of ac- knowledgment operation settings, where the maximum acknowledgment windows are 64 or 256 respectively. In SU scenario IEEE 802.11ax throughputs outperform IEEE 802.11ac by about 64% and 85% in reliable and unreliable channels respectively. In MU-MIMO scenario IEEE 802.11ax throughputs outperform IEEE 802.11ac by up to 263% and 270% in reliable and unreliable channels respectively. Also, as the number of stations increases, the advantage of IEEE 802.11ax in terms of the access delay also increases.
Potentiometric Glucose Detection by Paper-based Electrochemical Sensor on CMOS Chip

This paper presents a low cost portable medical device for biochemical sensor using CMOS chip and paper-based fluidic channel. We measured a potential produced by enzyme activity of glucose between the working and reference electrode on CMOS chip. A liquid sample is transported by paper-based fluidic channel, which is made of chromatography paper and silicone resin, and consists of the area for filtering a sample (filter layer) and that for reacting enzyme (enyzme layer). The paper-based fluidic channel is used by combining CMOS chip, and the solution with glucose is dropped from top of the paper-based fluidic channel. The concentrations of glucose are detected by potentiometry (open circuit potential time). The experimental results show that the glucose concentration is measured by CMOS chip and paper-based fluidic channel.
New cascade architecture for protein secondary structure prediction

La prédiction du repliement des protéines à partir de leur séquence (ab initio) est un problème majeur en biologie structurale. Du fait de sa difficulté, elle est ordinairement réalisée par étapes, l’une des principales étant la prédiction d’éléments structuraux périodiques locaux reliés entre eux par des éléments apériodiques. Ces éléments constituent la structure secondaire. La prédiction de cette structure consiste à affecter aux résidus d’une séquence protéique leur état conformationnel : hélice α, brin β ou apériodique (coil). Les méthodes statistiques fournissent actuellement les meilleures prédictions. L’architecture de base sur laquelle elles s’appuient est pratiquement toujours la même : deux classifieurs sont utilisés en cascade. Le premier, dit ”séquence-structure”, effectue la prédiction de la structure à partir de la séquence (ou d’un alignement multiple), le second, dit ”structurestructure”, lissant la prédiction initiale en exploitant la corrélation des états conformationnels des résidus consécutifs [5,6,7,8]. Cette brique de base est incorporée dans des combinaisons prenant des formes variées, ce qui soulève des questions relevant de la sélection de modèles. Ainsi, la méthode décrite dans [7] combine plusieurs centaines de perceptrons multicouches (PMC), tandis que les auteurs de la méthode Jnet [2] ont récemment fait passer la taille de la couche cachée des PMC qu’ils utilisent de neuf à cent. Nous proposons une version optimisée de l’architecture en cascade. Ses performances sont proches de l’état de l’art, pour une complexité en échantillon inférieure d’au moins un ordre de grandeur. Les sorties sont des estimations des probabilités a posteriori (p.a.p.) des catégories, ce qui autorise leur posttraitement par des modèles génératifs. Le premier niveau est constitué par les quatre principales machines à vecteurs support multi-classes (M-SVM) proposées dans la littérature, le second est une combinaison linéaire multivariée des sorties de ces machines.
METHOD FOR DETECTING AGING RELATED FAILURES OF PROCESS SENSORS VIA NOISE SIGNAL MEASUREMENT

In this article we examine the methods for detecting and predicting aging related process sensor failures by analyzing the noise of the sensor output signal. The study uses data from non-differential and differential pressure transmitters used in the pressure and water level measurements of the reactor pressure vessels of units 1 and 2 of the Olkiluoto nuclear power plant in Finland. The article contains a review of the current methods for detection of sensor failures. Additionally, we present a new method for detecting changes in the sensor output signal. The method creates fingerprints of the power spectra of the sensors by using Principal Component Analysis (PCA). The changes in these fingerprints together with the measurements of the redundant sensors can be used to detect indications of some of the impending sensor failures. In the experimental study we are able to produce stable fingerprints for both the non-differential and differential pressure transmitters. Also, a potential failure in one of the differential pressure transmitters in Olkiluoto unit 2 is detected by inspecting the fingerprints and analyzing the spectral changes of the transmitter output signal.
Improvement the Imbalanced Data Classification with Restarting Genetic Algorithm for Support Vector Machine Algorithm

—The general datamining algorithm also classify the balanced dataset, when the data have imbalanced the predicted rate over minority class is still low. The random sampling techniques has been applying to solve the imbalanced data, but sometimes the random technique has selected the features is clearly different from both, when the unseen data (from minority class) has features look like the majority class, the classification model show miss classification because the model learning sample data does not complete. To improve the performance to classify the data, the genetic algorithm is applying to finding the optimal parameter, but sometimes the genetic algorithm cannot find the best set of parameters because the random initial population is not cover the best set of parameters, in this research proposed the techniques to guarantee the genetic algorithm can find the optimal parameter by using restarting technique to re-create the initial population when the new generation show powerful less than the old population. The results show that proposed technique can improve the performance to classify the minority class from imbalanced dataset more than the other techniques.
Real-time door detection for indoor autonomous vehicle

Indoor Autonomous Vehicle(IAV) is used in many indoor scenes. Such as hotels and hospitals. Door detection is a key issue to guide the IAV into rooms. In this paper, we consider door detection in the use of indoor navigation of IAV. Since real-time properties are important for real-world IAV, the detection algorithm must be fast enough. Most monocular-camera based door detection model need a perfect detection of the four line segments of the door or the four corners. But in many situations, line segments could be extended or cut off. And there could be many false detected corners. And few of them can distinguish doors from door-like objects with door-like shape effectively. We proposed a 2-D vision model of the door that is made up of line segments. The number of parts detected is used to determine the possibility of a door. Our algorithm is tested on a database of doors.1 The robustness and real-time are verified. The precision is 89.4%. Average time consumed for processing a 640x320 figure is 44.73ms.
ANALYSIS OF SOFTWARE SECURITY TESTING TECHNIQUES IN CLOUD COMPUTING

-Cloud Security Testing is becoming a Popular field of Research Topic in Cloud Computing and Software Engineering. As the advance of cloud technology and services, more research work must be done to address the open issues and challenges in cloud security testing and More innovative testing techniques and solutions, Although there are many published papers discussing cloud Security testing, there is a lack of research papers addressing new issues, challenges, and needs in Software Security Testing. however, there is no clear methodology to follow in order to complete a cloud security testing. Since there is an increasing demand in Software usage there is more in for Software Security Testing. This paper presents an overview of Cloud Computing, Cloud security testing and comprehensive survey of security Testing Techniques and methods. from this we have identiﬁed problems in the current security testing techniques. This work has to presents a roadmap for new testers on the cloud with the necessary information to start their test.
The C++ Standard Library Extensions: A Tutorial and Reference

The current C++ standard library extends the core C++ language with common classes and functions. In recent years, to address limitations in that library, a number of components have been developed to extend the language even further. Compiled in a comprehensive technical report (TR1), the bulk of these extensions have been approved for the next revision of the C++ standard.In this book, Pete Becker describes in detail each component in the TR1 library, explaining new facilities for utilities, containers, call wrappers, type traits, numerics, regular expressions, and C compatibility. He draws on his own experience implementing these components to illustrate their value, clarifying the specifications when necessary and providing complete, tested code examples.Most chapters include exercises of various degrees of difficulty to help programmers get hands-on practice with the new components. Answers to the exercises, along with all code examples, are available on the Web. Appendixes comprise a summary of headers included in or extended by the TR1 library, as well as guidelines on how to use the components safely in multithreaded applications.The C++ Standard Library Extensions is for any programmer who wants to get a jump on the revised standard. It also makes the perfect companion to The C++ Standard Library, by Nicolai Josuttis, both books being tutorials and references essential for using C++ more effectively.
Optimal Voltage Control in Distribution Systems With Intermittent PV Using Multiobjective Grey-Wolf-Lévy Optimizer

The intermittent photovoltaic (PV) units significantly affect the performance of distribution systems, and they often cause several operational problems, most importantly, voltage rise/drop. At high PV penetration, excessive tap movements of transformers and high curtailed PV power are expected to completely solve the voltage violation problem. In this paper, we propose an optimal voltage control method for distribution systems considering the number of tap movements of transformers and the active power curtailment of PV units. The objective function of the proposed method comprises: 1) voltage drop violation, 2) voltage rise violation, 3) tap movement rate (TMR) of transformers, and 4) curtailed power of PV (CPPV). A multiobjective grey wolf optimizer integrated with a Lévy mutation operator (GWO-Lévy) is formulated to accurately solve the voltage control problem. A 24-h simulation is performed on the 119-bus distribution system with PV and different types of loads. The performance of GWO-Lévy is compared with three other optimizers, finding that it achieves the best performance. The simulation results demonstrate the efficacy of the proposed method for solving the voltage violation problem with PV while simultaneously optimizing TMR and CPPV.
Automatic target recognition and classification from synthetic aperture radar imagery using multi-stream Convolution Neural Network

The process of using a computer to identify or recognize a target from Synthetic Aperture Radar (SAR) images with or without human interference is known as Automatic Target Recognition (ATR). The recognition of target is the process of discovering the location, pose or class of a target with a particular spatial signature by using a high spectral resolution remotely sensed images, which belongs to a particular kind of object (vehicle). In recent years ATR is particular interest in military applications such as infrared surveillance and target acquisition, unmanned aerial vehicles, and autonomous missile. The traditional architecture of automatic target recognition (ATR) for synthetic aperture radar (SAR) consists of three stages: detection, discrimination, classification and recognition. In the last few years Many deep convolutional neural networks have been proposed and used for SAR-ATR and have obtained state-of-the-art results in many computer vision tasks, plus shown improvement from time to time, but most of them classify targets from target chips which is found from SAR imagery, due to limited training images in SAR-ATR, CNN yielded over-fitting when directly applied to SAR-ATR. This paper proposes a novel deep convolutional learning architecture, called Multi-Stream CNN (MS-CNN), for ATR in SAR by leveraging SAR images from multiple views. By deploying a multi-input architecture that fuses information from multiple views of the same target in different aspects enables it to make full use of limited SAR image data to improve recognition performance. In addition, the Fourier feature fusion framework derived which allows unraveling the highly nonlinear relationship between images and classes. The model proposed in this paper performs all the three tasks in the SAR-ATR architecture. The proposed CNN will train using the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set and to output scores of 10 classes.
Tightly Coupled Machine Learning Coprocessor Architecture With Analog In-Memory Computing for Instruction-Level Acceleration

Low-profile mobile computing platforms often need to execute a variety of machine learning algorithms with limited memory and processing power. To address this challenge, this work presents Coara, an instruction-level processor acceleration architecture, which efficiently integrates an approximate analog in-memory computing coprocessor for accelerating general machine learning applications by exploiting analog register file cache. The instruction-level acceleration offers true programmability beyond the degree of freedom provided by reconfigurable machine learning accelerators, and also allows the code generation stage of a compiler back-end to control the coprocessor execution and data flow, so that applications do not need high-level machine learning software frameworks with a large memory footprint. Conventional analog and mixed-signal accelerators suffer from the overhead of frequent data conversion between analog and digital signals. To solve this classical problem, Coara uses an analog register file cache, which interfaces the analog in-memory computing coprocessor with the digital register file of the processor core. As a result, more than 90% of data conversion overhead with ADC and DAC can be eliminated by temporarily storing the result of analog computation in a switched-capacitor analog memory cell until data dependency occurs. Cycle-accurate Verilog RTL model of the proposed architecture is evaluated with 45 nm CMOS technology parameters while executing machine learning benchmark computation codes that are generated by a customized cross-compiler without using machine learning software frameworks.
Simulating a Commercial Power Aggregator at Scale Design and Lessons Learned

To evaluate aggregation models in the context of a power system, a software tool (the SmartNet simulator) has been developed to look at the impact of managing Distributed Energy Resources (DERs) on networks’ technical operation (e.g. power flows and voltage levels) and simulates wholesale and ancillary services market conditions. This paper focusses on the design and implementation of one of the aggregation models that addresses the Curtailable Generator / Curtailable Load (CGCL) aggregator. The paper outlines the design of such a software aggregator agent and discusses the lessons learned in simulating a more realistic large power grid system. The aggregator is represented as an agent based object orientated model using a financed based buckets system to aggregate bids from up to 300,000 devices across 10 -20,000 power nodes. The concept/implementation can be extended to include more sophisticated bidding strategies and to use multiple perspectives on tranches. Simulation and testing of such a large simulation system was challenging, and we have proved that it is possible to simulate the aggregation and clustering of different types of flexibility into a number of manageable bids in a timely manner.
Teleoperating a mobile manipulation using a UAV camera without robot self-occlusions

The paper presents a novel teleoperation system that allows the simultaneous command of a mobile manipulator and a free ying camera, implemented using a UAV, from which the operator can monitor the task execution in real-time. A novel use of the kinematic redundancy is presented to prevent the robot parts from occluding the end-e ector to the operator view. Following an obstacle avoidance approach, an input is fed to the null space that keeps the robot links away from the end-e ector in the image plane as a sec ondary task. Simulations and the implementation in a real setup show the goodness of the proposed approach.
Assessing Technology Readiness for Artificial Intelligence and Machine Learning based Innovations

: Every innovation begins with an idea. To make this idea a valuable novelty worth investing in requires identification, assessment and management of innovation projects under two primary aspects: The Market Readiness Level (MRL) measures if there is actually a market willing to buy the envisioned product. The Technology Readiness Level (TRL) measures the capability to produce the product. The READINESSnavigator is a state of the art software tool that supports innovators and investors in managing these aspects of innovation projects. The existing technology readiness levels neatly model the production of physical goods but fall short in assessing data based products such as those based on Artificial Intelligence (AI) and Machine Learning (ML). In this paper we describe our extension of the READINESSnavigator with AI and ML relevant readiness levels and evaluate its usefulness in the context of 25 different AI projects.
An Iterative Kalman Filter with Reduced-Biased Kalman Gain for Single Channel Speech Enhancement in Non-stationary Noise Condition

This paper presents an iterative Kalman filter (IT-KF) with a reduced-biased Kalman gain for single channel speech enhancement in Non-stationary Noise Conditions (NNCs). The proposed IT-KF aims to offset the bias in Kalman gain through efficient parameter estimation leading to improve the speech enhancement performance. To do this, we introduce a Decision Directed (DD) and a posteriori SNR based noise variance estimation method controlled through Speech Activity Detector (SAD). The proposed SAD incorporates a majority voting of three distinct SAD fusions. The LPC parameters are computed from the pre-smoothing of noisy speech. With these initial estimated parameters, an IT-KF processes the noisy speech at first iteration. The parameters are re-estimated from the processed speech, re-adjust the Kalman gain, and the process is repeated at second iteration. It is shown that the adjusted Kalman gain enables the IT-KF to minimize the remaining artifacts of the processed speech, yielding the enhanced speech. Extensive simulation results reveal that the proposed method outperforms other benchmark methods in NNCs for a wide range of SNRs.
Saving Privacy in Trust-Based User-Centric Distributed Systems

—User-centricity is a design philosophy subsuming new models of Internet connectivity and resource sharing, whose development is mainly driven by what users offer and require. To promote user-centric services and collaborative behaviors, incentives are needed that are typically based on trust relations and remuneration. In this paper, we show that privacy-preserving mechanisms can favor user’s involvement if privacy can be traded with trust and cost. In particular, we present and evaluate formally a model ensuring an adequate level of ﬂexibility among privacy, trust, and cost in the setting of distributed systems.
Analysis of Clustered Multi-Antenna Small Cells With Intra-Cluster Interference Cancellation

This paper analyzes the performance of a clustered small cell network, where the base stations (BSs) are equipped with multiple antennas and the network topology is modeled as Matern cluster process (MCP). For such a clustered multi-antenna network, serious intra-cluster interference is a main bottleneck of the system performance. Utilizing the intra-cluster coordinated beamforming scheme is an effective approach for the intra-cluster interference cancellation. From this, we firstly derive an approximate expression for the users’ average data rate based on stochastic geometry, and then we discuss the optimal number of the maximum degrees of freedom (DoF) used for coordinated beamforming at the BS, which maximize the average data rate. Simulation results confirm the accuracy of our derivations and also present the performance with the intra-cluster interference cancellation is much better than that with no intra-cluster interference cancellation.
An analysis of online and blended learning environments : measuring approach and learning outcomes in corporate settings

Abstract Organizations use training as an investment with the desirable end goal of gaining a valuable employee, despite cost and time constraints on their organization. This review investigates how e-Learning and blended learning training methods are currently used in organizations. It measures the Return-On-Investment of e-Learning and blended learning training methods within corporate learning environments and examines means to improve learning outcomes for learners and the organization. For this review, peer-reviewed journals were evaluated to analyze e-Learning and blended learning methods and outcomes. Conclusions reveal that both e-Learning and blended learning training models are being successfully used in corporate training modules. Factors such as development costs, training time, and course design will affect learner outcome and productivity and profitability for organizations. Additional research is needed to verify long term ROI for organizations using online or blended learning methods for means of training.