Miracle-dz/UniFAR-FTUs huggingface dataset:
Virtual channel synchronous dynamic random access memory

PURPOSE: To provide a VCM semiconductor memory. CONSTITUTION: By reducing what is controlled by one line of Main Word in a WDRV basic CELL to only half of low-order 2-bit in Row address, an RA signal unified formerly at a boundary of the WDRV basic CELL is places at (the center) the inside of the basic CELL. By wiring two lines of the Main Word so as to cross with each other, the other half of low-order 2-bit in the Row address is controlled by an adjoining basic CELL, and different two Main Words having the same low-order bit are contained in one WDRV basic CELL. As shown in Fig. 2 and 8, the distance between a reference point and an origin of RED as the left side of a CELL boundary of a WDRV for RED is made the origin of RED is, as this design is compared with the conventional design, made shorter by a+b when Tr interval is 2b and protruding width from the CELL boundary is (a).
Network Design for Accurate Vehicle Localization

Network-based localization plays a key role on the introduction of emerging road applications, such as connected autonomous driving. These applications demand unprecedented precise, reliable and secure positioning, with localization requirements below 1 m. This stringent demand is pushing for the use of road-side units (RSUs) from fifth generation and vehicular networks for accurate vehicle localization. However, these networks are not typically designed for positioning but for data communication purposes, which follow a different paradigm for the network deployment and operation. This paper provides design guidelines on dedicated network deployments with the aim of achieving accurate vehicle-to-infrastructure positioning in road scenarios. First, the network layout or site placement is assessed with a geometrical metric. Then, the minimum density of RSUs along the road is bounded with line-of-sight probability models for urban street and highway scenarios. Finally, the Cramér–Rao bound for joint time-of-arrival (ToA) and angle-of-arrival (AoA) localization is used to maximize the distance between RSUs along the road, by exploiting multi-antenna deployments. According to the simulation results, the network sites are recommended to be located at alternate sides of the road, with a maximum distance between RSUs of 40 and 230 m for urban and rural environments, respectively. Following these design guidelines, there is also the need to exploit antenna arrays to combine uplink ToA and AoA estimates, in order to ensure a vehicle location accuracy below one meter on the 95% of the cases. The use of eight antenna elements at the RSUs is found to reduce nearly one and a half times the minimum network density in highway localization deployments.
Neural Network Based Named Entity Recognition

Czech named entity recognition (the task of automatic identification and classification of proper names in text, such as names of people, locations and organizations) has become a well-established field since the publication of the Czech Named Entity Corpus (CNEC). This doctoral thesis presents the author’s research of named entity recognition, mainly in the Czech language. It presents work and research carried out during CNEC publication and its evaluation. It further envelops the author’s research results, which improved Czech state-ofthe-art results in named entity recognition in recent years, with special focus on artificial neural network based solutions. Starting with a simple feed-forward neural network with softmax output layer, with a standard set of classification features for the task, the thesis presents methodology and results, which were later used in open-source software solution for named entity recognition, NameTag. The thesis finalizes with a recurrent neural network based recognizer with word embeddings and character-level word embeddings, based on recent advances in neural network research, which requires no classification features engineering and achieves excellent state-of-the-art results in Czech named entity recognition.
Assistive Robotic Hand Orthosis (ARHO) controlled with EMG: evaluation of a preliminary prototype*

Hand impairment severely limits basic activities of daily living (ADL). The use of motorized hand orthoses may provide enough functional assistance to perform basic tasks, such as grasping objects. Several prototypes have been proposed in the last decade, but there are still no solutions with the desired features regarding the weight, wearability and functionality. This paper describes the overall implementation of a prototype of an assistive robotic hand orthosis (ARHO) for object grasping, that can be triggered manually or by the detection of muscular activity in the forearm using surface electromyography (sEMG). The system is being specifically designed for a case study of a person with hemiplegia resulting from a peri-insular hemispherectomy. The proposed orthosis is a very preliminary but functional prototype, still far from the desired features mentioned above, but serves to show all the modules composing a low-cost implementation, and above all, to understand all the constraints and difficulties in designing such a system.
Journal of Biometrics & Biostatistics

Fingerprint is a unique biometric feature of individual. It is also known that fingerprints have differences in male and female with respect to ridge line details. Some studies in machine learning investigate a relationship between fingerprint and gender. In these studies by analyzing the fingerprint we get important information such as age and gender of a person. Statistical studies have been made in different geographical areas to identify the relationship between fingerprint and gender. This paper illustrates gender classification based on fingerprints through various machine learning techniques like naïve Bayes method, Decision Tree and Support Vector Machine algorithms, KNN, PCA, Wilcoxon-Mann-Whitney Test, Friedman Test. This study introduces the concept of epidermal ridge, minutiae, ridge areas, ridge density etc., and compare above stated machine learning techniques, their limitations and strengths based on experimental results for gender classification based on fingerprints. This study can be useful for legislative cases and for researchers to devise new machine learning techniques with improved results.
External Parameters Affecting on the Photoluminescence of InAs Spherical Layer Quantum Dot

Spherical layer quantum dots (SLQDs) attract a great deal of importance, and have various optoelectronics applications due to their outstanding optical and electrical properties. The photoluminescence (PL) and the electroluminescence (EL) spectra of InAs (SLQDs) were investigated theoretically under the presence of external parameters (pressure, temperature, electric field). Existing of both the temperature and the applied electric field lead to a significant decrease in photoluminescence peak energy (red-shift), while an increase ex-isted in presence of applied hydrostatic pressure (blue-shift). Also with increasing the quantum azimuthal number the photoluminescence peak energy increase. In addition, we found no effect on the band shape of the luminescence as a result of existing such parameters. The study indicates the importance of such parameters as fitting parameters for photoluminescence spectra.
Effects on Software Quality with Clean Room Software Coding

Software engineering and its maintenance is the most emerging topic now-a-days. As people are becoming more tech-savvy so with improvements in technology and in turn improvement in the internal working of the code and interface is new demand as well as prominent area of research. Software maintenance epitomize cost factor from customer point of view therefore developer tends to showcase software as brand new entity rather than improvised one. Defect avoidance is the key area rather than defect removal here. Keywords— Clean room processing, Coupling and cohesion, Refactoring, Software Maintenance International Journal of Engineering Research & Technology (IJERT)
SIC-MMSE Method based Wireless Precoding Technique for Millimetre-Wave MIMO System

A communication method is proposed using Minimum Mean Square Error (MMSE) precoding and Successive Interference Cancellation (SIC) technique for millimetre-wave multiple-input multiple-output (mm-Wave MIMO) based wireless communication system. Background: The mm-Wave MIMO technology for wireless communication system is the base potential technology for its high data transfer rate followed by data instruction and low power consumption compared to Long-Term Evolution (LTE). The mm-Wave system is already available in indoor hotspot and Wi-Fi backhaul for its high bandwidth availability and potential lead to rate of numerous Gbps/user. But, in mobile wireless communication system this technique is lagging because the channel faces relative orthogonal coordination and multiple node detection problem while rapid movement of nodes (transmitter and receiver) occur. Methods/Improvement: To improve the conventional mm-wave MIMO nodal detection and coordination performance, the system processes data using symbolized error vector technique for linearization. Then the MMSE precoding detection technique improves the link strength by constantly fitting the channel coefficients based on number of independent service antennas (M), Signal to Noise Ratio (SNR), Channel Matrix (CM) and mean square errors (MSE). To maintain sequentially encoded user data connectivity and to overcome data loss, SIC method is used in combination with MMSE. Improvements: MATLAB was used to validate proposed system performance. Simulation analysis shown that, with the increase number of antennas use, the spectral efficiency also increased and higher then millimetre-wave MIMO or Single MMSE system. This research observed that, hybrid controller or combined control method have the better efficiency then single method, where SIC-MMSE based hybrid controller is a good example.
Miniaturized Implantable Loop Antenna for Biomedical Applications

An implantable loop antenna is proposed for biomedical applications. The size of the proposed antenna is 13 x 13 x 1.27 mm is obtained. The proper feed and short pins for obtaining either RHCP or LHCP property can be realized. The proposed antenna is designed for Industrial, Scientific and Medical (ISM) (2-2.48 GHz) biomedical applications. The return loss of the loop antenna can be realized with S11 less than -10 dB and axial ratio below 3 dB. Keywords— Miniature antenna, Industrial, Scientific and Medical (ISM) band, RHCP, LHCP, implantable antenna.
THE PSYCHOLOGICAL IMPACT OF COVID-19 ON FRONTLINE HEALTHCARE WORKERS IN MOROCCO

Introduction: On March 11th 2020, the World Health Organization declared the infection of the virus severe acute respiratory syndrome coronavirus 2 or SARS-COV-2 a pandemic. The virus, which was first discovered in China in Wuhan province in December 2019, has affected more than 100 million people and more than two million deaths in more than 200 countries.Along with other epidemics and pandemics, the existence of this virus has led to psychiatric disorders in people in or without direct contact with the disease, especially frontline healthcare workers. Objective: To assess the impact of the virus on medical personnel working in Morocco, in particular stress, depression and sleep disorders. Methodology: To explore this topic, we used an anonymity questionnaire based on, in addition to individual status and conditions, scales assessing stress, depression and sleep.The questionnaire was sent via social networks to health personnel working in Morocco in the various hospitals and departments treating patients infected with this virus.The CES-D (Center of Epidemiological Studies - Depression) scales for depression, the PSS-10 (Cohens Perceived Stress Scale) for perceived stress and ISI (C. Morins Insomnia Severity Index) for disorders of sleep, were chosen given their frequent use in a number of studies on the same topic. Results: After sorting our responsesrigorously, we were able to collect 376 responses, of which 53.5% were women, with an average age of 28.45 years, 23.9% were nurses, 14.4% general practitioners, 19, 7% of internal doctors 37.2% of residents or specialists and 4.8% occupied other functions such as medical transport. 66% of our candidates were at the level of the different university hospital centers of Morocco.Our candidates respectively occupied 39.1% 28.2% 29% and 25.8% triage positions, observation of suspected cases, the COVID sector and resuscitation.Regarding our results, it was observed that 71% of our participants presented moderate stress, 44.9% had a risk of developing depression and 53.7 had insomnia of varying severity.The female sex, the lack of medical, surgical and psychiatric history influence the occurrence of the three abnormalities studied, it should also be noted that these last three maintain a relationship of influences between them. Conclusion: The exploration of stress, depression and insomnia revealed an impact of the state of health on the daily life of front-line health workers in Morocco.
Electricity Price-aware Consolidation Algorithms for Time-sensitive VM Services in Cloud Systems

Despite the salient feature of cloud computing, the cloud provider still suffers from electricity bill, which in part comes from 1) the power consumption of running physical machines (PMs) to guarantee the resource/time requirements of virtual machines (VMs), and 2) the dynamically varying electricity price offered by smart grids. In the literature, there exist viable solutions adaptive to electricity price variation to reduce the electricity bill. However, they are not applicable to serving time-sensitive VM requests. In serving time-sensitive VM requests, it is potential for the cloud provider to apply proper consolidation strategies to further reduce the electricity bill. Few prior works have provided theoretical solutions of VM consolidation strategies that are adaptive to electricity price variations in serving time-sensitive VM requests. In this work, to address this challenge, we develop electricity-price-aware consolidation algorithms for both the offline and online scenarios. For the offline scenario, we first develop a consolidation algorithm with constant approximation, which always approaches the optimal solution within a constant factor of 5. For the online scenario, we propose an <inline-formula><tex-math notation="LaTeX">$O(\log (\frac{L_{max}}{L_{min}}))$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mo form="prefix">log</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mfrac><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mfrac><mml:mo>)</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq1-2894742.gif"/></alternatives></inline-formula>-competitive algorithm that is able to approach the optimal offline solution within a logarithmic factor, where <inline-formula><tex-math notation="LaTeX">$\frac{L_{max}}{L_{min}}$</tex-math><alternatives><mml:math><mml:mfrac><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mfrac></mml:math><inline-graphic xlink:href="wang-ieq2-2894742.gif"/></alternatives></inline-formula> is the ratio of the longest length of the processing time requirement of VMs to the shortest one. Our trace-driven simulation results further demonstrate that the average performance of the proposed algorithms produce near-optimal electricity bill.
Efficient Clustering Technique with Feature Reduction Mechanism for Network Anomaly Detection

Today the information technology has grown largely with increased number of users accessing the network, which results in the increasing invasion of security threats. Anomaly detection systems (ADS) monitor the behaviour of a system and flag significant deviations from the normal activity as anomalies. Since any malicious activity on network may lead to serious consequences, the importance of information carried out in these networks makes the task of anomaly detection very crucial. Hence a robust anomaly detection technique, Efficient clustering technique with feature reduction mechanism for network anomaly detection is proposed. The proposed system has two phases. The first phase is used to reduce the dimensions of the dataset. For this, feature reduction method, Recursive feature Elimination technique is employed which selects the relevant features from the dataset while leaving the redundant ones. The Random forest classifier is used to find the Accuracy, Precision, Recall and F-measure of the Recursive feature elimination technique. Then the second phase is the clustering phase which is used to cluster the dataset to classify the labels of the dataset. Here in the proposed system the following clustering techniques are used and the results are compared. The clustering techniques that are evaluated are Hierarchical agglomerative Clustering, Density Based Clustering and K-Means Clustering. KeywordsHierarchical agglomerative Clustering, Density Based Clustering, K-means Clustering, Recursive Feature Elimination Technique.
Method and apparatus for machine learning a document relevance function

本发明提供了一种确定文档相关性函数的方法与计算机程序产品，该文档相关性函数用于估计数据库中的文档关于查询的相关性分数。 对于多个测试查询中的每个测试查询，收集相应的结果文档集。 对于每个测试查询，选择所述相应结果集中的文档子集，并且训练相关性分数集被分配给该子集中的文档。 在一个实施例中，至少一些训练相关性分数是由人类主观认识分配的，所述人类主观认识确定所提交的文档关于对应查询的个体相关性分数。 最后，基于所述多个测试查询、所述文档子集以及所述训练相关性分数集，确定相关性函数。
Towards safety in physically assistive robots: eating assistance

Safety is one of the base elements to build trust in robots. This paper studies remedies to unavoidable collisions using robotics assistive feeding as an example task. Firstly, we propose an attention mechanism so the user can control the robot using gestures and thus prevent collisions. Secondly, when unwanted contacts are unavoidable we compare two safety strategies: active safety, using a force sensor to monitor maximum allowed forces; and passive safety using compliant controllers. Experimental evaluation shows that the gesture mechanism is effective to control the robot. Also, the impact forces obtained with both methods are similar and thus can be used independently. Additionally, users experimenting on purpose impacts declared that the impact was not harmful.
Islanding and Detection of Distributed Generation Islanding using Negative Sequence Component of Current

There is a renewed interest in the distributed generation (DG) mainly due to the environmental concern and electricity market liberalization. Many utilities around the world already have a significant penetration of DG in their systems. But there are many issues to be resolved before DG becomes an integral part of the utilities around the world. One of the main issues with DG is islanding. Islanding occurs when a portion of the distribution system becomes electrically isolated from the remainder of power system yet continues to be energized by distributed generators. An important requirement to interconnect a DG to the power distributed system is the capability of the DG to detect islanding. Failure to trip islanded generators can lead to a number of problems to the generators and the connected loads. The current industry practice is to disconnect all distributed generators immediately after the occurrence of islands. Typically, a distributed generator should be disconnected within 100 to 300 ms after the loss of the main supply. To achieve such a goal, this paper proposes that each distributed generator must be equipped with an islanding detection device using a negative sequence component of the
Generative Adversarial Networks Time Series Models to Forecast Medicine Daily Sales in Hospital

The success of the work of Generative Adversarial Networks (GAN) has recently achieved great success in many fields, such as stock market prediction, portfolio optimization, financial information processing and trading execution strategies, because the GAN model generates seemingly realistic data with models generator and discriminator . Planning for drug needs that are not optimal will have an impact on hospital services and economics, so it requires a reliable and accurate prediction model with the aim of minimizing the occurrence of shortages and excess stock, In this paper, we propose the GAN architecture to estimate the amount of drug sales in the next one week by using the drug usage data for the last four years (2015-2018) for training, while testing using data running in 2019 year , the classification results will be evaluated by Actual data uses indicators of Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). From the results of the experiment, seen from the value ​​of MAE, RMSE and MAPE, the proposed model has promising performance, but it still needs to be developed to explore ways to extract factors that are more valuable and influential in the trend disease progression, thus helping in the selection of optimal drugs
Resource Scheduling for Postdisaster Management in IoT Environment

For postdisaster management, rescue and recovery operations are very critical. It is desired that the rescue and recovery operation should be handled through efficient resource management to minimize the postdisaster effects in terms of human loss and other types of damage. Resource management requires addressing various challenging issues like scheduling and monitoring of the resources which need real time information of various activities or events occurring anytime, anywhere, and anyplace. To satisfy such requirements, Internet of Things, an advanced upcoming technology, can be utilized for resource monitoring and scheduling. In this context, we propose resource scheduling algorithm for the postdisaster management. As mentioned above various tasks of rescue and recovery operation should be carried out with different priority and there should not be deadlock while availing the resources. In our approach, we estimate the waiting time using queuing theory for the availability of the resources for different activities that are to be performed at various locations. The simulation results of the proposed method are analyzed using different standard parameters like resource utilization and the waiting time for different activities. The proposed method is further visualized through real time annotation of resources and activities represented with the help of Google maps using android based application on the smartphone. The proposed algorithm is further compared in terms of computational complexity and fairness analysis for the effective utilization of the available resources.
RGB-D Indoor Simultaneous Location and Mapping Based on Inliers Tracking Statistics

In this paper, an algorithm based on Inliers tracking statistics is proposed to solve the problem of motion-blur interference of RGB-D SLAM front-end pose estimation and back-end optimization. Firstly, the feature extraction and matching of the RGB image are carried out and the inliers is obtained by the RANSAC algorithm. Then, the blur image affected by the camera motion is removed by the tracking and statistics of the inliers quantity, and the camera position is solved by the nonlinear optimization method. Finally, the motion trajectory and the three-dimensional dense point cloud are plotted by and optimized global pose. Experimental results based on standard test set show that the algorithm based on Inliers tracking can improve the robustness and precision of SLAM system.