Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of Optimization Techniques for Controlling an AVR System

The work proposed in this paper is study of cost function/error function in addition to different topologies of the optimizer like FFO, ACO, PSO to find an optimum PID for the AVR system. The system parameters depend on selected cost function and the optimizer ability to explore the search space. After setting the cost function and the parameters of optimizer, the well-known metaheuristic method has been applied in order to obtain the parameters of PID controller optimally. The closed loop PID consists of a generator, voltage sensor, amplifier and exciter. The whole system, which includes PID called as the AVR. The objective of the design technique proposed in this paper is to achieve high performance voltage control of the generator. The PID controlled system performance is examined by evaluating the step response characteristics. Upon comparing performances of PID controller using ACO, PSO, and FFO techniques it has been concluded that FFO-PID method is not only efficient, but also robust in improving the step response of an AVR system.
A Prospect of Regulating Traffic through Smart Traffic System

From the beginning of the 21st century, industrial researchers witnessed the development of a new technology which had the potential to change the prospects of networking. Recently, the capabilities and advantages of IoT have enabled humans to address varied real-life problems yet overgrowing population and traffic have compelled to look for alternative approaches in order to optimize current technology. These systems have limitations, are less efficient as they lack the flexibility of modification and adaptation on real-time data. This eventually adds up to environmental pollution and several health complications. To address these issues, smart systems have been proposed which focuses on the use of a network of sensors for sensing the real-time environment, processes this data to produce a decision; thus, optimizing the light switching to increase road capacity, saving time for travelling and prevent traffic congestion. The IoT based systems available today are more efficient and capable than the traditional fixed time systems as they display more potential, thus, providing options for a high degree of optimization in power consumed, speed and real-time adaptation. This paper compares presently deployed systems in various countries and focuses on the prospects of using an IoT inspired smart traffic regulation system.
VANET Protocols with Challenges- A Review

V2V (Vehicle to Vehicle), V2I (Vehicle to Infrastructure) and ITS (Intelligent Transportation System) is the approach to increase the safety of the transportation system. VANET (Vehicular Ad-Hoc Network) is a version of MANET (Mobile Ad-Hoc Network) in which Vehicles serve as active nodes of the network. A VANET is a system with high mobility. Routing protocols of the network layer are responsible for the transmission for data securely and efficiently. But these routing protocols have some challenges and issues such as delay, retransmission, security, QoS (Quality of Service) and scalability. In this manuscript, we have carried out a survey on a variety of routing protocols present in VANET system enlisting their advantages, disadvantages, and challenges associated with them. Provision of network communication, review of protocols is a necessity.
A MILP-based MPC Method for Traffic Signal Control of Urban Road Networks *

In this paper, a traffic signal control method via MILP-based model predictive control (MPC) was proposed. We use a macroscopic traffic flow model to describe traffic flows of urban road networks. In the model, 0-1 binary variables were used to describe the switch process of green lights and red lights at each intersection. Under the framework of MPC, aiming at reducing the computational complexity, an original MPC-based traffic signal control problem was reformulated as a mixed integer linear programming (MILP) problem which is conveniently calculated with efficient MILP solvers. Finally, numerical experiments based on a test network were implemented to study the validity of the reported method.
BioFET Technology: Aggressively Scaled pMOS FinFET as Biosensor

We report for the first time on a BioFET sensor using FinFETs with 10nm wide fins built in a 300 mm pilot line. Our minimally modified finfet process uses a ‘Replacement Gate’ strategy whereby the Si gate is pulled at the end of the process to form the cavity that holds the electrolyte. We used a pMOS FinFET as our electrolytic gate typically has a high work function of 5.0 eV. Changing the pH or binding biomolecules to the gate dielectric surface changes the threshold voltage (VT). Measuring the VT shift provides high sensitivity due to our high-quality gate dielectric. The median voltage referred 1/f noise of only ~500 μV2μm2/Hz (at 1Hz, at threshold), is significantly lower than other reported bioFETs. We obtain a pH sensitivity near the Nernstian limit of 57 mV/pH for HfO2. Drift of the sensor has been found to be adequate at -1 mV/hour. Finally, we have verified our device to act as a biomolecular transducer. We find the VT shift due to surface bound biomolecules for DNA grafting and PNA-DNA hybridization to be as expected from simulation. Based on our quantification of transduction and simulations we project which single molecule signal-to-noise ratios could ultimately be obtained in the future.
Control of Three-Stage AC-AC Solid State Transformer for Power Exchange Between Grids

In this paper, a solid-state transformer is studied, which adopts three-stage cascade control of input isolation output. The transformer can provide high-voltage and low-voltage DC bus, which can realize DC input and output of DC, and has strong compatibility. In this paper, the solid-state transformer is taken as the research object, focusing on the energy flow analysis of solid-state transformers and the coordinated control between solid-state transformers. The structure and control strategy of solid-state transformers are studied. The simulation model of solid state transformer is built by using Matlab/Simulink. The simulation results show that the coordinated control strategy system used in this paper has good dynamic performance, good anti-interference and fast response.
Tabu-Search-Based Pilot Assignment for Cell-Free Massive MIMO Systems

In this correspondence, an efficient pilot assignment scheme based on tabu search (TS) is proposed to mitigate the severe pilot contamination effect caused by pilot reuse in cell-free massive multiple-input multiple-output systems. By exploiting the local neighborhood search, we first define the neighborhood and search objective function. Then, the tabu list is introduced to record the prohibited solutions and to avoid being trapped in the local optimum. In addition, we consider a fair policy for the pilot reused times to further improve the system performance. We propose a TS-based pilot assignment iterative algorithm to search the suboptimal pilot assignment result with low complexity. Numerical results verify that the proposed TS-based pilot assignment scheme can significantly improve the 95$\%$-likely per-user throughput with similar complexity compared to the conventional random and greedy schemes.
A Reconfigurable Interconnect Technology based on Spoof Plasmon

The bottleneck of inter-chip communication speed, for instance that between logic and memory units is a long standing issue in acquiring ultra-fast computing. While research from two extreme end of technologies: electrical, and optical interconnect have their own pitfall in constructing an energy-economic route of communication with high bandwidth density, a radically different technology named as spoof plasmon based communication may bring new insight towards addressing the issue. This technology smoothly interweaves the advantages of electrical interconnect and that of optical ones. In the present work, we have given a proof-of-concept demonstration of the signal transfer capacity of spoof plasmon parallel data-bus. We demonstrated that spoof plasmon channel can accommodate two different kinds of mode: electro-SSPP mode and opto-SSPP mode. Depending on the data-traffic, SSPP channel can be dynamically reconfigured for supporting either of the two modes for information transfer in order to minimize energy consumption. In addition, we also endeavored to answer to the question that whether the metasurface based information transfer system can be reliable in case of patterning irregularity induced by fabrication process variations.
Implantable Microelectrode Arrays for Epileptiform Electrical Signals Detection in the Awake Epileptic Mice *

Millions of people are affecting by epilepsy due to the unclear pathogenesis. In this paper, we fabricated an implantable microelectrode array for spike patterns and local field potential signals detection in the hippocampus of awake epileptic mice. The abnormal epileptiform electrical signals were recorded when the mice were occurring a seizure. During a seizure, pyramidal neurons with higher amplitude appeared together with interneurons which firing intensively. The abnormal local field potential (LFP) waveform changed from the slow oscillation with low frequency and high amplitude, to the sharp-wave and spike-wave. The oscillation frequency of LFP waveform is low while amplitude is high at the beginning and ending of seizure. On the contrary, in the middle of seizure, frequency is high while amplitude is low. This detection technology provides us an effective method to understand the neuron activities at neuron level.
A Circularly Polarized Substrate Integrated Waveguide Slot Array Antenna with Enhanced Scanning Range

A circularly polarized (CP) substrate integrated waveguide (SIW) slot array antenna with enhanced scanning range is presented. The antenna can create a circularly polarized wave with a series of orthogonal linearly polarized (LP) slots. And its scanning range is enhanced by using a meandering structure. In the operating band of 12.5-17.5 GHz, simulated scanning angle of the proposed antenna is from −47° to +22°. The 3 dB axial ratio (AR) bandwidth of the antenna is 1.7 GHz (14.3-16.0Hz). In all, its simple structure, low-cost manufacture and great performance contribute to its potential applications in wireless communication and radar systems.
Prediction of Blood Glucose Concentration Based on EMD denoising and LSTM networks

As the number of diabetic patients continues to increase, improving the level of diabetes treatment becomes increasingly important. Blood glucose prediction plays a vital role in diabetes treatment; therefore, it is necessary to further improve the accuracy of blood glucose concentration prediction. Blood glucose data are a kind of sequential data, and the long short-term memory (LSTM) neural network, developed from the recurrent neural network (RNN), is a network structure for dealing with sequential data; therefore, in this study, LSTM networks were used to predict blood glucose concentration. If only LSTM networks are used for prediction, the phenomenon of prediction lag will occur. Therefore, to solve the lag problem and further improve the prediction accuracy, this paper proposes a blood glucose concentration prediction method based on empirical mode decomposition (EMD) denoising and LSTM networks. The final experimental results show that this method can remarkably attenuate the lag phenomenon of prediction and greatly improve the prediction accuracy; therefore, this method can well predict the blood glucose concentration.
A Robust Malware Detection Approach for Android System Against Adversarial Example Attacks

In recent years, Android has become the leading smartphone operating system across the world. However, due to their increasing popularity, Android devices have become the primary target to mobile malware. To address the arising security threats, many malware detection approaches have been studied that aim at providing strong defense mechanisms against malware. However, with more such malware detection systems being distributed and deployed, malware authors tend to generate adversarial examples by manipulating mobile applications to avoid being detected by the malware detection systems. In this paper, we investigate different types of adversarial example attacks while researching a viable approach to fight against them. More specifically, we first present the literature review on both existing malware detection approaches and adversarial example attacks against them. Then, we focus on the widely used evasion attack model that is applied to generate mutated samples. By working with various app features such as binary N-grams of API calls, we will generate feature sets consisting of a selected range of binary N-grams. As a result, we intend to use the manipulated dataset to develop and train our classifier to detect the evasion attack, and the goal of our approach is to further enhance the robustness of malware detection approach in the presence of adversarial example attacks.
CTProfiler measurement method for natural draft cooling towers

Natural draft cooling towers (NDCTs) are a systems for discharging waste heat into the atmosphere. In many cases NDCTs are not maintained properly and are exposed to slow degradation, which affect the performance of NDCTs in long term and consequently affects the operation of the power plant. We designed an innovative measuring solution, which enables high spatial resolution measurements of local temperatures, relative humidity and the airflow velocity inside the NDCTs. Based on the gathered information on environmental conditions it is possible to detect and evaluate degradation damages and thereby increase the performance of NDCTs.
A Vector Rotation-based Nonlinear Support Vector Regression Model for Envelope Tracking RF Power Amplifiers

In this paper, a novel behavior model for envelope tracking (ET) power amplifiers(PAs) is proposed by including the basis term of decomposed vector rotation (DVR) in the real value time-delay support vector regression (SVR) based model. Experiment results indicated that the SVR based model has the ability to model the behavior of ET PAs with distinct characteristic at different power levels. Compared to the original SVR model, more than 2 dB improvements in normalized mean square error (NMSE) can be achieved by the proposed model. Furthermore, when super-parameters changes, the proposed model shows better accuracy and stability.
Creation of Farmers' Awareness on Fall Armyworms Pest Detection at Early Stage in Rwanda using Deep Learning

In recent years, research has proven that cutting-edge technology is being used to revolutionize the agriculture sector more effectively and efficiently in both advanced and developing countries. A great number of researches have been conducted, very little research, however, has addressed the use of machine learning in Rwanda to improve farmer's knowledge and skills to be able to tackle agricultural issues related to crops pests. In this regards, we introduced the creation of extension material in the local language "Kinyarwanda" to enable smallholder farmers to find out the right pesticide to use after identifying the pest, how to fight the infection and how to prevent it from spreading. In order investigate the aforementioned method and proposed technology; we did binary classification where we collected unhealthy plant leaves images from the fields to be tested. We again obtained healthy plant leaves images without damage from a very well-known research and development unit called Plant Village where we collected our data. The findings of the current study are based on the trained model with Keras. The result of the classification model provided a satisfying accuracy 0f 99.5% and that model was deployed in an Android Mobile App to be used for both pest detection and creation of farmers' awareness on suitable crop pests' detection and diagnosis in a timely manner. Our findings contribute to the use of machine learning in agriculture literature and have important research in deep learning and practical implications.
Optimal Task Allocation for Minimizing Total Response Time in MEC Platform

In this paper, we propose a task allocation method to minimize total response time in Multi-access Edge Cloud (MEC) platform. In the proposed method, we formulate an optimization problem to minimize the total response time for all tasks and the optimal task allocation is decided by solving the optimization problem. We evaluate the performance of the proposed method with simulation and investigate the effectiveness of the proposed method. Numerical examples show that our proposed method is effective for the task allocation.
A Super-sensitive Flexible Strain Sensor with Ag/PDMS Composites for Wearable Physiological Signals Detection

This paper reports a flexible conductive Ag/PDMS film for physiological signals detection. The Ag/PDMS composite was prepared only with physical treatment, such as grinding, mixing, etc.; no chemical treatment was involved in the process, which results in a simple and short time-consuming microfabrication method without corrosive or hazardous chemical reagents. The results show that as the strain increase, the GF of the Ag-PDMS film increases, the sensitivity of the Ag-PDMS film increases. Compared to state of the art, the Ag/PDMS flexible sensor gauge factor (GF) can reach as high as 939. Applications of attaching the Ag-PDMS film around the finger detecting its movement was also conducted and the result is promising, which shows great potential to be used in physiological signals detection applications.
Attribute value generation using association rule mining

This paper presents an approach to generate attribute values of a record in a table using association rule mining. The proposed method generates a new record based on an existing record in the table. The generation of new values is subject to the constraints specified in the form of association rules. This approach can be used in automatic composition of music out of a ‘raaga’ and in generation of dance steps in choreography. The proposed approach has been experimented to generation a new composition on Indian classical music raaga Bhupali on Sitar.
Design Puncturing Polar Codes : The Odd-numbered Criterion

We propose the odd-numbered criterion to design puncturing polar codes and a new puncturing algorithm was put out in this paper. In view of the unreasonable puncturing selection standards, unsatisfactory decoding performance and high computational complexity of the current algorithms, we do many analysis and comparison and find some rules, then we present the odd-numbered criterion, analyzing its’ reliability and effectiveness. According to it we design a new algorithm based on correlation coefficient we defined, and the results manifest that the algorithm can greatly improve the decoding error rate performance of the polar code while reducing the computational complexity, it can bring great benefits to the application of the polar code in the digital communication system significantly.
Immune cells in experimental acute

Acute kidney injury (AKI) prolongs hospital stay and increases mortality in various clinical settings. Ischaemia-reperfusion injury (IRI), nephrotoxic agents and infection leading to sepsis are among the major causes of AKI. Inflammatory responses substantially contribute to the overall renal damage in AKI. Both innate and adaptive immune systems are involved in the inflammatory process occurring in post-ischaemic AKI. Proinflammatory damage-associated molecular patterns, hypoxia-inducible factors, adhesion molecules, dysfunction of the renal vascular endothelium, chemokines, cytokines and Toll-like receptors are involved in the activation and recruitment of immune cells into injured kidneys. Immune cells of both the innate and adaptive immune systems, such as neutrophils, dendritic cells, macrophages and lymphocytes contribute to the pathogenesis of renal injury after IRI, and some of their subpopulations also participate in the repair process. These immune cells are also involved in the pathogenesis of nephrotoxic AKI. Experimental studies of immune cells in AKI have resulted in improved understanding of the immune mechanisms underlying AKI and will be the foundation for development of novel diagnostic and therapeutic targets. This Review describes what is currently known about the function of the immune system in the pathogenesis and repair of ischaemic and nephrotoxic AKI.
Improving Bandit-Based Recommendations with Spatial Context Reasoning: An Online Evaluation

The pervasive deployment of low cost WiFi access points has accelerated the development of mobile computing to provide ubiquitous computing. Herein, we focus first on the discovery of urban districts, in several french cities, using the connection history of mobile users to a city-wide free public Wi-Fi network. The goal of our approach is to infer relevant spatial context features that can be exploitable by bandit-based recommendation systems and improve their performances. For the unsupervised context reasoning step, we use spectral clustering to deduce areas by grouping Wi-Fi access points according to their users' visitations. We have published an anonymized sample of our dataset and our results on the web. Then, we have integrated the deduced spatial context into a mobile cultural events visualization and recommendation app in order to evaluate the global performance online. Thus, over a year we have observed how such spatial context improves bandit-based recommendations in this app by comparing two use cases of the LinUCB algorithm: the first using the original context without the deduced geo-context, and the second using context enriched by our computed spatial context. Finally, our online evaluation shows that better results are obtained when combining spatial context reasoning with the bandit-based recommendation system, both in terms of accuracy and user participation.
Analysis of Grid Connected Wind Power System

The importance of renewable energy sources has increased rapidly in recent years. Among these renewable energy sources, wind energy comes to leading due to its advantages such as clean, sustainable and cost-effective. Wind power energy source is an intermittent energy source. The power output can vary widely because of the random variables of wind speed. The wind turbine output is equal to wind speed's cube. The grid-connected wind power which includes many paralleled wind turbines, transformers and collection lines, will affect stability of the power system. In the present study, the grid-connected wind power system has been analyzed for 30 km, 120 km transmission lines where wind speeds are 6 m/s, 11 m/s, respectively. Simulation results and conclusions have been given for impact of wind speed and transmission line length on active power, reactive power, and power factor.
Optimization of Ship Target Detection Algorithm Based on Random Forest and Regional Convolutional Network

Target detection can assist in detecting the position of the target ship, which is an important part of the intelligent ship visual aid system. With the development and perfection of deep learning, the convolutional neural network technology has been continuously optimized. And it can automatically learn and extract features of objects in images, providing stronger distinguishing power and representation ability. In this paper, various optimization algorithms of convolutional neural networks are compared. Aiming at the problem of unbalanced ship targets in remote sensing images of near-port areas, a ship target detection algorithm based on random forest and Faster-RCNN is proposed. The random forest algorithm is used for model optimization due to its insensitivity to multi-collinearity. The positive effect of the optimized algorithm on accuracy is verified through experiments.