Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Ceramic Capacitor Insulator Integrated with Transient Voltage Monitoring and Energy Self-extraction for Overhead Lines

This paper introduces a kind of novel ceramic capacitor insulator equipment, which is mainly composed of a composited insulator housing and a high voltage capacitor divider based on ceramic capacitors. Online monitoring function of transient voltage can be realized though supporting by the low voltage acquisition module and power energy selfextraction module, for the distribution overhead lines. The characteristics of the ceramic capacitor insulator are examined by numerical simulation and laboratory testing. The field testing has also been conducted by installing at a 10kV distribution overhead line. The testing results show that the ceramic capacitor insulator equipment can perfectly achieve the functions of the transient voltage online monitoring. For its integrated functions, it can greatly simplify the online monitoring equipment and has broad application prospects for any complicated distribution networks.
Optical-based Limit Order Book Modelling using Deep Neural Networks

We used a deep neural network to create a model of order books’ behaviors in a stock market using their VDO snapshots as an input. The snapshots were taken from a stock market application in time series format. Google’s Tesseract OCR was used to extract price data from these snapshots. A long short-term memory (LSTM) neural network was used to learn the price behaviors in order to predict their future trends, i.e. up, down, or neutral. The result showed that the system achieved an accuracy of 68.96% despite the noise from the OCR and the sampling effect of the snapshots.
Smart Parking System using IoT Technology

A simple and easy task such as parking is thought as a tedious and time-consuming process due to mismanagement of parking system. Current parking systems involve huge manpower for management and requires user to search for parking space floor by floor. Such conventional systems utilize more power, along with user’s valuable time. This paper presents a Smart Parking Energy Management solution for a structured environment such as a multi-storied office parking area. The system proposes implementation of state-of-the-art Internet of Things (IoT) technology to mold with advanced Honeywell sensors and controllers to obtain a systematic parking system for users. Unoccupied vehicle parking spaces are indicated using lamps and users are guided to an empty parking space, thus eliminating need for searching for a parking space. The occupied parking spaces are virtually stored to the cloud to be accessed by central system and direct the upcoming cars to empty spaces. The automatically controlled light illuminance helps reduce energy usage, along with lighting up the parking space to the user whilst in the parking space. The entire system being fully automatic leads to reduced manpower involved and improves illuminance aesthetics of the parking area. This paper aims at improving user’s time value and convenience in a parking system.
Short-Term Load Forecasting Method for AC/DC Distribution System Based on Ensemble Learning

There are more diverse types of loads in an AC/DC distribution system, so it is much more difficult to grasp the change rules. Accurate load forecasting is important for the scheduling of AC/DC distribution system. Aiming at the precision problem of traditional short-term load forecasting methods, such as neural network, grey theory and support vector machine, this study uses the ensemble learning to improve the traditional forecasting methods, and proposes a gradient boosting method based on shallow neural network (GBSNN) as a base learner. Meanwhile, by using the Huber function as the loss function, it is robust to abnormal load data and can reduce the generalization error. Through simulation results and comparison analysis, the proposed short-term load forecasting method based on GBSNN has higher precision than other methods and better performance in load forecasting of AC/DC distribution system.
The multistatic oceanographic HF radar network in Toulon

The Mediterranean Institute of Oceanography (MIO) and the University of Toulon operate a HF radar network for sea surface current monitoring in the Mediterranean Sea. This radar system was recently upgraded in the framework of the EU Interreg project SICOMAR-PLUS and is now running in a multistatic mode with 2 TX and 2 RX located on three distant sites. This original configuration combined with some innovations in the radar data processing allows for an increased coverage and improved spatiotemporal resolution of surface currents maps in the coastal region of Toulon. We present the main characteristics of this updated HF radar network together with a rapid overview of the specific processing techniques it requires. Preliminary results showing the potential performances of this network are presented.
Prototype Development and Experimental Performance Evaluation of FMCW Radar Using Iterative Interference Suppression Technique

Millimeter-wave FMCW (Frequency Modulated Continuous Wave) radar is a promising sensor for ADAS (Advanced Driver Assistance System) and automatic driving car. As radar-equipped vehicles are expected to be densely deployed, inter-radar interference causing miss-detection and/or false detection of targets will be a serious problem in near future. In order to solve this problem, the authors proposed a novel iterative interference suppression technique to suppress socalled wideband interference in various interference environments. To validate the proposed technique by experiments, a proto-type FMCW radar using the proposed technique is developed and evaluated. This paper describes proto-type development of FMCW radar using the proposed interference suppression technique and experimental performance evaluation results of the developed radar in real inter-radar interference environments.
Feature Selection and Classification for Detection of Seizures from EEG Signal

Epilepsy is a disorder in the neurological system which has abnormal neural activity in the brain. This abnormal activity can trace using EEG device. The identification of seizure activity through EEG is a demanding task and the finding of EEG signals by human scrutiny is imprecise as well as time consuming and is prone to errors. A number of attempts have been made to expand automatic system so as to help neurophysiologists to spot the seizures perfectly. In order to decrease the number of features and to pick only the majority pertinent set of features from the EEG signals becomes more difficult task. To obtain better, the searching speed of the CS algorithm, fuzzy function is used in this research work. Finally employ the Ensemble Enhanced Convolutional Neural Network (EECNN) for scrutiny of EEG signals. The proposed effort is implemented to detect the abnormal activity from the EEG based on different levels of EEG signals (normal, partially affected, and fully affected) classes. The proposed system is that the model yields promising classification accuracy.
Calculation of Parasitic Capacitance of Foil Winding Dry-type Power Transformer

Power transformers are critical components in the entire grid and are the bridges that connect different voltage levels. At the same time, harmonic pollution has become an essential factor in grid power pollution. It is a serious issue to curb harmonics into the public network and improve power quality. In the process of harmonic transmission, it is necessary to experience this part of the transformer. Therefore, this subject has established an important high-frequency equivalent circuit of the transformer through the study of the high-frequency parameters of the transformer, which has important practical significance for the filtering of harmonics. In this paper, the parasitic capacitance of the foil winding for low voltage side of the dry-type double-split power transformer model studied using the analytical method. According to the specific structure of the transformer, some of the circuits are adjusted to build a relatively accurate high-frequency equivalent circuit. Finally, the harmonics of the harmonic source AC/DC are analyzed to analyze the harmonic components in the waveform. Based on the previously established model, the impedance characteristics of the circuit in the frequency band of the harmonics explored.
A Novel Fuzzy based Intelligent Demand Side Management for Automated Load Scheduling

Developments in smart-grid technologies can be associated with rising awareness among general populace of renewable energy as well as the need of distributed generation via these sources. Improvement in efficiency of electrical energy from Renewable Sources (RS) can be achieved by collaborating advanced structures with intelligent metering technology. The smart meters along with the distributed generation sources are being widely used in smart grid applications. An intelligent energy management system is key to monitor and control the processes at consumer and supplier end. Thus, an intelligent system for various computation and procurements can be considered a part of smart-grid. It is within consideration that a part of the energy demand by the building is covered by this Intelligent Demand Management Structure (IDMS). The IDMS is an indispensable tool in order to guarantee greatest added value to the smart meter. The practical and theoretical integration and application of IDMS with the smart meter is presented in this article. The article proposes a novel algorithm based on fuzzy optimization logic employed to the intended system. Fuzzy Controller Logic (FCL) language was used to create the fuzzy rules while the execution was carried out in Python. The designed algorithm is tested in the real time with the load profile of a practical setup. The proposed FCL based algorithm saved a maximum of 15% energy in best cased scenarios.
Doppler Ambiguity Resolution for Binary-Phase-Modulated MIMO FMCW Radars

State-of-the-art FMCW automotive radar sensors contain Multiple-Input Multiple-Output (MIMO) arrays to obtain a good angular resolution. Binary phase modulation (BPM) is a technique used on these systems to achieve a high Signalto-noise ratio (SNR), compared to the commonly used Timedivision multiplexing (TDM). As several chirps are combined to transmit a binary code, the approach suffers from a reduced maximum unambiguous velocity. In this paper, we implement three approaches for the velocity disambiguation in a MIMO radar with four transmitters using Hadamard coding. The first two approaches require three heterogeneous frames and are based on an exact method (Chinese Remainder Theorem), and an unsupervised learning technique (Density Based Spatial Clustering of Applications with Noise) respectively. The third approach determines the true velocity by searching for the strongest response in the angular spectrum of a single frame, which suffers from a negative bias caused by the phase codes. The approaches are validated and compared in MATLAB simulations.
Authorization Model for IoT Healthcare Systems: Case Study

Internet of Things (IoT) is quickly expanding among consumers, companies, organizations, and governments. The spreading of smart systems where the IoT devices play a main role in accumulate, store, and share user data is discernibly increased. Moreover, the increasing integration of big data, cloud computing, and IoT in the future systems and organizations will increase the demands for computational resources, and the ability for the applications to interact and communicate with each other, within and across organizational boundaries. As cloud and mobile computing play a critical role in future systems. With all this advancement, the security challenges are generated, and it is considered as a developing concern. In this Work, we will discuss different security models provided for Cloud-Enabled IoT architectures security issues. Then, we will propose a security model using Amazon Web Service (AWS) cloud provider platform with Attribute-Based Access Control (ABAC) on different levels: IoT, Edge, and the cloud.
Biology of seagrasses : a treatise on the biology of seagrasses with special reference to the Australian region

Hardbound. Seagrasses are a common feature of inshore coastal waters of all continents except Antarctica. This book, the first to attempt a comprehensive coverage of seagrasses, will attract a wide audience including botanists, zoologists, geologists, and experts in fisheries and coastal management. There are individual chapters on all aspects of the plants themselves, from questions of taxonomy, reproduction and evolution, through ecology and biogeography, to the physiological aspects of photosynthesis, nutrition and salt balance.The fauna associated with seagrass beds are also dealt with extensively, including meiofauna, large grazers (including dugongs and turtles), and fish. The book examines the role of seagrass beds as nursery grounds for commercially important fish, and addresses the problems of human impact on seagrass communities, the conservation of seagrass beds, and their restoration.
MAGIC NAND within NOR gate

In-memory computing based on memristor has attracted significant interest for breaking the von Neumann bottleneck and developing high-efficient computing systems. Memristor-based logic gate is one of the most important part to achieve our goals. Recently, there are many methods which have been presented to achieve Boolean functions. Such as material implication (IMPLY) and memristor-aided logic (MAGIC). MAGIC NAND gate can be used as a standalone logic which is not placed within a crossbar array. However, it's unavoidable to describe it within a crossbar array in order to design more circuits which can achieve more functions. So we use a 4*3 array which consists of memristors and transistors (1T1R) to simulate MAGIC NAND gate within a crossbar array. And we deduce the mathematical relationship between the applied voltage and the threshold voltage. Furthermore, we have verified NAND logic within the array and implemented vector NAND computation.
Crop Selection and IoT Based Monitoring System for Precision Agriculture

Internet of Things (IoT) is the future. IoT is the change that is required in every field. Being able to monitor and control things from a distance makes any task effortless. Agriculture is a very important field and hence every possible technological advancement should be made in this field. With the rise in population worldwide, the demand for agriculture has increased drastically and unfortunately, farmers are failing to fulfill the never-ending demand. Instead of increasing the scale of agriculture, a better way will be implementing smart or precision agriculture techniques using IoT. The yield of any crop can be maximized with the help of precision agriculture by reducing the wastage. The proposed work does so by monitoring the environmental factors like temperature, humidity, soil moisture, etc. that affect the growth of the crop as well as helps the farmers to decide the idle crop that will be suitable for them according to the data collected and environmental conditions. This model can be very effective than the traditional methods as the risk of crop failure, less yield, excessive water supply or excessive use of fertilizers and pesticides, etc. can be reduced to a great extent. The data collected by the sensor nodes deployed all over the field in sent to the cloud and there the data is analyzed and visualized for the ease of farmers. With the help of visualized data farmers can take precise and effective decisions affecting their crops.
Cloud Computing Role in Internet of Things: Business Community Survey

The current revolution in the world of communication and information technology are geared by the Internet of Things and cloud computing services. This offers more opportunities for the development of a new system aimed mainly at facilitating people’s lives and eliminating the traditional complexities facing them. These drives have led to further development in the world of the Internet of Things, linking them with cloud computing, making them smarter and more sophisticated and available to all. In this paper, we discuss the integrating of the Internet of Things with cloud computing and how it used in various business sectors to be more interactive, intelligent and serving the community. Based on a systematic review, a number of perspectives from academia and industry have been drawn to establish a safe and intelligent community.
High Frequency Resonance Analysis of Distributed Storage Converter and Design of Output Filter

The application of distributed energy storage converter in modern distribution network has received sustained attention. The output filter, as the essential part in distributed energy storage converter, can effectively filter the switching ripple of the converter and improve the current quality. However, the output filter has many parameters, which are closely related to each other. Therefore, determine these parameters during designing is a difficult and vital step. Meanwhile, the study of this paper show that the filter and the current control process of the inverter are coupled to change the equivalent impedance of the inverter while the filter parameters have a decisive impact on the equivalent impedance in high frequency. When the parameters of the filter are unreasonable, zero impedance will appear in some high frequencies, which will result in the resonance of the inverters. When the number of parallel inverters increases, the damping of the system decreases due to the impedance parallel connection, and resonance phenomena become more obvious. Consequently, in this paper, the influence mechanism of output filter parameters on high frequency oscillation is obtained via establishing the mathematical model of converter under current closed-loop control. Combined with the requirements of filter performance, a design method for output filter parameters settings is summarized. Finally, experiments indicate the correctness and effectiveness of the proposed method.
Emerging Trends and Challenges in Data Science and Big Data Analytics

In the recent decade, several technologies have boomed up due to recent development in many technologies. These technologies have changed the life of human being and are increasing the profit for individual, organisations like NetFlix, Alibaba, Flipkart, etc. Today in this world, maximum people are surrounded by smart objects or using smart objects to make their life easier to live and convenient. But, on another side life is being made easier to live by analytics companies/ industries. For example, which user like which type of plays, movies, songs, etc., things are extracted by many companies. Such recommendations are being made and being provided improved services to the respective users. Such jobs are being done by Data Scientist. Whereas, Data science (a future of Artificial Intelligence) is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured (labelled) and unstructured (unlabelled) data. Moreover this, Big Data Analytics are the analysis mechanism used in Data Science by Data Scientist. Several tools like Hadoop, etc., are used to analysis the large amount of data and used in predicting the valuable information/ making decisions. But, in analysing we faces several concerns like complexity, scalability, privacy leaking and trust. So, this article discusses about such concerns, challenges (rising) in this emerging field in detail (with a comparative analysis/ taxonomy).
Music Response Based on Real-time Facial Expression Recognition

This research employs information and communication technology (ICT) to develop an App that plays music lists based on emotion. Music can regenerate brain cells and ease emotions, and when users are in a negative mood, the App will automatically play the appropriate music list. Emotions are the results of users' facial expression recorded by camera and predicted by deep learning model. The accuracy of each expression is as follows: 84% for happiness; 83% for surprise; 58% for anger, 55%; sadness and 58% for neutral. The dataset is the public data set fer2013 of the competition held by Kaggle in 2013, and some data are downloaded from the Internet, such as family members who mourned after the earthquake, families of victims of terrorist attacks, and weddings. Coupled with Internet of Things (IoT) technology, this study allows users to ease their emotions through music when they are depressed, so as to avoid any improper transfer of physical or mental suffering.
Analysing Conflicts in Online Football Communities of Reddit

Ever since social presence has moved online, understanding conflict in online communities has become a significant research problem. A successful analysis of conflict in online forums can help researchers understand how today’s online users participate and behave. Conflicts occur in a social platforms like Reddit when there are two different communities endorsing their own and rebuking the other. Soccer being a very common Subreddit where users support different teams, witnesses a large number of conflicts. So, in this work, we chose football communities in Reddit to understand inter-community conflicts. We first plotted various graphs to have a macro level understanding of such Reddit communities i.e. number of comments and unique users over months. Then to explore the Subreddits that are more involved in conflicts, we developed a framework for detecting magnitude as well as direction of conflicts and plot an intercommunity conflict graph with directed weighted edges where weights denote the intensity of conflicts. The conflict graphs then help us identify Subreddits that are most conflict-prone and also help us understand the timeline events when conflict occurs. The framework developed in this work can be used and extended for studying conflict in various online forums.
Development and Performance Evaluation of Integrated Automotive Mid-Range Radar System based on ROI preprocessing technique

In this paper, we developed an integrated mid-range automotive radar system with both blind spot detection (BSD) and lane change assistance (LCA) functions. Integrated automotive radar extends the maximum detection range to 80m with an update time that is within 50ms. By means of ROI preprocessing technique, the computation load of radar signal processing was improved. In order to evaluate the detection performance considering an actual driving environment, radar performance evaluations were conducted in various driving scenarios at an automotive proving ground.