Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrusion Detection and Prevention Systems in Smart Environments – A Survey

Intrusions in networks are considered to be an activity occurring on digital networks in an illegal manner. They steal the network's confidential data and resources in the which in turn degrades the network security and/or data security. By detecting and preventing these intrusion attacks, one can avoid malicious activities happening inside the network beforehand, analyze the quantity and types of attacks which might happen in the network and prevent it in advance. Intrusion detection and Prevention systems (IDPS) are being employed in various laboratories, industries, and public service organizations. Security controls to various enterprises and organizations can be provided and performance of IoT based smart environments can be improved. In this article, a detailed survey about how the intrusion attacks happen in smart environments, ways to detect and prevent them are discussed. Different types of IDS and IPS, Future research directions, challenges and scope of the work are outlined in this work.
An Approach for Detection of Epileptic Seizures from EEGs Considering Ictal-like Non-Ictal Signals

In the clinical diagnosis and treatment of epilepsy, it is a necessity to detect epileptic seizures automatically and accurately from the electroencephalography (EEG) data instead of manual detection. In most studies on this issue, the problem is simply regarded as a binary classification problem to distinguish ictal and non-ictal signals. However, EEG signals actually show characteristics of multimodal distribution, which should not be ignored in the detection. With consideration of the existence of such signals, a new workflow employing a k nearest neighbor algorithm and a generative adversarial network (GAN) is presented. The latter is developed to discern the ictal-like non-ictal EEGs before training the classifier. The GAN is used to find more implicit boundary-type samples. Consider the current task, a 16-layer CNN, which is the type most used to identify patterns in images and video, is transferred here to solve the problems of insufficient training data, long training time, and insufficient accuracy. Evaluated by a public EEG dataset, it proved effective to distinguish boundary signals prior to the classification of ictal and non-ictal signals.
PV-Grid Integrated Hybrid Charger for EV s with Contactless and Conductive Charging Capability

The imminent global fossil fuel crisis and ever-increasing global warming have opened up opportunities for green and energy efficient electric vehicles (EV). With the prevalence of EV in automobile market the charging infrastructure also needs to grow in parity. The objective is to provide a reliable, resilient and adaptive charging solution for existing and future EVs. Due to various pros and cons of plug-in as well as contactless charging both need to coexist in the same system for greater flexibility in charging. In this paper a solar PV -grid integrated hybrid EV charger is presented. The hybrid charger shares the same stages of power electronic converters for both conductive and contactless charging there by reducing the overall component count with reduced losses as compared to existing grid connected hybrid charging schemes. The proposed topology is simulated in MATLAB/Simulink platform for power same as that of standard level1 charger i.e., 3.3 kW. Power flow management and performance of the charger under various modes of operation is analyzed.
Comparative Analysis of Classification Techniques on Olympic Games Datasets

Olympic game is a prestigious ceremony that occurs after every four years. However, due to the spread of coronavirus in 2020, the game was held in 2021, which is post-Covid. The main aim of this research is to find out if there was a difference in the performance of nations in Rio 2016 Olympics (pre-Covid) and Tokyo 2020 Olympics (post-Covid). Statistical analysis is carried out to find the correlation between the different variables. One of the highly correlated variables (Gold Tally) is removed while performing the classification analysis. The idea is to see if the classifiers are able to do the comparative analysis without it or not. The classification algorithms utilized in this research are Decision Table, Decision Tree, Naïve Bayes, and Random Forest. The datasets used in this research are imbalanced sets, which were later transformed to balance sets through under-sampling. Random Forest was able to give 100% accuracy in both datasets whereas the True Positive Rate (TPR) was also 100%. After doing the comparative analysis it was found that irrespective of pre and post-Covid, the performance of athletes did not change. This paves the way for other researchers to investigate if Covid had any impact on the performance of the athletes or not. In the future, more vast variables will be investigated to do a more detailed comparative analysis.
Slam Algorithm based on Semi-Direct Visual Odometry

Due to the method of visual simultaneous localization and map building taking a long time and is easy to result in track failure with low pose estimation precision, in this paper, a semi-direct visual odometry is proposed. Semi-direct refers to the feature points in the image block in an image through direct matching to get the camera position, and not as direct as in the image matching method using a direct match. This method removes the need for expensive techniques for extracting and matching features in motion assessment and operates directly at the pixel level to achieve sub-pixel accuracy at high frame rates. The probabilistic diagram method is used to explicitly model abnormal measurements, to get fewer outliers and more certain points. The algorithm is improved based on ORB-SLAM2. It combines the direct method and feature method, tracks the key points without calculating the descriptor, and predicts the camera's movement according to the information of the small image blocks around the key points, which effectively improves the precision of the algorithm in the complex environment. In this paper, based on the TUM dataset, SVO is compared with the ORBSLAM2 algorithm. The results demonstrate that the property of the SVO algorithm is better than the traditional ORB-SLAM2 algorithm. According to the survey results, the algorithm is robust to moving object interference.
Interactive space 3D chistance measurement

Spacial 3D distance measurement technique is widely used in 3D medicine, 3D Geologic system. An algorithm presented to measure the spacial distance between any two points in a 3D object on computer screen: adding matrix transform to the screen coordinate of the points to get the spacial coordinate, the spacial distance through spacial coordinate of the points can be worked out.
Analysis of Stages Power in Asymmetrical Cascade H-Bridge Multilevel Inverter

Multi-level inverters (MLIs) are new generation of converters for DC-AC voltage and power conversion at medium and high levels. MLIs are divided into two types, symmetric and asymmetric, according to the value of DC voltage sources. In this study, an asymmetrical cascade H-bridge (ACH) MLI with different DC sources ratio is considered and the power between its stages is examined. Also, the harmonic of the output voltage waveform is analyzed, and the best ratio of ACH-MLI is obtained. The simulation by Simulink/MATLAB shows useful results of this study appropriate for industry design of ACH-MLIs.
A Hybrid Micro Energy Harvester based on Photovoltaic and Vibration Energy

A novel solar-vibration energy harvester is presented in this paper, which adopts the amorphous Si thin film and piezoelectric composite cantilever beam structure. The solar and vibration energy can both collected simultaneously by the proposed device.The model of he composite cantilever beam is given, and the output voltage, output power are obtained. In order to test the performance of the hybrid energy harvester, an experimental system is set up. The results show that, under the condition of1000 W/m2 light intensity, lg acceleration, and 20 Hz vibration frequency, the output power of the energy harvester is 425 mW. The hybrid energy harvester has good environmental adaptability and can be used in wireless sensor nodes.
Implementation of Synthetic Artificial Human Like Voice Over Robotic Voice

The creation of social robots has steadily increased during the past 20 years. Social robots differ from regular industrial robots such that they are meant to work closely with people in collaborative settings as opposed to frequently replacing people in labor-intensive tasks. Social robots are now much more prevalent in a variety of areas of the human life, including social well being, providing entertainment, and medical contexts, as well as in business and education. Roboticists are attempting to create social robots that replicate human look, behaviour, and emotional expression in these applications. The subject of how people perceive and react to social robots, particularly in terms of voice, is raised by the fact that these machines are becoming more and more integrated into people's lives. In this paper, we are going to review the literature on the communication part of the social robots and specifically the voice part which plays a key role in the Human Robot Interaction.
Textured conducting glass by nanosphere lithography for increased light absorption in thin-film solar cells

Nanoscale surface texturing in thin-film solar cells has been shown to enhance device efficiency by increasing light absorption through reduced reflectance and increased light scattering across a broad range of wavelengths and angles. However, light trapping in the industrial thin-film cells is still sub-optimal and creating optimized nanoscale texture over a large area remains challenging. In this article, we present a well-controlled low-cost process to fabricate a periodic nanocone texture optimized for maximum light absorption in thin-film microcrystalline silicon solar cells. The texture is fabricated using nanosphere lithography with the period controlled by the nanosphere diameter and the texture shape and aspect ratio controlled by the reactive ion etching conditions. Finite-difference time-domain optical simulations are used to optimize the texture in the state-of-the-art microcrystalline cells, and optical absorption measurements show that the same cells fabricated on the optimized nanocone-textured substrates exhibit a relative short-circuit current increase of close to 30% compared to a reference state-of-the-art cell with a randomly textured zinc oxide layer. This nanocone texturing technique is compatible with standard thin-film cell fabrication processes and can also be used for other thin-film cells (CIGS, CdTe, CZTS, etc) to maximize light absorption and minimize layer thickness enabling more efficient carrier collection and lower overall cost.
Modulation of ENSO-Based Long-Lead Outlooks of Southwestern U.S. Winter Precipitation by the Pacific Decadal Oscillation

Abstract Seasonal predictability of winter precipitation anomalies across the U.S. Southwest derived from knowledge of antecedent, late-summer Pacific Ocean surface temperatures is examined empirically. Previous studies have shown that equatorial Pacific SST anomalies associated with the El Nino–Southern Oscillation (ENSO) cycle, which are persistent from late summer through winter, exhibit a strong relationship with winter precipitation in Arizona and New Mexico. Here the degree to which seasonal predictability in this region is modulated by longer-term oceanic fluctuations associated with the Pacific decadal oscillation (PDO) is assessed. When all years from 1950 through 1997 are considered as a single dataset, inclusion of the PDO signal adds only slightly to the ENSO-based statistical predictability of Southwest winter precipitation anomalies. However, when the dataset is split into two subperiods delineated by a major shift in the PDO (before and after 1977), the ENSO-based predictability and, to a l.
Synchronization of Single-Phase Inverters using Deadzone and Hopf Oscillator based Controllers in Standalone Microgrid

Virtual oscillator control (VOC) is a very popular method for the control of parallel inverters. In this work, Deadzone oscillator (DZO) based VOC and Hopf oscillator (HO) based VOC is presented to regulate the parallel inverters in a standalone microgrid (MG). The voltage amplitude and frequency reference of the inverters are coming from the oscillator dynamics. The extensive comparison of DZO and HO based control methods with different types of loads is also presented in this work. The HO based control method minimizes harmonic distortion and achieves faster time response compared to DZO based control method. The simulation results show the efficacy of the HO based control method.
Deep Learning for Real Estate Trading

Artificial intelligence (AI) and Machine Learning techniques have been making impact on the real estate industry recently. Increasingly, several real estate companies have started to use a variety of AI techniques to optimize their property business. Ma-chine learning (ML) technology for providing support on real es-tate investment decisions, allows investigation of historical property sales data by computer algorithms to automatically predict house prices. Real estate professionals can leverage sophisticate ML techniques to analyse sales data as benchmarks and make appraisals for their home selling clients and customers. ML technologies not only make predictions or classifications, but also can assistant real estate professionals for investment purposes by providing a trading strategy. In this paper, we propose a novel machine learning model, based on a standard deep reinforcement learning (DRL) model, enhanced with a combination of two popular time series algorithms, the Gramian Angular Field (GAF) and long short-term memory (LSTM) algorithms for providing decision support on real estate trading strategy. Our goal is to explore if the proposed enhanced DRL model can make profitable trading strategy for a long-time investment, such as the real estate markets.
Simulation Analysis of Sensorless Control of Synchronous Reluctance Motor Based on Optimal Current Distribution Strategy

With the purpose of meeting the high-quality operation requirements of the synchronous reluctance motor, the pulsating high-frequency sinusoidal voltage injection is used to implement precise tracking of the rotor position to enable the motor to start smoothly and achieve closed-loop control. The real-time inductance parameters of the motor in different cases are obtained by using finite element method to adjust the current distribution strategy according to the inductance saturation of the motor, which effectively reduces the influence of magnetic circuit saturation on algorithm accuracy. The optimal current distribution strategy to maximize the torque is proposed, which improves the operating efficiency of the motor, and assists the controlling system to obtain good dynamic response and anti-load interference ability. A sensorless controlling system of synchronous reluctance motor on optimal current distribution strategy is established. The given values of quadrature and direct axis current can be obtained by combining the inductance parameters of the motor under current working conditions. The superiority of the control strategy in improving the operating efficiency of the motor is verified by Matlab/Simulink simulation.
Visualizing Cumulative Risk Across Work Contexts

How can we represent the risk that emerges when multiple processes interact? This research considers how safety incident information can be visualized using a narrative modeling method that represents the accumulation of effects through different contexts. Currently, organizations conceptualize safety incidents in terms of the formats in which they are classified - assets, roles, cost and deviations from procedures. However, some accidents should also be understood in terms of accumulation and context. An example is presented in which a reversing vehicle caused a fatal injury because safety modifications made for one work location actually reduced safety in another. This paper explores the inclusion of cumulative and contextual information in a workplace safety scenario by presenting it in a narrative modeling visualization. Animated examples will be shown in the presentation; this paper provides still images of those visualizations and links to the animations. The aim is to develop foundations for a formal approach that reveals how interventions can have a ripple effect across a distributed and dynamic system. A long-term goal is to develop an analytical modeling tool that tracks how influence promulgates through complex heterogenous systems, such as collaborative human-machine teams.
Inertial Vision Integrated Pedestrian Navigation Algorithm Based on Zero Velocity Update and Binocular Vision

Aiming at the divergence of heading angle in inertial pedestrian navigation, this paper presents an inertial visual combination pedestrian navigation algorithm based on zero-speed update and binocular vision. Firstly, the position and posture information of the pedestrian in the process of moving is obtained through the binocular visual odometry. Then gait intervals are divided by the generalized Likelihood Ratio Test (GLRT). The position and posture of pedestrians are obtained from a binocular visual odometry in the whole gait interval, and a speed error observation is added in the zero speed interval according to the pedestrian gait characteristics. The Kalman filter is used to estimate the pedestrian speed, position and attitude errors. Finally, the estimation error is used to correct the inertial pedestrian navigation system to improve the navigation positioning accuracy.
A machine learning-based depression detection on social media platforms for adolescents: A work in progress narrative review

A rise in depression episodes has prompted an increased focus on depression detection. This research paper aims to review the literature to discover the pros and cons of proposed solutions for this critical social problem. In this narrative review, specifically, we looked at machine learning (ML) based techniques that analyse text data from social media to diagnose depression symptoms. A thorough search technique across several databases for relevant articles, specifically Google Scholar, PubMed, Medline, ERIC, PsycINFO, and BioMed databases, were used to perform a narrative evaluation. Terms and definitions were used to filter the article titles, abstracts, and full texts. Approaches based on machine learning and text data from social media may be helpful in the diagnosis of depression and might be used in conjunction with other mental health services.
A dual polarized aperture coupled circular patch antenna using a C-shaped coupling slot

The design and development of a dual linearly polarized aperture coupled circular microstrip patch antenna at C-band are presented. The antenna uses a novel configuration of symmetric and asymmetric coupling slots. Variations in isolation between orthogonal feedlines and antenna axial ratio with the position of coupling slots are studied and broadband isolation and axial ratio are achieved. The prototype antenna yields 7.6 dBi peak gain, 70/spl deg/ 3-dB beam width, 25 dB cross-polarization levels and an isolation better than 28 dB between the two ports. With an external quadrature hybrid coupler connected to the two orthogonal feedlines, the antenna yields 3-dB axial ratio bandwidth of more than 30% at 5.8 GHz.
The prediction model of Irregular Cavity Volume based on CNN-GRNN neural network

In order to solve the problem that there are many influencing factors and difficult to accurately predict the volume detection of irregular cavity parts, this paper takes advantage of CNN's easy processing of irregular data, fast training speed of GRNN neural network model, and few human intervention factors to establish a CNN_GRNN of CNN model and GRNN neural network embedded combination model. The volume of irregular cavity parts was predicted by this model, and compared with the results predicted by CNN model alone, it was found that the error of the prediction result of the combined model was significantly better than that predicted by the CNN model alone. Therefore, the proposed volume prediction model can effectively predict the volume of irregular cavity parts and improve the detection efficiency.
Performances of Popular Programming Languages for Towers of Hanoi Algorithm

Looking at the growth of Popular Programming Languages(PPLs) like Python, Java and C++ in the fields of Artificial Intelligence, Data sciences and Machine learnings and other related concerns, and giving consideration to the recent research studies made in[11] [10] for nlogn and n2 class of algorithms, in this study we have considered the Exponential (a very high time complex) Algorithm of Towers of Hanoi problem by implementing it using C++, Java and Python in-order to observe and measure the performances and verify the behaviors. The results of our experiments have found to be interesting and also verifies the previous published work referred above. Finally the performances of PPLs are described with respects to the results obtained. These results shall play a role to Bench mark in selection of popular programming languages for Domain specific Applications.
CNN-GRU based fake data injection attack detection method for power grid

Fake data injection attack (FDIA), as a new attack method, seriously threatens the safe operation of the smart grid. It can tamper with the measurement information of the power system collected by the data acquisition and monitoring system (SCADA), thus seriously affecting the security of the power grid. Based on this, a false data injection attack detection model based on an improved neural network (CNN-GRU) is proposed. By adding an entry control unit (GRU) structure to CNN, the model realizes feature extraction of spatial and temporal characteristics of data. Finally, a comprehensive case study using the Mississippi open-source dataset is conducted to evaluate the performance of the model. The results show that this mechanism can achieve satisfactory accuracy of attack detection.
Fault detection and reconfiguration of the 12-pulse thyristor rectifier

Providing power through a silicon-controlled rectifier (SCR) is still commercially efficient. This type of rectifier is a well-known choice for converters in the high power range. Especially new exciter with SCR exciter is proliferating in generators. Monitoring, supervision, and safety checking of this kind of converter could increase reliability and decrease the risk of losses and penalties for interrupting the power supply of susceptible systems. In this paper, a fast, simple, robust fault detection and identification system with a small number of calculations have been proposed to detect open circuit fault of three-phase SCR (6-pulse, 12-pulse…). In addition, a strategy to reconfigure a 12-pulse converter is proposed. The simulation in MATLAB/SIMULINK has shown the acceptable performance of the proposed algorithm.