Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Uplink Detection and Channel Estimation for Large Scale MU-MIMO over Uncorrelated Rician Fading with Multi-Antenna Users

This paper considers a single cell large-scale Multi-User MIMO system, where the user terminals equipped with L antennas. To investigate the uplink achievable rate performance, two linear detectors; Maximum Ratio Combining (MRC) and Zero forcing (ZF), have been considered. In this investigation, training based estimation has been employed to estimate the channel state information (CSI) from the uplink pilots. Since in the practical multipath environments, the channels may consist of a line-of-sight (LOS) component and non-line-of-sight (NLOS) components, this work evaluates the system performance over Rician fading model. The effect of the LOS component on the achievable rate performance of the two considered detectors has been discussed with different values of K-factor. Furthermore, the effect of increasing the number of user antennas on the uplink sum-rate in the Rician fading scenario has been discussed. Simulation results show that an increase in the number of user’s antennas will result in improvement in the achievable rate of the considered linear detectors.
Design and Implementation of Electric Motor Controller for Two Wheeler Hybrid Vehicle

Vehicles that run on gasoline heavily burden the environment by releasing carbon dioxide and other greenhouse gases. Several types of research and published articles have proven the disadvantages of a gasoline engine only vehicle. Therefore, the need for an alternative mode of transportation increases. An example is a hybrid electric vehicle. A hybrid electric vehicle (HEV) uses two power sources: the internal combustion engine (ICE) and electric motor. This paper will focus on the design of a motor control system for hybrid electric two-wheeled vehicles. The motor controller system will employ a brushless DC motor unit as it offers better efficiency. The system will control the electric motor without interfering with the operation of the internal combustion engine. The vehicle will use two sources of propulsion- the gasoline engine as its primary source, and the electric engine as a secondary source. These implementations were constructed to lessen the fuel consumption of the vehicle. Based on theoretical calculations, the efficiency of the system is 83.51%, and upon simulation using Simulink, the efficiency resulted in 83.74%. The data gathered shows that 3.67 liters of fuel were saved in the actual implementation.
Prediction of Power Transformers Lifetime Using Thermal Modeling Analysis

Transformer is one of the main components in distribution system of electrical power system towards the consumers, thereby any damage to the transformers will hinder the distribution of electricity towards the consumers, and in turn will make the SAIDI and SAIFI levels go up. High temperature in transformers can cause degradation in the insulation of transformers which in turn will cause failure in transformers. When the temperature in winding reaches or goes beyond the limit of $110^{\circ}\mathrm{C}$, a degradation in insulation will start happening and the remaining life of transformers will decrease. Damage in transformers will cause disturbance in electrical power system and result in a major economic loss. Before damages occur, transformers need to be changed up until it is deemed to be no longer efficient, this can be done by replacing the transformer that is about to be damaged. To predict when a transformer is about to break, a calculation is made based on thermal modelling according to IEEE Std C57.91-1995 with its most prominent variable being Hot Spot Temperature (HST). HST is obtained by MATLAB programming using Annex G of IEEE Std. C57.91-1995. By obtaining HST, thus the remaining lifetime of transformers can be predicted. This study analysed the effect of loading, hot-spot temperature, and ambient temperature on the remaining lifetime of a transformer. The higher those three factors, the quicker the transformer will break, with loss of life percentage increasing exponentially. The highest loading, hot spot temperature, and ambient temperature given in this research gives percent loss of life of 0.0888332%, 0.0193394%, 0.020753% respectively.
Prediction of Electricity Load Growth of Tangerang City using SIMPLE-E

The electricity load growth should be planned appropriately to meet energy demand in a certain area. Electricity consumption growth is generally proportional with the economic and population growth. Using historical data on Gross Domestic Regional Product (GDRP) and population growth, the electricity load growth in the near future can be statistically predicted. The relationship between economic and population growth trend and total customers as well as the demands of contract electricity energy growth trend could be connected by the linear regression method to predict electricity energy consumptions. Through a Simple Econometric Simulation System (SIMPLE-E) program run in Microsoft Excel application, linear regression trend can be calculated. The purpose of study is to predict the growth of electricity consumption in Tangerang City-Indonesia, for the next ten consecutive years (2019-2028). Customer data that will be analyzed consist of five segments, which are Residential (R), Commercial (B), Industry (I), Public (P), and Social (S). Those can be combined with Gross Regional Domestic Product and human population using Multiples Linear Regression in SIMPLE-E. The correlation between dependent variables (total customers and contract of power) and independent variables (population and GRDP) will be computed in SIMPLE-E through rate value of correlation R, Adjusted R, and Durbin Watson test. The Actual trend data of total customer, contract power from 2012 to 2018, is compared to model equation from linear regression. The calculation of error use Root Mean Square Percentage Error and Mean Percentage Error, shows that the total error is less than 5%, meaning the simulation is scientifically accepted. This study shows that the average growth of electricity consumption in Tangerang city from 2019 to 2028 is 5.63%. It means that every 1% growth of economy in Tangerang will increase electricity load consumption for 0.94%.
Employment of Multi-classifier and Multi-domain Features for PCG Recognition

In this paper, multi-classifier of K-Nearest Neighbor and Support Vector Machine (SVM) classifiers with multi-domain features are employed, as a proposed methodology for recognizing the normality status of the heart sound recordings (so-called Phonocardiogram - PCG). The PhysioNet/CinC Challenge 2016 offers the dataset used in this paper. Heart sounds are complex signals and required trained clinicians for diagnosis, which motivated us to develop an algorithm for automatic classification of heart sounds into two classes normal and abnormal. Entropy, high-order statistics, Cyclo-stationarity, cepstrum, the frequency spectrum of records, energy, state amplitude, the frequency spectrum of states, and time interval, are the nine-domain features employed. These domain features are extracted to a total of 527 features. These features have been used to train the KNearest Neighbor and Support Vector Machine (SVM) classifiers. Fine-KNN classifier outperformed types of SVM classifiers by achieving the accuracy of 93.5% while Cubic-SVM classifier achieved 90.9% which is the highest accuracy of all SVMs. The Fine-KNN classifier and the proposed features are both efficient and significant for PCG recognition.
Foraging under Predation Risk: A test of giving-up densities with samango monkeys in South Africa

Animals frequently make a trade-off between food and safety and will sacrifice feeding effort if it means safety from predators. A forager can also vary its vigilance levels to manage predation risk. Giving-up densities (GUDs), the amount of food items left once a forager has quit an experimental food patch, have been used extensively as measures of foraging behaviour under risk of predation in a wide range of species. Vigilance also serves as an anti-predatory response to predation risk and has been the focus of a range of behavioural studies. However, very few studies have looked at these two measures together. The principal aim of this study was to determine the effect of habitat factors on the foraging behaviour of samango monkeys (Cercopithcus mitis erythrarchus) by measuring GUDs in artificial food patches and foraging behaviour, and relating this to height from the ground, canopy cover, habitat visibility and observed behaviour. The second objective was then to determine the extent to which the experimental approach matched observed behaviour in measuring primate responses to predation risk. The monkeys revealed lower GUDs with increasing height and with decreasing canopy cover and but were not affected by habitat visibility. Vigilance varied considerably with only conspecific and observer vigilance showing significant effects. Conspecific vigilance increased with height and decreasing canopy cover. Vigilance directed at observers increased with decreasing canopy cover. There was no effect of habitat visibility on any of the component behaviours of vigilance. The vigilance behaviour of the monkeys did not completely compliment the GUD results. The findings of this study confirm the prediction that habitat plays a key role in the foraging behaviour of samango monkeys but that vigilance is more sensitive to other factors such as sociality. Further work is required to determine the extent to which experimental approaches based on giving up densities match patterns of antipredatory behaviour recorded by observational methods.
Validation of Fiducial Marker Systems Performance with Rescue Robot Servosila Engineer Onboard Camera in Laboratory Environment

Typical tasks of service robotics and special robotics fields, including Urban Search and Rescue, set a number of challenges for mobile robotics with automatic performance of various functions of mobile robots being a key task. To pursue automatic camera calibration processes using on-body markers in this paper we demonstrated results of validation experiments on mobile robot Servosila Engineer using fiducial markers, which we selected based on the results of virtual experiments with fiducial marker systems.
Anti-Social Behavior Detection in Urdu Language Posts of Social Media

Social media such as Facebook and Twitter has gained strong attention for sharing information, worldwide connectivity and brand marketing all over the globe. The prevalent use of social networking sites has produced exceptional amounts of data. The mining of these social media applications has its potential to excerpt illegal activities which may be helpful for individuals, business and customers. The mining of data obtained from Facebook and Twitter, can be used to predict emotions of stakeholder and its analysis can provide very valuable information regarding behavioral inclinations of the writer. The automatic sentiment analysis to detect the emotional content in textual data has been widely used in many research fields. Most of the existing sentiment analysis techniques are tailored for English Language. This paper presents Urdu based antisocial behavior detection (ASB). We aim in particular to establish antisocial behavior detection method and defining its emotional state. We are intended to introduce a sentiment analysis based behavioral model that describes emotions related to antisocial behavior. In addition to describing the negative emotional state, our model will also use the concept of behavioral tendencies and evidence to predict the possible behavior of social media activists based on input text. We will outline the design of behavior detection systems based on social media posts. The learning algorithm learn about emotions from social media posts in the Roman Urdu language to predict user’s behavior regarding any specific post. The results of this study has verified that our method has outperformed state-of-the-art methods in terms of accuracy. A bilingual or multilingual ASB approach can be made in future.
On Data Management Optimization Strategies in Software as Service Systems

Two data management optimization strategies in Software as Service (SaaS) Systems are presented in this paper, adding index fields to the table structure of the Universal Table Layout schema-mapping technique for maintaining the logical table indexes, and dividing data into different data storage infrastructure when a tenant’s access is unaffordable. A method based on the foreign key of the logical table is proposed to partition data into different data storage infrastructures, which can partition joining operation tables into one data storage infrastructure as much as possible. Experimental results show that the table structure with logical index fields and the above data partition method effectively improve the performance of data management in SaaS systems.
Mapping of Prosopis Juliflora by a Fusion assisted Pattern Based Classification

This paper proposes a fusion assisted classification method to locate and map invasive plant species, Prosopis Juliflora using remote sensing techniques towards conservation of biodiversity. This is two stage method. At first, wavelet based fusion method is proposed for the multispectral image to produce high resolution multispectral image.In the second stage a texture based classification is performed ith pattern study.Minimum distance classifier is used to classify the input image based on weighted texture features. Accuracy is computed by collecting the ground truth points from the study site. Similar procedure is repeated for the Google Maps data and accuracy comparison of World View 2 and Google Maps is carried out. Thus World View 2 data outperform Google Maps data by achieving accuwracy of 80 percentage.
Development of Plantar Pressure and SEMG-based Artificial Intelligence System for Detection of Plantar Ulceration

Diabetic Peripheral Neuropathy (DPN) is one of the main complications that affect the neural function associated with diabetes, and is also the cause for the development of plantar ulceration due to the loss of protective sensation. In order to assess and eventually prevent plantar ulceration, DPN must be detected. The existing clinical solutions in detecting DPN are used for static responses only, and the dynamic response must also be considered. These dynamic responses are measured using plantar pressure sensors and surface electromyography (SEMG). The plantar pressure sensors locate potential location for plantar ulceration, while the SEMG detects DPN by inspecting the muscle movements in the lower limb. Support Vector Machine (SVM), which aims at minimizing the true error rate, was used in classifying the muscle response gathered from the SEMG in diagnosing the volunteers as normal (N), diabetic mellitus (DM) and diabetic with neuropathy (DN), with an accuracy of 75.86%. Linear regression, which can mathematically distinguish variables, was used in diagnosing the plantar region of the volunteers where ulceration has a high possibility of occurring and is identified as the Metatarsal 1 region.
Design of a String-Encoder and IMU Based 6D Pose Measurement System

Industrial robots have been a part of industrial production for a long time and they are expanding their sphere of influence with developing technologies. The use of robots requires the teaching of tasks to robots. For this teaching procedure, it is necessary to record the teaching tool's movements in high precision and accuracy. In this paper, it is aimed to measure the position and orientation, in other words the pose of the teaching tool in six dimensions using an inertial measurement unit and four string-encoders attached to the teaching tool. Then, the measured pose information can be transferred to a robot manipulator for tracking the recorded trajectory of the teaching tool. In that way, operator's movement can be copied and then used by robots to reproduce best product which has been produced by the operator. The 6D pose measurement system was designed and experimentally tested, and the obtained results show that the system satisfies accuracy and precision expectations for target applications.
Electro-Thermo-Mechanical Reliability of Recessed Barrier AlGaN/GaN Schottky Diodes Under Pulse Switching Conditions

Pulse reliability of AlGaN/GaN recessed Schottky diode is studied under transient overstress conditions, typically encountered in power converters. Degradation of the Schottky diode during both free-wheeling operation (high forward current injection) and reverse blocking state (high-voltage stress) is studied. Defect generation and the associated degradation were found to be uncorrelated with the nature of interface formed due to various surface treatments (at metal/GaN Schottky interface). During forward conduction, trap-assisted Schottky interface degradation is studied using on-the-fly <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> characterization under high-current stress. Under high-voltage stress, in the reverse blocking mode, mechanical strain evolution and defects generation were found to be dominant degradation modes, which are studied in detail using on-the-fly micro-Raman spectroscopy. Post-failure analysis was performed using SEM, TEM, and EDX, which reveals distinct failure signatures at the safe operating area (SOA) boundary. TCAD simulations are used to gain deeper physical insights into the observed degradation mechanism. Finally, a qualitative failure model, explaining the distinct failure physics, is presented based on observations and findings, from various electrical, optical, Raman spectroscopy, and electron microscopy investigations.
Implementation of Remote Control a Mower Robot

The goal of this paper is to save the mowing human resources and to decrease the loss of energy and time to make life more convenient. This paper focuses on developing an outdoor mower robot which uses sensors to detect the environment and automatically performs path planning navigation. The mower robot will have driving control, transmission mechanism, and path planning including the global path planning and automatic navigation. The purpose of the path planning is to use the outdoor positioning navigation system to assist the mower robot to perform the navigation efficiently.
In-memory PageRank using a Crosspoint Array of Resistive Switching Memory (RRAM) devices

Thanks to the high parallelism endowed by physical rules, in-memory computing with crosspoint resistive memory arrays has been applied to accelerate typical dataintensive tasks such as the training and inference of deep learning. Recently, it has been shown that a crosspoint resistive switching memory (RRAM) circuit with a feedback configuration can be used to solve linear systems, compute eigenvectors, and rank webpages in just one step. Here, we demonstrate the PageRank with a real database (the Harvard500) together with an 8-level RRAM model that is based on experimental measurements, including the max/min conductance ratio, the high conductance range and the standard deviation of each level. By using a verify algorithm for the RRAM device programming, the PageRank result from the crosspoint circuit shows a cosine similarity of 93.5% with respect to the floating-point solution. With more discrete conductance levels and a broader high conductance range in the RRAM model, a better performance of cosine similarity up to 97% can be achieved. This work supports the feasibility of in-memory PageRank with realistic RRAM devices for real-world networks.
Detecting Distraction in Drivers using Electroencephalogram (EEG) Signals

Driver distraction is considered as major factors in most of the traffic accidents. Driving errors may arise due to the distraction of the drivers. The aim of this paper is to analyze the EEG signals to detect distractive driving. Data from 10 different subjects were obtained and categorised into different frequency bands. Distractive driving is related with Theta band, so the Theta frequency band were decomposed using Discrete Wavelet Transform (DWT) and 17 different features were extracted. By enabling Principle Component Analysis (PCA) the accuracy rate was found for Cognitive Distraction and Visual Distraction using different machine learning algorithms. K Nearest Neighbour (KNN) performed well when compared to other machine learning algorithms with a better accuracy rate of 71.1%.
A Parallel Multibit Programing Scheme With High Precision for RRAM-Based Neuromorphic Systems

Resistive random access memory (RRAM) has been extensively studied as a promising candidate for neuromorphic computing. So far, high-precision multibit programing of RRAM for neuromorphic systems is done cell-by-cell, which can be very time-consuming. This brief demonstrates a row-by-row parallel program-verify scheme on a fabricated 160-Kb RRAM array using the incremental gate voltage programing (IGVP) method. Statistical analysis indicates that the optimal conductance tuning step size can accelerate the programing process and improve the success rate. Also, for the first time, the fundamental relations between programing success rate, critical path, and parallel efficiency for parallel program-verify methodology on the RRAM array are systematically studied. Moreover, a software-comparable recognition accuracy with only <0.3% accuracy loss is experimentally achieved for MNIST data set classification utilizing the proposed parallel program-verify scheme.
Energy Efficient & Fully Autonomous Residential Power Management Solution

In the proposed system, a load balancing system for the residential purpose has been developed which can be connected to solar energy and grid. The load balancing system will ensure the usage of solar power as much as possible. When solar energy is low, power from the AC grid system will be used for the loads. The load balancing system identifies automatically the required loads and switches off unwanted loads to reduce power consumption. The system is equipped with passive filters for the reduction of harmonic distortion. Based on user requirement, a mechanism to switch OFF/ON the loads in the house will be provided by the load balancing system. This system is demonstrated with IoT based controller which interface the communication between the connected devices.
Implementation of optimized digital filter using sklansky adder and kogge stone adder

In Digital era, filters can be used as memory elements to store data. The most commonly used adders to implement digital filters, are the full adders. Here in this project the implementation of digital filter is done with PPA. Among various PPA, the sklansky adder is more efficient than others. When implementing kogge stone adder, it experiences a larger hardware complexity. So that it is necessary to reduce the hardware complexity the architecture of sklansky adder has been introduced using Xilinx ISE. The comparative analysis made among various parameters such as numbers of logic gates and delay. The above proposed architecture tends to reduce the hardware complexity using various parameters.