Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of Three-Port Converter from existing dc-dc converters for PV based dc stand-alone system

A new approach is proposed in this paper for synthesizing Three Port Converter(TPC) by inserting an Energy Storage Element Cell(ESEC) in an existing two port converter. These TPCs are ideal for solar Photo-Voltaic(PV) based dc standalone schemes having battery as an energy storage element. New TPCs obtained through this approach are capable of Maximum Power Point(MPP) Tracking, charging and discharging of the energy storage element, and can feed power to the load while maintaining a desired load voltage. Control strategy and various possible operating modes for the newly developed PV standalone system are discussed. Topology derived from boost converter named Double Boost Input Output Converter(DBIOC) is taken as an example for establishing the feasibility of proposed topological design and control strategy. Performance analysis of DBIOC is done for various operating conditions and the same is validated by performing exhaustive simulation studies using MATLAB/Simulink.
Generating Political Scores Using RFM Model and Cluster Prediction by XGBoost

India is world's largest democracy and electing a right candidate in general parliament elections is not an easy task for electors. Though there are measures taken by Election Commission of India to educate electors, these information hardly reaches to the public. There is a need for basic scoring model which can measure candidate's viability and credibility. This study shows that Recency, Frequency and Monetary model can be applied to generate scores for political candidates who are contesting for elections. Based on scores candidates can be grouped into clusters to understand the behavior. These scores would evaluate candidates, thereby helps voters to make a decision. This study investigates machine learning techniques to generate political scores and predict candidate's loyalty towards party. The study also shows that XGBoost can predict candidate's party changing behavior.
VLC channel equalization simulator based on LMS algorithm and virtual instrumentation

This article focus on experimental evaluation of least mean squares (LMS) algorithm functionality in the form of software defined channel equalizer. Transmission channel is modelled for deployment in visible light communications (VLC), which can be impaired by multiple elements such as multipath transmission, intersymbol interference (ISI), etc. Equalizer is thoroughly tested in designed virtual device that allows modelling of different disturbance types, respectively different signal propagation models. Experiments are performed on multistate quadrature amplitude modulation (M-QAM). Results are evaluated by error vector magnitude (EVM) and modulation error rate (MER). In the conclusion, simulated results are compared to the real measured values of VLC technology.
Convolutional Neural Network for a Self-Driving Car in a Virtual Environment

Convolutional neural networks (CNNs) are machine learning models accomplishing state of the art results in a variety of computer vision tasks, decision making and visual recognition. For a long time, traditional computer vision based algorithms has been the primary method for analyzing camera footage, used for assisting safety functions, where decision making have been a product of manually constructed behaviors. During the last few years deep learning has showed its extraordinary capabilities for both visual recognition and decision making in end-to-end systems. this paper proposes a solution of introducing redundancy by combining deep learning methods with traditional computer vision based techniques for minimizing unsafe behavior in autonomous vehicles. A CNN has been trained to map raw pixels from a single front-facing camera directly to steering commands. The objective was to build a simple and reliable algorithm for a self-driving car and to implement a system that allow autonomous driving.
The Collective Trip Planning Query Processing Using G-Tree Index Structure

Since the sharing economy is defined as economic model based on combination of social relationships, many related services of sharing economy have emerged. Among these services, the location based ride sharing services have been the most common. We discuss a collective trip planning (CTP) query that minimizes the overall cost, one of the problems addressed in ride sharing services. The CTP query finds a meeting point that minimizes the overall cost to pick up passengers. Although many researches related to the CTP query have been conducted, there is a problem that the query performance is effective only in a specific situation. Therefore, we propose an effective query processing method for the CTP query using G-tree index structure on a road network. Furthermore, We analyze the limitations of the previous researches, and show that the proposed method can be obtained the optimal query result without being affected the limitations or constraints of the previous studies.
An Improved SMO for Position Sensorless Operation of PMSM Driven Solar Water Pumping System

An improved sliding mode observer (ISMO) for sensorless operation of permanent magnet synchronous motor (PMSM) driven solar water pumping system (SWPS) is proposed in this paper. The ISMO technique eliminates the chattering issue associated with conventional sliding mode observer (SMO). This is achieved through incorporating the synchronous frequency tracker based second order generalized integrator (SFT-SOGI), which filters the estimated back electro motive force (BEMF) and removes the chattering and accurately estimates the rotor position and speed. As the solar photovoltaic array offers a non-linear voltage current characteristic, a maximum power point tracking technique is required for extracting the peak power. Owing to its simple structure and ease of implementation, an incremental conductance algorithm is used for peak power extraction. The SWPS performance is validated experimentally in the laboratory with both the techniques, i.e. proposed ISMO control technique and conventional SMO technique. It has been observed that the proposed ISMO technique provides an effective estimation of rotor position and speed thereby improves the behavior of the SWPS.
Complex Network Metrics: Can Deep Learning Keep up With Tailor-Made Reference Algorithms?

Complex network metrics are used for ranking the importance of nodes in many different applications, e.g., spreader identification and percolation analysis. Examples for such node metrics include the degree centrality and betweenness centrality. The computation of some of these metrics is computationally expensive, e.g. the computation of betweenness takes $O(N^{3})$ of computation steps in the worst case, where $N$ is the number of nodes. In this study, we investigate the ability of deep learning via graph embedding to predict node centrality metrics in complex networks. Our study reveals that deep learning can identify vital nodes well for several of these metrics. Compared to exact computation with prohibitive costs, deep learning can get significant speedups, which scale up well with the size of the network. Further investigation on betweenness centrality reveals that the accuracy of deep learning is not as good as some tailor-made, tuned approximation algorithms. However, deep learning offers a nice trade-off between runtime and quality with its linear computational complexity regarding the network size, which makes it scalable on large networks and especially for these metrics with expensive computational costs. Our work is the first to explore the ability of deep learning on a wide range of networks metrics, and will hopefully induce future work on improved graph embeddings, tuned for specific network metrics.
Light-Weight Spiking Neuron Processing Core for Large-Scale 3D-NoC Based Spiking Neural Network Processing Systems

With the increasing demand for computing machines that more closely model the biological brain, the field of neuro-inspired computing has progressed to the exploration of Spiking Neural Networks (SNN), and to best the challenges of conventional Von Neumann architecture, several hardware-based (neuromorphic) chips have been designed. A neuromorphic chip is based on spiking neurons that process input information only when they receive spike signals. Given a sparsely-distributed input spike train, the power consumption for such event-driven hardware would be reduced since large portions of the network that are not driven by incoming spikes can be set into a power-gated mode. The challenges that need to be solved toward building in hardware such a spiking neuromorphic chip with a massive number of synapse include building small-sized spiking neuro-cores with low-power consumption, efficient neurocoding scheme, and lightweight on-chip learning algorithm. In this paper, we present the hardware implementation and evaluation of a light-weight spiking neuron processing core (SNPC) for our 3D-NoC SNN processor and the design of its on-chip learning block. The SNPC embeds 256 Leaky Integrate and Fire (LIF) neurons, and crossbar based synapses, covering a chip area of 0.12mm squared. Its performance is evaluated using MNIST dataset, achieving an inference accuracy of 97.55%
Damping of Power System Oscillations Using GA and GOA Optimized SSSC Based Controller

Growth of energy sector is one of the major factors in empowering the nation. And strengthening of power system is done by enhancing system reliability, security and quality. The main area to focus on is stability of the power system as it maintains the voltage and frequency of the system to a desired level. This paper presents a Single Machine Infinite Bus system with a Static Synchronous Series Compensator (SSSC) based supplementary damping controller in addition with PSS (Power System Stabilizer). The most favorable values of the SSSC based supplementary controller is determined using Genetic Algorithm and Grasshopper optimization Algorithm and comparison of both the optimization techniques is made. The coordinated design of controllers, thus formed is studied considering different fault conditions and system response against these fault conditions are analyzed.
Improve the robustness of MVDR beamforming method based on steering vector estimation and sparse constraint

Minimum variance distortionless response (MVDR) beamformer has two major drawback, High sidelobe level and direction of arrival (DOA) estimation sensitivity, many approaches was proposed to improve the robustness of the (MVDR) beamformer against array steering vector errors, and to deal with high sidelobe level. In this letter, We will propose a novel algorithm for robust MVDR depend on steering vector estimation and sparse constraint, so it deals with High sidelobe, and array steering vector errors in the same time, and compare its performance in different case of mismatch with other beamformers.
Super-Resolution of License Plate Images via Character-Based Perceptual Loss

License Plate Recognition (LPR) is an highly influential problem in computer vision. In this paper, we present a super-resolution model specialized for the license plate images, CSRGAN, trained with a novel character-based perceptual loss. Specifically, we focus on the character-level recognizability of the super-resolved images rather than the pixel-level reconstruction. Experimental results validate the benefits of our proposed method in both quantitative and qualitative aspects. In particular, our method achieves a higher character-level recognition accuracy over the state-of-the-art image super-resolution techniques.
Harmonic Mitigation in Grid Connected and Islanded Microgrid Via Adaptive Virtual Impedance

In an effort to overcome the economic and ecological challenges posed by conventional sources of energy, renewable energy based sources have come a long way. However, reliable and efficient commissioning of renewable energy sources in microgrids still have issues to be addressed. Power quality, power sharing and stability are the key issues in microgrids due to presence of power converters and non-linear loads. To improve power quality, harmonic compensation techniques have been used in past, but these are single objective control techniques and make the system complex and inefficient. zDroop control based control techniques have been adapted to improve power sharing, but again it is dependent on line impedance and seamless power sharing is not achieved due to mismatch. In this paper, a novel method of mitigating harmonics and improving power sharing has been implemented which also takes care of load dynamics. Capacitive virtual impedance has been implemented which improves harmonic current sharing, and offers decoupled control of active and reactive power. The capacitive virtual resistance have been implemented in the form of proportional resonant (PR) controllers which have added advantages over PI controller. Further, the resistance part in the capacitive virtual impedance has been made adaptive in nature, taking load dynamics into consideration. Simulation results have been provided to validate the proposed concept in both grid connected and islanded mode.
Artificial Neural Network (ANN) based Soil Electrical Conductivity (SEC) prediction

In this study, the ANN-based model has been developed to predict the SEC. SEC is an important indicator of soil degradation. If the SEC is high then vegetation growth will be decreased. The reason for the high SEC is the presence of soluble salts in the soil system. Environmental parameters were taken as covariates in developing the ANN model. Various covariates used in the study are, NDVI (Normalized Differential Vegetation Index), Rainfall, DEM (Digital Elevation Model) and slope of the study region. SEC data of the study region was obtained from Soil Health Card (SHC) for the year 2014 and was incorporated in training the model. The SHC data was divided in different ratios like 60:40, 70:30, and 80:20 for training and testing the model. The SCORPAN model has been considered as the base model for the development of the ANN based prediction model. The highest coefficient of determination (r square) between actual and predicted SEC was 0.63 and MSE (Mean Square Error) was 0.04 at 80:20 training, testing the samples.
Low-Complexity Incremental Search-Aided Hybrid Precoding and Combining for Massive MIMO Systems

The hybrid precoding and combining algorithms for mmWave massive multiple-input multiple-output (MIMO) systems must consider the trade-off between the complexity and performance of the system. Unfortunately, because of the unit-norm constraint imposed by the use of phase shifters, the optimization of the radio frequency (RF) precoder and combiner becomes a non-convex problem. As a consequence, the algorithm for hybrid precoding and combining design often incurs high complexity. This paper proposes a dictionary-constrained low-complexity algorithm for hybrid precoding and combining design. The proposed algorithm considers a decoupled optimization scheme between the RF and baseband domains for the spectral efficiency-maximization problem. In the RF domain, we propose an incremental successive selection method to find a subset of array response vectors from a dictionary, which forms the RF precoding/combining matrices. For the digital domain, we employ singular-value decomposition (SVD) of the low-dimensional effective channel matrix to generate the digital baseband precoder and combiner. Through numerical simulation, we show that the proposed algorithm achieves near-optimal performance while providing approximately up to 99% complexity reduction compared to the conventional hybrid precoding and combining algorithms.
Development of An Emperical Model for Dust Storm Attenuation Prediction

This paper proposes an specific sand and dust storm fade estimation model. The proposed model is based on an empirical assessment of the impacts of the dust and sand storms on a terrestrial link operating at21 GHz. The developed model is simple and reliable with minimum number of input parameters. Validation of the model with measurements at frequency range 14 GHz to 40 GHz showed close agreement.
Feasibility Study of a Consumer Multi-Sensory Wristband to Monitor Sleep Disorder

The new generation of consumer wristbands are now able to classify sleep stages based on multiple sensing data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic(PSG) parameters. Nevertheless, its feasibility in measuring sleep disorder remains unknown. This study aimed to evaluate the potential of Fitbit Charge 2 as a means of monitoring sleep disorders for workers. We recruited participants without mental and medical diseases from 3 sites consisting of department, hospital and research institute. Sleep data were obtained from 3-month night recording at each worker's home in 95 adults (22-64 years; 68 women; over 20 participants from each site). Our results indicate that there are some differences in sleep patterns in different occupations.
Slack's Knowledge Classification Mechanism for Architectural Knowledge Condensation

Agile methods adoption in global software development (GSD) has revealed architectural knowledge (AK) management as a challenge, since documentation debt prevails in these environments. Thus, knowledge dissemination/acquisition becomes difficult because of the four inherent distances in GSD, which, in turn causes AK vaporization. One way to reduce this loss of knowledge is to take advantage of the AK traces that different unstructured and textual electronic means (UTEM) leave (e.g. instant messengers and email), which developers prefer on AGSD environments. These traces are valuable, but their access is difficult because these means have no tools adapted to search AK and because this knowledge is scattered in different UTEM. The AK condensation concept proposes that trough a mechanism of classification and collection of messages contained in UTEM, AK could become available for the stakeholders. This article presents a Slack complement, which implements an AK classification mechanism based on social tagging. We evaluated this mechanism with undergraduate students through a replica of a previous study. We obtained positive results in regards to usefulness and ease of use, but with tagging behavior results that differ (not statistically significant) from those obtained previously. These evidences encourage us to evaluate the AK condensation concept in its whole, in real scenarios.
Investigation of Time-Reversal Based Far-Field Wireless Power Transfer From Antenna Array in a Complex Environment

The performance of time-reversal (TR) based far-field wireless power transfer (WPT) from an antenna array in a complex propagation environment is investigated in comparison with conventional array-based beamforming (BF). A two-step experiment is performed, namely 1) the propagation stage and 2) rectification stage. In the propagation stage, signal transmission is measured between the transmit array and receive antenna in an indoor multipath environment, for line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. The results demonstrate that TR results in higher peak voltage compared to BF given the same average transmit power. In addition, while BF loses its ability to selectively send waves at the receiver due to impairment of the beam from multipath, TR can selectively focus waves at the receiver by inherently taking advantage of multipath. In the rectification stage, the resulting signals from the propagation stage are fed into a broadband rectifier for RF-to-DC conversion. It is shown that the signals received using TR lead to higher rectified DC voltage and rectification efficiency. The overall results suggest that TR can outperform BF with higher rectification efficiency given the same average transmit power. Through optimization of the TR pulse interval, array configuration and transmit power, further improvement in the performance of TR based WPT in a complex propagation environment is possible.
An Intrusion Detection Model With Hierarchical Attention Mechanism

Network security has always been a hot topic as security and reliability are vital to software and hardware. Network intrusion detection system (NIDS) is an effective solution to the identification of attacks in computer and communication systems. A necessary condition for high-quality intrusion detection is the gathering of useful and precise intrusion information. Machine learning, particularly deep learning, has achieved a lot of success in various fields of industry and academic due to its good ability of feature representation and extraction. In this paper, deep learning methods are integrated into the NIDS. The intrusion activity is regarded as a time-series event and a bidirectional gated recurrent unit (GRU) based network intrusion detection model with hierarchical attention mechanism is presented. The influence of different lengths of previous traffic on the performance is then studied. Some experiments are performed on the dataset UNSW-NB15, in which the proposed hierarchical attention model achieves satisfactory detection accuracy of more than 98.76% and a false alarm rate (FAR) of lower than 1.2%. An attention probability map to reflect the importance of features is then visualized using the attention mechanism. The visualization ability assists in providing an understanding of the varied importance of the same features for different traffic classes and to determine feature selection in the future.
TLB Coalescing for Multi-Grained Page Migration in Hybrid Memory Systems

Superpages have long been proposed to enlarge the coverage of translation lookaside buffer (TLB). They are extremely beneficial for reducing address translation overhead in big memory systems, such as hybrid memory systems that composed of DRAM and non-volatile memories (NVMs). However, superpages conflict with fine-grained memory migration, one of the key techniques in hybrid memory systems to improve performance and energy efficiency. Fine-grained page migrations usually require to splinter superpages, mitigating the benefit of TLB hardware for superpages. In this paper, we present Tamp, an efficient memory management mechanism to support multiple page sizes in hybrid memory systems. We manage large-capacity NVM using superpages, and use a relatively small size of DRAM to cache hot base pages within the superpages. We find that there are remarkable contiguity exist for hot base pages in superpages. In response, we bind those contiguous hot pages together and migrate them to DRAM. We also propose multi-grained TLBs to coalesce multiple page address translations into a single TLB entry. Our experimental results show that Tamp can significantly reduce TLB misses by 62.4% on average, and improve application performance (IPC) by 16.2%, compared to a page migration policy without TLB coalescing support.
Deflection Analysis of Capacitive Micromachined Ultrasonic Transducer with InP Nanowires

In this paper, the clamped circular CMUT model developed using thin plate Kirchhoff’s theory is utilized for structural analysis. The CMUT with wurtzite indium-phosphide nanowires as membrane material has been analyzed, and results discussed for resonant frequency, capacitance variation, pull-in/collapse voltage along with deflection profile. This has been found that CMUT with indium phosphide membrane is suitable for air-borne applications.