Miracle-dz/UniFAR-FTUs huggingface dataset:
Load Frequency Control for Multi-Area Power System using Fuzzy Logic Controller

In the contemporary power system, the load frequency control (LFC) of two area thermal power plant has broadly expended in frequency control suitable to having the quick replies. The fuzzy logic controller is considered for two area power plant. The pattern of system with fuzzy controller is imitation in MATLAB/SIMULINK. The dynamic replies of LFC with fuzzy and it equated through PID of frequency control for system. The success of the proposed fuzzy is better-quality the outcomes of power system in provisions of settling time and peak undershoot time
Societal Development Analysis based on Social Network

This research work proposes societal development analysis of a particular region based on social network activities such as Facebook, Twitter etc. Academic & economic growth, cultural & criminal activities are proposed to be identified for a particular region in India, based on the information gathered from web resources and analyzing them for report generation is the main objective of this work. This framework works broadly in four stages. In the first stage, data will be collected from social networks. In second stage, the semantic relation link will be found and stored for future use. In third stage, the data will be clustered according to classified parameters. In fourth stage, the dense area of clustered data will be found from graphs and generate the output. After completion of all the stages, reports can be generated from the analysed output and made available to the public for social activity planning and decision-making.
Credible Agent-Based Simulation – An Illusion or Only A Step Awayƒ

During the World Café activity at the 2018 Winter Simulation Conference, we discussed Agent-based Simulation (ABS) credibility. The topic is important since credible ABS leads to an impact on society whereby ABS is implemented by users and they can benefit from it. This paper presents the perspective of three academic panelists and a practitioner on the credibility of ABS. The discussion reveals that the increasing use of ABS models to explain social phenomena or systems that exhibit emergent behavior pose a challenge for model credibility. Several points and suggestions are raised by the panelists, including evaluating ABS model credibility via its explanatory power, the multi-dimensionality of credibility and the role of software engineering approaches.
Analysis of a Dual Input DC-DC Converter Topology Based on SEPIC Configuration

Multi input converters are more reliable, efficient and has less complex structure compared to conventional methods of integrating various sources using multiple number of single input DC-DC converters. A dual input DC-DC converter is presented in this paper which can accommodate two different energy sources, two inductors, three power switches. Various modes of operation of the converter are discussed in this paper with the detailed analytical waveforms and equivalent circuits. The software simulation of the presented converter has been carried out using MATLAB/ Simulink® platform for the boost operation of the converter. A hardware prototype of the converter is also developed to check the feasibility of the converter performance in the experimental platform.
Convolutional Neural Networks for Predicting Words: A Lip-Reading System

In this paper, a relative study of lip reading models based on convolutional neural networks (CNN), like AlexNet and Inception V3, has been carried out. The novelty of the paper lies in studying the performance of speaker dependent and speaker independent models, in predicting ten words, making use of the two aforementioned CNNs. In this process a dataset consisting of ten words, fifteen speakers’ data named Miracl- VC1 has been used. From all the samples lip regions are extracted and concatenated for every word and used in training. Both the models are tested for known (speaker dependent) and unknown speakers (speaker independent). AlexNet reported the highest observed test accuracy of 86.6%, and Inception V3 reported an accuracy of 64.6% for speaker dependent Lip reading. Whereas, speaker independent test accuracies of both the models are at 37.1% in case of AlexNet and 17.6% for Inception-V3.
Defects Detection of Catenary Suspension Device Based on Image Processing and CNN

Catenary suspension device (CSD) is a critical part of the pantograph-catenary system, which is a fundamental power equipment to supply electricity to urban rail transit vehicles. Whether the CSD is in a normal state is of great significance to the safe operation of operating vehicles. Therefore, it is important to detect the defects of CSD in time and automatically. In this paper, innovative and intelligent methods using image processing technologies and convolutional neural network (CNN) are proposed. Firstly, the insulators and bolts of CSD are extracted in the detected images using template matching algorithm. After that, an improved Bag of Features (BOF) model is proposed for the defect detection of CSD. Furthermore, in order to further improve the detection efficiency, AlexNet is trained for the defect detection and identification of CSD. The experimental results show that the proposed methods can detect the defects of CSD in time, with high robustness and accuracy.
Joint Optimization of Offloading and Communication Resources in Mobile Edge Computing

Mobile-Edge Computing (MEC) is a novel computing paradigm proposed in recent years. MEC addresses the shortcomings of mobile devices (MDs) in terms of computational performance and energy efficiency by offloading computation tasks to the edge of the network. In this study, we consider a multi-user MEC system in which MD users can offload tasks to the MEC cloud via wireless channel. This study aims to minimize the total system overhead of the MEC system by jointly optimizing computation offloading decision making and communication channel allocation. In particular, the mobile user not only needs to determine whether to offload, but also needs to decide the channel to be offloaded. The advantage of genetic algorithm (GA) is used to solve this computation offloading optimization problem. The efficiency of GA is studied by simulation and compared that of with other baseline algorithms to verify its performance.
Efficient Methods for Agile Earth Observation Satellite Scheduling

Agile Earth observation satellite (AEOS) scheduling is a complex optimization problem with longer visible time and Time-dependent transition time constraint. We address one method to transform the AEOS scheduling problem to Maximum weight independent set (MWIS) problem to reduce the difficulty of modeling and solving. We also propose reduction and decomposition strategy to reduce the scale of the problem. Experiments proved IP model established by these methods can get optimal solution of the problem, whose size is larger than previous research. Additional, iterated local search hybrid with Variable neighborhood search (VNS-ILS) is designed to solve MWIS problem from AEOS scheduling. The proposed algorithm almost increases all solution quality than ALNS and ALNS-TS adopting in previous research for AEOS scheduling.
Maximal Algorithmic Caliber and Algorithmic Causal Network Inference: General Principles of Real-World General Intelligenceƒ

Ideas and formalisms from far-from-equilibrium thermodynamics are ported to the context of stochastic computational processes, via following and extending Tadaki’s algorithmic thermodynamics. A Principle of Maximum Algorithmic Caliber is proposed, providing guidance as to what computational processes one should hypothesize if one is provided constraints to work within. It is conjectured that, under suitable assumptions, computational processes obeying algorithmic Markov conditions will maximize algorithmic caliber. It is proposed that in accordance with this, real-world cognitive systems may operate in substantial part by modeling their environments and choosing their actions to be (approximate and compactly represented) algorithmic Markov networks. These ideas are suggested as potential early steps toward a general theory of the operation of pragmatic generally intelligent systems.
Dual-Band Circular Polarized Printed Antenna for WiMAX and LTE Applications

In this paper, the design and testing of a dual-band circular polarized (CP) printed antenna is presented. The proposed structure is used as a simple approach to obtain dual-band with CP operation. Initially, the three parasitic elements are implemented at beside the left and right of the coaxial feed to achieve the dual-band. Additionally, another a parasitic element is used to improve impedance bandwidth. Besides that, the two H-shaped stubs and corner truncated is implemented to obtain the impedance match and achieve the CP. Additionally, a shorting pin is used for a better impedance match. The measured resonant frequencies are generated at 3.5 and 3.9 GHz (simulate: 3.5 and 3.8 GHz), which have covered the band of the worldwide interoperability of microwave access (WiMAX) and long term evolution (LTE) applications. The measured results show an impedance bandwidth of 2.13, 5.71 % and the 3-dB axial ratio bandwidth of 1.71, 4.1 % at the corresponding resonant frequencies, respectively. The maximum measured peak realized gain and radiation efficiency of the designed antenna are 3.99 dBic, 74 % for the first resonant frequency and 3.72 dBic, 69 %, for the second resonant frequency
Some Aspects of Binaural Signal Selection

Two experiments are reported which measure the increase in intelligibility occasioned by listening binaurally to running speech imbedded in interfering signals. In the first experiment, the frequency range of the speech is restricted to one of three ranges: 200–1600, 880–2200, 1660–6100. The interfering signal is broad‐band random noise. Under difficult listening (27% intelligibility for homophasic listening) the lowest range shows a binaural improvement of 33% for a change in interaural polarity, and of 28% for an interaural time disparity of 500 μsec. The middle‐ and high frequency ranges show less binaural gain, but do afford some advantage to two eared listening under difficult conditions.The second experiment compares the effects of different speech waves masking the wanted speech in binaural listening. For the maskers used, the binaural system helps most when the interfering wave is the speaker's own voice or a multiple mixture of many voices. The measured differences between homophasic and binaural.
Membrane System-based Optimization Algorithm for Numeric Optimization Problem

In this paper, an optimization algorithm based on membrane system is proposed for numerical optimization problems. In the proposed algorithm, we designed two mechanisms to simulate the movement of molecules in arbitrary direction and a certain direction to balance global exploration and local exploitation. To test the performance of the proposed algorithm, eight benchmark functions were chosen. The simulation results show that the proposed algorithm is more advantageous than other experimental algorithms in solving numerical optimization problems.
A computational study on the effect of climatic parameters on air pollution in Coimbatore city

Rapidly increasing pollution in atmosphere degrades the human health and ecosystem globally. The climatological parameters have a vital role in the distribution, transition as well as elimination of pollutant concentration in the atmosphere. In this paper, the influence of climate parameters on the intensity of pollutants is analysed. To analyse the influence, multiple linear regression and artificial neural network techniques are applied. The effect of climatological data on pollutant concentration data such as particulate matter, nitrogen oxides, carbon monoxide of Coimbatore region are used in this work. The parameters that are positively correlated with the atmospheric pollutants are utilised to estimate the pollutant concentration of PM2.5, NOx and CO through MLR and ANN models. Performance evaluation of the models is done by using statistical measures and it is found that ANN has resulted in better estimation.
Robust Multipitch Estimation of Piano Sounds Using Deep Spiking Neural Networks

In this paper, we propose a robust multi-label classification system based on deep spiking neural networks to handle multi-pitch estimation tasks. We employ constantQ transform spectrogram as a time-frequency representation. A keypoint detection technique is used for noise suppression and the extraction of relevant information. We also propose a novel biological spiking coding method that fits the expression of musical signals. This coding method can encode time, frequency, intensity information into spatiotemporal spike trains. And the spatio-temporal credit assignment (STCA) algorithm is used to train deep spiking neural networks. We perform the multipitch evaluation on the MAPS data set, and our work compares with the state-of-the-art methods by using the F1-score metric. Experimental results show that the proposed scheme has achieved better performance than other state-of-the-art methods and reveal the system’s robustness to environmental noise.
A Novel Approach for Improving the Workflow Management System in an Extremely Large Scale Enterprise

In any enterprise level organization, it is necessary to have an efficient and well-designed workflow for the seamless movement of task across its various participating units. In such a situation, the management of the workflow of the large scale enterprise is centralized on an integrated information system called Enterprise Resource Planning (ERP). It is incorporated across various functional areas of the company focussing on solving the needs and resources of the units in the organization. Such a kind of system must be able to manage data from all the departments in their large cloud storage. An implementation of such a scale and value has positive benefits to the overall performance of the large work flow based organization. In this paper we will see the implementation, drawbacks and how to improve the workflow management system in a way that is best suited for an extremely large scale organization.
Wideband Beamforming Using Modified Farrow Structure FIR Filtering Method for Sonar Applications

A novel wideband beamforming method is proposed where the beamforming is achieved by using Modified Farrow Structure FIR Filtering method. This method provides efficient time delay compensation since it considers only the fractional part of delay unlike other time domain FIR filtering methods. The simulation results show the magnitude response of Modified Farrow Structure FIR Filter is almost unity up to half of the nyquist frequency which is most desired in performing time delay compensation for wideband beamforming in sonar applications.
Priority Policy in Multi-Queue Data Center Networks via per-Port ECN Marking

Explicit Congestion Notification (ECN) as a main part of the Internet congestion control was originally designed with a single queue in mind. But, nowadays data centers industry trends to have multiple queues per-port in its switches. This type of mismatch can affect the scheduling schemes and hence leads to remarkable performance degradation in multi-queue switches. For instance, when some of the queue buffers of the switch exceed the considered thresholds, all the packets belonging to the same shared port could get ECN mark. To cope with this challenge, we propose Priority-ECN, a first systematic solution to this problem. The first intuition of Priority-ECN lies in the fact that if any packet gets marked, it should be prioritized to pass a route quicker than the others to notify the condition of the network in an early stage. This presents a full view of instantaneous demand from all senders. The second intuition is that if any packet gets marked in the queue buffer, Priority-ECN will not consider them in the marking decision again in the output port buffer. The third intuition is to reach the goal of high throughput without sacrificing the latency. So, we use an approach similar to Cut Payload (CP), which drops the payloads of packets, rather than the metadata, when a queue reaches the threshold. We do so through dual couple buffer in output port and catch the advantage of CP to implement an efficient scheme to obtains near optimal flow completion times across different flow sizes with very low latency.
A Face Detection Algorithm Based on Two Information Flow Block and Retinal Receptive Field Block

Recently, significant progress has been made in the field of face detection. However, despite the demand for its high accuracy and recall rate, the efficiency of face detection algorithm is another key factor in evaluating its performance, which puts forward serious challenges to current models. To boost up the efficiency of face recognition, in this paper, we propose a lightweight rapid framework, called LRNet, which has fewer convolutional layers and a higher efficiency. In particular, our framework is consist of two major modules. One is the Feature Map Fast Shrink Module (FMFSM), which leads to a fast reduction in the size of feature maps and time consumed for detection by using Two Information Flow Block (TIFB). Another module, namely Variable Scale Face Detection Module (VSFDM), is consist of the Retinal Receptive Field Block (RRFB) and designed to prevent a single or composite feature map from undertaking too much tasks. In addition, we propose a new anchor strategy that considers not only the density of anchors with different scales but also the position and central symmetry of the features. Our proposed LRNet achieves high accuracy and efficiency on the challenging FDDB dataset for face detection. When the number of false positives is 2000, its True Positive Rate (TPR) under discrete and continuous scores can achieve 0.951 and 0.725 respectively. When running on GTX 1080Ti, given images with a resolution of $1024\times 1024$ , the average time consumed for detection is merely 8.88 ms.
Fusion Methods & Applications – A Survey

Data fusion is the process in which integration of multiple data sources produce more consistent, efficient, and useful information than that provided by any of the individual data source. The fusion methods can be broadly classified with the help of class information. The fusion methods such as serial feature fusion, parallel feature fusion and canonical correlation analysis (CCA) does not contain any class information. That is, they are unsupervised fusion methods. The methods such as cluster CCA, Generalized Multiview Analysis, Linear Discriminant Analysis, Multiview Discriminant Analysis, Discriminative Multiple CCA, Locality Preserving CCA (LPCCA), BGLPCCA, MGLPCCA contain class information. Applications of each fusion methods are also taken into consideration.
Analysis of Space Vector PWM algorithms on Open End Winding Induction Motor Drive using dSPACE

Coupled and Decoupled space vector PWM algorithms are presented for the V/f control of the open end winding induction motor (OEWIM) drive. The complexity of analyzing the 64 switching states of dual inverter is reduced in coupled inverter using only 27 switching states to generate three level switching states. Coupled inverter for OEWIM analyzes all the switching states in seven sub hexagons and six sectors in each sub hexagon. The quality of output voltage, total harmonic distortion (THD) are enhanced in coupled PWM inverter but common mode voltage (CMV) is high compared to decoupled inverter. However the merits and demerits of both the inverters for OEWIM drive are experimentally analyzed in dSPACE and results are presented.
Finger Multimodal Features Fusion and Recognition Based on CNN

Finger-based multimodal fusion recognition has attracted increasing attention due to its high stability and security in practical application. Traditional fusion methods exist some challenges in scale inconsistency and universality. In additon, multimodal dimension standardization has not been well realized. In this paper, we propose a novel convolutional neural network(CNN) framework for finger multimodal fusion and recognition, which obtains the fusion features by network learning automatically. The fusion network makes full use of the complementary information among three modals of finger to make the fusion features more stable and effective. Firstly, the three parallel CNNs have been designed to extract unimodal features. Then, a size standardization method based on principal component analysis(PCA) is utilized for different unimodal features. Finally, the high-level unimodal features are integrated to learn fusion features with better representation capability. Extensive experiments on the finger multimodal dataset show that the proposed multimodal fusion network performs better than other state-of-the-arts.
Improving the Reliability of Embedded Memories using ECC and Built-In Self-Repair Techniques

System on Chip (SoC) process technology is shrinking day by day resulting in increased complexity. In the presence of faults, the reliability of embedded memories in deep submicron technology is becoming a significant challenge. Embedded Memories are highly prone to soft errors and hard faults. Hence, hard repair techniques combined with Error Correction Codes (ECCs) can improve the reliability of embedded memories. An integrated ECC and Built-In Self-Repair (BISR) technique is proposed in this paper can correct 8 faulty bits for a 16-bit input. Higher error correction and repair capability gives the higher reliability. The proposed integrated ECC and BISR has less area and more faulty bit correction capability compared to Enhanced Built -In Self-Repair (EBISR) technique.
Improving the Accuracy of License Plate Detection and Recognition in General Unconstrained Scenarios

Nowadays, Automatic License Plate Recognition (ALPR) is widely used in commercial applications. Some of the existing methods focus on the detection of license plate (LP) images with approximate frontage, while others deal with the unconstrained LP of images, but the result of LP recognition under some extreme conditions is unsatisfactory. This work mainly focuses on improving the existing unconstrained LP capturing method [1] to extract the LP and recognize it in several extreme cases as many LPs may be seriously distorted, even not parallelogram. Our main contributions are the following. First is to modify the loss function of the existing Convolutional Neural Networks (CNNs) so that the output parameters can form a parallelogram directly, then, LPs could be detected more accurate in a single input image first. Secondly, two parameters need to be estimated are added to the output of the networks’ fully connected layer structure to make the corresponding output parameters form an arbitrary quadrilateral, which is closer to the LP shape of the actual imaging. In this way, it can detect and correct LPs with various shapes (LP may be deformed into irregular quadrilateral due to various reasons) in a single image. Thirdly, a feedback mechanism would be added to identify and process the LP images which are not detected correctly after the LP characters are recognized by Optical Character Recognition (OCR) network, then, return the processed image to the detection network for re-detection. In general, LP can be detected correctly. Compared to the other benchmark methods, the experimental results have demonstrated that the proposed method achieved the best performance, especially in the extreme conditions.