Miracle-dz/UniFAR-FTUs huggingface dataset:
Object Detection and Human Identification using Raspberry Pi

The aim of this paper is to investigate the development of a navigation system for blind and visually impaired people along with human identification. It is based on a microprocessor with synthetic speech output. This solution is designed to give details about human whomever they encounter. The proposed system detects the any obstacle in the path of the blind via stereoscopic sonar system using ultrasonic waves and sends back vibro-tactile feedback to inform the blind about its localization. And if there is an obstacle and then the second system kicks into to check whether the obstacle is human or not, and if proven human, it matches with the existing system database to find out the person details and provide it to the user. Face Detection is an important step in any face recognition systems, for the purpose of localizing and extracting face region from the rest of the images.
Developing A Multi Agent System in JADE for Micro Grids

Tremendous growth in electricity consumption has been faced over past few years, due to increase in population and intensification in methodologies. Thus, it is important to improve sensing and monitoring techniques in micro grid while endorsing its intelligence level. Micro grid is an automated power system that incorporates advanced and modern technology into power grid. Multi Agent systems (MAS) is employed with micro grid framework to enhance its intelligence. The Multi Agent model is simulated in Java Agent Development Environment (JADE) for the proposed model accounting the intermittent nature of renewable resources, randomness of load and variation in demand. Further, this proposed multi agent model is expected to improve operational efficiency, thus reduce Peak Load Demand and operational cost using energy cloud.
Least Recently Frequently Used Replacement Policy Named Data Networking Approach

Named Data Networking (NDN) is a new paradigm of a network by using Content-Centric Network (CCN) theory. NDN provides better network performance since the caching policy of the NDN router saving the content requested by the user and make it accessible by another user. The current popular replacing policy that adopted from TCP/IP network is Least Recently Used (LRU) and Least Frequency Used (LFU). However, in a low condition of cache size, both algorithms not powerful yet. In this paper, we evaluate replacement policy in NDN using Least Recently/Frequency Used (LRFU) compared with LRU algorithm. Moreover, our approach based on C++ programming language to simulate the LRFU caching policy in Named Data Networking. Overall results shows that LRFU replacement policy in NDN provides higher hit rate and lower miss rate. Based on 20000 generated interest, LRFU replacement policy achieve 3578 average hit which is 18.398% greater than LRU with average hit 3022. Based on miss rate LRFU replacement policy achieve 16391 miss rate which is 3.28% lower than LRU with 16948 miss rate.
Calculation Method of Spectrum Requirement for IMT-2020 eMBB and URLLC With Puncturing Based on M/G/1 Priority Queuing Model

The major use cases for IMT-2020 include enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC). URLLC packets occur sporadically, but each time, available resources with favorable conditions are required for their timely and trusted transmissions. To this end, an overlapping scheme of puncturing a part of resources scheduled to an eMBB packet for URLLC packets is proposed. Such a scheme causes inevitable damages to eMBB packets. Thus, additional delays due to possible retransmissions of damaged packets should be considered in the calculation of spectrum requirement for IMT-2020. So far, the method provided by International Telecommunication Union Radiocommunication Sector (ITU-R) based on an M/G/1 non-preemptive priority queuing model has been widely used in calculating spectrum requirements for mobile broadband services. In this work, we extend it to reflect the puncturing of URLLC on eMBB. We fully consider additional delays due to retransmissions by exploiting the concept of delay cycle. Numerical simulations validate the accuracy of our analytical results. Furthermore, we provide an algorithm to calculate spectrum requirement for IMT-2020 using the same input parameters as in the method of ITU-R.
MIGRATE: Mobile Device Virtualisation Through State Transfer

Delegation of processing tasks to the network has moved from cloud-based schemes to edge computing solutions where nearby servers process requests in a timely manner. Virtualisation technologies have recently given data cloud and network providers the required flexibility to offer such on-demand resources. However, the maintenance of close computing resources presents a challenge when the served devices are on the move. In this case, if processing continuity is desired, a transference of processing resources and task state should be committed to maintain the service to end devices. The solution here presented, MIGRATE, proposes the concept of virtual mobile devices (vMDs) implemented as Virtual Functions (VxF) and acting as virtual representatives of physical processing devices. vMDs are instantiated at the edge of the access network, following a Multi-Access Edge Computing (MEC) approach, and move across different virtualisation domains. MIGRATE provides seamless and efficient transference of these software entities to follow the real location of mobile devices and continue supporting their physical counterparts. Software Defined Networks and Management and Operation functions are exploited to “migrate” vMDs to new virtualisation domains by forwarding data flows to the former domain until the new one is prepared, while a distributed data base avoids the transference of data. The solution has been deployed in a reference vehicular scenario at the Institute of Telecommunications Aveiro premises within the 5GINFIRE European project. In particular, the system has been evaluated under different virtualisation domains to study the operation of the migration approach in a vehicular monitoring scenario. The results validate the system from the application viewpoint with a Web monitoring tool, and the migration of the digital twin provided as VxF is analysed attending to the modification of data flows, indicating a seamless transition between virtualisation domains in a timely manner.
Impact of Fin Height on Bias Temperature Instability of Memory Periphery FinFETs

Fin height dependence of negative and positive Bias Temperature Instability (N/PBTI) on logic for memory high-κ metal gate (HKMG) FinFETs transistors is reported for the first time. It was observed that NBTI degradation is less severe when increasing the physical height of the silicon fin. The increased fin height results in a lower effective defect density, believed to be related to a reduced role of the defective fin corners and/or top surface. PBTI results reveal a similar, albeit less severe, impact of fin height, suggesting an impact of fin height on the high-κ layer, with again an increased defectivity at the fin corners and/or top surface, whose effective role is reduced in the case of taller fin.
High Spectral Purity Optical Source Stabilized on a Fiber Ring Resonator

This letter demonstrates the stabilization of an external cavity semiconductor laser at 1550 nm on an ultra-high Q factor fiber ring resonator (FRR). Based on a Pound-Drever-Hall technique, our system is optimized in order to take into account the properties of the FRR which features a large Q factor (<inline-formula> <tex-math notation="LaTeX">$2\times 10^{9}$ </tex-math></inline-formula>) but a relatively small free spectral range (2 MHz). The measured performances of the stabilized laser shows a frequency noise level as low as 0.4 Hz<sup>2</sup>/Hz in the 1 kHz to 10 kHz range and a linewidth lower than 30 Hz for 5 ms integration time.
A Novel Algorithm for Missing Data Imputation on Machine Learning

Missing data value plays a significant role in medical research and its presence causes an adverse effect on machine learning and AI models which leads to the wrong insights for decision making. Past few decades, researchers have developed and applied various imputation approaches to real-world applications. In addition, imputation methods help us to build effective models to discover hidden patterns in medical applications that can provide insightful outcomes for better decision-making. In this paper, a new approach is proposed to impute the missing data value using XGBoost (eXtreme Gradient Boosting) of ensemble learning method for continuous attributes in medical datasets. The proposed methods are continuous type attribute imputations for continuous and discrete data attributes. In this approach, we impute each missing data attribute value by predicting its data value from non-missing data attributes. The experiments are conducted on benchmark medical datasets missing values ranging from 1.98% to 50.65% and compared with iterative imputation, KNN imputation, and missForest imputation. In our study, we observe that missXGBoost can successfully handle missing data attributes of continuous types of attributes and it outperforms other imputation methods.
Development of Advance Energy Management Strategy for Standalone Hybrid Wind & PV System Considering Rural Application

This paper is an emphasis on advanced energy management strategy and control of a stand-alone hybrid system. The hybrid systems consist of PV, wind and battery storage for rural domestic applications. Several types of different DC-DC converters utilized to control the power flow between load and source. The DC bus in the given system has MPPT controllers which provide optimal torque (OT). The storage system i.e. the battery is connected via DC-DC converter which is bidirectional in nature to the DC bus. The fuzzy based controller Inverter is used as the interface between the AC bus and DC bus. The hybrids system for power generation works within the constraints to satisfactorily fulfil power demand. Using an efficient control strategy for all the sources a continuous power supply is being aimed in this paper. Also, battery's state of charge (SOC) is aimed to be above 80%. The study and simulation results obtained shows that the system stabilizes within two cycle when load suddenly changes. Fuzzy based controller along with the energy management system (EMU) effectively maintains the supply demand balance.
Traffic Light Detection using Convolutional Neural Networks and Lidar Data

This paper presents a traffic light detection system based on convolutional neural networks and lidar data. The proposed approach contains two stages. In the first detection stage, the map information is adopted to assist the object detection, and two cameras with different focal lengths are used to detect traffic lights at different distances. In the following recognition stage, we combine detector and classifier to deal with the problem of many light states. A dataset is created with urban road scenes in Taiwan. The experiments have demonstrated the advance in traffic light detection and recognition.
A Study on Detection and Segmentation of Ischemic Stroke in MRI and CT Images

An ischemic stroke occurs when a vessel providing blood to the cerebrum is impeded. It represents around 87 percent of all strokes. Quickly advancing imaging innovation is revolutionizing the administration of stroke patients, and the field of ischemic stroke treatment is experiencing transformation. Imaging of the cerebrum in stroke with CT and MR imaging modalities has turned into an invaluable tool for the conclusion of stroke. Nowadays the computerized approach of diagnosing the ischemic stroke is increasingly being used and giving satisfactory results. The computerized investigation of ischemic stroke includes several stages: pre-processing, segmentation, specific features extraction, comparison of brain tissue area and detection. The mechanized conclusion strategy for the stroke lesion will enable the radiologist to quicken the treatment so the mortality rate and the long-term disability for the reason that of cerebrum stroke can be decreased. This review focuses on the procedure of computer-aided investigation and the identification of ischemic stroke in MRI and CT images.
VOLTTRON based Multi-agent System for Residential Micro-grid

VOLTTRON™ is an open-source decentralized sensing platform. Its control strategies include the use of agents and transaction-based controls. In this manuscript, various aspects of VOLTTRON™ platform has been studied while focusing on Multi-agent System (MAS) integration environment. The paper expresses possible commercial and industrial market contributions of the platform. This identifies the roles and strategies used by the distributed environment and analysis case study based upon the communication latency in the interactive MAS environment.
Compact Augmented Reality Combiner Using Pancharatnam-Berry Phase Lens

A compact augmented reality (AR) combiner using Pancharatnam-Berry phase (PBP) lens and holographic optical element (HOE) is proposed. PBP lens is activated as a convex lens or a concave lens according to the polarization state of the input light wave. By using this characteristic, a thin AR eyepiece with a large numerical aperture (NA) is constructed. The AR eyepiece operates as a convex lens for the input light with a right-handed circular polarization state and operates as a transparent plate for the left-handed circularly polarized light. In order to adopt this AR eyepiece to the AR combiner, a chromatic aberration should be compensated. To resolve the chromatic aberration, HOE which diffracts the light rays with different profiles according to the input wavelength is designed. By combining this HOE with the AR eyepiece and a light guide, a compact AR combiner providing a wide field of view (FOV) is designed. A prototype of the proposed AR combiner is presented along with experimental results and analysis.
Streamer characteristics of PEEK surface in nitrogen driven by positive repetitive pulse voltage

In this work, we present a detailed analysis on the observed streamer characteristics of PEEK (polyetheretherketone) surface in nitrogen at atmospheric pressure driven by positive repetitive pulse voltage, including the forward discharge streamer and the back discharge streamer. Fast oscilloscopic technique and digital photographic technique were employed to observe the streamers of surface discharges, and current pulse waveform analysis technique and image analysis technique were used to analyze the characteristics of the observed streamers. The experimental results show that the current pulse of forward discharge streamer has a shorter rise time, a higher current amplitude, a faster current rising rate, a shorter fall time, a faster current falling rate and a smaller charge quantity than those of back discharge streamer at the same voltage amplitude. Besides, the forward discharge streamer has a longer stopping length, a faster propagation velocity and a stronger maximum light intensity than back discharge streamer at the same voltage amplitude. Furthermore, the correlation between the electrical characteristics and optical characteristics of the streamer was analytically derivated, and the current rising rate of the streamer is proportionally correlated to the product of the streamer stopping length and the streamer velocity square.
Irreversible Privacy-Preserving Images Holding Spatial Information for HOG Feature Extraction

In this paper, we propose a generation method of visually protected images and its application to privacy-preserving machine learning. Images generated by the proposed method hold the gradient direction information of the original images, but have no the information. Histogram-of-Oriented-Gradients (HOG) features are extracted from the protected images, and the features are applied to machine learning algorithms. In addition, the proposed generation method is an irreversible one, so there is no need to manage secret keys, unlike encryption methods. In an experiment, a face classification task is carried out under the use of a support vector machine algorithm with the HOG features to demonstrate the effectiveness of the proposed method.
Saliency Estimation via Integration of PDDL & MRF for Video Encoding (SEIPM-Video Encoding)

Recently, there are many applications that broadcast multimedia data through internet and many other ways, as the multimedia data are captured with better quality, the size of the data is getting huge. Thus, because of increase in size there are many issues occurring while broadcasting the media data. To resolve this issue, there are many compression techniques implemented for compressing the size of video by maintaining the quality of the video. The state-of-art techniques are not able to maintain the quality of the video after compressing the video as the quality of the video must be compromised when the size of the video is minimized. Thus, we are implementing a new technique naming as “Saliency Estimation using Integration of Processing-Division-of-Description-Layer and Markov-Random-Field for Video Encoding” in short known as “SEIPM-Video Encoding”. In here, we are compressing the size of the video without compromising the quality of the video. We are implementing this technique based on spatial and temporal saliency prediction by combining Processing-Division-of-Description-Layer (PDDL) and Markov-Random-Field (MRF), where PDDL is used to recognize the features that are attracting the eyes very much and MRF is used analyze the saliency frames of the video to recognize the region that has motions and movements within the frames. By recognizing the region of movement and regions that attracts eye, we only encode that region and remaining pixel is taken from the previous frame. Because of this, we get compressed size with better quality of video.
Function Block-Based Robust Firmware Update Technique for Additional Flash-Area/Energy-Consumption Overhead Reduction

Energy consumption and flash-memory usage are very limited in microcontrollers that make up the sensor network, accordingly, the process of updating the embedded firmware should also be low cost and energy efficient. This work proposes a technique that overcomes limitations due to increased costs of configuring the sensor network by additional memory usages and increased energy consumption resulting from firmware updates. Instead of dealing with the whole firmware, we split the firmware into function blocks and managed them with a function map that indicate each function block address. Further, by only updating function blocks where differences exist, we successfully reduced flash memory usage and energy consumption that occurred during the firmware update process. We implemented the proposed technique with the target measurement environment, and the result shows that maximum flash memory usage reduced by 91.4% and that 71.4% reduction in execution time resulted in a 69% reduction in energy consumption over the conventional method.
A Review of Deep Learning Approaches for Image Analysis

Convolutional neural network (CNN) is the generally used efficient methodology for image analysis. Deep learning involves machine learning methods that uses the unsupervised, semi-supervised or supervised learning strategies for hierarchical representation and classification. Deep learning have shown feasible results for segmentation and classification of images on multiple domains. This review provides an explicit knowledge on methods of convolutional neural network and deep learning procedures. applied for image segmentation and computer-aided analysis. The objective of this survey is to give an extensive discussion on deep learning approaches for image analysis and segmentation in existence. This paper aims to outline significant technology progress and also gives knowledge into areas wherein deep learning can enhance research over and highlight new research to implement the application of deep learning. The survey put on a valuable reference for new deep learning exponents including those searching to innovate in the deep learning applications.
Distant Sound Source Suppression Based on Multichannel Nonnegative Matrix Factorization with Bases Distance Maximization Penalty

In this research, we address distant sound source suppression based on Multichannel Nonnegative Matrix Factorization (MNMF), and propose a new penalized method. A conventional method based on MNMF separates an observed signal into a target signal and other distant sound sources. Unfortunately, MNMF often degrades the separation performance owing to the basis-sharing problem. Our penalized method forces the target basis to become different from the nontarget one. Experimental results show that the proposed method can improve the separation capability more than the conventional one.
Arrhythmia Classification Using Deep Neural Network

Research on biomedical signal to support the physician is boom of current research. In this paper, the cardiac signal is considered for arrhythmia detection and classification. The data from MIT-BIH database is taken for experiment. In first phase the signals preprocessed using different types of filters as low pass filter and median filter. Further the efficient technique discrete wavelet transform (DWT) is utilized to extract the features. As the feature set is large, deep neural network (DNN) is considered for the classification model. In this case the model can be used for medical data mining and self optimization process. Due to such advantages the model is chosen. The accuracy is found 98.66 % which is better than the earlier methods. It is exhibited in result section
Study of the Extended Phase Shift Migration for Three-Dimensional MIMO-SAR Imaging in Terahertz Band

In this paper, the extended phase shift migration (PSM) for three-dimensional (3-D) multi-input-multi-output synthetic aperture radar (MIMO-SAR) imaging in terahertz (THz) band was studied. Based on one-dimensional MIMO arrays combined with synthetic aperture scan along another dimension, MIMO-SAR imaging scheme allows the number of array elements to be greatly reduced compared with the two-dimensional (2-D) MIMO arrays. By analyzing the derived analytical expression of the scattered waves in the frequency-wavenumber domain, the MIMO-SAR data in a certain frequency is mapped to another frequency of the ‘explode fields’ of the monostatic form in accordance with a newly defined 3-D dispersion relations. By multiplying the modified phase shift terms in each frequency of ‘explode fields’, the ‘explode fields’ at different range planes can be reconstructed. Finally, the ‘explode fields’ at time t = 0 can be successfully derived to realize fast imaging reconstruction for MIMO-SAR, with great reduction on the time consuming as compared with the BP algorithm and better accuracy compared with the Stolt migration. Additionally, due to its iterative nature in the range direction, the proposed algorithm is more flexible in treating more complicated scenarios, such as the image reconstruction in the multi-layer medium. A bistatic prototype imager was designed for the proof-of-principle experiments in THz band. The 3-D imaging results of different targets and computational complexity were also given to demonstrate the good performance of the proposed algorithm for THz MIMO-SAR imaging.