Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Motion Compensation for Forensic Analysis of Egocentric Video using Joint Stabilization and Tracking

Stabilization and tracking of objects in egocentric videos captured by law enforcement body-worn cameras are often much more challenging compared to standard videos captured by regular mobile cameras. That is due to extreme motion caused either by the camera or by objects in the video frames. Therefore, standard stabilization and tracking methods may be less effective on such video clips, and more robust methods are required. The work presented in this paper describes robust methods for video frame stabilization and in-frame object stabilization and tracking for egocentric video analysis. During forensic investigations, sometimes more than one type of analysis is required for egocentric videos, captured in a variety of motion conditions. Hence we first define four types of use-cases that influence the requirements from the stabilization and tracking algorithms. These use-cases are categorized according to the camera motion vector, the type, size and number of objects in the scene, and to the relative motion between the objects. The methods we provide for those four use-cases are specifically adapted for forensic investigation, and have the ability to simultaneously stabilize and track both background as well as foreground regions in the video frames. The proposed methods are robust to the frame content, perform joint estimation and filtering of the camera path, and handle multiple moving objects in the scene, as demonstrated in our experiments.
A DC-DC Step Down Converter for Unprecedented Higher Efficiency

A DC-DC step down converter is an encyclopedic DC-DC converter that adroitly converts a high voltage to a low voltage aptly. Adequate power conversion prolongs battery life, curtails heat and acquiesces for smaller gadgets to be fabricated. An unprecedented higher efficiency DC-DC step down converter using MOSFET switch, two inductors and switched capacitor is proposed. The use of DC-DC buck converter for high step down DC-DC conversion is very limited due to its narrow duty cycle. Proposed converter subsists of two inductors in lieu of single inductor, which used in conventional DC-DC step down converter, to get higher efficiency in attaining same low voltage gain. It transfers energy from DC source to load resistor using one inductor and capacitor whilst another inductor is used to step down the voltage and also get higher efficiency than conventional DC-DC step down converter. The circuit operation depends on the conduction state of the MOSFET. In case of on state condition, two inductors are in parallel and off state condition those are in series connection, so particular inductors charged the output capacitor to get surpassing efficiency and also make comparison between proposed and conventional buck converter. The steady state operation of the proposed circuit is ascertained by simulation results.
Development of an Adaptive In-Pipe Inspection Robot with Rust Detection and Localization

In response to addressing the issue of pipe quality checking, the researchers developed an adaptive in-pipe inspection robot that is able to detect rust as well as map the rust on the pipe network. The robot is traversed in a pipe network of horizontal, vertical, elbow, and tee type with diameters of 8, 10 and 12 inches for all. Hence, the test features the versatility, adaptability, and robustness of the robot. The leg expansion of the robot is inspired by the scissors mechanism. On the other hand, rust detection was done through a per pixel classification via image processing. To effectively map the rust, checkpoints were used as a guide of the robot. Testing of the robot were supported in both simulation and actual testing, wherein it yields a 96.45% success rate on the site. Likewise, its rust detection program proved to be successful with a high percentage accuracy of 99.18%. The localization on the other hand yielded an accuracy of 85%. Given the obtained data and results, the researchers were able to go beyond their target objective of 70%.
A wide-field SAR polar format algorithm based on quadtree sub-image segmentation

The approximation of planar wavefront in traditional Polar Format Algorithm(PFA) limits the effective scene size of PFA for high-resolution squinted Synthetic Aperture Radar (SAR). In this paper, a new wide-field PFA based on quadtree sub-image segmentation is proposed. The original phase history of the full-beam is filtered recursively to generate multiple sub-beams by sub-image recursive segmentation. As long as each sub-beam is filtered narrow enough, standard PFA could be implemented to produce a number of fully focused sub-image. Finally, all fully focused sub-images are mosaicked to get a big image perfectly focused. This divide-and-conquer approach breaks the image size limit in traditional PFA, extensively enlarges the effective focused scene. The processing flows are derived in detail and the algorithm is validated by measured data.
Wavelet Neural Network based Controller Performance Analysis of a PMBLDC Motor

Wavelet Neural Networks (WNN) are computational tools used now a day for control and estimation purposes. This paper describes a wavelet neural network based control system designed for Permanent Magnet Brushless DC (PMBLDC) Motor drive. The WNN controller is trained online through gradient descent algorithm for controlling the motor output in different operating conditions. A software is developed in C++ and was used to test the performance of the drive system. The results show that drive control system works effectively after comparison of the results with those obtained using PI controller based system.
Comparative Study of Linear Superconductivity Machine With Different Stator and Winding Configurations

Compared with rotary electrical machines in linear drive applications, linear machines without rotary–linear motion conversion equipment can have higher force density and efficiency. Linear superconductivity machines (LSMs), consisting of high-performance superconducting magnets instead of permanent magnets, can offer very high force density and efficiency compared with other linear machines, such as linear induction machine, linear permanent magnet machine, and so on. In this paper, LSMs with different stators and winding configurations are investigated, specifically LSMs with concentrated or distributed windings, and unilateral or bilateral side stators. The electromagnetic parameters and performance of these LSMs are calculated and compared by using the finite element method, and then, the main difference between various design methods in LSM has been presented.
Multi-resolution classification techniques for PTSD detection from audio interviews.

We describe a set of novel techniques used for identifying PTSD in patients from audio interviews. In particular, to improve classification performance, we utilize a multi-resolution decomposition of the audio signal into coarser and finer scales. We describe a simple ensemble probability averaging method together with a bagging approach, both of which outperform the classification scheme based on only the features of the untransformed signal. In addition, we describe our segmentation strategy and simple filtering techniques which can aid classification performance for audio recordings obtained in non-standardized recording conditions.
Lost Robot Self-Recovery via Exploration Using Hybrid Topological-Metric Maps

A robot might get lost due to abrupt wheel slippage, unsteady movements on uneven floors, collision with obstacles, blocked perception sensors or kidnapping. When this happens, the robot has to recover by itself. This is necessary but has not been satisfactorily resolved. In this paper, a generic lost robot self-recovery framework is proposed. It has self-exploration capability assisted by an efficient place recognition module using hybrid topological-metric maps. The metric map is used for path planning and navigation while the topological map is used for re-localization when lost. As soon as the robot detects that it is lost, self exploration is activated and starts to explore while performing place recognition using the topological map. Once the place is re-identified, the proximate global location is obtained and the robot performs fine localization using the metric map, recovery itself and continue to its destination. The proposed system has been implemented on a mobile robot operating in a typical office environment. Experiments conducted show a robot can be efficiently and reliably recover itself when it gets lost within or outside of the map.
Joint Ideal Ratio Mask and Generative Adversarial Networks for Monaural Speech Enhancement

Speech enhancement is the task of improving some perceptual aspects of noisy speech. Recently, Generative Adversarial Networks (GAN) is becoming a popular deep learning method and different GAN’s structures have been proposed [1], [2]. In this paper, we propose a new framework for speech enhancement task by using GAN. We train two models: a generative model G and a discriminative model D. The G and D are both defined by the feedforward multilayer perceptions (MLPs) [3]. The difference between the generator and the discriminator is the generator G employs deep neural network (DNN) based on the masking technique in which the magnitude spectrum of noise and the magnitude spectrum of clean speech are estimated from noisy speech features simultaneously. Meanwhile, the discriminator D uses the MLPS structure to directly predict clean speech magnitude spectrum. The model D discriminates data that comes from clean speech or generated speech by G network. Moreover, in our work, G network is used to perform the speech enhancement. The objective evaluation and experimental results show that the proposed framework significantly improves the performance of traditional deep neural network (DNN) and recent GAN-based speech enhancement methods.
Source Resizing and Improved Power Distribution for High Available Island Microgrid: A Case Study on a Tunisian Petroleum Platform

A microgrid is a small-scale smart network that contains distributed resources and loads and serves several technical, economic, and environmental aims. In this kind of power system, the energy generated by different sources is often collected in an adequate bus system (ac or dc busses) and transported to loads across a distribution system. In case of islanding operation of a microgrid, each production insufficiency or distribution system fault can cause a partial or total interruption of power supply required by loads of the microgrid. This unavailability can last longer because of the randomness and intermittent behavior of renewable sources and the importance of the maintenance actions of the distribution system. To guarantee high availability of power supply, microgrid must be able to produce and to transfer the power energy requested by loads. The sizing of distributed sources and the design of the distribution system present an important challenge to achieve this goal. This paper offers a new global methodology carried out in two steps: in the first, it aims to optimize the source sizing by adding some microsources for ensuring the balance between sources production and loads requirement, then, in a second step, it aims to enhance the distribution system design by the creation of new paths of power transmission for transferring the produced energy from sources to loads. The proposed optimization methodology aims to achieve the requested availability rate requested by each load (considering their priorities) by taking into account some technical and economic factors. In this methodology based on genetic algorithms, objective functions are defined, and several electrical and economic constraints are formulated. The graph theory is used to represent the architecture of the microgrid distribution system, and Matpower tool of MATLAB is used to model it. An application and implementation of the proposed optimization methodology in a Tunisian petroleum platform indicate that the proposed solution is efficient in optimizing of power supply availability in its microgrid.
Speech Enhancement Method Based On LSTM Neural Network for Speech Recognition

Long Short-Term Memory (LSTM), a special kind of Recurrent Neural Network (RNN), is capable of learning long-term dependencies. In this paper, a kind of speech enhancement method is proposed for LSTM network structure to cope with the speech features, with the purpose of improving the speech recognition rate. This method utilizes the LSTM structure in reference to the acoustic model and crossover residual network to construct the front-end enhancement module. We trained and compared DNN, CNN, LSTM and BLSTM models with various numbers of parameters. The experimental results show that, the LSTM model performs the best in the test set and the real scene. The noise reduction effects are the best when the noise is reduced from 31.23% to 25.89% on the Xiaomi speaker test set.1
Biometric Authentication: A Holistic Review

Information Security is of growing concern for each individual as computer technology has become all too pervasive. A user’s personal and sensitive data is present on the device and is prone to attacks. Hence, it is necessary to authenticate the devices or restrict physical access of the devices only to the authorized users. Former methods of knowledge-based and token-based authentication are gradually becoming insufficient and thus obsolete as attackers are finding several methods to circumvent these methods. Biometric authentication is a considerable advancement in authenticating an individual. Biometric traits are closely associated with an individual and are a powerful tool in authorizing access to users. The paper reviews the various biometric traits commonly used in a biometric system and different types of biometric systems-Unimodal and Multimodal Systems. It also reviews the various biometric factors which determine whether a biometric characteristic qualifies as biometric measure in a system.
SET-THEORETIC LEARNING FOR DETECTION IN CELL-LESS C-RAN SYSTEMS

Cloud-radio access network (C-RAN) can enable cell-less operation by connecting distributed remote radio heads (RRHs) via fronthaul links to a powerful central unit. In the conventional C-RAN, baseband signals are forwarded after quantization/compression to the central unit for centralized processing/detection in order to keep the complexity of the RRHs low. However, the limited capacity of the fronthaul is a significant bottleneck that prevents C-RAN from supporting large systems (e.g. massive machine-type communications (mMTC)). We propose a learning-based C-RAN in which the detection is performed locally at each RRH and, in contrast to the conventional C-RAN, only the likelihood information is conveyed to the central unit. To this end, we develop a general set-theoretic learning method for estimating likelihood functions. Our method can be used to extend existing detection methods to the C-RAN setting.
Detection of Arsenic Contamination in Drinking Water using Color Sensor

Arsenic is one kind of chemical element that can poison surface water with causing many human difficulties. For detecting and giving a clear view of arsenic contamination in water there are different types of researches going on. Most of the researches are developed for laboratory work. In this work, not only a system was developed but also a simple theory was described that will detect the arsenic percentage of water easily. Modern arsenic detecting kit and simple color sensor have been used for developing the system. Some sample of water was collected from different region of Khulna, Bangladesh to measure the percentage of arsenic contamination. The data was recorded and analyzed to verify the system.
Carrier-Based PWM Equivalent to Multilevel Multiphase Space Vector PWM Techniques

The space vector pulsewidth modulation (SVPWM) techniques enhance the performance of multilevel multiphase inverters. With multilevel (three-phase) inverters and with (two-level) multiphase inverters, it is widely accepted that the typical SVPWM strategies have an equivalent carrier-based pulsewidth modulation (CBPWM) counterpart, which produces identical results. However, the conclusions reached in the articles that show these cannot be applied, nor even extended, to SVPWM techniques with more than two levels and three phases. This article shows that the most widely accepted multilevel multiphase SVPWM techniques have a fully equivalent CBPWM counterpart, which consists of a phase disposition pulsewidth modulation with an appropriate zero-sequence injection scheme. Closed-form expressions to calculate the zero sequences are provided. The proposed modulation techniques are simulated and then implemented in a field-programmable gate array, showing that the equivalent CBPWM techniques produce identical results as the original SVPWM ones, but with a significant reduction of hardware requirements. The proposed methodology can be generalized to other multilevel multiphase SVPWM techniques.
Effects of Interference Mitigation Methods on Scintillation Detection

The use of Global Navigation Satellite Systems (GNSS) signals as signal of opportunity for ionospheric sounding is becoming quite popular. In particular, for scintillation monitoring, it is essential that the affected GNSS signal is not distorted by any other artificial interference. As a matter of fact, the Ionospheric Scintillation Monitoring Receivers (ISMR) used to observe scintillation activities could operate in scenarios where communication systems or even jammers are present. Such sources can provide scintillation-like performance in the signal processing stage, leading to misconceptions about the behavior of actual scintillation phenomena. This paper investigates the estimation of the S4 index, generally used to detect the presence of a scintillation event, under five types of anthropogenic interference, including continuous wave, narrow band, wide band, chirp and pulsed interference. Furthermore, the study also addresses the use of notch filtering and wavelet packet decomposition to mitigate the anthropogenic interference from a scenario in which both scintillation and artificial interference are present, and the S4 is estimated on the mitigated data.
Self-Adaptive Framework with Game Theoretic Decision Making for Internet of Things

The Internet of Things (IoT) connects several objects within environments that dynamically change, and so requirements may be added and changed at runtime. Therefore, requirements may be satisfied at dynamic change. Self-adaptive software can alter their behavior to satisfy requirements in dynamic environments. In this perspective, the concept of self-adaptive software is suitable for IoT environments. In this study, a self-adaptive framework is proposed for decision making in IoT environments at runtime. The framework includes finite-state machine model designs and game theoretic decision-making methods to extract efficient strategies. The framework is implemented as a prototype, and experiments are performed to evaluate runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime.
Structural Alignment based Zero-shot Classification for Remote Sensing Scenes

Zero-shot classification aims to classify unseen classes instances without any training data. However, the problem of class structure in consistency between visual space and semantic space severely affects zero-shot classification performance for remote sensing scenes. In order to tackle this problem, we employ semi-supervised Sammon embedding algorithm to modify semantic space prototypes to have a more consistent class structure with visual space prototypes. Then, unseen class prototypes in visual space can be effectively synthesized by transferring unseen knowledge from semantic space to visual space. Thus, classification task could be ultimately accomplished by the nearest neighbor method with the unseen class prototypes in visual space. The proposed method is extensively evaluated on two benchmark remote sensing scenes datasets, achieving the state-of-the-art performance.
Size Reduction of Cavity Backed Slot Antenna using Half Mode Substrate Integrated Waveguide Structure

Nowadays, the RF system requires a small size for compact package passive and active devices, include antenna devices. This paper proposes the half mode substrate integrated waveguide (HMSIW) structure combined with the antenna slot known as HMSIW cavity backed slot antenna (CBSA). It is achieved by bisecting the conventional SIW along the fictitious magnetic wall. By using parametric studies for the slot position and length in the ground layer, the frequency resonance shift to the lower frequency and it can be applied for antenna size reduction. The simulation results give return loss at 18.55 dB with resonance frequency of 2.43 GHz, fractional bandwidth of 1.65 % and gain of 4.12 dB where it is suitable for industrial scientific and medical (ISM) radio bands. The measurement of fabrication antenna gives resonance frequency at 2.45 GHz with fractional bandwidth of 1.22 % and gain of 3.64 dBi. Therefore, it is shown that the proposed antenna can effectively reduce its size without sacrificing its performance.
Tapered-Ratio Compression for Residual Network

Residual network is a commonly used convolutional neural network, but as the network becomes wider and deeper, it is becoming more difficult for IoT devices or embedded systems to adopt it due to their hardware constraints. Network compression using knowledge distillation is helpful to overcome this problem. In this paper, we propose a tapered compression ratio for residual network compression, which achieves higher accuracy with fewer multiplications on CIFAR datasets.
Role of Spatio-Temporal Feature Position in Recognition of Human Vehicle Interaction

This paper presents a solution for incorporating the structural information along with local features to enhance the recognition accuracy of human-vehicle interaction activities. Proposed system aims to exploit Bag of Words for extracting structural information both spatial and temporal relationship between features from video data to help achieve better recognition accuracy for complex interaction scenes. Traditional Bag of Words (BOW) approach is inefficient in representing structural information, feature positions and their temporal relationships which makes it difficult for the classifier to recognise interaction and complex scenes. The classifier uses BOW along with spatial and temporal positions of features. Random Forest and kNN are used as classifiers to compare classification results and to find a trade-off between recognition accuracy and computational complexity. We have used state of the art dataset VIRAT (Video and Image Retrieval and Analysis Tool) for validation of our scheme. Random Forest and modified BOW (RF+mBOW) gives better recognition accuracy at the cost of higher computational time whereas kNN and modified BOW (kNN+mBOW) takes less time for computations while giving remarkable recognition results. We observed that Random Forest and modified BOW (RF+mBOW) outperforms all state of art methodologies.