Face RGB-D Data Acquisition System Architecture for 3D Face Identification Technology

The three-dimensional approach in face identification technology had gained prominent significance as the state-of-the-art breakthrough due to its ability to address the currently developing issues of identification technology (illumination, deformation and pose variance). Consequently, this trend is also followed by rapid development of the three-dimensional face identification architectures in which some of them, namely Microsoft Kinect and Intel RealSense, have become somewhat today's standard because of its popularity. However, these architectures may not be the most accessible to all due to its limited customisation nature being a commercial product. This research aims to propose an architecture as an alternative to the pre-existing ones which allows user to fully customise the RGB-D data by involving open source components, and serving as a less power demanding architecture. The architecture integrates Microsoft LifeCam and Structure Sensor as the input components and other open source libraries which are OpenCV and Point Cloud Library (PCL). The result shows that the proposed architecture can successfully perform the intended tasks such as extracting face RGB-D data and selecting out region of interest in the face area

Performance Analysis of Noise Subspace-based Narrowband Direction-of-Arrival (DOA) Estimation Algorithms on CPU and GPU

High-performance computing of array signal processing problems is a critical task as real-time system performance is required for many applications. Noise subspace-based Direction-of-Arrival (DOA) estimation algorithms are popular in the literature since they provide higher angular resolution and higher robustness. In this study, we investigate various optimization strategies for high-performance DOA estimation on GPU and comparatively analyze alternative implementations (MATLAB, C/C++ and CUDA). Experiments show that up to 3.1x speedup can be achieved on GPU compared to the baseline multi-threaded CPU implementation. The source code is publicly available at the following link: https://github.com/erayhamza/NssDOACud

Modulation recognition of low-SNR UAV radar signals based on bispectral slices and GA-BP neural network

In this paper, we address the challenge of low recognition rates in existing methods for radar signals from unmanned aerial vehicles (UAV) with low signal-to-noise ratios (SNRs). To overcome this challenge, we propose the utilization of the bispectral slice approach for accurate recognition of complex UAV radar signals. Our approach involves extracting the bispectral diagonal slice and the maximum bispectral amplitude horizontal slice from the bispectrum amplitude spectrum of the received UAV radar signal. These slices serve as the basis for subsequent identification by calculating characteristic parameters such as convexity, box dimension, and sparseness. To accomplish the recognition task, we employ a GA-BP neural network. The significant variations observed in the bispectral slices of different signals, along with their robustness against Gaussian noise, contribute to the high separability and stability of the extracted bispectral convexity, bispectral box dimension, and bispectral sparseness. Through simulations involving five radar signals, our proposed method demonstrates superior performance. Remarkably, even under challenging conditions with an SNR as low as −3 dB, the recognition accuracy for the five different radar signals exceeds 90%. Our research aims to enhance the understanding and application of modulation recognition techniques for UAV radar signals, particularly in scenarios with low SNRs

Applications of MEMS Gyroscope for Human Gait Analysis

After decades of development, quantitative instruments for human gait analysis have become an important tool for revealing underlying pathologies manifested by gait abnormalities. However, the gold standard instruments (e.g., optical motion capture systems) are commonly expensive and complex while needing expert operation and maintenance and thereby be limited to a small number of specialized gait laboratories. Therefore, in current clinical settings, gait analysis still mainly relies on visual observation and assessment. Due to recent developments in microelectromechanical systems (MEMS) technology, the cost and size of gyroscopes are decreasing, while the accuracy is being improved, which provides an effective way for qualifying gait features. This chapter aims to give a close examination of human gait patterns (normal and abnormal) using gyroscope-based wearable technology. Both healthy subjects and hemiparesis patients participated in the experiment, and experimental results show that foot-mounted gyroscopes could assess gait abnormalities in both temporal and spatial domains. Gait analysis systems constructed of wearable gyroscopes can be more easily used in both clinical and home environments than their gold standard counterparts, which have few requirements for operation, maintenance, and working environment, thereby suggesting a promising future for gait analysis

An Architecture for Biometric Electronic Identification Document System Based on Blockchain †

This paper proposes an architecture for biometric electronic identification document (e-ID) system based on Blockchain for citizens identity verification in transactions corresponding to the notary, registration, tax declaration and payment, basic health services and registration of economic activities, among others. To validate the user authentication, a biometric e-ID system is used to avoid spoofing and related attacks. Also, to validate the document a digital certificate is used with the corresponding public and private key for each citizen by using a user’s PIN. The proposed transaction validation process was implemented on a Blockchain system in order to record and verify the transactions made by all citizens registered in the electoral census, which guarantees security, integrity, scalability, traceability, and no-ambiguity. Additionally, a Blockchain network architecture is presented in a distributed and decentralized way including all the nodes of the network, database and government entities such as national register and notary offices. The results of the application of a new consensus algorithm to our Blockchain network are also presented showing mining time, memory and CPU usage when the number of transactions scales up

Incorporating Fine-grained Events in Stock Movement Prediction

Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement prediction. Firstly, we propose a professional finance event dictionary built by domain experts and use it to extract fine-grained events automatically from finance news. Then we design a neural model to combine finance news with fine-grained event structure and stock trade data to predict the stock movement. Besides, in order to improve the generalizability of the proposed method, we design an advanced model that uses the extracted fine-grained events as the distant supervised label to train a multi-task framework of event extraction and stock prediction. The experimental results show that our method outperforms all the baselines and has good generalizability.Comment: Accepted by 2th ECONLP workshop in EMNLP201

Comparison of Mathematical Methods for Compensating a Current Signal Under Current Transformers Saturation Conditions

Current measurements from electromagnetic current transformers are essential for the construction of secondary circuit systems, including for protection systems. Magnetic core of these transformers are at risk of saturation, as a result of which maloperation of protection algorithms can possibly occur. The paper considers methods for recovering a current signal in the saturation mode of current transformers. The advantages and disadvantages of methods for detecting the occurrence of current transformers core saturation are described. A comparative analysis of mathematical methods for recovering a current signal is given, their approbation was carried out, and the most promising of them was revealed. The stability and sensitivity of recovery methods were tested by adding white noise to the measured signal and taking into account the initial flux density (remanent magnetization) in the current transformers core. Their comparison is given on the basis of angular, magnitude, and total errors at a given simulation interval. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This work was supported in part by the International Cooperation Project of National Natural Science Foundation of China under Grant 41761144079, in part by the Strategic Priority Research Program of the Chinese Academy of Sciences, in part by the Pan-Third Pole Environment Study for a Green Silk Road under Grant XDA20060303, in part by the K. C.Wong Education Foundation under Grant GJTD-2020-14, in part by the CAS PIFI Fellowship under Grant 2021PC0002, in part by the Xinjiang Tianchi Hundred Talents Program under Grant Y848041, in part by the CAS Interdisciplinary Innovation Team under Grant JCTD-2019-20, in part by the project of the Research Center of Ecology and Environment in Central Asia under Grant Y934031, and in part by the Regional Collaborative Innovation Project of Xinjiang Uygur Autonomous Regions under Grant 2020E01010

Алгоритм покращення результатів аналізу епілептичних сигналів ЕЕГ

Робота присвячена розробці програмного алгоритму для автоматичного розпізнавання та прогнозування епілетриформних частотних ритмів в сигналах ЕЕГ, за допомогою методів машинного навчання. Метою є створення програмної моделі для автоматичного розпізнавання та прогнозування епілетриформних частотних ритмів в сигналах ЕЕГ, за допомогою методів машинного навчання. Об’єктом дослідження є сигнали електроенцефалограми. Предметом дослідження виступають методи машинного навчання. У магістерській дисертації визначені основні напрямки досліджень штучного інтелекту, з використанням сигналів електроенцефалограми; досліджені переваги та недоліки методів аналізу; проведена попередня обробка сирих даних та сформовані набори вхідних даних; відфільтровано найефективніший та найінформативніший набір ознак; на основі платформи програмування Python 2.7.15 побудовано модель класифікації сигналів ЕЕГ; проведені дослідження з прогнозування епілепсії за допомогою методів машинного навчання. За результатами роботи опубліковано: стаття «Classification of epileptiform activity in EEG using machine learning techniques» у науковому журналі «Science, Research, Development» (червень 2018 року); тези «Розпізнавання епілептичної активності в сигналах ЕЕГ за допомогою методів машинного навчання» у науково-практичному журналі «Інформаційні системи та технології в медицині» ISM-2018 (листопад 2018 року).The volume of the report is 85 pages, 42 figures, 6 tables, 7 formulas, two applications are included. In total 47 references were analyzed. Epilepsy is the fourth most common neurological problem in the world. When diagnosing epilepsy, the most informative is the registration of EEG, which helps distinguish epileptic seizures from non˗epileptic seizures and classify them. Aim: EEG signal classification model based on machine learning methods. In the master's dissertation were determined the basic directions of research of artificial intelligence, using signals of an electroencephalogram. Were investigated the advantages and disadvantages of the analysis methods. Preprocessing of raw data have been done and formed input datasets. The most effective and informative set of features filtered out. A model of the classification of EEG signals was constructed using the programming platform Python 2.7.15. Researches have been conducted on the prediction of epilepsy with by the machine learning methods. The article «Classification of epileptiform activity in EEG using machine learning techniques» was published in the journal «Science, Research, Development» (June 2018) and thesis «Recognition of epileptic activity in EEG signals using machine learning methods» was published in the journal «Information systems and technologies in medicine ISM–2018» (November 2018) based on research results