Miracle-dz/UniFAR-FTUs huggingface dataset:
Segment Based Abnormality Detection in EEG Recordings

Electroencephalograms or EEGs are essential tests employed to diagnose any abnormalities in the brain waves that can be related to numerous brain disorders. These EEG recordings are ordered for a minimum length of 20 minutes and can go up to an hour or more, which makes the process of analysing it cumbersome and time consuming for medical professionals, and makes diagnosis very subjective. The aim of this paper is to aid in diagnosing EEG recordings with higher efficiency and accuracy. This is achieved by flagging segments of the recording into different classes that need special attention. This includes normal sleep wave patterns like POSTS, vertex waves and spindles, artifacts like ECG artifacts and Eyes open/close artifacts which can lead to misdiagnosis, and abnormal waveforms like spikes and slow waves. Most of the existing literature focuses on classifying the entire EEG as either abnormal or normal. However, in practice, a medical professional interprets the EEGs by looking at only particular sections of the recording. Therefore, our approach is to have individual segments classified instead. EEG signals, owing to their highly dynamic nature, are difficult for machine learning models to process and analyze effectively. To achieve this, we explore different methods of signal decomposition, feature elimination and classification, and find the best combination of these for the annotation task. Our combination of Empirical Wavelet Transform (EWT) for signal decomposition, Recursive Feature Elimination and Linear SVM model for classification, achieved an accuracy of 70% on the Temple University dataset and 90.78% on a private dataset.
Staking approach to the problem of predicting bankruptcies

In this work, staking methods of predicting bankruptcy are improved and expanded. The main topic of this study is the use of different types of models: multilayer perceptrons, decision trees, Kohonen networks. In various combinations, they are selected and combined into ensembles and meta-ensembles in such a way as to consider the strengths and weaknesses of each type of basic models in the context of the task.
Design and Implementation of a Fast Convolution Algorithm for Embedded Platform

In recent years, deep learning has been gradually applied to the industry with great success. As the demand for the lightweight intelligent devices increases, the deployment of deep learning models on embedded platforms to meet users' needs for real-time performance has become a trend in the development of intelligence. However, due to the pursuit of higher accuracy, existing deep learning frameworks are becoming richer in functionality and more complex in computation. A large amount of memory requirements and computational power demands make it challenging to deploy neural network computing frameworks on embedded platforms with limited resources and computational power. The WPOC algorithm is proposed and integrated into the Darknet framework to address real-time image processing based on the Winograd algorithm. Tested on the ZYNQ-7010 platform was passed. The results show that the WPOC algorithm proposed in this paper can effectively speed up image recognition by about six times under the VGG-16 model while ensuring the same accuracy rate.
Edge Computing Based Abnormal Behavior Learning for Mental Disorder Detection Through UAV Surveillance

The population of people with severe mental disorders in global is soaring year by year, violence or crimes imposed by them always shock the public and lead to terrible consequences. How to utilize reasonable means to predict dangerous intention of people with severe mental disorders, prevent their potential hazards against the public, is significant for social security. In this paper, we release Unmanned Aerial Vehicles to achieve real-time behavioral sensing on people with severe mental disorders, deploy hybrid model in edge cloud to extract global features from behavioral data of people with severe mental disorders, then utilize these global features to construct decent classification algorithms for action recognition and behavioral semantic cognition. With cooperation of behavioral data and algorithms, we can understand the instantaneous emotions of patients. On the basis of behavioral data and emotional state of patients, the correlation between daily behavior and dangerous intention of the patients has been revealed by utilizing behavioral big data analysis, which is a key foundation for the prediction of patients' dangerous intention. This paper proposes a novel pattern for the effective management of people with severe mental disorders, which can effectively eliminate a part of possible dangers caused by those patients.
Fabrication of Nanostructured Skutterudite-Based Thermoelectric Module and Design of a Maximum Power Point Tracking System for the Thermoelectric Pile

In a bid to realize the applications of skutterudite-based thermoelectric modules and maximally utilize the output power of its thermoelectric pile, first, nanocomposite n-type skutterudite-based material was prepared by adding the nano phase AgSbTe2, giving rise to a dimensionless figure-of-merit of 0.91. Then, skutterudite-based modules were fabricated and tested, which showed high area-ratio power of nearly 0.244W · cm−2. At last, in order to obtain the maximum output power for the skutterudite-based thermoelectric pile and improve the energy efficiency, a power management system based on maximum power point tracking (MPPT) technology was designed and tested. The perturbation observation method was adopted for the MPPT. Test results showed that the maximum efficiency of the system was over 98%. Meantime, when the open-circuit voltage of the thermoelectric pile was over 21.2 V, the system operating efficiency was larger than 90%.
Metamaterial Superstrate Based High Gain Antenna for Wi-Fi Applications

In this paper, a novel technique to improve the gain of the microstrip antenna using a metamaterial lens for S-band applications is discussed. An Omega-shaped structure is proposed to focus the electromagnetic field in the desired manner to increase gain and bandwidth. omega formations are combinations of a line and a ring to produce negative permeability and permittivity. The approach and process for extracting negative refractive index from scattering components in metamaterials are investigated. The metamaterial is designed and placed on the antenna which acts as a lens for focusing electromagnetic waves to improve the antenna gain. The simulation results are presented. The band with of antenna is (2.434GHz to 2.490GHz) 56MHz and the gain enhancement of 3.41dB was achieved. The results are compared with published work. When compared to a standard microstrip design bandwidth of the metamaterial-enhanced proposed antenna has increased by 10.1%.
Quality management principles in generating statistical data

This paper proposes a general overview of regulatory documents and data submission forms on the example of statistical data published on the official website of the Federal State Statistics Service (Rosstat). Our analysis is realized in terms of meeting the needs of the population, the scientific community, commercial organizations and entrepreneurs in a variety of objectives and complete statistical information. The article contains methodology for assessing the quality management principles implementation in the statistical data forming.
Word-Based Bantu Language Identification using Naïve Bayes

Language identification of text has become increasingly important as large quantities of text are processed or filtered automatically. It is one of the preprocessing steps in Natural Language Processing (NLP) tasks such as information retrieval and machine translation. Few studies have worked on Bantu Languages in automatic language identification. Language identification is a challenge in Bantu languages because of lack of data and in addition to that, languages which are written similarly like Setswana and Sesotho are also challenging. In this paper, we present a word-based Naïve Bayes classifier to identify words of Sesotho and Setswana language. The classifier was trained with words from both Setswana and Sesotho in a supervised manner. Adjectives, pronouns, adverbs and enumeratives are also included. The classifier shows that the two languages can be individually identified as it gives an accuracy of 71.4%. Despite that when we increase the data to double the number of words, the model increased performance to 78%. We also report that the classifier fails with homographs. The performance could be improved by using more data. Additionally, the syllable identification and sentence identification could be implemented along with word-based classifier.
Design and Analysis of Graphene based Octagonal Short-angular Circular Patch MIMO Antenna for Terahertz Communications

In this research, a Graphene based octagonal short-angular circular patch (OSACP) 2x2 multi-input-multi-output (MIMO) antenna is designed for Terahertz (THz) short-range communications. Graphene, as a 2D material, has gained momentum for the realisation of the plasmonic THz antennas due to its unique electromagnetic properties, can fetch the property of surface plasmon polariton (SPP) at THz regime. The graphene layers of thickness 0.01 um have been placed on a Silicon Nitride (Si3N4) substrate of thickness 15 um to increase the efficiency of MIMO antenna. The radiation characteristics of the proposed antenna model are compared to those of a Si3N4 substrate structure based microstrip patch antennas using FDTD technique with a cross-sectional area of 1232 × 1232 um2. It exhibits the return loss (S11) better than -10 dB over the frequency range of 0.259 - 0.324 THz.
ECG Noise Removal Using FCN DAE Method

An electrocardiogram (ECG) is a straightforward test that measures your heart rate and electrical activity. Electrical signals produced by your heart are detected by skin-connected nerves each time it beats. ECG signals are susceptible to noise contamination in real-world conditions, which can lead to misunderstanding. Baseline wanders and power line interference are the two main sources of noise in the ECG signal. To tackle these problems and eliminate inaccuracies, special emphasis has been dedicated to interpreting the ECG in order to achieve a precise diagnosis and analysis. To recycle pure data in its audio version, a denoising autoencoder (DAE) might be utilized. The results of experiments on ECG signals with various degrees of SNR input reveal that FCN outperforms fully connected neural network-and convolutional neural-based denoising network models significantly.
CARE: IoT enabled Cow Health Monitoring System

It is extremely difficult to care for animals' health in remote areas, particularly in India. It is too difficult to care for cattle not only in remote areas, but also on large farms with a large number of them. As a result, this paper's primary goal is to develop a health monitoring system capable of routinely monitoring dairy cow health. The monitoring system's goal is to detect various diseases based on behavioural changes and symptoms. We installed different sensors on the cow's body as well as in various locations around the farm to record the dairy cows' behavioural changes. Those sensory readings are sent to the cloud. CARE, our proposed algorithm, will classify possible diseases based on recorded cow behaviour. The proposed algorithm detects cow diseases with high accuracy. This framework was created as part of the smart health monitoring system.
Dual Band Millimeter Wave MIMO Antenna Loaded with SRRs for 5G Applications

This paper presents a four element dual band MIMO patch antenna for millimeter-wave (mm-wave) wireless communication in Ka-band (26-40 GHz) applications. The proposed design consists of one U-shaped monopole element along with two split ring resonators (SRRs) symmetrically placed with respect to the feed line on the top layer and a defected ground plane. By precisely optimizing the design parameters, the geometry radiates at 28 GHz and 36 GHz with peak gains of 4.21 dBi and 6.05 dBi, respectively. A single antenna element has initially been designed with detailed analysis and subsequently the four port MIMO antenna has been realized. Both the antennas exhibit desired characteristics to be used for mm-wave 5G and future wireless applications.
Identification of Tomato Plant Diseases Using CNN- A Comparative Review

Background: Tomatoes are extensively farmed as a vegetable or fruit in many regions of the country, and many forms of tomato pests and diseases are met over the whole life cycle of tomatoes; thus, early identification and diagnosis of these diseases is critical. The application of several deep learning approaches for identification of plant diseases has recently gained popularity, with encouraging results obtained for different diseases. Problem: The advancement of architectures based on Convolutional Neural Network (CNN) has considerably improved diagnostic accuracy of tomato diseases by using images of tomato leaves. The integration of CNN in the agricultural sector ensures an increased produce of tomato crop in a sustainable manner. However, complexity and performance time of CNN technique is a significant concern. Objective: In most deep learning models, all characteristics generated at various layers are given equal weighting. Significant characteristics should be learned and transferred to higher layers of the network for more exact classification. To improve the CNN performance, reuse and sharing of images (large dataset) is a great work required for tomato disease detection to get high accuracy. Methods: In this paper, we have presented a comprehensive review of twenty recent and notable works which employ CNN-based detection of tomato plant diseases. A comparative analysis of these works is given, taking accuracy as the metric. Results: A complete assessment of Bacterial Canker and Speck, Yellow Leaf Curl, Brown Rugose Fruit, Crown and Root Rot, Early Blight, Bunchy Top, Stolbur disease identification studies employing CNNs in this study. Conclusion: It is observed that CNN scan identify diseases with high accuracy when enough training data is provided. We anticipate that our study will be a useful resource for agricultural disease researchers employing technology for early disease diagnosis and management.
An Evaluation of Prediction Accuracy of Machine Learning Algorithms for Arteriosclerosis of the Human Heart

The advent of Machine Learning and development in statistics has made it possible to gain crucial insights from immense data obtained from surveys. It has made it easy to interpret huge numbers and made it possible to make predictions with extreme accuracy in a myriad of fields. Healthcare is one such field in which this technology can be extensively applied to make early and precise predictions of diseases based on the medical information of the patient. Arteriosclerosis of the human heart is a major concern worldwide as it is responsible for the majority of deaths. Early signs of arteriosclerosis of human heart prediction based on the health and lifestyle parameters of a patient can prove lifesaving. This research aims to create and train a machine learning model which can predict whether an individual faces a risk of arteriosclerosis. The highest prediction accuracy obtained was 86.8293% by logistic regression.
Virtual Reality in Financial Trading and Investing: A Review of Literature and Applications

The proliferation of virtual reality (VR) technology is finding new applications in various domains, such as financial trading and investing, where individuals buy and sell financial assets with the intention of increasing their capital gains. The purpose of this review is to summarize the literature and practices regarding the adoption of VR in financial trading and investing. Online searches were conducted to identify studies on the subject; in parallel, an examination of the VR applications for trading and investing was performed. We found convergence when observing that conventional stock trading and investing usually use 2D applications on desktop PC devices. This generates numerous setbacks, predominantly because of poor data visualization due to the limited 2D representation, interaction via mouse and keyboard, poor data presentation capabilities, and restricted support for collaboration among traders and investors. To overcome those setbacks, both academics and practitioners advocate the adoption of an immersive virtual environment for stock trading and investing, in which significant advantages in terms of multidimensional data visualization, data presentation, and user collaboration can be achieved.
Creation of one-dimensional nanostructures based on zinc oxide

One-dimensional nanostructured metal oxides present great potential for both research and practical applications. Due to their catalytic properties, high chemical and thermal stability, pronounced surface chemistry and biocompatibility, such nanostructures have attracted increasing research interest. Much attention is currently devoted to the development of reliable methods for producing such nanomaterials, which require specific growth conditions, including those based on alternative processes that exploit novel physical effects. Nanostructures based on zinc oxide are currently used in sensing applications and present interest as functional electrical contact materials. Pulsed-periodic laser irradiation is promising for the creation of zinc oxide-based nanomaterials.
Essential Standard Cell Library Composition

Current industrial standard cell libraries consist of at least a few hundred to a thousand cells per threshold voltage (VT), and this number keeps increasing with every new process node. These standard cell libraries are composed of a very large set of combinational and sequential logic functions. Generating and maintaining such large libraries is extremely cost intensive. In this paper, we determine the essential standard cell library which is the smallest subset, i.e., comprising the fewest cell functions and fewest drive strengths, of the full standard cell library that can produce power, performance, and area (PPA) results close to that of a full industrial ASIC library for a set of benchmarks. We used a 12nm FinFET-based process node by Global Foundries and an ARM designed standard cell library. State-of-the-art commercial electronic design automation (EDA) tools, Cadence’s Genus and Innovus, were used for logic synthesis and physical synthesis of the benchmark designs, respectively. Our post-routing results show that a standard cell library containing only 14 combinational logic functions and 4 sequential logic functions achieves PPA results with less than 1.7% degradation compared to a full library. This represents a reduction from 838 library cells down to 88, nearly an order of magnitude reduction in library size and its concomitant cost of maintenance. We did not observe any deviation of this conclusion over process corners, nor any dependence on the clock frequency, and there was no observed dependence on the designed chip. Hence, we term this the essential standard cell library since adding any additional cells or sizes to it is unlikely to produce any meaningful reduction in PPA, and whereas reducing any cells or sizes from it appreciably degrades PPA.
Re-configurable Gain, Bandwidth, Chopper Stabilized, Biosignal Amplifier Design In 180nm CMOS Process

An amplifier design with re-configurable gain and bandwidth is presented in the paper for the amplification of three biopotential signals: the electrocardiogram (ECG), the electroencephalogram (EEG), and neural signals. This amplifier uses chopper stabilization to decrease the input-referred noise (IRN) and suppresses the low-frequency flicker noise. The proposed circuit operates in two modes and is designed in 180nm CMOS technology. The low pass frequency is set at 0.03 Hz to 1 Hz, and the upper pass frequency is set at 150 Hz - 5 kHz. With a closed-loop gain of 35.5 dB - 60 dB, the amplifier exhibits $3.61 \mu \mathrm{W}$ of power consumption for 1.8 V supply and $0.3 \mu \mathrm{V}_{rms}$ of input-referred noise over the range 0.1 Hz to 150 Hz. With action potential neural signals (0.1 Hz - 5 kHz), the amplifier i s configured with input-referred noise of $2.63 \mu \mathrm{V}_{rms}$. Inclusion of the chopper stabilization circuit decreased the IRN by 86.6%, and 77.6%, to $0.04{\mu} \mathrm{V}_{rms}$and $0.58{\mu} \mathrm{V}_{rms}$, respectively.
Estimation of reflection phase due to a pair of Jerusalem Cross elements using an equivalent circuit model

This paper proposes a methodology to estimate the reflection phase due to a pair of Jerusalem Cross (JC) elements using an equivalent circuit model. Importantly, the mutual coupling between the elements is taken into account by using a novel approach. The broad picture is as follows: the individual element's reflection phase is determined using the circuit model. Then, the coupling between the two adjacently placed JCs is estimated by considering their mutual inductance. The reflection phases of the two elements are updated using the estimated mutual coupling factor. The updated reflection phase thus obtained is validated with simulation. The study is undertaken for three different separation distances.
Miniaturized Planar Dual Band Monopole UWB Antenna using Capacitively Loaded Loop Resonator with Notch Characteristics

The increasing popularity of wireless wideband communications has raised the concerns of users for faster connectivity and better quality of service and the reliability of these networks. Ultra wideband (UWB) supports multi users and provide them with more information at higher data rates. UWB technology is a type of wireless communication that uses very short pulses to cover a wide bandwidth. UWB is a wireless technology that is progressed in order to transfer data at high rates over very short distances at very low power densities with potential applications. UWB is used to transport the sensor information wirelessly and also work as a sensor of respiration, heartbeat, and in some instance for medical imaging. UWB pulses provide extremely high data rate implementation in multi-user network applications. In this project a miniaturized planar dual band monopole Ultra wideband (UWB) antenna using CLLRs (capacitively loaded loop resonator) with notch band characteristics is designed. The antenna structure is dual band having 3.1-10.6 GHz UWB band, 2.31 WLAN band (wireless local area network). The UWB band is realized by using half of a conventional rectangular radiator size and the other half is used for realization of other bands by folded CLLRs. Notch band is also introduced for UWB application. The dual-band antenna has an overall size of $25 \times 30\ \text{mm}^{2}$.
TSRSY - Traffic Sign Recognition System using Deep Learning

Most of the traffic injuries are triggered because of carelessness, lack of knowledge of the rules and neglecting traffic signboards. The occurrence of street accidents in our country is alarming. When a person disobeys the traffic signs and signals, he/she is putting one's life at risk in addition to the life of the pedestrians. The traffic signs and signals are designed to regulate traffic and thus reduces the severity of traffic injuries. Today, drivers are distracted a lot while driving. For example, talking to someone on the passenger seat, listening to music or news, or using the phone which increases the chances of an accident. To tackle this issue, we had designed Traffic Sign Recognition System using YOLOv5 (TSRSY), which detects traffic signs, classifies them, and provides an appropriate audio alert message.