Electronics and biomedical engineering

In this study we propose an automatic method for solving convolutive mixtures separation. The independent components are extracted by frequency domain analysis, where the convolutive model can be solved by instantaneous mixing model approach. The signals are reconstructed back in the observation space resolving the ICA model ambiguities. Simulations are carried out to test the validity of the proposed method

Objective: to identify theoretical and legal problems associated with the steadily increasing spread of digital and biotechnologies’ development products; to assess the risks associated with this process that can change the position of a human in the society of the future; to develop and substantiate proposals to minimize risks and eliminate the identified problems through legal regulation.

Methods: the research is based on the methods of generalization of scientific and technical information and theoretical analysis used while studying the source materials; axiological and systematic approaches; the formal legal method and, in addition, methods of legal forecasting, primarily extrapolation, which made it possible to highlight the prospects for reforming law due to technological expansion.

Results: include a description of the dynamics of digital and bio-technologies’ development; a description of the changing social landscape with the emerging new types of entities that can affect the actual status of a human; a classification of risks threatening people due to the digital and biotechnologies development; a list of tasks whose solution based on law will help to eliminate, postpone or, at least, significantly reduce the severity of problems by increasing the time limit allotted to humanity to comprehend a number of conceptual points. The author presents arguments substantiating the need to develop special legal regulation in relation to new types of entities, the appearance of which becomes an inevitable result of the mentioned technologies’ development.

Scientific novelty: consists, first, in a comprehensive study of the development of interconnected groups of digital and biotechnologies, taking into account their increasing convergence; and, second, in the formulation of legal problems that need to be resolved due to the potential emergence of new types of entities with cognitive functions and capable of having a targeted intellectual impact on the environment and legal entities.

Practical significance: it is present in the answers given to the formulated legal questions, which contain suggestions and recommendations on the necessary adjustment of legal regulation and focus the attention of legal scholars on the problems arising from the pace and vectors of scienceconsuming technologies’ development.

Annotatsiya: Ushbu maqolada energiya samarador binolar haqida ma'lumot berildi. Aqilli uy haqida va uning imkoniyatlari aytib o'tildi. Aqilli uyning ijobiy va salbiy tomonlari sanab o'tildi. Aqilli uylar nima sababdan keng tarqalayotganligi haqida qisqacha so'z yuritildi. Kalit so'zlar: energiya samarador, energiya tejamkor, passiv uylar, aktiv uylar, aqilli shahar, aqilli uy.

Due to the continuous monitoring process of critical patients, Intensive Care Units (ICU) generate large amounts of data, which are difficult for healthcare personnel to analyze manually, especially in overloaded situations such as those present during the COVID-19 pandemic. Therefore, the automatic analysis of these data has many practical applications in patient monitoring, including the optimization of alarm systems for alerting healthcare personnel. In this paper, explainable machine learning techniques are used for this purpose, with a methodology based on age-stratification, boosting classifiers, and Shapley Additive Explanations (SHAP) proposed. The methodology is evaluated using MIMIC-III, an ICU patient research database. The results show that the proposed model can predict mortality within the ICU with AUROC values of 0.961, 0.936, 0.898, and 0.883 for age groups 18–45, 45–65, 65–85 and 85+, respectively. By using SHAP, the features with the highest impact in predicting mo...

Bugungi kunda barcha sohalar axborotlashuvi yuzaga kelmoqda. Bu esa kompyuter texnologiyasini yaxshi biladigan kadrlarga bo'lgan ehtiyojning oshishiga sabab bo'lyapti. Maktablarda ham informatika va axborot texnologiyalari fanining chuqurlashtirilib o'tilishi bejizmas, albatta. Ushbu maqola maktab o'quvchilariga informatika darslarida grafik muharrirlar to'g'risida tushunchalar berishga bag'ishlangan.

This paper deals with the problem of signal modeling using fractional-order linear prediction. In this research, we obtain the closed-form expression of the optimum sampling frequency of the One-Parameter Fractional-order Linear Prediction (OPFLP) and examine the performance when the fractional order (alpha) is in (0<=alpha<=2). Our investigation focuses on determining optimum alpha within the individual ranges of 0<=alpha<=1 and 1<=alpha<=2 while considering various influential parameters, such as sampling frequency and environmental interferences. We initiate our study by examining the impact of the sampling frequency, a critical parameter that demands meticulous selection, on the optimal value ‎of‎‎ alpha. Simulation Results demonstrate that if the sampling rate falls within five to six times the maximum frequency of the signal under scrutiny, the optimal range for alpha resides within 1<=alpha<=2. Conversely, when the sampling frequency exceeds six times the maximum signal frequency, the optimal alpha shifts to 0<=alpha<=1. This observation underscores the crucial relationship between sampling frequency and the appropriate selection of the fractional order alpha for effective OPFLP performance. In the next step, we assess the robustness of OPFLP in handling challenging signal processing tasks, particularly in hands-free speech acquisition applications. We evaluate the model's performance and robustness against environmental interferences in three scenarios: noisy environments, reverberant environments, and noisy-reverberant settings. Simulation outcomes highlight OPFLP's superior robustness compared to second-order LP in handling environmental interferences. Furthermore, our investigations elucidate that noise exerts a more detrimental impact on OPFLP performance than reverberation, emphasizing the nuanced effects of these interferences on the model's efficacy.

The detection of Paroxysmal Atrial Fibrillation (PAF) is a fairly complex process performed manually by cardiologists or electrophysiologists by reading an electrocardiogram (ECG). Currently, computational techniques for automatic detection based on fast Fourier transform (FFT), Bayes optimal classifier (BOC), k-nearest neighbors (K-NNs), and artificial neural network (ANN) have been proposed. In this study, six features were obtained based on the morphology of the P-Wave, the QRS complex and the heart rate variability (HRV) of the ECG. The performance of this methodology was validated using clinical ECG signals from the Physionet arrhythmia database MIT-BIH. A feedforward neural network was used to detect the presence of PAF reaching a general accuracy of 97.4%. The results obtained show that the inclusion of the information of the P-Wave, HRV and QR Electrical alternans increases the accuracy to identify the PAF event compared to other works that use the information of only one or...

One of the common way or method to authenticate identity of person is biometric recognition system.With the growing employment of the iris recognition systems and associated research to support this, the need for large databases of iris images is growing. Iris is considered to be the most unique attribute possessed by an individual and is regarded as the most reliable form of biometric authentication. Iris portion of the eye image is complex annular part it contains many distinctive features such as arching ligaments, furrows and ridges. The compression algorithms developed for iris images have to preserve the details present in the iris part of the image, which are used for subsequent biometric processes. We propose here, a compression scheme of iris images using Mallat based wavelet transform using Directional Filter Bank. Mallat algorithm is based on the multiresolution, and it represents the wavelet transform as a pyramid. Directional Filter Bank provides the flexibility to obta...

In the realm of wearable medical device design, the paramount goal is to enhance battery life while navigating time and cost barriers to deliver an optimal user experience. Balancing the performance-power trade-off is a formidable task for developers as power consumption escalates with system performance in battery-powered devices. Wearable medical devices, enabled by portability, often enhanced utility by obviating the need for cumbersome equipment. The trend towards handheld or wearable products necessitates microcontroller units (MCUs) with low power consumption and heightened energy efficiency. The evolution of smaller yet potent wearables challenges battery suppliers to engineer compact, high-capacity batteries, thereby reducing the dependence on non-renewable energy sources for recharging. Solar-powered wearable medical devices converge healthcare technology with renewable energy, inspiring innovative solutions for health monitoring with sustained device operability. Energy optimization and prolonged battery life are paramount in designing these devices to ensure seamless functionality in monitoring vital signs and biometric data. This research endeavours to elevate energy efficiency and operational longevity in solar-powered medical wearables through strategic optimization of performance, energy efficiency, and reliability across varied indoor and outdoor scenarios while adhering to stringent design prerequisites. Leveraging generative AI techniques empowers device design with adaptability, heightened reliability, and clinician-endorsed validation [8] [9].

The Jaipur limb has revolutionized the treatment of amputees in India costs only about $30 compared to a prosthetic foot in the U.S. costing $8,000. Jaipur limb is given away free to the many handicapped poor who have lost a limb.  The attempt was made to replicate the model on a smaller scale without any financial help from others in a small private hospital in Surat is presented. The manufacturing process of the Jaipur foot, its advantages and drawbacks, its cost, the short coming in the project and possible reasons of inability to sustain the project is presented here.

In this paper, a circularly polarized printed monopole antenna (CPPMA) is proposed for medical microwave imaging and health monitoring applications. The proposed CPPMA is optimized to operate at the Industrial, Scientific and Medical (ISM) band. A prototype of the designed antenna is fabricated and printed on the low-cost FR-4 substrate that has a compact size of 34 × 28 × 1.5 mm 3. The simulated results indicate that the designed CPPMA operates between 2.425 GHz and 2.475 GHz while the measured results range between 2.32 GHz and 2.515 GHz. The designed CPPMA also reveals a circular polarization performance at 2.45 GHz (2.4386 GHz-2.4633 GHz). The suitability of CPPMA for microwave imaging is confirmed by checking its aptitudes to detect the presence of breast tumors and brain strokes. A great detection capability is achieved for breast tumors and brain strokes of various sizes inserted at different positions with a high sensitivity to changes or anomalies in the dielectric properties of human tissues. In addition, the usefulness of the proposed CPPMA for wearable application is justified experimentally. Excellent agreement is achieved between the simulated results and the measured ones. Index Terms-Breast tumor and brain stroke detection, circularly polarized printed monopole antenna (CPPMA), industrial scientific and medical (ISM) band, medical microwave imaging and health monitoring, phantom models, wearable antenna.

This project discusses about  car-park system using peripheral interface controller (PIC) Microcontroller application.  This system includes three blocks; parking space monitoring unit, seven segment display unit and entrance and exit monitoring unit.  The parking space monitoring unit includes mechanical normally open push buttons as space sensors and green light emitting diodes, indicator module with a small scaled model, located at the entrance, interfaced with peripheral interface controller (PIC) Microcontroller. Entrance and exit monitoring unit detects the presence of car at the entrance and exit respectively and provides data to PIC Microcontroller in real time. Seven segments display, displays the number of parking space available, the number of cars that are in the car park at present and the number of cars that utilizes the car park for a day. And, the seven segment display indicates full when the car park is filled to its capacity. The PIC microcontroller processes this data in order to update the information supplied to the ports.

This paper considers the problem of two-channel speech enhancement with a single target speaker in a diffuse noise field and proposes a novel algorithm for estimating the power spectral density (PSD) of the background noise. First, a reference noise signal is computed as the output of a blocking matrix based on blind source separation (BSS). Then the ratio between the target noise PSD and the reference noise PSD is calculated by exploiting the separation matrix obtained from BSS and the spatial coherence of the noise field which is assumed to be known. The proposed algorithm can also operate during speech active periods, which is advantageous especially in fast-changing non-stationary noise fields. Experimental results show that the proposed algorithm outperforms existing methods, both in terms of noise PSD estimation and noise suppression.

Real-time continuous tracking of seizure state is necessary to develop feedback neuromodulation therapy that can prevent or terminate a seizure early. Due to its high temporal resolution, high scalp coverage, and non-invasive applicability, electroencephalography (EEG) is a good candidate for seizure tracking. In this research, we make multiple seizure state estimations using a mixed-filter and multiple channels found over the entire sensor space; then by applying a Kalman filter, we produce a single seizure state estimation made up of these individual estimations. Using a modified wrapper feature selection, we determine two optimal features of mixed data type, one continuous and one binary analyzing all available channels. These features are used in a state-space framework to model the continuous hidden seizure state. Expectation maximization is performed offline on the training and validation data sets to estimate unknown parameters. The seizure state estimation process is performed for multiple channels, and the seizure state estimation is derived using a square-root Kalman filter. A second expectation maximization step is utilized to estimate the unknown square-root Kalman filter parameters. This method is tested in a real-time applicable way for seizure state estimation. Applying this approach, we obtain a single seizure state estimation with quantitative information about the likelihood of a seizure occurring, which we call seizure probability. Our results on the experimental data (CHB-MIT EEG database) validate the proposed estimation method and we achieve an average accuracy, sensitivity, and specificity of 92.7%, 92.8%, and 93.4%, respectively. The potential applications of this seizure estimation model are for closed-loop neuromodulation and longterm quantitative analysis of seizure treatment efficacy.

Mitral valve prolapse (MVP) is one of the cardiovascular valve abnormalities that occurs due to the stretching of mitral valve leaflets, which develops in around 2 percent of the population. MVP is usually detected via auscultation and diagnosed with an echocardiogram, which is an expensive procedure. The characteristic auscultatory finding in MVP is a mid-to-late systolic click which is usually followed by a highpitched systolic murmur. These can be easily detected on a phonocardiogram which is a graphical representation of the auscultatory signal. In this paper, we have proposed a method to automatically identify patterns in the PCG that can help in diagnosing MVP as well as monitor its progression into Mitral Regurgitation. In the proposed methodology the systolic part, which is the region of interest here, is isolated by preprocessing and thresholded Teager-Kaiser energy envelope of the signal. Scalogram images of the systole part are obtained by applying continuous wavelet transform. These scalograms are used to train the convolutional neural network (CNN). A two-layer CNN could identify the event patterns with nearly 100% accuracy on the test dataset with varying sizes (20%-40% of the entire data). The proposed method shows potential in the quick screening of MVP patients.

Mitral valve prolapse (MVP) is one of the cardiovascular valve abnormalities that occurs due to the stretching of mitral valve leaflets, which develops in around 2 percent of the population. MVP is usually detected via auscultation and diagnosed with an echocardiogram, which is an expensive procedure. The characteristic auscultatory finding in MVP is a mid-to-late systolic click which is usually followed by a highpitched systolic murmur. These can be easily detected on a phonocardiogram which is a graphical representation of the auscultatory signal. In this paper, we have proposed a method to automatically identify patterns in the PCG that can help in diagnosing MVP as well as monitor its progression into Mitral Regurgitation. In the proposed methodology the systolic part, which is the region of interest here, is isolated by preprocessing and thresholded Teager-Kaiser energy envelope of the signal. Scalogram images of the systole part are obtained by applying continuous wavelet transform. These scalograms are used to train the convolutional neural network (CNN). A two-layer CNN could identify the event patterns with nearly 100% accuracy on the test dataset with varying sizes (20%-40% of the entire data). The proposed method shows potential in the quick screening of MVP patients.

Stroke is the second cause of death and the third cause of disability in the world. One of the treatments for post-stroke patients is physiotherapy. This treatment is expensive and requires mobility. Additionally, the level of adherence of post-stroke patients in carrying out physiotherapy also influences the effectiveness of healing. Therefore, this research proposes a new tool-named "the Post-Stroke Range of Motion (ROM) Measurement Tool with Black Box Boundary Value Analysis Testing-for physiotherapy telemonitoring of post stroke patients. This tool is designed on the left hand for measuring the ROM with the integration of MPU6050, MAX30102, and Flex sensors connected to ESP WROOM 32. This study uses a quantitative research type with a laboratory and field experimental method approach. The flex sensor was tested by measuring the minimum and maximum resistance values, which are converted into straight (0<11), bending (11<46), and fully bending (46<90), and the software was tested using the Black Box Boundary Value Analysis method. The outcomes unveil that the proposed system-with its various features-has the potential to be widely applied, and readily utilized by patients in physiotherapy rehabilitation exercise at home.

Due to the various dangers involved, especially tropical cyclones, they are one of the most common and deadly calamities in the world. The very important primary step in monitoring this destructive disaster is by estimating its intensity. The aim of this research is to determine intensity of cyclones. For the estimation of cyclones and determination of their intensity, various methods have been created. It is a difficult task which requires speed and efficiency. This paper shows comparison between various types of deep learning algorithms on infrared satellite imagery dataset for tropical cyclone intensity estimation. A side-by-side comparison research revealed that the detection of the tropical cyclone intensity evaluated via the models derived from different machine learning algorithms is significantly impacted by the use of various infrared (IR) channels.

In the frequency domain, convolutive mixes with blind source separation can be successfully resolved. But the permutation issue in frequency-domain blind source separation needs to be resolved. We investigated the impact of frequency space and separation performance at each frequency bin on the amplitude correlation permutation algorithm with the goal of addressing the permutation ambiguity problem in frequency-domain blind source separation of convolutive mixtures, and we proposed an enhanced permutation algorithm. The improved algorithm uses spacing influence weight and performance influence weight to control the influence of the frequency bins sorted in the neighborhood on the frequency bins unsorted. Experiments have shown that the two influence weights are effective. Finally, blind source separation experiments are performed on the speech signals under the two convolutive mixing models and the simulated room mixing model. According to experiments, the increased signal to interference plus noise ratio of separated signals demonstrates that the improved algorithm outperforms the amplitude correlation permutation algorithm in terms of separation performance and robustness.

Intelligent machines are increasingly being used to restore and augment the user’s body. The restorative or augmentative nature of implantable chips, bionic limbs, biomimetic organs, neuroprostheses, etc., has been interpreted in many ways (see, among others, de Vignemont 2024). There are two basic ways of approaching the question of technological restoration and augmentation of the human body. The first way emphasizes the aspect of replacement. Sophisticated artificial intelligence (AI) can, at least ideally, replace existing motor, sensory, and body-related subjective abilities of the human user. This approach, which I call ‘body replacement’, builds on a conventional interpretation of computing as an artificial activity that uses human-made machines to process information in digital form. The second way sees computing as an activity inherent in the natural body that can be used by humans to create nature-inspired intelligent machines. In the so-called natural computing perspective (Adamatzky 2017), AI complements the human body by enhancing human bodily capabilities (‘body supplementation’). In this article, I present and discuss the two approaches, namely body replacement and body augmentation, following a case study strategy. The so-called neuro-reality (Houser 2017), generated by an advanced brain-computer interface (nanometric brain chips receive signals with ‘single-neuron resolution’ and transmit them wirelessly to extracorporeal computers or smartphones. See Drew 2024), is analyzed as a relevant case of body replacement. On the other hand, neuroprosthetics, especially visual neuroprostheses (implantable medical devices capable of reactivating visual neurons through electrical stimulation. For details, see Borda and Ghezzi 2022), are studied as a highly informative example of the supplementation of the user’s body by intelligent machines. The analysis of the case study brings to light the philosophical underpinnings of both the replacement and augmentation approaches to restorative and augmentative technology. Body replacement is found to be informed by a radical functionalism, according to which bodily states are identified by their functional role, i.e. by 'what they do'. Otherwise, material constituents are centralized through bodily supplementation. To understand what bodily states are, you have to look at what they are made of. What kind of representation of the human being is supported by the replacement and the suppletive approach?

A baby incubator is a biomedical equipment that can provide warmth, humidity, and adjust the amount of oxygen input into a chamber for premature newborn babies for ideal conditions similar to those of the mother. Newly born babies or preterm babies are very sensitive to rough environments. Newborn mortality has been a global issue worldwide. Sleep Apnea disorder is a major concern in newborn neonates. Adding to this, newborns may have immature, underdeveloped lungs that necessitate care and additional oxygen. Existing models available in the market are expensive and at the same time very difficult to be used in remote locations like rural maternity centers, makeshift primary health centers or war-affected areas. Due to these issues, a Portable baby incubator is necessary that can provide the same environmental conditions as a mother's womb does and monitors the baby's vital conditions such as heartbeat, blood oxygenation level, skin temperature etc. The COVID-19 epidemic has made healthcare a top priority. Being physically present with the patients has become a huge problem for doctors. In such cases, implementing an application with hospital equipment such as a Smart Baby Incubator is necessary. Our goal is to make a smart baby incubator that can detect a baby's vital parameters and indicate its deviation, from its normal state. The model is integrated with the mobile app so that doctors can track the baby's condition remotely. In the presented model baby's temperature, heart rate and SpO 2 are monitored continuously. The efficiency of the model is tested on neonates and different age groups. The results obtained are found within the standard medical condition limits.

International scientific and scientific-technical conference on theme "Innovations in construction, seismic safety of buildings and structures" Международная научная и научно-техническая конференция на тему «Инновации в строительстве, сейсмическая безопасность зданий и сооружений» «Қурилишда инновациялар, бинолар ва иншоотларнинг сейсмик хавфсизлиги» Халқаро миқёсидаги илмий ва илмий-техник конференция МАТЕРИАЛЛАРИ ТЎПЛАМИ Наманган 14 декабрь, 2023 йил

In Minimally Invasive Surgery (MIS), surgeons should acquire many skills before carrying out a real operation. The Fundamentals of Laparoscopic Surgery (FLS) tasks are currently used as an assessment tool for laparoscopic skills. However, the current training methods still require the presence of an expert surgeon to assess the surgical dexterity of the trainee. This process is time-consuming and may lead to subjective assessment. This research aims to extend the application of image processing and analysis methods to detect and track the tips of the laparoscopic instruments and to localize the circle center to calculate the distance between the scissors' tips and the circle's center in each processed frame. The data obtained will feed the fuzzy system to assess the trainer's performance with a processing speed of 4 frames per second. The proposed system can provide a final text report that lists an error counter value and the fuzzy assessment at each time slot of 0.29 s on average. Additionally, a BMP image that summarizes the tips of the scissors within the predefined circles during the test is created. Finally, a Canny edge detector is applied to detect whether any circle line was cut during the test. This work enables a summative assessment of the precision-cutting task without altering the original setup of the FLS box trainer. Additionally, this can expedite the development of surgery skills and assess the trainees' performance using a single-input-single-output fuzzy logic system. The output of the fuzzy logic assessment is the performance evaluation for the surgeon, and it is quantified in percentages.

The detection of Paroxysmal Atrial Fibrillation (PAF) is a fairly complex process performed manually by cardiologists or electrophysiologists by reading an electrocardiogram (ECG). Currently, computational techniques for automatic detection based on fast Fourier transform (FFT), Bayes optimal classifier (BOC), k-nearest neighbors (K-NNs), and artificial neural network (ANN) have been proposed. In this study, six features were obtained based on the morphology of the P-Wave, the QRS complex and the heart rate variability (HRV) of the ECG. The performance of this methodology was validated using clinical ECG signals from the Physionet arrhythmia database MIT-BIH. A feedforward neural network was used to detect the presence of PAF reaching a general accuracy of 97.4%. The results obtained show that the inclusion of the information of the P-Wave, HRV and QR Electrical alternans increases the accuracy to identify the PAF event compared to other works that use the information of only one or...

The sparse data in PM2.5 air quality monitoring systems is frequently happened on large-scale smart city sensing applications, which is collected via massive sensors. Moreover, it could be affected by inefficient node deployment, insufficient communication, and fragmented records, which is the main challenge of the high-resolution prediction system. In addition, data privacy in the existing centralized air quality prediction system cannot be ensured because the data which are mined from end sensory nodes constantly exposed to the network. Therefore, this paper proposes a novel edge computing framework, named Federated Compressed Learning (FCL), which provides efficient data generation while ensuring data privacy for PM2.5 predictions in the application of smart city sensing. The proposed scheme inherits the basic ideas of the compression technique, regional joint learning, and considers a secure data exchange. Thus, it could reduce the data quantity while preserving data privacy. Th...

In this paper the design of a Graphical User Interface (GUI) is proposed for healthcare applications. The GUI is designed in MATLAB environment and is specifically suitable for advanced phonocardiography (PCG). This diagnostic method is more and more important nowadays, due to the advances in signal processing methods and computer processors performances in order to detect with a simple PC based electronic stethoscope, pathologies otherwise detectable with much more expensive and time-consuming medical techniques and devices.

Sleep is an essential phase in human circadian rhythm with importance in restoring human vigour and vitality. Conventional sleep examination is done using Polysomnography with many sensors connected to various parts of human body. Recently, research in sleep is geared toward alternative feasibility of using only ECG signal. In this research, sleep stages classification using only features derived from single-lead ECG is conducted. The capability of Generalized Learning Vector Quantization (GLVQ) in discriminating sleep stages is tested. We showed that GLVQ which is configured with multi codebooks shows promising result in discriminating complex sleep stages data.

Ponencia presentada en el XXI Congreso Internacional de Ingenieria Electronica, Electrica y Computacion - INTERCON 2014, organizada por el Programa Profesional de Ingenieria Electronica y de Telecomunicaciones de la Universidad Catolica San Pablo, Arequipa - Peru.

Traditionally, direction-of-arrival (DOA) and pitch estimation of multichannel, periodic sources have been considered as two separate problems. Separate estimation may render the task of resolving sources with similar DOA or pitch impossible, and it may decrease the estimation accuracy. Therefore, it was recently considered to estimate the DOA and pitch jointly. In this paper, we propose two novel methods for DOA and pitch estimation. They both yield maximum-likelihood estimates in white Gaussian noise scenarios, where the SNR may be different across channels, as opposed to state-of-the-art methods. The first method is a joint estimator, whereas the latter use a cascaded approach, but with a much lower computational complexity. The simulation results confirm that the proposed methods outperform state-of-the-art methods in terms of estimation accuracy in both synthetic and real-life signal scenarios.

This paper reviews Independent Components Analysis (ICA) and Blind Signal Separation (BSS) problems. An overview on the main statistical principles that guide the search for the independent components is formulated, methods for blind signal separation that require both high-order and second-order statistics are also illustrated. Some of the most successful algorithms for both ICA and BSS are derived. Experimental applications in different signal processing tasks such as passive sonar, nondestructive ultrasound inspection and electrical-load time series are presented.

This work describes a condition based reporting technique of a multi-machine System using GSM technology. In this present approach, a dedicated microcontroller based hardware unit (DHU) has been developed to continuously measure the parameters of the motors viz. voltage, current, speed etc. to monitor the running condition of each motor in a plant. In a multi-machine monitoring system each motor is connected with one such DHU, which are also connected to a Global System for Mobile Communication (GSM) modem. The preliminary level of fault or abnormality in operation of each machine is diagnosed by the respective DHU and the fault or abnormalities details are reported to the pre-assigned operator through an SMS service. In extreme case, the provision of machine shut down by a return SMS is also provided. A proto-type lab model is set up and is working satisfactorily.

Avtomobillarni qayta jihozlangan vositalar yordamida boshqarishda qo‗llanilgan qismlar tahlili Qayumov B.A., Mirzayev B.N., Voxobov R.A. Буғланма совитиш жараѐнига контактли насадкаларни таъсирини ўрганиш Усмонов Н.О. Повышение конкурентоспособности предприятий-жизненная необходимость Султанов А.А.

One of the major challenges facing functional electrical stimulation (FES) cycling is the design of an automatic control system that addresses the problem of disturbance with unknown bound and time-varying behavior of the muscular system. The previous methods for FEScycling are based on the system modeling and require preadjustment of the control parameters which are based on the model parameters. These will degrade the FES-cycling performance and limit the clinical application of the methods. In this paper, a distributed cooperative control framework, which is based on an adaptive higher-order sliding mode (AHOSM) controller, is proposed for simultaneous control of torque and cadence in FES-cycling. The proposed control system is free-model which does not require any pre-adjustment of the control parameters and does not need the boundary of the disturbance to be known. Another major issue in FES-cycling is the stimulation pattern. In the paper, an automatic pattern generator is proposed which is capable of providing not only the regions of the crank angle in which each muscle group should be stimulated but also a specific gain for each muscle group. The results of the simulation studies and experiments on three spinal cord injuries showed that the proposed control strategy significantly increases the efficiency and tracking accuracy of motorassisted FES-cycling in paraplegic patients and decreases the power consumption compared to HOSM controller with the fixed stimulation pattern. Reducing power consumption can slow down muscle fatigue and consequently increase cycling endurance. The average of cadence and torque tracking errors over three subjects using the proposed method are 5.77±0.5% and 5.23±0.8%, respectively. Index Terms-Adaptive control, FES-cycling, functional electrical stimulation (FES), fuzzy logic control (FLC), higher order sliding mode (HOSM). I. INTRODUCTION F UNCTIONAL electrical stimulation (FES) with a history of more than 50 years, is one of the most commonly used techniques for therapy, restoration and maintenance of

Emotions are the accelerators of human intellect and innovation and creativity, so the ability to recognize emotions is in high demand. Real-time hardware has hurdles of Noise and hardware factors as compared to simulations. An electrocardiogram (ECG) sensor (AD8232), a temperature sensor (LM35), and a signal processing circuit is hardware of the proposed real-time emotion identification. The RR intervals are calculated from the ECG data. Emotions prediction using machine learning makes use of RR intervals and body temperature as features. One of the four emotions (namely 1. Happy 2. Stressed 3. Neutral 4. Sad.) is visualized at the serial port of the processor by using WESAD benchmark dataset and the HRV, serial, and pickle libraries.This article's innovation factors are (1) Use of ECG for emotion detection rather than disease detection with Emotion induction method, RR interval capturing and design of RR interval GUI for real time capture of temperature and ECG (2) Display of current emotion on Arduino serial port. (3) Measurement of Class performance using F1 score, macro average, and weighted average instead of general term accuracy. (4) Use of the probability based Navies Bayes as compared to traditional KNN, SVM, Random Forest nethods (5) Class wise performance for example Navies Bayes' specificity or accuracy is lower than SVM's (0.96), but its recall or sensitivity is higher (0.97) vs. (0.94) for stress.In this article, we presented performance parameters in terms of interactive computations, tabular form and graphical display.

Background: Sickle cell anaemia is a molecular disease. WHO recognises it as a global public health problem. In India, it is common among tribal communities. Increased HbS is a culprit. Therapeutic research is focused on maintaining high levels of HbF and decreasing 2,3, BPG to target disease.
Aim: To assess the role of naturally compensated haemoglobin variants in tribal Sickle cell anaemic patients of North East Gujarat.
Settings and Design: Prospective, analytical, case control study conducted on randomly selected fifty tribal Sickle cell anaemic patients having disease for more than 5 years. Fifty age and sex-matched, healthy control subjects.
Materials and Methods: Each fifty Tribal sickle cell anaemic patients and healthy control were included in the study. Total Haemoglobin level, Sickling test by NESTROFT method and Haemoglobin variants were analysed by alkaline haemoglobin electrophoresis. Frequencies of clinical crises were recorded by oral questioning. The results were analysed using SPSS version 20. Student unpaired t- test was employed to assess the significance of the differences. P-values < 0> Results: We observed decreased levels of total haemoglobin, high levels of HbF and HbA2 along with reduced HbA0. Compensatory increase of HbF in tribal SCA patients have shown lower frequency of clinical crises.
Conclusion: Reduced HbA0 in sickle cell anaemia is compensated by naturally elevated HbF and HbA2 in tribal patients which has a beneficial influence on their general health.

Keywords: Sickle cell anaemia, Electrophoresis, Haemoglobin, HbF, Tribal, Haemoglobin S. General health.

During the present paper a number of cardiovascular patients were classified using features of cepstral coefficients based on national standards. Heart sounds include a healthy heart and fourth unhealthy heart, The data used in this paper was obtained from Michigan university website. After the separation of the heart murmurs from each heart sound, the cepstral coefficients based on Mel scale for each class was calculated as feature vectors and method support vector machine was used for the classification of the diseases. The diagnostic accuracy was %100. The results indicated the accuracy, reliability, and high performance capacity in method design.

We address the problem of identifying the constituent sources in a single-sensor mixture signal consisting of contributions from multiple simultaneously active sources. We propose a generic framework for mixture signal analysis based on a latent variable approach. The basic idea of the approach is to detect known sources represented as stochastic models, in a single-channel mixture signal without performing signal separation. A given mixture signal is modeled as a convex combination of known source models and the weights of the models are estimated using the mixture signal. We show experimentally that these weights indicate the presence/absence of the respective sources. The performance of the proposed approach is illustrated through mixture speech data in a reverberant enclosure. For the task of identifying the constituent speakers using data from a single microphone, the proposed approach is able to identify the dominant source with up to 8 simultaneously active background sources in a room with RT60 250 ms, using models obtained from clean speech data for a Source to Interference Ratio (SIR) greater than 2 dB.

A new technique to measure phase distribution of ultrasound beams is presented. This method is based on the phase measurement of the Doppler signal backscattered by a moving target traveling at constant velocity, which represents a scanning element exploring the field under examination. It is produced by mechanical equipment consisting of a modified string phantom in which the commonly used cotton thread was substituted by a thin nylon thread with some tight knots over it. The thread diameter was chosen small enough to produce knots smaller than the acoustic field wavelength in order to have "pointlike" moving targets. This paper describes how the Doppler signal phase is related to the phase field and the numerical method to extract a phase field distribution from the backscattered signal. Experimental results obtained for two circular transducers, strongly and weakly focused, respectively, are finally shown and commented.

Introduction. Wireless diagnostics of patients' functional state is important for ensuring timely detection and treatment of diseases. In this article, the development of a medical hardware-software system for remote monitoring of the state of biological objects is considered on the example of cardiovascular diseases. Aim. To develop a medical hardware-software system for remote monitoring of the main cardiovascular parameters and to expand the scope of medical remote telemetry equipment, which can eventually lead to improved medical services. Materials and methods. On the example of cardiovascular diseases, an analysis of the existing medical hardwaresoftware system for remote monitoring of the state of biological objects was carried out, taking into account the cardiac monitoring of the electrocardiogram. This method is widely used for in the diagnostics and treatment of cardiovascular diseases. Results. A method was proposed for creating a hardware-software system for remote monitoring of the main cardiovascular parameters to ensure timely detection and treatment of diseases. The general structure of such a system, including its advantages and disadvantages and the routing of information, is described. The proposed technology of prolonged remote monitoring of the patient's health state makes it possible to increase the efficiency of detecting dangerous heartbeat arrhythmia by about 30 %. Conclusions. The proposed system solves the problem of registering biological parameters without significant interference in the patient's vital activity, which allows typical conditions to be traced. Remote data collection also facilitates the load on medical personnel, reduces the number of contacts with patients and improves their psychological state.

I International Workshop on Applied Biosensing (WBIO 2024) will provide an excellent international forum for aims to bring together researchers and practitioners from academia and industry to focus on Biosensing platforms from all sort of technologies focusing on advancements, recent systems and techniques in the broad field of Biosensing. Original research papers, state-of-the-art reviews are invited for publication in all areas of biosensing involving Electrical, Biomedical and Electronics Engineering, Medical Physics, Computing Science, and related applications related to chemistry, environment, food quality, biology and medical diagnosis.

ИССЛЕДОВАНИЯ ПО ПОДБОРУ ХИМРЕАГЕНТОВ ДЛЯ ПОЛИМЕРНОГО ЗАВОДНЕНИЯ Молдабаева Г. Ж. д.т.н. ассоц. проф. кафедры «Нефтяная инженерия». КазНИУ, г. Алматы, Турдиев М.Ф. к.ф.м.н., профессор кафедры «Нефтегазовое дело». АУНГ, Р. Казахстан, Сулейменова Р.Т. докторант PhD кафедры «Нефтяная инженерия». КазНИУ, г. Алматы. Аннотация. Обсуждается лабораторные и промысловые исследования по подбору химреагентов для полимерного заводнения нефтяных залежей на основе комплекса гидродинамических исследований. Ключевые слова: Полимерное заводнение, нефтяной пласт, вязкость нефти и воды, неоднородность нефтяного пласта, ГИС, индикаторный метод, поддержание пластового давления. Полимерное заводнение является одним из высокоэффективных методов физико-химического воздействия на продуктивный пласт. Основное свойство Казахский национальный исследовательский университет имени Каныша Сатпаева. Факультет «ИГиНГД». Кафедра «Нефтяная инженерия». Алматы. Сатпаева 22а. Молдабаева Гульназ Жаксылыковна. д.т.н. ассоц.профессор кафедры «Нефтяная инженерия». Казахский национальный исследовательский университет имени Каныша Сатпаева. Алматы. Сатпаева 22а. moldabaeyva@gmail.com Сулейменова Райхан Таупиховна. Докторант 2 курса кафедры «Нефтяная инженерия». Казахский национальный исследовательский университет имени Каныша Сатпаева. Алматы. Сатпаева 22а. raika_83@mail.ru 87773551182 Турдиев Мурадим Файзиевич. к.ф.м.н., профессор кафедры «Нефтегазовое дело». Атырауский университет нефти и газа. Атырау.

Many systemic medications have been used off‐label in cutaneous diseases. Use of β‐adrenergic‐blocking agents has risen in popularity among dermatologists since the discovery of their efficacy in treating infantile haemangioma. There has also been an increase in the interest of the applications of β‐blockers in other skin disorders. Overall, β‐blockers are effective in treating diseases of vascular origin and promote wound healing. They are relatively safe and inexpensive medications that could be included in the armamentarium against skin diseases.

Annotatsiya: Ushbu maqola MATLAB dasturi yordamida reaktiv quvvatni boshqarish jarayonini modellashtirishga qaratilgan. U MATLAB dan reaktiv quvvatni boshqarish tizimlarining xatti-harakatlarini taqlid qilish va tahlil qilish vositasi sifatida foydalanishni o'rganadi. Maqolada energiya tizimining barqarorligini saqlash va energiya sifatini yaxshilashda reaktiv quvvatni boshqarishning ahamiyati muhokama qilinadi. U MATLAB-dan modellashtirish va simulyatsiya qilish uchun foydalanishning afzalliklarini, jumladan, uning moslashuvchanligi, foydalanish qulayligi va funksiyalar va vositalarning keng kutubxonasini ta'kidlaydi. Maqolada, shuningdek, MATLAB-da reaktiv quvvatni boshqarish jarayonlarini modellashtirish uchun turli xil texnika va yondashuvlar taqdim etilgan, masalan, matematik tenglamalar, uzatish funktsiyalari va holat-fazo tasvirlaridan foydalanish. Umuman olganda, maqola reaktiv quvvatni boshqarish jarayonlarini modellashtirish va tahlil qilish uchun MATLAB dasturini qo'llash bo'yicha qimmatli tushunchalarni beradi.

With the advent of the IoT and Big data, there is an enormous supply of datasets, which are captured by the sensors installed in the relevant places. For the air pollution monitoring system, several sensors provide a huge dataset. They continuously capture pollutant concentration as well as the meteorological factors to form the voluminous, multivariate time-series dataset, which includes both. Those sensors are paired with low-capacity batteries and limited memory, transmission, and computational components. Transmitting the high volume data to the monitoring system deployed in the cloud demands high energy dissipation and higher bandwidth. The single solution for all these constraints is data compression. Data compression reduces the volumes of the datasets. The reduced volume of data saves energy and it is easier to transmit the compressed data through the limited bandwidth. In this paper, a lossless data compression algorithm is proposed. On successful implementation of the algorithm, the data is compressed with absolutely no data loss. To evaluate the efficiency of the algorithm, its performance is compared with some classical and several state-of-the-art compression schemes. The experimental results show that the proposed compression algorithm outperforms the classical as well as state-of-the-art models concerning the performance evaluating parameters like Compression Ratio, Compression Factor, and most importantly Power Saving.