Biomedical signal and image processing

The model suggested for detection of financial fraud detection a Modified Graph Convolutional Network with Temporal Features Network (MGCN-TFNet) for spatial as well as temporal data analysis. The model initiates with acquiring the dataset and normalizing it, followed by applying Graph Convolutional Networks to learn about transactional structural patterns and embeddings of features. Temporal attention layers emphasize corresponding time-based features and identify transactions according to relevance for various windows of time. An integration layer of temporal-structural embeddings enhances fraud detection. A fine-tune and final classification fully connected layer by a SoftMax classifier detect valid and invalid transactions. The performance of the model is tested based on a confusion matrix, with an accuracy of 0.991, precision of 0.992, sensitivity of 0.990, specificity of 0.992, and 0.991 in Fmeasure. The Negative Predictive Value (NPV) is 0.939, which is indicative of superior performance in identifying true fraudulent transactions. This methodology is very effective in integrating structure learning and temporal pattern discovery for detecting financial fraud.

Abstract—Recent years have seen significant research in finding closed form expressions for the delay of the RLC interconnect which improves upon the Elmore delay model. However, several of these formulae assume a step excitation. But in practice, the input waveform does have a non zero time of flight. There are few works reported so far which do consider the ramp inputs but lacks in the explicit nature which could work for a wide range of possible input slews. Elmore delay has been widely used as an analytical estimate of ...

In this paper, we describe the use of deep learning-based model for classifying kidney stone images in order to overcome the lack of medical infrastructures within the hospitals in the development countries like Madagascar. Moreover, the use of mobile phones such as smartphone is very popularized in these countries. This situation leads us to perform a lightweight deep learning-based model inspired from the architecture of SSDLiteX. The Balancing technical was used to achieve higher performance. Our system provides 100% of accuracy, validation accuracy and F1-score. The result shows that this model surpass all lightweight models recorded to the state of the art about kidney stone classification. According to its nature, the proposed approach could run on the low resource environment like Smartphone. Thus, it is reliable and answer the need of the specialist within the hospitals in the development country.

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

In this paper we compare the performance of different algorithms employed in solving frequency domain blind source separation of convolutive mixtures. The convolutive model is an extension of the instantaneous one and it allows to relax the hypothesis of a linear mixing process in which all the sources are supposed to reach the electrodes at the same time. This test is carried out in the frequency domain, where the algorithms developed for independent component analysis can be employed with minor modifications. The decomposition performance of such algorithms is evaluated on simulated dataset of convultive mixtures of biomedical signals.

Chronic kidney disease (CKD) is considered a risk factor for the development of cardiovascular disease. QT interval is an electrocardiographic parameter that quantifies the duration of ventricular repolarization. An increase of its spatial variability measured from the selected leads of a standard electrocardiogram (ECG), named QT dispersion (QTd), is considered a risk factor for malign ventricular arrhythmias and sudden death in the CKD. An algorithm for automatic measurement of QTd in the ECG leads DI, aVF and V2 using the continuous wavelet transform with splines is presented. Validation of QRS complex detection has been done on records from MIT-BIH database, and the accuracy is 99.5%. Validation of detection of QRS wave onset and T wave end has been done on records from CSE and QT databases, and the measurements were within the tolerance limits for deviations with respect to the manual measurements defined by the experts. The algorithm was applied in two studies. In the first st...

There are two main processes to create a 3D animatable facial model from photographs. The first is to extract features such as eyes, nose, mouth, and chin curves on the photographs. The second is to create 3D individualized facial model using extracted feature information. The final facial model is expected to have an individualized shape, photograph-realistic skin color, and animatable structures. Here, we describe our novel approach to detect features automatically using a statistical analysis for facial information. We are not only interested in the location of the features but also the shape of local features. How to create 3D models from detected features is also explained and several resulting 3D facial models are illustrated and discussed.

Over the years people have been monitoring some vital signs in human's health. Notably, in the physiological study of mankind, various methods and techniques are being used to track the heartbeat, body temperature, respiratory rate and arterial blood pressure. This quest was to ensure that humanity is in good condition at all time. Since health is wealth, the human's health becomes a crucial and vital to every medical professionals and scientists as well. Up till today, it is still in progress, and more ways of solving human's health problems keep on unfolding on daily bases. This work takes a critical survey of human health monitoring system, with focus on heart beat, body temperature and blood pressure monitoring devices and systems, how they evolved over the years, and what the human health monitoring system of today are capable of. It also noted some research gaps and sets some agenda for further research work. The ideas presented in this paper will open up more areas for further studies. It will enable researchers to further advance the methods and tools used in monitoring the identified vital signs in human health.

Smart agriculture uses the internet of things, usually for irrigation purposes. IoT is employed in this case to manage the water log on fields. The soil moisture sensor is used to keep track of the soil moisture in agricultural areas. The Cloud Service Brokerage issues a directive to the relay telling it to turn ON the suction motor when the moisture level reaches a specific level. The farmland's extra water is removed by the suction motor. With this proposal, we want to use IoT to analyze the availability of micronutrients and control the excess water log in farming. This semi-automated water management system can operate independently or be managed using a smartphone app.The weather at that particular place may be analyzed using the DHT sensor values, and it can be projected using a prediction algorithm for the following 10 days. IoT refers to the idea of linking all gadgets to the internet and enabling internet-based communication between them. The Internet of Things (IoT) is a vast network of interconnected gadgets that collectively gather and share information about their usage and the settings in which they function. IoT is an agricultural use of contemporary information and communication technologies. A system for agricultural monitoring is constructed using sensors

Very low bit-rate video coding has recently become one of the most important areas of image communication and a large variety of applications have already been identified. Since conventional approaches are reaching a saturation point, in terms of coding efficiency, a new generation of video coding techniques, aiming at a deeper "understanding" of the image, is being studied. In this context, image analysis, particularly the identification of objects or regions in images (segmentation), is a very important step. This paper describes a segmentation algorithm based on split and merge. Images are first simplified using mathematical morphology operators, which eliminate perceptually less relevant details. The simplified image is then split according to a quad tree structure and the resulting regions are finally merged in three steps: merge, elimination of small regions and control of the number of regions.

Monitoring weather plays a crucial role in human life, making the collection of data on climate changes imperative. This paper aims to develop a weather station to monitor climate parameters in off-grid regions. The project includes sensors for detecting temperature, humidity, rainfall, LPG levels, carbon monoxide, smoke, altitude, and barometric pressure. The Arduino gathers information from the sensors, displays it on an LCD screen, and additionally sends SMS alerts via a GSM module. Accurate weather updates are essential for successful planning in agriculture, fishing, and various other activities. Unfortunately, weather information often lacks accuracy for specific regions, relying instead on data from the nearest weather station. This project calculates and compares the error level between national climate forecasts and actual readings. It is designed to assist individuals in remote and off-grid areas who rely on weather information for their livelihoods.

As fossil fuels are decreasing day by day, renewable power turns out to be the main energy source. Anyway, the interest is still lower than expected. Among them, one of the newest energy-gathering techniques is changing the mechanical energy from falling raindrops into power. This task focuses on the small, creating power with the help of falling raindrops. The benefits of this paper are that it can help to add to minimizing the disadvantages of diminishing fossil-based fuels. Regardless, in the utilization of making power from the rain, the available head and stream rate is extremely low for the utilization of Pelton, Francis, Kaplan, or various kinds of water-driven turbomachines. This task revolves around the use of the developed Savonius rotor, which is mounted in an even circumstance to isolate energy from falling raindrops. In this examination, water is first stored in a tank before being conveyed like a stream of water onto a rotor. This rotor is unequivocally planned for this application and is an adjusted version of the Savonius water rotor. This instrument was used to generate a small amount of power. SOLIDWORKS was used to design the modified water turbine, which was then analyzed using Computational Fluid Dynamics (CFD) in ANSYS simulation software. The performance is analyzed with the test variables of rotational speed, torque, power, torque coefficient, tip speed ratio, and power coefficient. Then the experimented values are compared with the numerical values to know the accuracy of the values. The output of the experimental value was 15.452 watts, and in the numerical analysis, it was found to be 16.501 watts.

The Electrocardiogram (ECG) signal is used to record the electrical activity of heart. The subtle variations in ECG attributes are used by cardiologists for diagnosis of heart anomalies. But, for prognosis of cardiac ailments feature extraction from electrocardiographic signal becomes extremely difficult due to presence of noise. With the aim of noise reduction, a hybrid technique involving Extended Kalman filter along with Discrete Wavelet transform for effectively improving signal quality is focused as a powerful tool. The performance of denoising algorithm is evaluated in terms of signal to noise ratio and mean square error. On denoised signal, a quick, simple and effectual approach based on Principal Component Analysis is proposed for R-peak and QRS complex detection. The beat detector performance is validated with MIT-BIH arrhythmia database, yielding a sensitivity of 99.93%, positive predictivity of 99.98% and a 0.079% detection error rate, being a positive outcome in comparison with recent researches. Later, different types of arrhythmias are detected on the basis of heart rate and morphological characteristics of ECG waveform.

Oscilloscope is very important equipment in the industry as a measuring instrument. There are few types of oscilloscopes: digital, analog, digital storage and digital sampling oscilloscope. The easiest and earliest type of oscilloscope also called as cathode ray (C.R.O.) consist of a vertical amplifier, a time base, a cathode ray tube (CRT), a horizontal amplifier and a power supply. In contrary, a digital oscilloscope is a complex electronic device composed of various software and electronic hardware modules that work together to capture, process, display and store data that represents the signals of interest of an operator. In general oscilloscopes are also massive in size and so they are not easy to carry. Therefore, in industries, whenever there is a need of measurement of the parameters it was often hectic because of the oscilloscope’s size and weight. However portable and touch screen oscilloscope can be very small in size, lighter in weight, cheap in cost and also having less...

Purpose: Cardiologists use 12-lead electrocardiograph (ECG) and prolonged continuous Holter monitoring to detect abnormalities in heart rhythm, which could diagnose or hint at different cardiac diseases. However, the availability of a costly ECG machine and the expertise to interpret ECG is suboptimal in our country. The current state of the art proposes mitigating the cost and easy diagnosis by physicians with the use of low cost photoplethysmograph (PPG), which will deliver ECG information in terms of ECG fiducial parameters. Methods: The MIMIC-III matched paired datasets of PPG/Lead-II ECG signals with the six classes (right bundle branch block (RBBB), hypertension (HYPER), arrhythmia-atrial fibrillation (ARYTH-ATRFAB), dilated cardiomyopathy (DCM), complete heart block (CHB) and NORMAL) associated cardiovascular diseases (CVDs) syndromes of 30 patients with 5 minutes duration each were taken from Physionet. The proposed work tested by predicting ECG fiducial parameters (P, Q, R, S and T) through Gaussian process regression(GPR)-based predictor by using the PPG fiducial parameters with a training and testing ratio of 70:30. The performance was analyzed by different performance evaluation parameters e.g., RMSE, rRMSE, etc. Results: Although the shape of the ECG may differ in various ways from that of the normal ECG, the methodology can predict the ECG fiducials from PPG for a variety of CVD patients with a performance accuracy of 95-99% by evaluating through seven performance metrics. Conclusion: The ECG fiducial parameters can be used to analyze CVDs captured from a low-cost, handy, skincompatible continuous and non-invasive optical recording device well known as a PPG sensor.

A notion of band limited functions is considered in the case of the hyperbolic plane in its Poincare upper half-plane H realization. The concept of band-limitedness is based on the existence of the Helgason-Fourier transform on H. An iterative algorithm is presented, which allows to reconstruct bandlimited functions from some countable sets of their values. It is shown that for sufficiently dense metric lattices a geometric rate of convergence can be guaranteed as long as the sampling density is high enough compared to the band-width of the sampled function.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The aim of the study was to simulate T-wave alternans (TWA) magnitude distribution maps on the body surface employing ECGSIM program and then to compare obtained results with data measured from patients. The toolbox for ECGSIM program was developed for TWA simulation. Graphical representation of spatial distribution of TWA magnitude was implemented. Body surface maps of TWA were simulated for different locations of the disturbances on the heart. Simulated data were compared with recordings from patients and similarities were found. Preliminary study shows that ECGSIM program can be used for approximate location of action potential disturbances in the heart cells.

The aim of this study was to investigate the application of wavelet denoising in noise reduction of multichannel high resolution ECG signals. In particular, the influence of the selection of wavelet function and the choice of decomposition level on efficiency of denoising process were considered and whole procedures of noise reduction were implemented in MatLab environment. The Fast Wavelet Transform was use. The advantage of used denoising method is noise level decreasing in ECG signals, in which noise reduction by averaging has limited application, i.e. in case of arrhythmia, or in presence of extrasystols.

The aim of the study was to assess the relations between QRST and STT, QRS integral maps in three groups: healthy volunteers, patients without arrhythmia and patients with implanted cardioverter-defibrillator (ICD). The obtained results showed that the mean correlation coefficient between STT and QRST integral maps was highest in the group of healthy subjects while the mean correlation coefficient between QRS and QRST integral maps was lowest in the same group of subjects. The relation of depolarization phase with repolarization phase and the influence of depolarization sequence on repolarization sequence is noticeably disrupted in patients with impaired systolic function. In such cases the QRST integral maps seems to reflect depolarizationrepolarization disorder rather than only repolarization dispersion.

The aim of the study was a selection of best ECG leads to get the significant T-wave alternans signal (TWA). The group of 16 patients with implantable cardioverterdefibrillator (ICD) was examined. The 64 lead ECG system was used. Three lead sets were analyzed: 64 BSPM lead set, orthogonal lead set (XYZ) and standard 12 lead set. Patients were divided into two groups: TWA positive (TWA(+)) 5 patients and TWA negative (TWA(-)) 11 patients. In TWA(+) group mean alternans ratio was equal 5.4 (SD 8.09) and in TWA(-) group 0.73 (SD 0.26). Both standard ECG leads and XYZ leads are less sensitive to TWA than specially selected leads from 64 lead set

T-wave alternans (TWA) allows for identification of patients at an increased risk of ventricular arrhythmia. Stress test, which increases heart rate in controlled manner, is used for TWA measurement. However, the TWA detection and analysis are often disturbed by muscular interference. The evaluation of wavelet based denoising methods was performed to find optimal algorithm for TWA analysis. ECG signals recorded in twelve patients with cardiac disease were analyzed. In seven of them significant Twave alternans magnitude was detected. The application of wavelet based denoising method in the pre-processing stage increases the T-wave alternans magnitude as well as the number of BSPM signals where TWA was detected.

The aim of the study was to evaluate diagnostic value of TCRT (spatial QRS-T angle) parameter in different sets of ECG leads and to select the ECG lead set, for which the TCRT parameter would have the best sensitivity and specificity in identifying patients threatened by ventricular tachycardia (VT pts). Two groups of patients after myocardial infarction were studied: 13 non VT pts and 30 VT pts. The TCRT parameter values differentiated VT pts group from non VT pts group for all analyzed sets of ECG leads. Considering sensitivity of the TCRT parameter in identifying VT patients the best set of ECG leads was selected.

This paper introduces a novel low complexity highly accurate on-chip architecture for the detection of fragmented QRS (f-QRS) feature including notches and local extrema in the QRS complexes and subsequently identifies its various morphologies (Notched S, rsR', RsR' without elevation etc.) under the real-time environment targeting remote personalized health care. The proposed architecture uses the outcome of recently proposed Hybrid feature extraction algorithm (HFEA) [1] Level 3 detailed coefficients and detects and identifies the fragmentation feature from the QRS complex based on the criteria of the positions, and the magnitudes of the extrema (maxima and minima) and notches from the wavelet coefficients with no extra cost in terms of arithmetic complexity. To verify the proposed architecture 100 patients were randomly selected from the MIT-BIH PhysioNet PTB database and their ECG was examined by two experienced cardiologists individually and the results were compared with those obtained from the architecture output wherein we have achieved 95 % diagnostic matching.

This paper introduces a novel low complexity highly accurate on-chip architecture for the detection of fragmented QRS (f-QRS) feature including notches and local extrema in the QRS complexes and subsequently identifies its various morphologies (Notched S, rsR', RsR' without elevation etc.) under the real-time environment targeting remote personalized health care. The proposed architecture uses the outcome of recently proposed Hybrid feature extraction algorithm (HFEA) [1] Level 3 detailed coefficients and detects and identifies the fragmentation feature from the QRS complex based on the criteria of the positions, and the magnitudes of the extrema (maxima and minima) and notches from the wavelet coefficients with no extra cost in terms of arithmetic complexity. To verify the proposed architecture 100 patients were randomly selected from the MIT-BIH PhysioNet PTB database and their ECG was examined by two experienced cardiologists individually and the results were compared with those obtained from the architecture output wherein we have achieved 95 % diagnostic matching.

Background Osteoporosis is recognized as a worldwide skeletal disorder problem. In India, the older as well as postmenopausal women population suffering from osteoporotic fractures has been a common issue. Bone mineral density measurements gauged by dual-energy X-ray absorptiometry (DXA) are used in the diagnosis of osteoporosis. Objectives (1) To evaluate osteoporosis in south Indian women by radiogrammetric method in a comparative perspective with DXA. (2) To assess the capability of KJH; Anburajan's Empirical formula in the prediction of total hip bone mineral density (T.BMD) with estimated Hologic T.BMD. Methods In this cross-sectional design, 56 south Indian women were evaluated. These women were randomly selected from a health camp. The patients with secondary bone diseases were excluded. The standard protocol was followed in acquiring BMD of the right proximal femur by DPX Prodigy (DXA Scanner, GE-Lunar Corp., USA). The measured Lunar Total hip BMD was converted into estimated Hologic Total hip BMD. In addition, the studied population underwent chest and hip radiographic measurements. Combined cortical thickness of clavicle has been used in KJH; Anburajan's Empirical formula to predict T.BMD and compared with estimated Hologic T.BMD by DXA. The correlation coefficients exhibited high significance. The combined cortical thickness of clavicle and femur shaft of total studied population was strongly correlated with DXA femur T.BMD measurements (r = 0.87, P \ 0.01 and r = 0.45, P \ 0.01) and it is also having strong correlation with low bone mass group (r = 0.87, P \ 0.01 and r = 0.67, P \ 0.01) KJH; Anburajan's Empirical formula shows significant correlation with estimated Hologic T.BMD (r = 0.88, P \ 0.01) in total studied population. The empirical formula was identified as better tool for predicting osteoporosis in total population and old-aged population with a sensitivity (88.8 and 95.6 %), specificity (89.6 and 90.9 %), positive predictive value (88.8 and 95.6 %) and negative predictive value (89.6 and 90.9 %), respectively. Conclusion The results suggest that combined cortical thickness of clavicle and femur shaft using radiogrammetric method is significantly correlated with DXA. Moreover, KJH; Anburajan's Empirical formula is useful and better index than other simple radiogrammetry measurements in the evaluation of osteoporosis from the economical and widely available digital radiographs.

This paper presents a new compression scheme for single channel ECG, by delineating each ECG cycle. It uses multirate processing to normalize the varying period beats, followed by amplitude normalization. These beats are coded using vector quantization, with each beat being treated as a vector of uniform dimension. The average amplitude scale factor and the average beat period are made available at the decoder, along with the codebook of period and amplitude normalized beat vectors, to facilitate reconstruction of the signal. The actual beat period and the actual maximum amplitude of the beat are sent to the decoder by DPCM. It achieves a high quality approximation at less than 30 bits per second, with a compression ratio of around 100 : 1 to 200 : 1. To assess the technique properly we have evaluated two measures of error. Finally, the merits and demerits of the technique are discussed.

Electrocardiography (ECG) signals are important in medical engineering to determine the condition of the heart. The proper processing of ECG signal and its accurate detection is very much essential for easy diagnosis. Generally ECG gets corrupted by noise and human artifacts. The denoising of this signal is very important issue in medical field. In this proposed work concentrated on denoising of ECG signal from white Gaussian noise using wavelet transform. Initially the noisy signal is transformed using wavelet transform to generate approximate and detailed coefficients. These detailed coefficients are thresholded by soft thresholding to remove the white Gaussian noise. At last IDWT (Inverse Discrete Wavelet Transform) is applied on thresholded detailed coefficient and approximated coefficients to generate denoise ECG signal. Finally the performance of proposed method is evaluated with SNR (Signal to Noise Ratio) value, RMSE (Root Mean Square Error) value and correlation value and c...

To diagnose a variety of conditions, from torn ligaments to tumors,
doctors make use of MRI and CT scans. An application developed using
python, installed in a mobile or tablet render the data of MRI and CT
scans and let the doctor understand patient health condition. A method for
transforming MRI/CT scan data in DICOM form to HDF5 with python.
Variety of tools available now to develop android applications for mobile
with python.

This paper aims at developing an elaborate system for forest fire detection. Disasters are one of the most important and a common type is forest fire. This particular application is chosen to overcome the shortcomings of MODIS and Basic Wireless Sensor Network. To support for step-wise development and implementation of these as more sensors are added aside from the temperature sensor, they were broken down into five smaller separate components. It introduced a new operational model to identify forest fires and its zero models for wireless sensor networks using ZigBee, according to common practice. The approach has been designed to be as close to real time due the rapid nature of forest fires which leaves no scope for delay. This system has been implemented using three fire sensors. If anything is sensed by one of the sensors then water pump will activated at that moment and information transmitted to base station through zigbee. Developed and implemented an IoT-based Zigbee Wireless Sensor Networks (WSN) for the smart forest fire alarm system. Every output parameter is tested Using ZigBee wireless communication technology; the system can be automatically defended and detected for flames. This approach allows Zigbee to meet the requirements of fire parameter value coverage and low power consumption while using easy-to-use air communication.

The conventional method for assessing the working memory performance of children is time-consuming and potentially inaccurate, especially when dealing with many samples. Therefore, an automated system that can produce swift and accurate results is required. Electroencephalograms (EEG) can be used to analyse the working memory status of children by extracting specific features from the EEG signal, which can be incorporated into an automatic system to reduce manpower and processing time for analysis. This project used EEG recording to identify children's working memory status while they were performing working memory tasks. EEG signals were acquired from both children and adults using an automated computer-based working memory assessment tool, processed, and analyzed. The discrete wavelet transform (DWT) was then employed to identify five distinct working memory statuses: distracted, confused, daydreaming, losing focus, and active. DWT was also used to extract features that demonstrate these various statuses. The results showed that DWT could accurately identify the working memory status of both children and adults from their EEGs. This work has thus provided a more efficient method for extracting features from EEG signals to identify working memory statuses in both children and adults.

Abstract: Brain tumor is inherently serious and life-threatening disease. Brain tumor is an abnormal growth of cells within the brain or inside the skull, which can be cancerous or noncancerous. Early detection and classification of brain tumors is very important in clinical practice. With recent development in the medical engineering and instruments, EEG instruments are able to record the brain electric activities with high accuracy, which establishes EEG as a primary tool for diagnosing the brain abnormalities. This paper represents earlier detection of brain tumor using artificial neural networks (ANNs).EEG signals carry the information of human brain. But EEG signal is contaminated with artifacts. These artifacts are removed by adaptative filtering. Then the spectral analysis method is applied for extracting generic features in an EEG signal. Fast Fourier Transform for spectral analysis is used to separate the signal features present in noise. The clean EEG is obtained and fed t...

The developing of deep learning systems that are used for chronic disease diagnosing is a challenge. Furthermore, the localisation and identification of objects like white blood cells (WBCs) in leukemia without preprocessing or traditional hand segmentation of cells is a challenging matter due to the irregular and distorted of nucleus. This paper proposed a system for computer-aided detection depend completely on deep learning with three models of computer-aided detection (CAD3) to detect and classify three types of WBC which is fundamental of leukemia diagnosing. The system used modified you only look once (YOLO v2) algorithm and convolutional neural network (CNN). The proposed system was trained and evaluated on a dataset created and prepared especially for the addressed problem without any traditional segmentation or preprocessing on microscopic images. The study proved that dividing the addressed problem into sub-problems will achieve better performance and accuracy. Furthermore, the results show that the CAD3 achieved an average precision (AP) of up to 96% in the detection of leukocytes and an accuracy of 94.3% in leukocytes classification. Moreover, the CAD3 gives a report containing complete information on WBC. Finally, the CAD3 proved its efficiency on the other datasets, such as the acute lymphoblastic leukemia image database (ALL-IBD1) and blood cell count dataset (BCCD).

Forensik merupakan bidang ilmu pengetahuan yang membantu proses individu untuk kepentingan hukum. Bite mark adalah bekas gigitan yang terdapat pada organ tubuh korban dan pelaku kriminal. Hal ini banyak di temukan pada kasus kekerasan, pemerkosaan dan penganiyayaan anak. Adapun hambatan untuk proses identifikasi bite mark yaitu prosesnya yang membutuhkan waktu lama dan untuk menganalisisnya dengan menggunakan kasat mata. Oleh karena itu, sangat dibutuhkan pengolahan citra pola bite mark untuk mendapatkan ketepatan identifikasi jenis kelamin pelaku atau korban kriminalitas dengan membutuhkan waktu yang cepat. Pada Tugas Akhir ini, sistem mampu melakukan identifikasi citra pola bitemark menjadi sebuah keluaran berupa jenis kelamin pelaku dan korban kriminalitas. Klasifikasi ciri pada tugas akhir ini menggunakan metode Discrete Wavalet Transform (DWT) selanjutnya menggunakan metode klasifikasi K-Nearest Neighbor (KNN). Nilai akurasi terbesar pada pengujian ini yaitu 82,9787% dengan waktu komputasi 0,7078s. Parameter jarak kaninus menunjukan bahwa jarak kaninus perempuan lebih kecil 0,95% dibanding jarak kaninus laki-laki. Dengan metode ektrasi ciri DWT menggunakan level dekomposisi 5 dan filter LL. Sedangkan pada proses klasifikasi K-NN jenis distance terbaik yang bisa digunakan adalah Euclidean dan nilai k = 1. Ukuran citra yang digunakan 800x1600 piksel.

12th International Conference on Signal and Image Processing (SIGL 2025) is a forum for presenting new advances and research results in the fields of Digital Image Processing. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. The scope of the conference covers all theoretical and practical aspects of the Signal, Image Processing & Pattern Recognition.

Es indudable que en los últimos años las técnicas de procesamiento digital de señales han evolucionado fuertemente con el uso de computadores. El instrumental destinado a los ensayos de alta tensión no ha estado ajeno a dicha evolución. Las nuevas técnicas no sólo permiten variadas visualizaciones durante las pruebas, sino que además posibilitan almacenar digitalmente los resultados para su posterior interpretación, estudio y análisis. En la puesta en marcha se ha considerado como referencia excluyente, la norma IEC 60270-2000 [1]. Para la implementación del nuevo instrumento fue necesario adaptar el resto de los elementos que componen el sistema de medición (divisores capacitivos, filtros, etc.), de forma que la instalación resultante cumpla con los requerimientos establecidos en la norma de referencia. Descargas parciales -evolución de instrumental -adaptaciones y calibración -ensayos normalizadosnorma IEC 60270-2000. Un sistema de medición de DPs básicamente consta de un medidor o adquisidor "M"; un conjunto de