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Wyniki wyszukiwania dla: TIME SERIES CLASSIFICATIONLEARNING SYSTEMSCAPSULE NETWORKSDATA MININGMULTI-HEAD CONVOLUTIONAL NEURAL NETWORKSSIGNAL PROCESSING
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The influence of image masks definition onsegmentation results of histopathological imagesusing convolutional neural network
PublikacjaAbstract—In the era of collecting large amounts of tissue materials, assisting the work of histopathologists with various electronic and information IT tools is an undeniable fact. The traditional interaction between a human pathologist and the glass slide is changing to interaction between an AI pathologist with a whole slide images. One of the important tasks is the segmentation of objects (e.g. cells) in such images. In this...
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Excited state properties of a series of molecular photocatalysts investigated by time dependent density functional theory.
PublikacjaTime dependent density functional theory calculations are applied on a series of molecular photocatalysts of the type [(tbbpy)2M1(tpphz)M2X2]2+ (M1 = Ru, Os; M2 = Pd, Pt; X = Cl, I) in order to provide information concerning the photochemistry occurring upon excitation of the compounds in the visible region. To this aim, the energies, oscillator strengths and orbital characters of the singlet and triplet excited states are investigated....
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The influence of different time durations of thermal processing on berries quality
PublikacjaBioactive compounds (polyphenols, flavonoids, flavanols, tannins, anthocyanins and ascorbic acid) and the level of antioxidant activity by ABTS, DPPH, FRAP and CUPRAC of water, acetone and hexane extracts of Chilean 'Murtilla' (Ugni molinae Turcz) and 'Myrteola' berries (Myrtaceae, Myrteola nummularia (Poiret) Berg.), Chilean and Polish blueberries (Vaccinium corymbosum), Chilean raspberries (Rubus idaeus), and Polish black chokeberry...
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Abdominal Aortic Aneurysm segmentation from contrast-enhanced computed tomography angiography using deep convolutional networks
PublikacjaOne of the most common imaging methods for diagnosing an abdominal aortic aneurysm, and an endoleak detection is computed tomography angiography. In this paper, we address the problem of aorta and thrombus semantic segmentation, what is a mandatory step to estimate aortic aneurysm diameter. Three end-to-end convolutional neural networks were trained and evaluated. Finally, we proposed an ensemble of deep neural networks with underlying...
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Multimedia interface using head movements tracking
PublikacjaThe presented solution supports innovative ways of manipulating computer multimedia content, such as: static images, videos and music clips and others that can be browsed subsequently. The system requires a standard web camera that captures images of the user face. The core of the system is formed by a head movement analyzing algorithm that finds a user face and tracks head movements in real time. Head movements are tracked with...
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Efficient Calibration of Cost-Efficient Particulate Matter Sensors Using Machine Learning and Time-Series Alignment
PublikacjaAtmospheric particulate matter (PM) poses a significant threat to human health, infiltrating the lungs and brain and leading to severe issues such as heart and lung diseases, cancer, and premature death. The main sources of PM pollution are vehicular and industrial emissions, construction and agricultural activities, and natural phenomena such as wildfires. Research underscores the absence of a safe threshold for particulate exposure,...
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Artificial Neural Networks for Prediction of Antibacterial Activity in Series of Imidazole Derivatives
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CMOS implementation of an analogue median filter for image processing in real time
PublikacjaAn analogue median filter, realised in a 0.35 μm CMOS technology, is presented in this paper. The key advantages of the filter are: high speed of image processing (50 frames per second), low-power operation (below 1.25 mW under 3.3 V supply) and relatively high accuracy of signal processing. The presented filter is a part of an integrated circuit for image processing (a vision chip), containing: a photo-sensor matrix, a set of...
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Modeling and Simulation for Exploring Power/Time Trade-off of Parallel Deep Neural Network Training
PublikacjaIn the paper we tackle bi-objective execution time and power consumption optimization problem concerning execution of parallel applications. We propose using a discrete-event simulation environment for exploring this power/time trade-off in the form of a Pareto front. The solution is verified by a case study based on a real deep neural network training application for automatic speech recognition. A simulation lasting over 2 hours...
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Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series
PublikacjaThis study assessed the usefulness of algorithms in estimating energy consumption and carbon dioxide emissions in Viet- nam, in which the training dataset was used to train the models linear regression, random forest, XGBoost, and AdaBoost, allowing them to comprehend the patterns and relationships between population, GDP, and carbon dioxide emissions, energy consumption. The results revealed that random forest, XGBoost, and AdaBoost...
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Irregular variations in GPS time series by probability and noise analysis
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Neural networks and deep learning
PublikacjaIn this chapter we will provide the general and fundamental background related to Neural Networks and Deep Learning techniques. Specifically, we divide the fundamentals of deep learning in three parts, the first one introduces Deep Feed Forward Networks and the main training algorithms in the context of optimization. The second part covers Convolutional Neural Networks (CNN) and discusses their main advantages and shortcomings...
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Noise Analysis of Continuous GPS Time Series of Selected EPN Stations to Investigate Variations in Stability of Monument Types
PublikacjaThe type of monument that a GPS antenna is placed on plays a significant role in noise estimation for each permanent GPS station. In this research 18 Polish permanent GPS stations that belong to the EPN (EUREF Permanent Network) were analyzed using Maximum Likelihood Estimation (MLE). The antennae of Polish EPN stations are placed on roofs of buildings or on concrete pillars. The analyzed data covers a period of 5 years from 2008...
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A nine-input 1.25 mW, 34 ns CMOS analog median filter for image processing in real time
PublikacjaIn this paper an analog voltage-mode median filter, which operates on a 3 × 3 kernel is presented. The filter is implemented in a 0.35 μm CMOS technology. The proposed solution is based on voltage comparators and a bubble sort configuration. As a result, a fast (34 ns) time response with low power consumption (1.25 mW for 3.3 V) is achieved. The key advantage of the configuration is relatively high accuracy of signal processing,...
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Flooding Extent Mapping for Synthetic Aperture Radar Time Series Using River Gauge Observations
PublikacjaThe flooding extent area in a river valley is related to river gauge observations such as discharge and water elevations. The higher the water elevations, or discharge, the larger the flooding area. Flooding extent maps are often derived from synthetic aperture radar (SAR) images using thresholding methods. The thresholding methods vary in complexity and number of required parameters. We proposed a simple thresholding method that...
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Impact of Shifting Time-Window Post-Processing on the Quality of Face Detection Algorithms
PublikacjaWe consider binary classification algorithms, which operate on single frames from video sequences. Such a class of algorithms is named OFA (One Frame Analyzed). Two such algorithms for facial detection are compared in terms of their susceptibility to the FSA (Frame Sequence Analysis) method. It introduces a shifting time-window improvement, which includes the temporal context of frames in a post-processing step that improves the...
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Exploring Neural Networks for Musical Instrument Identification in Polyphonic Audio
PublikacjaThe purpose of this paper is to introduce neural network-based methods that surpass state-of-the-art (SOTA) models, either by training faster or having simpler architecture, while maintaining comparable effectiveness in musical instrument identification in polyphonic music. Several approaches are presented, including two authors’ proposals, i.e., spiking neural networks (SNN) and a modular deep learning model named FMCNN (Fully...
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Rough Set Based Modeling and Visualization of the Acoustic Field Around the Human Head
PublikacjaThe presented research aims at modeling acoustical wave propagation phenomena by applying rough set theory in a novel manner. In a typical listening environment sound intensity is determined by numerous factors: a distance from a sound source, signal levels and frequencies, obstacles’ locations and sizes. Contrarily, a free-field is characterized by direct, unimpeded propagation of the acoustical waves. The proposed approach is...
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Deep neural network architecture search using network morphism
PublikacjaThe paper presents the results of the research on neural architecture search (NAS) algorithm. We utilized the hill climbing algorithm to search for well-performing structures of deep convolutional neural network. Moreover, we used the function preserving transformations which enabled the effective operation of the algorithm in a short period of time. The network obtained with the advantage of NAS was validated on skin lesion classification...
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Neural network approach to 2D Kalman filtering in image processing
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Task Allocation and Scalability Evaluation for Real-Time Multimedia Processing in a Cluster Envirinment
PublikacjaAn allocation algorithm for stream processing tasks is proposed (Modified best Fit Descendent, MBFD). A comparison with another solution (BFD) is provided. Tests of the algorithms in an HPC environment are descrobed and the results are presented. A proper scalability metric is proposed and used for the evaluation of the allocation algorithm.
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Static Series and Shunt-series PE Voltage-quality Controllers
PublikacjaAs presented in the Chap. 7 static shunt power electronics (PE) voltage-quality controllers protect the utility electrical system from the unfavorable impact of customer loads. Shunt controllers, as shown in Chap. 6, are recommended mainly for mitigation of the causes of disturbances, and not their effects in distanced nodes of a power-electronics system. In the case when reduction of disturbances effects is required, which leads...
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Application of PCA and time series analysis in studies of precipitation in Tricity (Poland).
PublikacjaPrzedstawiono wyniki monitoringu zanieczyszczenia atmosfery Trójmiasta. Próbki wody opadowej pobierano w cyklach miesięcznych przez 4 lata (1998-2001)w 10 punktach. Wyniki poddano statystycznej i chemometrycznej analizie (szeregi czasowe, analiza wariancji, analiza głównych składowych). Wykazano wpływ lokalizacji punktów monitoringowych i bliskości Morza Bałtyckiego na zawartość jonów nieorganicznych w analizowanych próbkach.
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Influence of YARN Schedulers on Power Consumption and Processing Time for Various Big Data Benchmarks
PublikacjaClimate change caused by human activities can influence the lives of everybody onthe planet. The environmental concerns must be taken into consideration by all fields of studyincludingICT. Green Computing aims to reduce negative effects of IT on the environment while,at the same time, maintaining all of the possible benefits it provides. Several Big Data platformslike Apache Spark orYARNhave become widely used in analytics and...
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1D convolutional context-aware architectures for acoustic sensing and recognition of passing vehicle type
PublikacjaA network architecture that may be employed to sensing and recognition of a type of vehicle on the basis of audio recordings made in the proximity of a road is proposed in the paper. The analyzed road traffic consists of both passenger cars and heavier vehicles. Excerpts from recordings that do not contain vehicles passing sounds are also taken into account and marked as ones containing silence....
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Neural network breast cancer relapse time prognosis
PublikacjaPrzedstawiono architekturę i wyniki testowania sztucznej sieci neuronowej w prognozowaniu czasu nawrotu choroby u kobiet chorych na raka piersi. Sieć neuronowa uczona była na danych zgromadzonych przez 20 lat. Dane opisują grupę 439 pacjentów za pomocą 40 parametrów. Spośród tych parametrów wybrano 6 najistotniejszych: liczbę przerzutowych węzłów chłonnych, wielkość guza, wiek, skalę według Blooma oraz stan receptorów estrogenowych...
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Deep neural networks approach to skin lesions classification — A comparative analysis
PublikacjaThe paper presents the results of research on the use of Deep Neural Networks (DNN) for automatic classification of the skin lesions. The authors have focused on the most effective kind of DNNs for image processing, namely Convolutional Neural Networks (CNN). In particular, three kinds of CNN were analyzed: VGG19, Residual Networks (ResNet) and the hybrid of VGG19 CNN with the Support Vector Machine (SVM). The research was carried...
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Head movement compensation algorithm in multi-display communication by gaze
PublikacjaAn influence of head movements on the gaze estimation accuracy when using a head mounted eye tracking system is discussed in the paper. This issue has been examined for a multi-display environment. It was found that head movement (rotation) to some extent does not influence on the gaze estimation accuracy seriously. Acceptable results were obtained when using eye-tracker to communicate with a computer via in two displays simultaneously.
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Time series analysis and impact assessment of the temperature changes on the vegetation and the water availability: A case study of Bakun-Murum Catchment Region in Malaysia
PublikacjaThe Bakun-Murum (BM) catchment region of the Rajang River Basin (RRB), Sarawak, Malaysia, has been under severe threat for the last few years due to urbanization, global warming, and climate change. The present study aimed to evaluate the time series analysis and impact assessment of the temperature changes on the vegetation/agricultural lands and the water availability within the BM region. For this purpose, the Landsat data for...
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Video of LEGO Bricks on Conveyor Belt Dataset Series
PublikacjaThe dataset series titled Video of LEGO bricks on conveyor belt is composed of 14 datasets containing video recordings of a moving white conveyor belt. The recordings were created using a smartphone camera in Full HD resolution. The dataset allows for the preparation of data for neural network training, and building of a LEGO sorting machine that can help builders to organise their collections.
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Resource constrained neural network training
PublikacjaModern applications of neural-network-based AI solutions tend to move from datacenter backends to low-power edge devices. Environmental, computational, and power constraints are inevitable consequences of such a shift. Limiting the bit count of neural network parameters proved to be a valid technique for speeding up and increasing efficiency of the inference process. Hence, it is understandable that a similar approach is gaining...
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Processing of musical data employing rough sets and artificial neural networks
PublikacjaArtykuł opisuje założenia systemu automatycznej identyfikacji muzyki i dźwięków muzycznych. Dokonano przeglądu standardu MPEG-7, ze szczególnym naciskiem na parametry opisowe dźwięku. Przedyskutowano problemy analizy danych audio, związane z zastosowaniami wykorzystującymi MPEG-7. W oparciu o eksperymenty przedstawiono efektywność deskryptorów niskiego poziomu w automatycznym rozpoznawaniu dźwięków instrumentów muzycznych. Przedyskutowano...
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Processing of musical data employing rough sets and artificial neural networks
PublikacjaArtykuł opisuje założenia systemu automatycznej identyfikacji muzyki i dźwięków muzycznych. Dokonano przeglądu standardu MPEG-7, ze szczególnym naciskiem na parametry opisowe dźwięku. Przedyskutowano problemy analizy danych audio, związane z zastosowaniami wykorzystującymi MPEG-7. W oparciu o eksperymenty przedstawiono efektywność deskryptorów niskiego poziomu w automatycznym rozpoznawaniu dźwięków instrumentów muzycznych. Przedyskutowano...
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Neural Network Subgraphs Correlation with Trained Model Accuracy
PublikacjaNeural Architecture Search (NAS) is a computationally demanding process of finding optimal neural network architecture for a given task. Conceptually, NAS comprises applying a search strategy on a predefined search space accompanied by a performance evaluation method. The design of search space alone is expected to substantially impact NAS efficiency. We consider neural networks as graphs and find a correlation between the presence...
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Price bubbles in commodity market – A single time series and panel data analysis
PublikacjaThis paper examines thirty-five commodities, grouped into three market sectors (energy, metals, agriculture & livestock) in terms of the occurrence of price bubbles. The study was based on monthly data for each commodity separately and, in a panel approach, for selected sectors and for all commodities combined. The GSADF test and its version for panel data – panel GSADF – were used to identify bubbles. The beginning and end of...
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Application of time-series-cross-section data in case of sale forecasting in an enterprise
PublikacjaW artykule wskazano możliwości wykorzystania danych przestrzenno-czasowych do prognozowania sprzedaży w przedsiębiorstwie. Przedstawiono różne podejścia do prognozowania ekonometrycznego przy użyciu tego typu danych. Wyznaczono krótkookresowe prognozy sprzedaży benzyny bezołowiowej Pb95 w przekroju województw oraz dokonano oceny ich jakości przy użyciu mierników ex-post. Dwie najdokładniejsze metody prognozowania wykorzystano do...
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Autocovariance based weighting strategy for time series prediction with weighted LS-SVM
PublikacjaPrzedstawiono metodę konstrukcji algorytmów z funkcją jądra, a także dwa algorytmy uzyskane poprzez użycie różnych funkcji straty. Zaproponowano kowariacyjną strategię ważenia algorytmów z kwadratową funkcją straty do problemu predykcji chaotycznych przebiegów czasowych.
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Experimental and analytical analysis of punching shear in flat slabs supported on column topped with concrete head
PublikacjaAn experimental laboraatory test of the two series of slab-column elements topped with drop panels of varying sizes is described in this paper. The scope of the paper is to investigate the influence of the drop panel size and stiffness on the behaviour of the connection between the flat slab and the column topped by the concrete head. The impact of the head size and stiffness is analysed analytically and experimentally. The experimental...
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Real-Time Multimedia Stream data Processing in a Supercomputer Environment
PublikacjaRozdział opisuje doświadczenia uzyskane przez autorów podczas pracy w projekcie MAYDAY EURO 2012. Przedstawiono główny cel projektu - stworzenie systemu umożliwiającego rozwijanie i równolegle wykonywanie usług multimedialnych w środowisku klastra obliczeniowego dużej mocy. opisano tematykę przetwarzania dużej liczby strumieni multimedialnych na komputerach dużej mocy. Następnie zaprezentowano możliwości platformy KASKADA: tworzenie...
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The influence of different time duration of thermal processing on berries quality
PublikacjaOznaczano zawartość związków bioaktywnych (polifenole, flawonoidy, taniny, antocyjany i kwas askorbinowy) oraz poziom aktywności przeciwutleniającej próbek ekstraktów (wodnych, heksanowych i acetonowych) uzyskanych z różnych gatunków owoców jagodowych. Do pomiaru poziomu aktywności przeciwutleniającej wykorzystano takie testy jak ABTS, DPPH, FRAP i CUPRAC. Zbadano wpływ czasu trwania procesu obróbki termicznej na zawartość bioaktywnych...
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Improving Accuracy of Respiratory Rate Estimation by Restoring High Resolution Features With Transformers and Recursive Convolutional Models
PublikacjaNon-contact evaluation of vital signs has been becoming increasingly important, especially in light of the COVID- 19 pandemic, which is causing the whole world to examine people’s interactions in public places at a scale never seen before. However, evaluating one’s vital signs can be a relatively complex procedure, which requires both time and physical contact between examiner and examinee. These re- quirements limit the number...
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Multi-layered tissue head phantoms for noninvasive optical diagnostics
PublikacjaExtensive research in the area of optical sensing for medical diagnostics requires development of tissue phantoms with optical properties similar to those of living human tissues. Development and improvement of in vivo optical measurement systems requires the use of stable tissue phantoms with known characteristics, which are mainly used for calibration of such systems and testing their performance over time. Optical and mechanical...
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Applying artificial neural networks for modelling ship speed and fuel consumption
PublikacjaThis paper deals with modelling ship speed and fuel consumption using artificial neural network (ANN) techniques. These tools allowed us to develop ANN models that can be used for predicting both the fuel consumption and the travel time to the destination for commanded outputs (the ship driveline shaft speed and the propeller pitch) selected by the ship operator. In these cases, due to variable environmental conditions, making...
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Image Processing Techniques for Distributed Grid Applications
PublikacjaParallel approaches to 2D and 3D convolution processing of series of images have been presented. A distributed, practically oriented, 2D spatial convolution scheme has been elaborated and extended into the temporal domain. Complexity of the scheme has been determined and analysed with respect to coefficients in convolution kernels. Possibilities of parallelisation of the convolution operations have been analysed and the results...
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GIS for processing multidimensional marine data in SAAS model
PublikacjaGeographic Information Systems (GIS) have always been a useful tool for visualization and processing of geospatial data. However, their capabilities of analysis non-standard information such as hydroacoustic soundings has thus far been very limited. This paper proposes a general-purpose GIS which uses techniques such as OLAP, WCS and WCPS for processing of multidimensional spatio-temporal data. The versatility of the GIS is exemplified...
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Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform
PublikacjaTraffic monitoring from closed-circuit television (CCTV) cameras on embedded systems is the subject of the performed experiments. Solving this problem encounters difficulties related to the hardware limitations, and possible camera placement in various positions which affects the system performance. To satisfy the hardware requirements, vehicle detection is performed using a lightweight Convolutional Neural Network (CNN), named...
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Neural Networks Based on Ultrafast Time-Delayed Effects in Exciton Polaritons
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Characterizing the Scalability of Graph Convolutional Networks on Intel® PIUMA
PublikacjaLarge-scale Graph Convolutional Network (GCN) inference on traditional CPU/GPU systems is challenging due to a large memory footprint, sparse computational patterns, and irregular memory accesses with poor locality. Intel’s Programmable Integrated Unffied Memory Architecture (PIUMA) is designed to address these challenges for graph analytics. In this paper, a detailed characterization of GCNs is presented using the Open-Graph Benchmark...
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Neural network training with limited precision and asymmetric exponent
PublikacjaAlong with an extremely increasing number of mobile devices, sensors and other smart utilities, an unprecedented growth of data can be observed in today’s world. In order to address multiple challenges facing the big data domain, machine learning techniques are often leveraged for data analysis, filtering and classification. Wide usage of artificial intelligence with large amounts of data creates growing demand not only for storage...
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A Simple Neural Network for Collision Detection of Collaborative Robots
PublikacjaDue to the epidemic threat, more and more companies decide to automate their production lines. Given the lack of adequate security or space, in most cases, such companies cannot use classic production robots. The solution to this problem is the use of collaborative robots (cobots). However, the required equipment (force sensors) or alternative methods of detecting a threat to humans are usually quite expensive. The article presents...