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Wyniki wyszukiwania dla: deepfm
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Deep brain stimulation: new possibilities for the treatment of mental disorders
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The Impact of Dredging Deep Pits on Organic Matter Decomposition in Sediments
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Problem of selection of reference plane with deep and wide valleys analysis
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Deep learning-based waste detection in natural and urban environments
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Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
PublikacjaWe present a novel deep learning model for the detection and reconstruction of dysarthric speech. We train the model with a multi-task learning technique to jointly solve dysarthria detection and speech reconstruction tasks. The model key feature is a low-dimensional latent space that is meant to encode the properties of dysarthric speech. It is commonly believed that neural networks are black boxes that solve problems but do not...
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Using deep learning to increase accuracy of gaze controlled prosthetic arm
PublikacjaThis paper presents how neural networks can be utilized to improve the accuracy of reach and grab functionality of hybrid prosthetic arm with eye tracing interface. The LSTM based Autoencoder was introduced to overcome the problem of lack of accuracy of the gaze tracking modality in this hybrid interface. The gaze based interaction strongly depends on the eye tracking hardware. In this paper it was presented how the overall the...
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Deep learning for ultra-fast and high precision screening of energy materials
PublikacjaSemiconductor materials for energy storage are the core and foundation of modern information society and play important roles in photovoltaic system, integrated circuit, spacecraft technology, lighting applications, and other fields. Unfortunately, due to the long experiment period and high calculation cost, the high-precision band gap (the basic characteristic parameter) of semiconductor is difficult to obtain, which hinders the...
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A Comprehensive Analysis of Deep Neural-Based Cerebral Microbleeds Detection System
PublikacjaMachine learning-based systems are gaining interest in the field of medicine, mostly in medical imaging and diagnosis. In this paper, we address the problem of automatic cerebral microbleeds (CMB) detection in magnetic resonance images. It is challenging due to difficulty in distinguishing a true CMB from its mimics, however, if successfully solved it would streamline the radiologists work. To deal with this complex three-dimensional...
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Face with Mask Detection in Thermal Images Using Deep Neural Networks
PublikacjaAs the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature based on the detected face, which brings new perspectives and opportunities to use such an approach for health control purposes. The...
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Autonomous pick-and-place system based on multiple 3Dsensors and deep learning
PublikacjaGrasping objects and manipulating them is the main way the robot interacts with its environment. However, for robots to operate in a dynamic environment, a system for determining the gripping position for objects in the scene is also required. For this purpose, neural networks segmenting the point cloud are usually applied. However, training such networks is very complex and their results are unsatisfactory. Therefore, we propose...
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Deep learning approach on surface EEG based Brain Computer Interface
PublikacjaIn this work we analysed the application of con-volutional neural networks in motor imagery classification for the Brain Computer Interface (BCI) purposes. To increase the accuracy of classification we proposed the solution that combines the Common Spatial Pattern (CSP) with convolutional network (ConvNet). The electroencephalography (EEG) is one of the modalities we try to use for controlling the prosthetic arm. Therefor in this...
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DEEP LEARNING BASED ON X-RAY IMAGING IMPROVES COXARTHROSIS DETECTION
PublikacjaObjective: The purpose of the study was to create an Artificial Neural Network (ANN) based on X-ray images of the pelvis, as an additional tool to automate and improve the diagnosis of coxarthrosis. The research is focused on joint space narrowing, which is a radiological symptom showing the thinning of the articular cartilage layer, which is translucent to X-rays. It is the first and the most important of the radiological signs...
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Experimental and predicted physicochemical properties of monopropanolamine-based deep eutectic solvents
PublikacjaIn this work, the novel deep eutectic solvents (DESs) based on 3-amino-1-propanol (AP) as hydrogen bond donor (HBD) and tetrabutylammonium bromide (TBAB) or tetrabutylammonium chloride (TBAC) or tetraethylammonium chloride (TEAC) as hydrogen bond acceptors (HBAs) were synthesized with different molar ratios of 1:4, 1:6 and 1:8 salt to AP. Fourier Transform Infrared Spectroscopy measurements were performed to provide an evidence...
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Deep learning based thermal image segmentation for laboratory animals tracking
PublikacjaAutomated systems for behaviour classification of laboratory animals are an attractive alternative to manual scoring. However, the proper animals separation and tracking, especially when they are in close contact, is the bottleneck of the behaviour analysis systems. In this paper, we propose a method for the segmentation of thermal images of laboratory rats that are in close contact during social behaviour tests. For this, we are...
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Deep Features Class Activation Map for Thermal Face Detection and Tracking
PublikacjaRecently, capabilities of many computer vision tasks have significantly improved due to advances in Convolutional Neural Networks. In our research, we demonstrate that it can be also used for face detection from low resolution thermal images, acquired with a portable camera. The physical size of the camera used in our research allows for embedding it in a wearable device or indoor remote monitoring solution for elderly and disabled...
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Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks
PublikacjaThe increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated...
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Deep Learning: A Case Study for Image Recognition Using Transfer Learning
PublikacjaDeep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities,...
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Assessment and design of greener deep eutectic solvents – A multicriteria decision analysis
PublikacjaDeep eutectic solvents (DES) are often considered as green solvents because of their properties, such as negligible vapor pressure, biodegradability, low toxicity or natural origin of their components. Due to the fact that DES are cheaper than ionic liquids, they have gained many applications in a short period of time. However, claims about their greenness sometimes seem to be exaggerated. Especially, bearing in mind lots of data...
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Experimental and predicted physicochemical properties of monopropanolamine-based deep eutectic solvents
PublikacjaIn this work, the novel deep eutectic solvents (DESs) based on 3-amino-1-propanol (AP) as hydrogen bond donor (HBD) and tetrabutylammonium bromide (TBAB) or tetrabutylammonium chloride (TBAC) or tetraethylammonium chloride (TEAC) as hydrogen bond acceptors (HBAs) were synthesized with different molar ratios of 1: 4, 1: 6 and 1: 8 salt to AP. Fourier Transform Infrared Spectroscopy measurements were performed to provide an evidence...
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Deep learning approach for delamination identification using animation of Lamb waves
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Deep learning super-resolution for the reconstruction of full wavefield of Lamb waves
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OmicSelector: automatic feature selection and deep learning modeling for omic experiments
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Simulation of backup rolls quenching with experimental study of deep cryogenic treatment
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Deep Data Analysis of a Large Microarray Collection for Leukemia Biomarker Identification
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Deep sea habitats in the chemical warfare dumping areas of the Baltic Sea
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Deep eutectic solvents – Ideal solution for clean air or hidden danger?
PublikacjaThe industrial sector is one of the fastest-growing sources of greenhouse gases, due to its excessive energy consumption to meet the rapidly growing demand for energy-intensive products. The use of deep eutectic solvents (DESs) has been studied extensively in order to cope with these harmful gases, but their usage can be an issue in respect to ecological reasons. Do deep eutectic solvents harm the atmosphere? Yes, these solvents...
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Optimized Deep Learning Model for Flood Detection Using Satellite Images
PublikacjaThe increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection...
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Ionic Liquids and Deep Eutectic Mixtures: Sustainable Solvents for Extraction Processes
PublikacjaIn recent years, ionic liquids and deep eutectic mixtures have demonstrated great potential in extraction processes relevant to several scientific and technological activities. This review focuses on the applicability of these sustainable solvents in a variety of extraction techniques, including but not limited to liquid- and solid-phase (micro) extraction, microwave-assisted extraction, ultrasound-assisted extraction and pressurized...
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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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Influence of parameters of deep grinding on nanohardness and surface roughness of C45 steel
PublikacjaPrzedstawiono wyniki badań wpływu głębokości współbieżnego szlifowania powierzchni płaskich na chropowatość i nanotwardość warstwy wierzchniej stali C45 o strukturze ferrytyczno-perlitycznej i średniej wielkości ziarna 20 μm. Dla wszystkich wartości głębokości szlifowania uzyskano znaczny wzrost twardości warstwy wierzchniej przedmiotu obrabianego.
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Selected Technical Issues of Deep Neural Networks for Image Classification Purposes
PublikacjaIn recent years, deep learning and especially Deep Neural Networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the Convolutional Neural Networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good...
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Analysis of 2D Feature Spaces for Deep Learning-based Speech Recognition
Publikacjaconvolutional neural network (CNN) which is a class of deep, feed-forward artificial neural network. We decided to analyze audio signal feature maps, namely spectrograms, linear and Mel-scale cepstrograms, and chromagrams. The choice was made upon the fact that CNN performs well in 2D data-oriented processing contexts. Feature maps were employed in the Lithuanian word recognition task. The spectral analysis led to the highest word...
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Purification of model biogas from toluene using deep eutectic solvents
PublikacjaBiogas from landfills and wastewater treatment facilities typically contain a wide range of volatile organic compounds (VOCs), that can cause severe operational problems when biogas is used as fuel. Among the contaminants commonly occur aromatic compounds, i.e. benzene, ethylbenzene, toluene and xylenes (BTEX). In order to remove BTEX from biogas, different processes can be used. A promising process for VOCs removal is their absorption...
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Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters
PublikacjaThis paper focuses on convolution neural network quantization problem. The quantization has a distinct stage of data conversion from floating-point into integer-point numbers. In general, the process of quantization is associated with the reduction of the matrix dimension via limited precision of the numbers. However, the training and inference stages of deep learning neural network are limited by the space of the memory and a...
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Evaluation of Facial Pulse Signals Using Deep Neural Net Models
PublikacjaThe reliable measurement of the pulse rate using remote photoplethysmography (PPG) is very important for many medical applications. In this paper we present how deep neural networks (DNNs) models can be used in the problem of PPG signal classification and pulse rate estimation. In particular, we show that the DNN-based classification results correspond to parameters describing the PPG signals (e.g. peak energy in the frequency...
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SYNTHESIZING MEDICAL TERMS – QUALITY AND NATURALNESS OF THE DEEP TEXT-TO-SPEECH ALGORITHM
PublikacjaThe main purpose of this study is to develop a deep text-to-speech (TTS) algorithm designated for an embedded system device. First, a critical literature review of state-of-the-art speech synthesis deep models is provided. The algorithm implementation covers both hardware and algorithmic solutions. The algorithm is designed for use with the Raspberry Pi 4 board. 80 synthesized sentences were prepared based on medical and everyday...
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Lignocellulosic waste biosorbents infused with deep eutectic solvents for biogas desulfurization
PublikacjaThis paper introduces an innovative method for treating biogas streams, employing lignocellulosic biosorbents infused with environmentally friendly solvents known as deep eutectic solvents (DES). The primary focus of this study was the elimination of volatile organosulfur compounds (VSCs) from model biogas. Biosorbents, including energetic poplar wood, antipka tree, corncobs, and beech wood, were used, each with varying levels...
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Bimodal deep learning model for subjectively enhanced emotion classification in films
PublikacjaThis research delves into the concept of color grading in film, focusing on how color influences the emotional response of the audience. The study commenced by recalling state-of-the-art works that process audio-video signals and associated emotions by machine learning. Then, assumptions of subjective tests for refining and validating an emotion model for assigning specific emotional labels to selected film excerpts were presented....
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New Carvone-Based Deep Eutectic Solvents for Siloxanes Capture from Biogas
PublikacjaDuring biogas combustion, siloxanes form deposits of SiO2 on engine components, thus shortening the lifespan of the installation. Therefore, the development of new methods for the purification of biogas is receiving increasing attention. One of the most effective methods is physical absorption with the use of appropriate solvents. According to the principles of green engineering, solvents should be biodegradable, non-toxic, and...
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Application of deep eutectic solvents for separation and determination of bioactive compounds in medicinal plants
PublikacjaThe medicinal plants industry, particularly in regard to products rich in biologically active substances for maintaining health, has grown by leaps and bounds in the last decade, with sales of over-the-counter drugs containing these substances growing by billions of dollars. Attention has thus also been paid to the safety and effectiveness of these medicines. We are currently witnessing a rapid increase in the number of publications...
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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
PublikacjaFollowing the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides...
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Efficient Extraction of Fermentation Inhibitors by Means of Green Hydrophobic Deep Eutectic Solvents
PublikacjaThe methods for hydrogen yield efficiency improvements, the gaseous stream purification in gaseous biofuels generation, and the biomass pretreatment are considered as the main trends in research devoted to gaseous biofuel production. The environmental aspect related to the liquid stream purification arises. Moreover, the management of post-fermentation broth with the application of various biorefining techniques gains importance....
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Autonomous Perception and Grasp Generation Based on Multiple 3D Sensors and Deep Learning
PublikacjaGrasping objects and manipulating them is the main way the robot interacts with its environment. However, for robots to operate in a dynamic environment, a system for determining the gripping position for objects in the scene is also required. For this purpose, neural networks segmenting the point cloud are usually applied. However, training such networks is very complex and their results are unsatisfactory. Therefore, we propose...
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Pursuing the Deep-Learning-Based Classification of Exposed and Imagined Colors from EEG
PublikacjaEEG-based brain-computer interfaces are systems aiming to integrate disabled people into their environments. Nevertheless, their control could not be intuitive or depend on an active external stimulator to generate the responses for interacting with it. Targeting the second issue, a novel paradigm is explored in this paper, which depends on a passive stimulus by measuring the EEG responses of a subject to the primary colors (red,...
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VOCs absorption from gas streams using deep eutectic solvents – A review
PublikacjaVolatile organic compounds (VOCs) are one of the most severe atmospheric pollutants. They are mainly emitted into the atmosphere from anthropogenic sources such as automobile exhaust, incomplete fuel combustion, and various industrial processes. VOCs not only cause hazards to human health or the environment but also adversely affect industrial installation components due to their specific properties, i.e., corrosive and reactivity....
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Deep eutectic solvents – A new platform in membrane fabrication and membrane-assisted technologies
PublikacjaDeep eutectic solvents (DESs) are a new class of solvents that can offset some of the primary drawbacks of typical solvents and ionic liquids (ILs). Since DESs fall into the guidelines of “Twelve Principles of Green Chemistry”, their implementation in several types of applications has exponentially increased over the last years. The usage of DESs has been directed to the designing, manufacture and purification of new materials...
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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
PublikacjaThe field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and...
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Novel “acid tuned” deep eutectic solvents based on protonated L-proline
PublikacjaThe paper presents new types of deep eutectic solvents (DESs) based on L-proline protonated using three different acids (hydrochloric, sulfuric and phosphoric)and playing the role of a hydrogen bond acceptor(HBA). Glucose and xylitol were used as hydrogen bond donors (HBD). A series of deep eutectic solvents with various mole ratios were obtained for the systems L-proline: glucose and L-proline: xylitol. Density, melting point,...
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Deodorization of model biogas by means of novel non-ionic deep eutectic solvent
PublikacjaThe paper presents new non-ionic deep eutectic solvent (DES) composed of natural and non-toxic components i.e. guaiacol, camphor and levulinic acid in 1:1:3 molar ratio as a promising absorbent for removal of selected volatile organic compounds (VOCs) including dichloromethane, toluene, hexamethyldisiloxane and propionaldehyde from model biogas. The affi nity of DES for VOCs was determined as vapour-liquid coeffi cients and the...
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Benchmarking Deep Neural Network Training Using Multi- and Many-Core Processors
PublikacjaIn the paper we provide thorough benchmarking of deep neural network (DNN) training on modern multi- and many-core Intel processors in order to assess performance differences for various deep learning as well as parallel computing parameters. We present performance of DNN training for Alexnet, Googlenet, Googlenet_v2 as well as Resnet_50 for various engines used by the deep learning framework, for various batch sizes. Furthermore,...