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Search results for: CONVOLUTION
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Discrete convolution based on polynomial residue representation
PublicationThis paper presents the study of fast discrete convolution calculation with use of the Polynomial Residue Number System (PRNS). Convolution can be based the algorithm similar to polynomial multiplication. The residue arithmetic allows for fast realization of multiplication and addition, which are the most important arithmetic operations in the implementation of convolution. The practical aspects of hardware realization of PRNS...
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Delamination Identification Using Global Convolution Networks
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Implementation of discrete convolution using polynomial residue representation
PublicationConvolution is one of the main algorithms performed in the digital signal processing. The algorithm is similar to polynomial multiplication and very intensive computationally. This paper presents a new convolution algorithm based on the Polynomial Residue Number System (PRNS). The use of the PRNS allows to decompose the computation problem and thereby reduce the number of multiplications. The algorithm has been implemented in Xilinx...
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Alternative convolution approach to friction in unsteady pipe flow
PublicationIn the paper some aspects of the unsteady friction in pipe flow expressed by the convolution are analyzed. This additional term introduced into the motion equation involves the accelerations of fluid occurring in the past and a weighting function. The essence of such approach is to assume the appropriate form of weighting function. However, until now no fully reliable formula for this function has been found. To avoid some inconveniences...
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Implementation of discrete convolution using polynomial residue representation
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Computation of the convolution with use of the polynomial residue number system.
PublicationPrzedstawiono użycie wielomianowych systemów resztowych do obliczania splotu w cyfrowych układach dużej skali integracji VLSI.
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Sign Language Recognition Using Convolution Neural Networks
PublicationThe objective of this work was to provide an app that can automatically recognize hand gestures from the American Sign Language (ASL) on mobile devices. The app employs a model based on Convolutional Neural Network (CNN) for gesture classification. Various CNN architectures and optimization strategies suitable for devices with limited resources were examined. InceptionV3 and VGG-19 models exhibited negligibly higher accuracy than...
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EFFICIENT LINE DETECTION METHOD BASED ON 2D CONVOLUTION FILTER
PublicationThe article proposes an efficient line detection method using a 2D convolution filter. The proposed method was compared with the Hough transform, the most popular method of straight lines detection. The developed method is suitable for local detection of straight lines with a slope from -45˚ to 45˚. Also, it can be used for curve detection which shape is approximated with the short straight sections. The new method is characterized...
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Comparing some convolution-based methods for creation of surround sound
PublicationW referacie przedstawiono eksperymenty związane z symulacją dźwięku dookólnego w sali koncertowej. W tym celu wykorzystano splot odpowiedzi impulsowej z danego wnętrza (wielokanałowe nagrania odpowiedzi impulsowej) z nagraniami z komory bezechowej. Uzyskany w ten sposób sygnał został następnie przypisany do odpowiednich kanałów w systemie dookólnym. Uzyskane w ten sposób nagrania były następnie porównywane w testach subiektywnych...
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Alternative Approach to Convolution Term of Viscoelasticity in Equations of Unsteady Pipe Flow
PublicationIn the paper the selected aspects concerning description of viscoelastic behavior of pipe walls during unsteady flow are analyzed. The alternative convolution expression of the viscoelastic term is presented and compared with the corresponding term referring to unsteady friction. Both approaches indicate similarities in the forms of impulse response functions and the parameter properties. The flow memory was introduced into convolution...
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An Analog Sub-Miliwatt CMOS Image Sensor With Pixel-Level Convolution Processing
PublicationA new approach to an analog ultra-low power medium-resolution vision chip design is presented. The prototype chip performs low-level image processing algorithms in real time. Only a photo-diode, MOS switches and two capacitors are used to create an analog processing element (APE) that is able to realize any convolution algorithm based on a full 3x3 kernel. The proof-of-concept circuit is implemented in 0.35 µm CMOS technology,...
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Design of a complex multiplier based on the convolution with the use of the polynomial residue number system
Publicationzaproponowano realizację mnożnika zespolonego opartego na algorytmie dekompozycyjnym skavantzosa i stouraitisa. mnożenie zespolone jest wykonywane jako splot 8-punktowy. przedstawiono przykład obliczeniowy i architekturę mnożnika dla małych liczb.
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Ontology-based text convolution neural network (TextCNN) for prediction of construction accidents
PublicationThe construction industry suffers from workplace accidents, including injuries and fatalities, which represent a significant economic and social burden for employers, workers, and society as a whole.The existing research on construction accidents heavily relies on expert evaluations,which often suffer from issues such as low efficiency, insufficient intelligence, and subjectivity.However, expert opinions provided in construction...
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Single-Slope ADC With Embedded Convolution Filter for Global-Shutter CMOS Image Sensors
PublicationThis brief presents an analog-to-digital converter (ADC) suitable for acquisition and processing of images in the global-shutter mode at the pixel level. The ADC consists of an analog comparator, a multi-directional shift register for the comparator states, and a 16-bit reversible binary counter with programmable step size. It works in the traditional single-slope mode. The novelty is that during each step of the reference ramp,...
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Hybrid Technique Combining the FDTD Method and Its Convolution Formulation Based on the Discrete Green's Function
PublicationIn this letter, a technique combining the finite-difference time-domain (FDTD) method and its formulation based on the discrete Green's function (DGF) is presented. The hybrid method is applicable to inhomogeneous dielectric structures that are mutually coupled with wire antennas. The method employs the surface equivalence theorem in the discrete domain to separate the problem into a dielectric domain simulated using the FDTD method...
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Comparing some convolution-based methods for creation of surround sound. W: [CD-ROM] Collected papers. First Pan-American/Iberian Meeting on Acoustics. 144 Meeting of the Acoustical Society of America. III Iberoamerican Cong- ress of Acoustics. 9o Mexican Congress of Acoustics. Cancun, Q. R. Mxico, 2-6 Dec. 2002. [B.m.:ASA]**2002 paper 2pPP22, 7 s. 8 rys. 1 tab. bibliogr. 12 poz. system nagrań dźwięku dookólnego z wykorzystaniem splotu odpowiedzi impul- sowej sali.
PublicationW referacie przedstawiono eksperymenty związane z symulacją dźwięku dookól-nego w sali koncertowej. W tym celu wykorzystano splot odpowiedzi impulsowejz danego wnętrza (wielokanałowe nagrania odpowiedzi impulsowej) z nagraniamiz komory bezechowej. Uzyskany w ten sposób sygnał został następnie przypisa-ny do odpowiednich kanałów w systemie dookólnym. Uzyskane w ten sposób nag-rania były następnie porównywane w testach subiektywnych...
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Performance Analysis of Convolutional Neural Networks on Embedded Systems
PublicationMachine learning is no longer confined to cloud and high-end server systems and has been successfully deployed on devices that are part of Internet of Things. This paper presents the analysis of performance of convolutional neural networks deployed on an ARM microcontroller. Inference time is measured for different core frequencies, with and without DSP instructions and disabled access to cache. Networks use both real-valued and...
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Architectural Modifications to Enhance Steganalysis with Convolutional Neural Networks
PublicationThis paper investigates the impact of various modifications introduced to current state-of-the-art Convolutional Neural Network (CNN) architectures specifically designed for the steganalysis of digital images. Usage of deep learning methods has consistently demonstrated improved results in this field over the past few years, primarily due to the development of newer architectures with higher classification accuracy compared to...
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Characterizing the Scalability of Graph Convolutional Networks on Intel® PIUMA
PublicationLarge-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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Clothes Detection and Classification Using Convolutional Neural Networks
PublicationIn this paper we describe development of a computer vision system for accurate detection and classification of clothes for e-commerce images. We present a set of experiments on well established architectures of convolutional neural networks, including Residual networks, SqueezeNet and Single Shot MultiBox Detector (SSD). The clothes detection network was trained and tested on DeepFashion dataset, which contains box annotations...
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Digits Recognition with Quadrant Photodiode and Convolutional Neural Network
PublicationIn this paper we have investigated the capabilities of a quadrant photodiode based gesture sensor in the recognition of digits drawn in the air. The sensor consisting of 4 active elements, 4 LEDs and a pinhole was considered as input interface for both discrete and continuous gestures. Index finger and a round pointer were used as navigating mediums for the sensor. Experiments performed with 5 volunteers...
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Automatic Breath Analysis System Using Convolutional Neural Networks
PublicationDiseases related to the human respiratory system have always been a burden for the entire society. The situation has become particularly difficult now after the outbreak of the COVID-19 pandemic. Even now, however, it is not uncommon for people to consult their doctor too late, after the disease has developed. To protect patients from severe disease, it is recommended that any symptoms disturbing the respiratory system be detected...
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Deep convolutional neural network for predicting kidney tumour malignancy
PublicationPurpose: According to the statistics, up to 15-20% of removed solid kidney tumors turn out to be benign in postoperative histopathological examination, despite having been identified as malignant by a radiologist. The aim of the research was to limit the number of unnecessary nephrectomies of benign tumors. Methods or Background: We propose a machine-aided diagnostic system for kidney...
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Automatic Breath Analysis System Using Convolutional Neural Networks
PublicationDiseases related to the human respiratory system have always been a burden for the entire society. The situation has become particularly difficult now after the outbreak of the COVID-19 pandemic. Even now, however, it is common for people to consult their doctor too late, after the disease has developed. To protect patients from severe disease, it is recommended that any symptoms disturbing the respiratory system be detected as...
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An Improved Convolutional Neural Network for Steganalysis in the Scenario of Reuse of the Stego-Key
PublicationThe topic of this paper is the use of deep learning techniques, more specifically convolutional neural networks, for steganalysis of digital images. The steganalysis scenario of the repeated use of the stego-key is considered. Firstly, a study of the influence of the depth and width of the convolution layers on the effectiveness of classification was conducted. Next, a study on the influence of depth and width of fully connected...
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Recognition of Emotions in Speech Using Convolutional Neural Networks on Different Datasets
PublicationArtificial Neural Network (ANN) models, specifically Convolutional Neural Networks (CNN), were applied to extract emotions based on spectrograms and mel-spectrograms. This study uses spectrograms and mel-spectrograms to investigate which feature extraction method better represents emotions and how big the differences in efficiency are in this context. The conducted studies demonstrated that mel-spectrograms are a better-suited...
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From Linear Classifier to Convolutional Neural Network for Hand Pose Recognition
PublicationRecently gathered image datasets and the new capabilities of high-performance computing systems have allowed developing new artificial neural network models and training algorithms. Using the new machine learning models, computer vision tasks can be accomplished based on the raw values of image pixels instead of specific features. The principle of operation of deep neural networks resembles more and more what we believe to be happening...
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Highlighting interlanguage phoneme differences based on similarity matrices and convolutional neural network
PublicationThe goal of this research is to find a way of highlighting the acoustic differences between consonant phonemes of the Polish and Lithuanian languages. For this purpose, similarity matrices are employed based on speech acoustic parameters combined with a convolutional neural network (CNN). In the first experiment, we compare the effectiveness of the similarity matrices applied to discerning acoustic differences between consonant...
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Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks
PublicationThe presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods....
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System for monitoring road slippery based on CCTV cameras and convolutional neural networks
PublicationThe slipperiness of the surface is essential for road safety. The growing number of CCTV cameras opens the possibility of using them to automatically detect the slippery surface and inform road users about it. This paper presents a system of developed intelligent road signs, including a detector based on convolutional neural networks (CNNs) and the transferlearning method employed to the processing of images acquired with video...
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A Novel Iterative Decoding for Iterated Codes Using Classical and Convolutional Neural Networks
PublicationForward error correction is crucial for communication, enabling error rate or required SNR reduction. Longer codes improve correction ratio. Iterated codes offer a solution for constructing long codeswith a simple coder and decoder. However, a basic iterative code decoder cannot fully exploit the code’s potential, as some error patterns within its correction capacity remain uncorrected.We propose two neural network-assisted decoders:...
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Using Convolutional Neural Networks for Corneal Arcus Detection Towards Familial Hypercholesterolemia Screening
PublicationFamilial hypercholesterolemia (FH) is a highly undiagnosed disease. Among FH patients, the onset of premature coronary artery disease is 13 times higher than in the general population. Early diagnosis and treatment is essential to prevent cardiovascular diseases and their complications, and to prolong life. One of the clinical criteria of FH is the occurrence of a corneal arcus (CA) among patients, especially those under 45 years...
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1D convolutional context-aware architectures for acoustic sensing and recognition of passing vehicle type
PublicationA 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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The influence of image masks definition onsegmentation results of histopathological imagesusing convolutional neural network
PublicationAbstract—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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A novel approach exploiting properties of convolutional neural networks for vessel movement anomaly detection and classification
PublicationThe article concerns the automation of vessel movement anomaly detection for maritime and coastal traffic safety services. Deep Learning techniques, specifically Convolutional Neural Networks (CNNs), were used to solve this problem. Three variants of the datasets, containing samples of vessel traffic routes in relation to the prohibited area in the form of a grayscale image, were generated. 1458 convolutional neural networks with...
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Speaker Recognition Using Convolutional Neural Network with Minimal Training Data for Smart Home Solutions
PublicationWith the technology advancements in smart home sector, voice control and automation are key components that can make a real difference in people's lives. The voice recognition technology market continues to involve rapidly as almost all smart home devices are providing speaker recognition capability today. However, most of them provide cloud-based solutions or use very deep Neural Networks for speaker recognition task, which are...
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Improving Accuracy of Respiratory Rate Estimation by Restoring High Resolution Features With Transformers and Recursive Convolutional Models
PublicationNon-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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Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-Learned Features
PublicationNematodes Caenorhabditis elegans (C. elegans) have been used as model organisms in a wide variety of biological studies, especially those intended to obtain a better understanding of aging and age-associated diseases. This paper focuses on automating the analysis of C. elegans imagery to classify the muscle age of nematodes based on the known and well established IICBU dataset. Unlike many modern classification methods, the proposed...
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A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multi-Head Convolutional Attention
PublicationTogether with fast advancement of the Internet of Things (IoT), smart healthcare applications and systems are equipped with increasingly more wearable sensors and mobile devices. These sensors are used not only to collect data, but also, and more importantly, to assist in daily activity tracking and analyzing of their users. Various human activity recognition (HAR) approaches are used to enhance such tracking. Most of the existing...
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Classification of Covid-19 using Differential Evolution Chaotic Whale Optimization based Convolutional Neural Network
PublicationCOVID-19, also known as the Coronavirus disease-2019, is an transferrable disease that spreads rapidly, affecting countless individuals and leading to fatalities in this worldwide pandemic. The precise and swift detection of COVID-19 plays a crucial role in managing the pandemic's dissemination. Additionally, it is necessary to recognize COVID-19 quickly and accurately by investigating chest x-ray images. This paper proposed a...
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DEEP CONVOLUTIONAL NEURAL NETWORKS AS A DECISION SUPPORT TOOL IN MEDICAL PROBLEMS – MALIGNANT MELANOMA CASE STUDY
PublicationThe paper presents utilization of one of the latest tool from the group of Machine learning techniques, namely Deep Convolutional Neural Networks (CNN), in process of decision making in selected medical problems. After the survey of the most successful applications of CNN in solving medical problems, the paper focuses on the very difficult problem of automatic analyses of the skin lesions. The authors propose the CNN structure...
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Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures
PublicationAbstract Background In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments...
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Smart Approach for Glioma Segmentation in Magnetic Resonance Imaging using Modified Convolutional Network Architecture (U-NET)
PublicationSegmentation of a brain tumor from magnetic resonance multimodal images is a challenging task in the field of medical imaging. The vast diversity in potential target regions, appearance and multifarious intensity threshold levels of various tumor types are few of the major factors that affect segmentation results. An accurate diagnosis and its treatment demand strict delineation of the tumor affected tissues. Herein, we focus on...
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Abdominal Aortic Aneurysm segmentation from contrast-enhanced computed tomography angiography using deep convolutional networks
PublicationOne 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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GPU Power Capping for Energy-Performance Trade-Offs in Training of Deep Convolutional Neural Networks for Image Recognition
PublicationIn the paper we present performance-energy trade-off investigation of training Deep Convolutional Neural Networks for image recognition. Several representative and widely adopted network models, such as Alexnet, VGG-19, Inception V3, Inception V4, Resnet50 and Resnet152 were tested using systems with Nvidia Quadro RTX 6000 as well as Nvidia V100 GPUs. Using GPU power capping we found other than default configurations minimizing...
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Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks
PublicationAge prediction from X-rays is an interesting research topic important for clinical applications such as biological maturity assessment. It is also useful in many other practical applications, including sports or forensic investigations for age verification purposes. Research on these issues is usually carried out using high-resolution X-ray scans of parts of the body, such as images of the hands or images of the chest. In this...
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Flexible Knowledge–Vision–Integration Platform for Personal Protective Equipment Detection and Classification Using Hierarchical Convolutional Neural Networks and Active Leaning
PublicationThis work is part of an effort to develop of a Knowledge-Vision Integration Platform for Hazard Control (KVIP-HC) in industrial workplaces, adaptable to a wide range of industrial environments. The paper focuses on hazards resulted from the non-use of personal protective equipment (PPE). The objective is to test the capability of the platform to adapt to different industrial environments by simulating the process of randomly selecting...
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Activation maps of convolutional neural networks as a tool for brain degeneration tracking in early diagnosis of dementia in Parkinson's disease based on magnetic resonance imaging
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Image Processing Techniques for Distributed Grid Applications
PublicationParallel 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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Inverse Flood Routing Using Simplified Flow Equations
PublicationThe paper considers the problem of inverse flood routing in reservoir operation strategy. The aim of the work is to investigate the possibility of determining the hydrograph at the upstream end based on the hydrograph required at the downstream end using simplified open channel flow models. To accomplish this, the linear kinematic wave equation, the diffusive wave equation and the linear Muskingum equation are considered. To achieve...