The aerospace industry is a very unforgiving field, where even the smallest error could have catastrophic consequences.To reduce the risk of disaster, multiple systems are put into place to provide accurate informatio...The aerospace industry is a very unforgiving field, where even the smallest error could have catastrophic consequences.To reduce the risk of disaster, multiple systems are put into place to provide accurate information for informed decisionmaking. The field of optics has played a pivotal role in advancing space exploration and technology. From enabling preciseobservations of distant celestial objects to facilitating communication across vast interstellar distances, optics hasbecome an indispensable tool in space science and supported by significant advances in the last few years, new and improvedapplications continue to arise. This review aims to explore the diverse applications of optical systems and technologiesin the aerospace industry, highlighting recent developments regarding navigation, communications, process andstructural health monitoring, as well as the monitorization of astronauts' health.展开更多
Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remain...Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.展开更多
In this paper, we propose and evaluate outage performance of a mixed amplify-and-forward(AF) and decode-and-forward(DF) relaying protocol in underlay cognitive radio. Different from the conventional AF and DF protocol...In this paper, we propose and evaluate outage performance of a mixed amplify-and-forward(AF) and decode-and-forward(DF) relaying protocol in underlay cognitive radio. Different from the conventional AF and DF protocols, in the proposed protocol, a secondary source attempts to transmit its signal to a secondary destination with help of two secondary relays. One secondary relay always operates in AF mode, while the remaining one always operates in DF mode. Moreover, we also propose a relay selection method, which relies on the decoding status at the DF relay. For performance evaluation and comparison, we derive the exact and approximate closedform expressions of the outage probability for the proposed protocol over Rayleigh fading channel. Finally, we run Monte Carlo simulations to verify the derivations. Results presented that the proposed protocol obtains a diversity order of three and the outage performance of our scheme is between that of the conventional underlay DF protocol and that of the conventional underlay AF protocol.展开更多
The field of mobility prediction has been widely investigated in the recent past,especially the reduction of the coverage radius of cellular networks,which led to an increase in hand-over events.Changing the cell cove...The field of mobility prediction has been widely investigated in the recent past,especially the reduction of the coverage radius of cellular networks,which led to an increase in hand-over events.Changing the cell coverage very frequently,for example,may lead to service disruptions if a predictive approach is not deployed in the system.Although several works examined mobility prediction in the new-generation mobile networks,all of these studies focused on studying the time features of mobility traces,and the spectral content of historical mobility patterns was not considered for prediction purposes as yet.In the present study,we propose a new approach to mobility prediction by analyzing the effects of a proper mobility sampling frequency.The proposed approach lies in the mobility analysis in the frequency domain,to extract hidden features of the mobility process.Thus,we proposed a new methodology to determine the spectral content of mobility traces(considered as signals)and,thus,the appropriate sampling frequency,which can provide numerous advantages.We considered several types of mobility models(e.g.pedestrian,urban,and vehicular),containing important details in the time and frequency domains.Several simulation campaigns were performed to observe and analyze the characteristics of mobility from real traces and to evaluate the effects of sampling frequency on the spectral content.展开更多
Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods...Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.展开更多
An intersection of two or more roads poses a risk for potential conflicts among vehicles.Often the reasons triggering such conflicts are not clear,as they might be too subtle for the human eye.The environment also pla...An intersection of two or more roads poses a risk for potential conflicts among vehicles.Often the reasons triggering such conflicts are not clear,as they might be too subtle for the human eye.The environment also plays a part in understanding where,when,and why a particular vehicle interaction has occurred in a certain way.Therefore,it is of paramount importance to dive deeper into the vehicle interaction at a micro-scale within the embedded geographical environment,particularly at the intersections.This would in turn assist in evaluating the association of vehicle interactions with conflict risks and near-miss accidents.Moreover,detection of such micro traffic interactions could also be used to improvise the complexity of the already established transport infrastructure.Conversely,traffic at intersections has been explored mainly for flow estimation,capacity and width measurements,and traffic congestion,etc.,whereas the detection of micro-scale traffic interactions at intersections remains relatively under-explored.In this paper,we present a novel approach to retrieve and represent micro-scale traffic movement interactions at a non-signalized T-junction by extending a recently introduced qualitative spatiotemporal Point-Descriptor-Precedence(PDP)representation.We study how the PDP representation offers a fine solution to study the interaction of traffic flows at intersections.This permits tracking the micro-movement of vehicles in much finer detail,which is used later to retrieve movement patterns from a motion dataset.Unlike conventional approaches,we start our approach with the actual movements before modeling the static intersection environment.Additionally,with the aid of illustrative examples,we discuss how the length,width,and speed of the vehicles can be exploited in our approach to detect specific patterns more accurately.Additionally,we address the potential benefits of our approach for traffic safety assessment and how it can be extended to a network of intersections using different transport modes.展开更多
The paper presents a comparative study on the electric, dielectric and microwave properties of natural rubber based composites comprising dual phase fillers prepared from furnace carbon black or conductive carbon blac...The paper presents a comparative study on the electric, dielectric and microwave properties of natural rubber based composites comprising dual phase fillers prepared from furnace carbon black or conductive carbon black with a different amount of silica. It has been established that, the specifics of the carbon phase have a marked strong effect upon the properties mentioned above. The interpenetration of the two filler phases and the grade of isolation of the conductive carbon phase by the dielectric one depend on the ratio between them. On the other hand, that leads to a change in all properties of the studied composites, which allows tailoring those characteristics.展开更多
This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous ...This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART vehicles.Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground noise.This article presents the new concept of a hybrid system which is implemented as a virtual instrument.The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction.The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles.On average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.展开更多
Real-Time segmented pulse compression-detection is one of the key technologies of space-borne tracking receiver.Its implementation requires an optimized and dedicated hardware.The real-time processing places several c...Real-Time segmented pulse compression-detection is one of the key technologies of space-borne tracking receiver.Its implementation requires an optimized and dedicated hardware.The real-time processing places several constraints such as area occupied,power comumption,and speed.A number of segmented compression techniques have been proposed to overcome these limitations and decrease the processing latency.However,relatively high power loss in the partial field could limit their implementation in many current real-time systems.A good theoretical model was designed with intersection signal accumulation to enhance signal-noise-ratio(SNR)gain of detecting signal in the paper.From the experimental results it is known that this approach works well for pulse compression-detection,which is better suited for implementation in the high performance of current field programmable gate array(FPGA)with dedicated hardware multipliers.展开更多
Industry leaders are currently setting out standards for 5G networks projected for 2020 or even sooner. Future generation networks will be heterogeneous in nature as no single network type will be capable of optimally...Industry leaders are currently setting out standards for 5G networks projected for 2020 or even sooner. Future generation networks will be heterogeneous in nature as no single network type will be capable of optimally meeting all the rapid changes in customer demands. With the advent of multi-homed devices and heterogeneous network (HetNet) solution, great concerns arise in the processes involved for successful handover. Active calls that get dropped or cases of poor quality of service experienced by mobile users can be attributed to the phenomenon of delayed handover (HO) or an outright case of an unsuccessful handover procedure. This work compares multiple criteria handover basis to its traditional single relative signal strength (RSS) base counterpart. It analyses the performance of a fuzzy-based VHO algorithm scheme in a Wi-Fi, WiMAX, UMTS and LTE integrated network using OMNeT++ event simulator. The loose coupling network architecture is adopted and simulation results analysed for the two major categories of handover;the multiple and single criteria. Results obtained show a better overall throughput, better call dropped rate and shorter handover time for the multiple criteria based decision method as compared to the single criteria based technique. This work also highlights current research trends, challenges of seamless handover and initiatives for Next Generation HetNet.展开更多
The analysis of the principle operation of the solar element is given in the work. The efficiency of the creation of combined high-efficiency converters of network energy in electric and thermal is indicated. A method...The analysis of the principle operation of the solar element is given in the work. The efficiency of the creation of combined high-efficiency converters of network energy in electric and thermal is indicated. A method for creating a combined photo thermo converter with a high value of the efficiency of a solar element has been found. A method for separating light from optical lenses is shown. The calculation of the light intensity into diffraction pattern is calculated using the Huygens-Fresnel principle. A new design of a highly efficient combined light-thermal converter into an electrical, with a solar element, operating on selective photoactive radiation is presented. The process of conversion of non-active radiation into electrical radiation by means of a thermo electronic converter is described. The ways of the solution of the problem connected with the reduction of the coefficient of its full effect as a function of temperature and characteristics are indicated.展开更多
Parkinson’s disease is a neurological disease which is incurable according to current clinical knowledge. Therefore, early detection and provision of appropriate treatment are of primary importance. Speech is one of ...Parkinson’s disease is a neurological disease which is incurable according to current clinical knowledge. Therefore, early detection and provision of appropriate treatment are of primary importance. Speech is one of the biomarkers that enable the detection of Parkinson’s disease affection. Numerous researches are based on recordings from controlled environments;nonetheless fewer apply real circumstances. In the present study, three objectives were examined: recording fragmentation (paragraph, sentences, time-based), variable encodings (Pulse-Code Modulation [PCM], GSM-Full Rate [FR], G.723.1) and majority voting on 8 kHz records using multiple classifiers. Support Vector Machine (SVM), Long Short-Term Memory (LSTM), i-vector and x-vector classifiers were evaluated in contrast with SVM as baseline. The highest results in accuracy and F1-score were achieved using i-vector models. Although variable encodings generally caused decrease in Parkinson-disease recognition, decline was within 2% - 3% at best. Moreover, fragmentation did not yield a clear outcome though some classifiers performed with the very similar efficiency along the differently fragmented sets. Majority voting did produce a slight increase in classification performance compared to as if no aggregation is used.展开更多
The work presents studies on the complex permittivity and permeability of composites based on acrylonitrile butadiene rubber containing combinations of conductive fillers which include carbon black and nickel powder. ...The work presents studies on the complex permittivity and permeability of composites based on acrylonitrile butadiene rubber containing combinations of conductive fillers which include carbon black and nickel powder. The properties of those composites, containing each of the fillers at the same amount were compared. The permittivity and permeability values of the composites are influenced remarkably by their morphology and structure as well as by the morphological and structural specifics of both fillers. As electron scanning microscopy studies confirm, those parameters are predetermined by the nature of the composites studied—particle size, particles arrangement in the matrix and their tendency to clustering. Last but not least matrix-filler interface phenomena also impact the characteristics in question. The possibilities for applications of the composites in antennae have been studied, in particular, as substrates and insulating layers in flexible antennae for body centric communications (BCCs). The research results allow the conclusion that these materials can find such applications indeed. Composites of higher conductivity can be used where surface waves are generated to provide on-body communications, while composites of lower conductivity may be used for antennae that will be on the body of a person and will transmit to and receive from other antennas that are not on the body of the same person (off-body communications). It is clear that one can engineer the properties of antennae substrates at microwave frequencies by adjusting the filler content and the type of filler and thus control and tailor the antenna performance specific for a particular application.展开更多
In this paper, the optimization of quantizer’s segment threshold is done. The quantizer is designed on the basis of approximative spline functions. Coefficients on which we form approximative spline functions are cal...In this paper, the optimization of quantizer’s segment threshold is done. The quantizer is designed on the basis of approximative spline functions. Coefficients on which we form approximative spline functions are calculated by minimization mean square error (MSE). For coefficients determined in this way, spline functions by which optimal compressor function is approximated are obtained. For the quantizer designed on the basis of approximative spline functions, segment threshold is numerically determined depending on maximal value of the signal to quantization noise ratio (SQNR). Thus, quantizer with optimized segment threshold is achieved. It is shown that by quantizer model designed in this way and proposed in this paper, the SQNR that is very close to SQNR of nonlinear optimal companding quantizer is achieved.展开更多
The paper presents the synthesis and characterization of carbon black/silicone dioxide hybrid fillers obtained by an impregnation technology. The electromagnetic interference shielding effectiveness of the composites ...The paper presents the synthesis and characterization of carbon black/silicone dioxide hybrid fillers obtained by an impregnation technology. The electromagnetic interference shielding effectiveness of the composites filled with carbon black/silicone dioxide hybrid fillers was measured in wide frequency range of 1 - 12 GHz. The dc and ac electrical conductivity of composites also have been investigated. The relationship between electrical (dc and ac) conductivity and shielding effectiveness was analyzed. A positive correlation was found between the absorptive shielding effectiveness and ac conductivity for composites comprising conductive carbon black/silica filler, when the filler loading is above the percolation threshold.展开更多
Close-range hyperspectral images are a promising source of information in plant biology,in particular,for in vivo study of physiological changes.In this study,we investigate how data fusion can improve the detection o...Close-range hyperspectral images are a promising source of information in plant biology,in particular,for in vivo study of physiological changes.In this study,we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information.The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation,disease infections,and environmental conditions have in plants.We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing.Experimental results demonstrate the efficiency of our fusion approach,with significant improvements over some conventional methods.展开更多
基金funded by National Funds through the Portuguese Science and Technology Foundation(FCT I.P.)under the scope of the project CICECO(LA/P/0006/2020,UIDB/50011/2020,UIDP/50011/2020)The research was co-funded by the financial support of the European Union under the REFRESH–Research Excellence For Region Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transitionsupported by the Ministry of Education,Youth,and Sports of the Czech Republic conducted by the VSB-Technical University of Ostrava,under grants no.SP2025/039 and SP2025/021.
文摘The aerospace industry is a very unforgiving field, where even the smallest error could have catastrophic consequences.To reduce the risk of disaster, multiple systems are put into place to provide accurate information for informed decisionmaking. The field of optics has played a pivotal role in advancing space exploration and technology. From enabling preciseobservations of distant celestial objects to facilitating communication across vast interstellar distances, optics hasbecome an indispensable tool in space science and supported by significant advances in the last few years, new and improvedapplications continue to arise. This review aims to explore the diverse applications of optical systems and technologiesin the aerospace industry, highlighting recent developments regarding navigation, communications, process andstructural health monitoring, as well as the monitorization of astronauts' health.
基金This study is financed by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001.
文摘Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.
基金supported by the 2016 research fund of University of Ulsan
文摘In this paper, we propose and evaluate outage performance of a mixed amplify-and-forward(AF) and decode-and-forward(DF) relaying protocol in underlay cognitive radio. Different from the conventional AF and DF protocols, in the proposed protocol, a secondary source attempts to transmit its signal to a secondary destination with help of two secondary relays. One secondary relay always operates in AF mode, while the remaining one always operates in DF mode. Moreover, we also propose a relay selection method, which relies on the decoding status at the DF relay. For performance evaluation and comparison, we derive the exact and approximate closedform expressions of the outage probability for the proposed protocol over Rayleigh fading channel. Finally, we run Monte Carlo simulations to verify the derivations. Results presented that the proposed protocol obtains a diversity order of three and the outage performance of our scheme is between that of the conventional underlay DF protocol and that of the conventional underlay AF protocol.
基金supported by the Czech Ministry of Education,Youth and Sports under project Reg.No.SP2021/25partially from the project“e-Infrastructure CZ”Reg.No.LM2018140.
文摘The field of mobility prediction has been widely investigated in the recent past,especially the reduction of the coverage radius of cellular networks,which led to an increase in hand-over events.Changing the cell coverage very frequently,for example,may lead to service disruptions if a predictive approach is not deployed in the system.Although several works examined mobility prediction in the new-generation mobile networks,all of these studies focused on studying the time features of mobility traces,and the spectral content of historical mobility patterns was not considered for prediction purposes as yet.In the present study,we propose a new approach to mobility prediction by analyzing the effects of a proper mobility sampling frequency.The proposed approach lies in the mobility analysis in the frequency domain,to extract hidden features of the mobility process.Thus,we proposed a new methodology to determine the spectral content of mobility traces(considered as signals)and,thus,the appropriate sampling frequency,which can provide numerous advantages.We considered several types of mobility models(e.g.pedestrian,urban,and vehicular),containing important details in the time and frequency domains.Several simulation campaigns were performed to observe and analyze the characteristics of mobility from real traces and to evaluate the effects of sampling frequency on the spectral content.
基金Ministry of Education,Youth and Sports of the Chezk Republic,Grant/Award Numbers:SP2023/039,SP2023/042the European Union under the REFRESH,Grant/Award Number:CZ.10.03.01/00/22_003/0000048。
文摘Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.
基金supported by the Higher Education Commission(HEC),Pakistan[grant number 50040696]Bernard De Baets and Guy De Tréreceived funding from the Flemish Government under the“Onderzoeksprogramma Artificiële Intelligentie(AI)Vlaanderen”program.
文摘An intersection of two or more roads poses a risk for potential conflicts among vehicles.Often the reasons triggering such conflicts are not clear,as they might be too subtle for the human eye.The environment also plays a part in understanding where,when,and why a particular vehicle interaction has occurred in a certain way.Therefore,it is of paramount importance to dive deeper into the vehicle interaction at a micro-scale within the embedded geographical environment,particularly at the intersections.This would in turn assist in evaluating the association of vehicle interactions with conflict risks and near-miss accidents.Moreover,detection of such micro traffic interactions could also be used to improvise the complexity of the already established transport infrastructure.Conversely,traffic at intersections has been explored mainly for flow estimation,capacity and width measurements,and traffic congestion,etc.,whereas the detection of micro-scale traffic interactions at intersections remains relatively under-explored.In this paper,we present a novel approach to retrieve and represent micro-scale traffic movement interactions at a non-signalized T-junction by extending a recently introduced qualitative spatiotemporal Point-Descriptor-Precedence(PDP)representation.We study how the PDP representation offers a fine solution to study the interaction of traffic flows at intersections.This permits tracking the micro-movement of vehicles in much finer detail,which is used later to retrieve movement patterns from a motion dataset.Unlike conventional approaches,we start our approach with the actual movements before modeling the static intersection environment.Additionally,with the aid of illustrative examples,we discuss how the length,width,and speed of the vehicles can be exploited in our approach to detect specific patterns more accurately.Additionally,we address the potential benefits of our approach for traffic safety assessment and how it can be extended to a network of intersections using different transport modes.
文摘The paper presents a comparative study on the electric, dielectric and microwave properties of natural rubber based composites comprising dual phase fillers prepared from furnace carbon black or conductive carbon black with a different amount of silica. It has been established that, the specifics of the carbon phase have a marked strong effect upon the properties mentioned above. The interpenetration of the two filler phases and the grade of isolation of the conductive carbon phase by the dielectric one depend on the ratio between them. On the other hand, that leads to a change in all properties of the studied composites, which allows tailoring those characteristics.
基金This research was funded by the European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems project, project number CZ.02.1.01/0.0/0.0/16_019 /0000867by the Ministry of Education of the Czech Republic, Project No. SP2021/32.
文摘This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART vehicles.Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground noise.This article presents the new concept of a hybrid system which is implemented as a virtual instrument.The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction.The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles.On average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.
基金Correspondence should be addressed to:YANG Jian-xi,E-mail:navicom@foxmail.com
文摘Real-Time segmented pulse compression-detection is one of the key technologies of space-borne tracking receiver.Its implementation requires an optimized and dedicated hardware.The real-time processing places several constraints such as area occupied,power comumption,and speed.A number of segmented compression techniques have been proposed to overcome these limitations and decrease the processing latency.However,relatively high power loss in the partial field could limit their implementation in many current real-time systems.A good theoretical model was designed with intersection signal accumulation to enhance signal-noise-ratio(SNR)gain of detecting signal in the paper.From the experimental results it is known that this approach works well for pulse compression-detection,which is better suited for implementation in the high performance of current field programmable gate array(FPGA)with dedicated hardware multipliers.
文摘Industry leaders are currently setting out standards for 5G networks projected for 2020 or even sooner. Future generation networks will be heterogeneous in nature as no single network type will be capable of optimally meeting all the rapid changes in customer demands. With the advent of multi-homed devices and heterogeneous network (HetNet) solution, great concerns arise in the processes involved for successful handover. Active calls that get dropped or cases of poor quality of service experienced by mobile users can be attributed to the phenomenon of delayed handover (HO) or an outright case of an unsuccessful handover procedure. This work compares multiple criteria handover basis to its traditional single relative signal strength (RSS) base counterpart. It analyses the performance of a fuzzy-based VHO algorithm scheme in a Wi-Fi, WiMAX, UMTS and LTE integrated network using OMNeT++ event simulator. The loose coupling network architecture is adopted and simulation results analysed for the two major categories of handover;the multiple and single criteria. Results obtained show a better overall throughput, better call dropped rate and shorter handover time for the multiple criteria based decision method as compared to the single criteria based technique. This work also highlights current research trends, challenges of seamless handover and initiatives for Next Generation HetNet.
文摘The analysis of the principle operation of the solar element is given in the work. The efficiency of the creation of combined high-efficiency converters of network energy in electric and thermal is indicated. A method for creating a combined photo thermo converter with a high value of the efficiency of a solar element has been found. A method for separating light from optical lenses is shown. The calculation of the light intensity into diffraction pattern is calculated using the Huygens-Fresnel principle. A new design of a highly efficient combined light-thermal converter into an electrical, with a solar element, operating on selective photoactive radiation is presented. The process of conversion of non-active radiation into electrical radiation by means of a thermo electronic converter is described. The ways of the solution of the problem connected with the reduction of the coefficient of its full effect as a function of temperature and characteristics are indicated.
文摘Parkinson’s disease is a neurological disease which is incurable according to current clinical knowledge. Therefore, early detection and provision of appropriate treatment are of primary importance. Speech is one of the biomarkers that enable the detection of Parkinson’s disease affection. Numerous researches are based on recordings from controlled environments;nonetheless fewer apply real circumstances. In the present study, three objectives were examined: recording fragmentation (paragraph, sentences, time-based), variable encodings (Pulse-Code Modulation [PCM], GSM-Full Rate [FR], G.723.1) and majority voting on 8 kHz records using multiple classifiers. Support Vector Machine (SVM), Long Short-Term Memory (LSTM), i-vector and x-vector classifiers were evaluated in contrast with SVM as baseline. The highest results in accuracy and F1-score were achieved using i-vector models. Although variable encodings generally caused decrease in Parkinson-disease recognition, decline was within 2% - 3% at best. Moreover, fragmentation did not yield a clear outcome though some classifiers performed with the very similar efficiency along the differently fragmented sets. Majority voting did produce a slight increase in classification performance compared to as if no aggregation is used.
文摘The work presents studies on the complex permittivity and permeability of composites based on acrylonitrile butadiene rubber containing combinations of conductive fillers which include carbon black and nickel powder. The properties of those composites, containing each of the fillers at the same amount were compared. The permittivity and permeability values of the composites are influenced remarkably by their morphology and structure as well as by the morphological and structural specifics of both fillers. As electron scanning microscopy studies confirm, those parameters are predetermined by the nature of the composites studied—particle size, particles arrangement in the matrix and their tendency to clustering. Last but not least matrix-filler interface phenomena also impact the characteristics in question. The possibilities for applications of the composites in antennae have been studied, in particular, as substrates and insulating layers in flexible antennae for body centric communications (BCCs). The research results allow the conclusion that these materials can find such applications indeed. Composites of higher conductivity can be used where surface waves are generated to provide on-body communications, while composites of lower conductivity may be used for antennae that will be on the body of a person and will transmit to and receive from other antennas that are not on the body of the same person (off-body communications). It is clear that one can engineer the properties of antennae substrates at microwave frequencies by adjusting the filler content and the type of filler and thus control and tailor the antenna performance specific for a particular application.
基金Serbian Ministry of Education and Science through Mathematical Institute of Serbian Academy of Sciences and Arts(Project III44006)Serbian Ministry of Education and Science(Project TR32035)
文摘In this paper, the optimization of quantizer’s segment threshold is done. The quantizer is designed on the basis of approximative spline functions. Coefficients on which we form approximative spline functions are calculated by minimization mean square error (MSE). For coefficients determined in this way, spline functions by which optimal compressor function is approximated are obtained. For the quantizer designed on the basis of approximative spline functions, segment threshold is numerically determined depending on maximal value of the signal to quantization noise ratio (SQNR). Thus, quantizer with optimized segment threshold is achieved. It is shown that by quantizer model designed in this way and proposed in this paper, the SQNR that is very close to SQNR of nonlinear optimal companding quantizer is achieved.
文摘The paper presents the synthesis and characterization of carbon black/silicone dioxide hybrid fillers obtained by an impregnation technology. The electromagnetic interference shielding effectiveness of the composites filled with carbon black/silicone dioxide hybrid fillers was measured in wide frequency range of 1 - 12 GHz. The dc and ac electrical conductivity of composites also have been investigated. The relationship between electrical (dc and ac) conductivity and shielding effectiveness was analyzed. A positive correlation was found between the absorptive shielding effectiveness and ac conductivity for composites comprising conductive carbon black/silica filler, when the filler loading is above the percolation threshold.
文摘Close-range hyperspectral images are a promising source of information in plant biology,in particular,for in vivo study of physiological changes.In this study,we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information.The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation,disease infections,and environmental conditions have in plants.We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing.Experimental results demonstrate the efficiency of our fusion approach,with significant improvements over some conventional methods.
