Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate...Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.展开更多
Recently,Multicore systems use Dynamic Voltage/Frequency Scaling(DV/FS)technology to allow the cores to operate with various voltage and/or frequencies than other cores to save power and enhance the performance.In thi...Recently,Multicore systems use Dynamic Voltage/Frequency Scaling(DV/FS)technology to allow the cores to operate with various voltage and/or frequencies than other cores to save power and enhance the performance.In this paper,an effective and reliable hybridmodel to reduce the energy and makespan in multicore systems is proposed.The proposed hybrid model enhances and integrates the greedy approach with dynamic programming to achieve optimal Voltage/Frequency(Vmin/F)levels.Then,the allocation process is applied based on the availableworkloads.The hybrid model consists of three stages.The first stage gets the optimum safe voltage while the second stage sets the level of energy efficiency,and finally,the third is the allocation stage.Experimental results on various benchmarks show that the proposed model can generate optimal solutions to save energy while minimizing the makespan penalty.Comparisons with other competitive algorithms show that the proposed model provides on average 48%improvements in energy-saving and achieves an 18%reduction in computation time while ensuring a high degree of system reliability.展开更多
A method for improving the level of reliability of distribution systems is presented by employing an integrated voltage sag mitigation method that comprises a two-staged strategy,namely,distribution network reconfigur...A method for improving the level of reliability of distribution systems is presented by employing an integrated voltage sag mitigation method that comprises a two-staged strategy,namely,distribution network reconfiguration(DNR)followed by DSTATCOM placement.Initially,an optimal DNR is applied to reduce the propagated voltage sags during the test period.The second stage involves optimal placement of the DSTATCOM to assist the already reconfigured network.The gravitational search algorithm is used in the process of optimal DNR and in placing DSTATCOM.Reliability assessment is performed using the well-known indices.The simulation results show that the proposed method is efficient and feasible for improving the level of system reliability.展开更多
Average(mean)voter is one of the commonest voting methods suitable for decision making in highly-available and long-missions applications where the availability and the speed of the system are critical.In this paper,a...Average(mean)voter is one of the commonest voting methods suitable for decision making in highly-available and long-missions applications where the availability and the speed of the system are critical.In this paper,a new generation of average voter based on parallel algorithms and parallel random access machine(PRAM)structure are proposed.The analysis shows that this algorithm is optimal due to its improved time complexity,speed-up,and efficiency and is especially appropriate for applications where the size of input space is large.展开更多
Orthogonal Frequency Division Multiplexing (OFDM) is a multi-carrier scheme used in modern broadband wireless communication systems to transmit data over a number of orthogonal subcarriers. When transmitted signals ar...Orthogonal Frequency Division Multiplexing (OFDM) is a multi-carrier scheme used in modern broadband wireless communication systems to transmit data over a number of orthogonal subcarriers. When transmitted signals arrive at the receiver by more than one path of different length, the received signals are staggered in time;this is multipath propagation. To mitigate the effect of dispersed channel distortion caused by random channel delay spread, Cyclic Prefix (CP) is introduced to eliminate Inter-Symbol Interference (ISI). In the literature, researchers have focused on carrying out investigations (or studies) mainly on the two existing CP insertions, namely: normal and extended CPs. Both CPs have limitations with respect to handling channel delay spreads. In the current work, a new CP, herein referred to as “ultra extended” CP is proposed to address delay spreads beyond the limits of the normal and extended CPs. The efficacy of the proposed ultra extended CP is tested via simulation under different scenarios. It is shown by the results obtained that the proposed CP can efficiently handle delay spreads beyond the limits of the existing normal and extended CP, and can indeed be implemented in the design of future telecommunication systems to accommodate higher channel delay spreads and it ensures wider cell coverage.展开更多
Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveill...Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.展开更多
Face detection is a critical component inmodern security,surveillance,and human-computer interaction systems,with widespread applications in smartphones,biometric access control,and public monitoring.However,detecting...Face detection is a critical component inmodern security,surveillance,and human-computer interaction systems,with widespread applications in smartphones,biometric access control,and public monitoring.However,detecting faces with high levels of occlusion,such as those covered by masks,veils,or scarves,remains a significant challenge,as traditional models often fail to generalize under such conditions.This paper presents a hybrid approach that combines traditional handcrafted feature extraction technique called Histogram of Oriented Gradients(HOG)and Canny edge detection with modern deep learning models.The goal is to improve face detection accuracy under occlusions.The proposed method leverages the structural strengths of HOG and edge-based object proposals while exploiting the feature extraction capabilities of Convolutional Neural Networks(CNNs).The effectiveness of the proposed model is assessed using a custom dataset containing 10,000 heavily occluded face images and a subset of the Common Objects in Context(COCO)dataset for non-face samples.The COCO dataset was selected for its variety and realism in background contexts.Experimental evaluations demonstrate significant performance improvements compared to baseline CNN models.Results indicate that DenseNet121 combined with HOG outperforms other counterparts in classification metrics with an F1-score of 87.96%and precision of 88.02%.Enhanced performance is achieved through reduced false positives and improved localization accuracy with the integration of object proposals based on Canny and contour detection.While the proposed method increases inference time from 33.52 to 97.80 ms,it achieves a notable improvement in precision from 80.85% to 88.02% when comparing the baseline DenseNet121 model to its hybrid counterpart.Limitations of the method include higher computational cost and the need for careful tuning of parameters across the edge detection,handcrafted features,and CNN components.These findings highlight the potential of combining handcrafted and learned features for occluded face detection tasks.展开更多
Low-salinity water(LSW)and CO_(2) could be combined to perform better in a hydrocarbon reservoir due to their synergistic advantages for enhanced oil recovery(EOR);however,its microscopic recovery mechanisms have not ...Low-salinity water(LSW)and CO_(2) could be combined to perform better in a hydrocarbon reservoir due to their synergistic advantages for enhanced oil recovery(EOR);however,its microscopic recovery mechanisms have not been well understood due to the nature of these two fluids and their physical reactions in the presence of reservoir fluids and porous media.In this work,well-designed and inte-grated experiments have been performed for the first time to characterize the in-situ formation of micro-dispersions and identify their EOR roles during a LSW-alternating-CO_(2)(CO_(2)-LSWAG)process under various conditions.Firstly,by measuring water concentration and performing the Fourier transform infrared spectroscopy(FT-IR)analysis,the in-situ formation of micro-dispersions induced by polar and acidic materials was identified.Then,displacement experiments combining with nuclear magnetic resonance(NMR)analysis were performed with two crude oil samples,during which wettability,interfacial tension(IFT),CO_(2) dissolution,and CO_(2) diffusion were quantified.During a CO_(2)-LSWAG pro-cess,the in-situ formed micro-dispersions dictate the oil recovery,while the presence of clay minerals,electrical double-layer(EDL)expansion and multiple ion exchange(MIE)are found to contribute less.Such formed micro-dispersions are induced by CO_(2) via diffusion to mobilize the CO_(2)-diluted oil,alter the rock wettability towards more water-wet,and minimize the density contrast between crude oil and water.展开更多
In the production of AlCuFe alloy for a special application,the growth rate was changed and the results were evaluated.Changes in the eutectic spacing(microstructure)of a material due to the growth rate are known to a...In the production of AlCuFe alloy for a special application,the growth rate was changed and the results were evaluated.Changes in the eutectic spacing(microstructure)of a material due to the growth rate are known to affect its mechanical,electrical and thermal properties.To evaluate its microstructure,the eutectic composition of Al−32.5wt.%Cu−0.5wt.%Fe was prepared and directional solidification experiments were conducted using a Bridgman-type furnace at a constant temperature gradient(G=8.50 K/mm)and five growth rates(V=8.25,16.60,41.65,90.05,164.80μm/s).The effect of the growth rate on the eutectic spacing was then determined,and the resulting microhardness and ultimate tensile strength were obtained based on the change in the microstructure by regression analysis and Hall−Petch correlations.Despite the fact that the growth rate increased by approximately twenty times,the eutectic spacing decreased by a factor of approximately 5,and these changes in the growth rate and microstructure caused the mechanical properties to change by a factor of approximately 1.5.展开更多
In digital signal processing (DSP), Nyquistrate sampling completely describes a signal by exploiting its bandlimitedness. Compressed Sensing (CS), also known as compressive sampling, is a DSP technique efficiently acq...In digital signal processing (DSP), Nyquistrate sampling completely describes a signal by exploiting its bandlimitedness. Compressed Sensing (CS), also known as compressive sampling, is a DSP technique efficiently acquiring and reconstructing a signal completely from reduced number of measurements, by exploiting its compressibility. The measurements are not point samples but more general linear functions of the signal. CS can capture and represent sparse signals at a rate significantly lower than ordinarily used in the Shannon’s sampling theorem. It is interesting to notice that most signals in reality are sparse;especially when they are represented in some domain (such as the wavelet domain) where many coefficients are close to or equal to zero. A signal is called K-sparse, if it can be exactly represented by a basis, , and a set of coefficients , where only K coefficients are nonzero. A signal is called approximately K-sparse, if it can be represented up to a certain accuracy using K non-zero coefficients. As an example, a K-sparse signal is the class of signals that are the sum of K sinusoids chosen from the N harmonics of the observed time interval. Taking the DFT of any such signal would render only K non-zero values . An example of approximately sparse signals is when the coefficients , sorted by magnitude, decrease following a power law. In this case the sparse approximation constructed by choosing the K largest coefficients is guaranteed to have an approximation error that decreases with the same power law as the coefficients. The main limitation of CS-based systems is that they are employing iterative algorithms to recover the signal. The sealgorithms are slow and the hardware solution has become crucial for higher performance and speed. This technique enables fewer data samples than traditionally required when capturing a signal with relatively high bandwidth, but a low information rate. As a main feature of CS, efficient algorithms such as -minimization can be used for recovery. This paper gives a survey of both theoretical and numerical aspects of compressive sensing technique and its applications. The theory of CS has many potential applications in signal processing, wireless communication, cognitive radio and medical imaging.展开更多
Polyaniline/Attapugite/ PE(PAn-ATTP/PE)composites containing particles with core-shell structure were obtained via the two-step blending processs. The experimental condition is as follows: Organo-attapulgite and PAn w...Polyaniline/Attapugite/ PE(PAn-ATTP/PE)composites containing particles with core-shell structure were obtained via the two-step blending processs. The experimental condition is as follows: Organo-attapulgite and PAn was obtained by modifying attapulgite with laury benzenesulfonic acid sodium salt and, then added to PE. The electrical conductivity, structure and properties of the composites were studied. Under the function of shear stress, core-shell structure particles with ATTP as the core and PAn as the shell were formed in the composites. The structure of PAn-ATTP/PE composites were characterized by FTIR,XRD,SEM, etc, respectively. The effects of concentration of doping agent on the conductivity and mechanical property of the composites were investigated. The mechanical properties and impact fracture surface of the ternary composites were studied by means of the tensile tester, SEM, etc. The results show that polyaniline encapsulated ATTP enhances the strength of the PE. And the conductivity of PAn-ATTP/PE composites of is improved effectively when polyaniline encapsulated ATTP is added. The composite have good conductivity when 10% polyaniline encapsulated ATTP is added.展开更多
One of the most important features of the modern ma ch ining system in an "unmanned" factory is to change tools that have been subjec ted to wear and damage. An integrated tool condition monitoring system co...One of the most important features of the modern ma ch ining system in an "unmanned" factory is to change tools that have been subjec ted to wear and damage. An integrated tool condition monitoring system composed of multi-sensors, signal processing devices and intelligent decision making pla ns is a necessary requirement for automatic manufacturing processes. An intellig ent tool wear monitoring system will be introduced in this paper. The system is equipped with power consumption, vibration, AE and cutting force sensors, signal transformation and collection apparatus and a microcomputer. Tool condition monitoring is a pattern recognition process in which the characte ristics of the tool to be monitored are compared with those of the standard mode ls. The tool wear classification process is composed of the following parts: fea ture extraction; determination of the fuzzy membership functions of the features ; calculation of the fuzzy similarity; learning and tool wear classification. Fe atures extracted from the time domain and frequency domain for the future patter n recognition are as follows. Power consumption signal: mean value; AE-RMS sign al: mean value, skew and kutorsis; Cutting force, AE and vibration signal: mean value, standard deviation and the mean power in 10 frequency ranges. These signa l features can reflect the tool wear states comprehensively. The fuzzy approachi ng degree and the fuzzy distance between corresponding features of different obj ects are combined to describe the closeness of two fuzzy sets more accurately. A unique fuzzy driven neural network based pattern recognition algorithm has bee n developed from this research. The combination of Artificial Neural Networks (A NNs) and fuzzy logic system integrates the strong learning and classification ab ility of the former and the superb flexibility of the latter to express the dist ribution characteristics of signal features with vague boundaries. This methodol ogy indirectly solves the automatic weight assignment problem of the conventiona l fuzzy pattern recognition system and let it have greater representative power, higher training speed and be more robust. The introduction of the two-dimensio nal weighted approaching degree can make the pattern recognition process more re liable. The fuzzy driven neural network can effectively fuse multi-sensor i nformation and successfully recognize the tool wear states. Armed with the advan ced pattern recognition methodology, the established intelligent tool condition monitoring system has the advantages of being suitable for different machini ng conditions, robust to noise and tolerant to faults. Cooperated with the contr ol system of the machine tool, the optimized machining processed can be achieved .展开更多
Detection of brain tumors in MRI images is the first step in brain cancer diagnosis.The accuracy of the diagnosis depends highly on the expertise of radiologists.Therefore,automated diagnosis of brain cancer from MRI ...Detection of brain tumors in MRI images is the first step in brain cancer diagnosis.The accuracy of the diagnosis depends highly on the expertise of radiologists.Therefore,automated diagnosis of brain cancer from MRI is receiving a large amount of attention.Also,MRI tumor detection is usually followed by a biopsy(an invasive procedure),which is a medical procedure for brain tumor classification.It is of high importance to devise automated methods to aid radiologists in brain cancer tumor diagnosis without resorting to invasive procedures.Convolutional neural network(CNN)is deemed to be one of the best machine learning algorithms to achieve high-accuracy results in tumor identification and classification.In this paper,a CNN-based technique for brain tumor classification has been developed.The proposed CNN can distinguish between normal(no-cancer),astrocytoma tumors,gliomatosis cerebri tumors,and glioblastoma tumors.The implemented CNN was tested on MRI images that underwent a motion-correction procedure.The CNN was evaluated using two performance measurement procedures.The first one is a k-fold cross-validation testing method,in which we tested the dataset using k=8,10,12,and 14.The best accuracy for this procedure was 96.26%when k=10.To overcome the over-fitting problem that could be occurred in the k-fold testing method,we used a hold-out testing method as a second evaluation procedure.The results of this procedure succeeded in attaining 97.8%accuracy,with a specificity of 99.2%and a sensitivity of 97.32%.With this high accuracy,the developed CNN architecture could be considered an effective automated diagnosis method for the classification of brain tumors from MRI images.展开更多
Bio based nitrogen doped carbon dots(N-CDs)were obtained from empty fruit bunch carboxymethylcellulose and ethylenediamine(EDA)through one-pot hydrothermal carbonization route.The optimum as-formed NCDs were thoroughl...Bio based nitrogen doped carbon dots(N-CDs)were obtained from empty fruit bunch carboxymethylcellulose and ethylenediamine(EDA)through one-pot hydrothermal carbonization route.The optimum as-formed NCDs were thoroughly characterized via Transmission electron microscopy(TEM),high-resolution TEM(HRTEM),Fourier transform infrared(FTIR),X-ray photoelectron spectra(XPS),UV–vis spectra(UV–Vis)and Fluorescence spectra(PL).Response surface methodology was statistically used to assess three independent variables that have major influence on the fluorescence quantum yield(QY),including temperature(230–270℃),time(2–6 h)and EDA mass(10%–23.3%).Based on analysis of variance(ANOVA)results,synthesis temperature was found to be the most influential factor on the QY,followed by time and EDA mass.Higher temperature,long synthesis time and high amount of EDA were satisfactorily enough for efficient carbonization conversion rate and obtaining highest QY of N-CDs.The obtained quadratic model(R^2=0.9991)shows a good correlation between the experimental data and predicted values.The optimum synthetic parameters are of 270℃temperature,6 h reaction time and 23.3%of EDA mass.The optimized as-made N-CDs exhibited blue photoluminescence with both excitation dependent/independent phenomena and high nitrogen content.The maximum emission intensity was 426 nm at a maximum excitation wavelength of 320 nm,with a QY of up to 22.9%.XPS and FTIR data confirmed the existence of polar containing groups,such as carbonyl,carboxyl,hydroxyl and amino groups over the surface of N-CDs whereas nitrogen species in the form of(pyridinic and graphitic-N)were introduced in the aromatic carbon domains,which imparts the hydrophilic and photostability of N-CDs.Taking into account the low-cost and sustainable production of N-CDs,this method considered a feasible route for converting low quality waste into value-added nanomaterials and utilizing for different functionalization processes and analytical applications.展开更多
This study aimed to produce a prototype system for non-contact vital sign monitoring of the elderly using microwave radar with the intention of reducing the burdens on monitored individuals and nursing caregivers. In ...This study aimed to produce a prototype system for non-contact vital sign monitoring of the elderly using microwave radar with the intention of reducing the burdens on monitored individuals and nursing caregivers. In addition, we tested the ability of the proposed prototype system to measure the respiratory and heart rates of the elderly in a nursing home and discussed the systems effectiveness and problems by examining results of real-time monitoring. The prototype system consisted of two 24-GHz microwave radar antennas and an analysis system. The antennas were positioned below a mattress to monitor motion on the body surface for measuring cardiac and respiratory rates from the dorsal side of the subjects (23.3 ± 1.2 years) who would be lying on the mattress. The heart rates determined by the prototype system correlated significantly with those measured by electrocardiography (r = 0.92). Similarly, the respiratory rates determined by the prototype correlated with those obtained from respiration curves (r = 0.94). Next, we investigated the effectiveness of the prototype system with 7 elderly patients (93.3 ± 10.56 years) at a nursing home. The proposed system appears to be a promising tool for monitoring the vital signs of the elderly in a way that alleviates the need to attach electrodes overnight to confirm patient safety.展开更多
An alternative method was introduced for voltage sag source location based on S and TT transformed disturbance powers. It is done to avoid the wrong and inconclusive detection of conventional disturbance power method ...An alternative method was introduced for voltage sag source location based on S and TT transformed disturbance powers. It is done to avoid the wrong and inconclusive detection of conventional disturbance power method proposed in the literature. Unlike in the case of the traditional method, the proposed method first transforms the recorded voltage and current during the sag event to some special features before calculating the new version of disturbance powers. The effectiveness of the proposed method has been verified through simulation and actual data from an industrial power system. The results show that the presented method can correctly detect the location of voltage sag source.展开更多
文摘Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.
文摘Recently,Multicore systems use Dynamic Voltage/Frequency Scaling(DV/FS)technology to allow the cores to operate with various voltage and/or frequencies than other cores to save power and enhance the performance.In this paper,an effective and reliable hybridmodel to reduce the energy and makespan in multicore systems is proposed.The proposed hybrid model enhances and integrates the greedy approach with dynamic programming to achieve optimal Voltage/Frequency(Vmin/F)levels.Then,the allocation process is applied based on the availableworkloads.The hybrid model consists of three stages.The first stage gets the optimum safe voltage while the second stage sets the level of energy efficiency,and finally,the third is the allocation stage.Experimental results on various benchmarks show that the proposed model can generate optimal solutions to save energy while minimizing the makespan penalty.Comparisons with other competitive algorithms show that the proposed model provides on average 48%improvements in energy-saving and achieves an 18%reduction in computation time while ensuring a high degree of system reliability.
基金Project(DIP-2012-30)supported by the Universiti Kebangsaan,Malaysia
文摘A method for improving the level of reliability of distribution systems is presented by employing an integrated voltage sag mitigation method that comprises a two-staged strategy,namely,distribution network reconfiguration(DNR)followed by DSTATCOM placement.Initially,an optimal DNR is applied to reduce the propagated voltage sags during the test period.The second stage involves optimal placement of the DSTATCOM to assist the already reconfigured network.The gravitational search algorithm is used in the process of optimal DNR and in placing DSTATCOM.Reliability assessment is performed using the well-known indices.The simulation results show that the proposed method is efficient and feasible for improving the level of system reliability.
文摘Average(mean)voter is one of the commonest voting methods suitable for decision making in highly-available and long-missions applications where the availability and the speed of the system are critical.In this paper,a new generation of average voter based on parallel algorithms and parallel random access machine(PRAM)structure are proposed.The analysis shows that this algorithm is optimal due to its improved time complexity,speed-up,and efficiency and is especially appropriate for applications where the size of input space is large.
文摘Orthogonal Frequency Division Multiplexing (OFDM) is a multi-carrier scheme used in modern broadband wireless communication systems to transmit data over a number of orthogonal subcarriers. When transmitted signals arrive at the receiver by more than one path of different length, the received signals are staggered in time;this is multipath propagation. To mitigate the effect of dispersed channel distortion caused by random channel delay spread, Cyclic Prefix (CP) is introduced to eliminate Inter-Symbol Interference (ISI). In the literature, researchers have focused on carrying out investigations (or studies) mainly on the two existing CP insertions, namely: normal and extended CPs. Both CPs have limitations with respect to handling channel delay spreads. In the current work, a new CP, herein referred to as “ultra extended” CP is proposed to address delay spreads beyond the limits of the normal and extended CPs. The efficacy of the proposed ultra extended CP is tested via simulation under different scenarios. It is shown by the results obtained that the proposed CP can efficiently handle delay spreads beyond the limits of the existing normal and extended CP, and can indeed be implemented in the design of future telecommunication systems to accommodate higher channel delay spreads and it ensures wider cell coverage.
基金funded by A’Sharqiyah University,Sultanate of Oman,under Research Project grant number(BFP/RGP/ICT/22/490).
文摘Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.
基金funded by A’Sharqiyah University,Sultanate of Oman,under Research Project Grant Number(BFP/RGP/ICT/22/490).
文摘Face detection is a critical component inmodern security,surveillance,and human-computer interaction systems,with widespread applications in smartphones,biometric access control,and public monitoring.However,detecting faces with high levels of occlusion,such as those covered by masks,veils,or scarves,remains a significant challenge,as traditional models often fail to generalize under such conditions.This paper presents a hybrid approach that combines traditional handcrafted feature extraction technique called Histogram of Oriented Gradients(HOG)and Canny edge detection with modern deep learning models.The goal is to improve face detection accuracy under occlusions.The proposed method leverages the structural strengths of HOG and edge-based object proposals while exploiting the feature extraction capabilities of Convolutional Neural Networks(CNNs).The effectiveness of the proposed model is assessed using a custom dataset containing 10,000 heavily occluded face images and a subset of the Common Objects in Context(COCO)dataset for non-face samples.The COCO dataset was selected for its variety and realism in background contexts.Experimental evaluations demonstrate significant performance improvements compared to baseline CNN models.Results indicate that DenseNet121 combined with HOG outperforms other counterparts in classification metrics with an F1-score of 87.96%and precision of 88.02%.Enhanced performance is achieved through reduced false positives and improved localization accuracy with the integration of object proposals based on Canny and contour detection.While the proposed method increases inference time from 33.52 to 97.80 ms,it achieves a notable improvement in precision from 80.85% to 88.02% when comparing the baseline DenseNet121 model to its hybrid counterpart.Limitations of the method include higher computational cost and the need for careful tuning of parameters across the edge detection,handcrafted features,and CNN components.These findings highlight the potential of combining handcrafted and learned features for occluded face detection tasks.
基金support by The CO_(2) Flooding and Storage Safety Monitoring Technology(Grant 2023YFB4104200)The Dynamic Evolution of Marine CO_(2) Geological Sequestration Bodies and The Mechanism of Sequestration Efficiency Enhancement(Grant U23B2090)The Efficient Development Technology and Demonstration Project of Offshore CO_(2) Flooding(Grant KJGG-2022-12-CCUS-0203).
文摘Low-salinity water(LSW)and CO_(2) could be combined to perform better in a hydrocarbon reservoir due to their synergistic advantages for enhanced oil recovery(EOR);however,its microscopic recovery mechanisms have not been well understood due to the nature of these two fluids and their physical reactions in the presence of reservoir fluids and porous media.In this work,well-designed and inte-grated experiments have been performed for the first time to characterize the in-situ formation of micro-dispersions and identify their EOR roles during a LSW-alternating-CO_(2)(CO_(2)-LSWAG)process under various conditions.Firstly,by measuring water concentration and performing the Fourier transform infrared spectroscopy(FT-IR)analysis,the in-situ formation of micro-dispersions induced by polar and acidic materials was identified.Then,displacement experiments combining with nuclear magnetic resonance(NMR)analysis were performed with two crude oil samples,during which wettability,interfacial tension(IFT),CO_(2) dissolution,and CO_(2) diffusion were quantified.During a CO_(2)-LSWAG pro-cess,the in-situ formed micro-dispersions dictate the oil recovery,while the presence of clay minerals,electrical double-layer(EDL)expansion and multiple ion exchange(MIE)are found to contribute less.Such formed micro-dispersions are induced by CO_(2) via diffusion to mobilize the CO_(2)-diluted oil,alter the rock wettability towards more water-wet,and minimize the density contrast between crude oil and water.
基金This research was supported financially by the Scientific and Technical Research Council of Turkey(TUBİTAK)under Contract No.112T588The author is grateful to the Scientific and Technical Research Council of Turkey(TUBİTAK)for its financial support。
文摘In the production of AlCuFe alloy for a special application,the growth rate was changed and the results were evaluated.Changes in the eutectic spacing(microstructure)of a material due to the growth rate are known to affect its mechanical,electrical and thermal properties.To evaluate its microstructure,the eutectic composition of Al−32.5wt.%Cu−0.5wt.%Fe was prepared and directional solidification experiments were conducted using a Bridgman-type furnace at a constant temperature gradient(G=8.50 K/mm)and five growth rates(V=8.25,16.60,41.65,90.05,164.80μm/s).The effect of the growth rate on the eutectic spacing was then determined,and the resulting microhardness and ultimate tensile strength were obtained based on the change in the microstructure by regression analysis and Hall−Petch correlations.Despite the fact that the growth rate increased by approximately twenty times,the eutectic spacing decreased by a factor of approximately 5,and these changes in the growth rate and microstructure caused the mechanical properties to change by a factor of approximately 1.5.
文摘In digital signal processing (DSP), Nyquistrate sampling completely describes a signal by exploiting its bandlimitedness. Compressed Sensing (CS), also known as compressive sampling, is a DSP technique efficiently acquiring and reconstructing a signal completely from reduced number of measurements, by exploiting its compressibility. The measurements are not point samples but more general linear functions of the signal. CS can capture and represent sparse signals at a rate significantly lower than ordinarily used in the Shannon’s sampling theorem. It is interesting to notice that most signals in reality are sparse;especially when they are represented in some domain (such as the wavelet domain) where many coefficients are close to or equal to zero. A signal is called K-sparse, if it can be exactly represented by a basis, , and a set of coefficients , where only K coefficients are nonzero. A signal is called approximately K-sparse, if it can be represented up to a certain accuracy using K non-zero coefficients. As an example, a K-sparse signal is the class of signals that are the sum of K sinusoids chosen from the N harmonics of the observed time interval. Taking the DFT of any such signal would render only K non-zero values . An example of approximately sparse signals is when the coefficients , sorted by magnitude, decrease following a power law. In this case the sparse approximation constructed by choosing the K largest coefficients is guaranteed to have an approximation error that decreases with the same power law as the coefficients. The main limitation of CS-based systems is that they are employing iterative algorithms to recover the signal. The sealgorithms are slow and the hardware solution has become crucial for higher performance and speed. This technique enables fewer data samples than traditionally required when capturing a signal with relatively high bandwidth, but a low information rate. As a main feature of CS, efficient algorithms such as -minimization can be used for recovery. This paper gives a survey of both theoretical and numerical aspects of compressive sensing technique and its applications. The theory of CS has many potential applications in signal processing, wireless communication, cognitive radio and medical imaging.
文摘Polyaniline/Attapugite/ PE(PAn-ATTP/PE)composites containing particles with core-shell structure were obtained via the two-step blending processs. The experimental condition is as follows: Organo-attapulgite and PAn was obtained by modifying attapulgite with laury benzenesulfonic acid sodium salt and, then added to PE. The electrical conductivity, structure and properties of the composites were studied. Under the function of shear stress, core-shell structure particles with ATTP as the core and PAn as the shell were formed in the composites. The structure of PAn-ATTP/PE composites were characterized by FTIR,XRD,SEM, etc, respectively. The effects of concentration of doping agent on the conductivity and mechanical property of the composites were investigated. The mechanical properties and impact fracture surface of the ternary composites were studied by means of the tensile tester, SEM, etc. The results show that polyaniline encapsulated ATTP enhances the strength of the PE. And the conductivity of PAn-ATTP/PE composites of is improved effectively when polyaniline encapsulated ATTP is added. The composite have good conductivity when 10% polyaniline encapsulated ATTP is added.
文摘One of the most important features of the modern ma ch ining system in an "unmanned" factory is to change tools that have been subjec ted to wear and damage. An integrated tool condition monitoring system composed of multi-sensors, signal processing devices and intelligent decision making pla ns is a necessary requirement for automatic manufacturing processes. An intellig ent tool wear monitoring system will be introduced in this paper. The system is equipped with power consumption, vibration, AE and cutting force sensors, signal transformation and collection apparatus and a microcomputer. Tool condition monitoring is a pattern recognition process in which the characte ristics of the tool to be monitored are compared with those of the standard mode ls. The tool wear classification process is composed of the following parts: fea ture extraction; determination of the fuzzy membership functions of the features ; calculation of the fuzzy similarity; learning and tool wear classification. Fe atures extracted from the time domain and frequency domain for the future patter n recognition are as follows. Power consumption signal: mean value; AE-RMS sign al: mean value, skew and kutorsis; Cutting force, AE and vibration signal: mean value, standard deviation and the mean power in 10 frequency ranges. These signa l features can reflect the tool wear states comprehensively. The fuzzy approachi ng degree and the fuzzy distance between corresponding features of different obj ects are combined to describe the closeness of two fuzzy sets more accurately. A unique fuzzy driven neural network based pattern recognition algorithm has bee n developed from this research. The combination of Artificial Neural Networks (A NNs) and fuzzy logic system integrates the strong learning and classification ab ility of the former and the superb flexibility of the latter to express the dist ribution characteristics of signal features with vague boundaries. This methodol ogy indirectly solves the automatic weight assignment problem of the conventiona l fuzzy pattern recognition system and let it have greater representative power, higher training speed and be more robust. The introduction of the two-dimensio nal weighted approaching degree can make the pattern recognition process more re liable. The fuzzy driven neural network can effectively fuse multi-sensor i nformation and successfully recognize the tool wear states. Armed with the advan ced pattern recognition methodology, the established intelligent tool condition monitoring system has the advantages of being suitable for different machini ng conditions, robust to noise and tolerant to faults. Cooperated with the contr ol system of the machine tool, the optimized machining processed can be achieved .
基金the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project Number PNU-DRI-RI-20-029.
文摘Detection of brain tumors in MRI images is the first step in brain cancer diagnosis.The accuracy of the diagnosis depends highly on the expertise of radiologists.Therefore,automated diagnosis of brain cancer from MRI is receiving a large amount of attention.Also,MRI tumor detection is usually followed by a biopsy(an invasive procedure),which is a medical procedure for brain tumor classification.It is of high importance to devise automated methods to aid radiologists in brain cancer tumor diagnosis without resorting to invasive procedures.Convolutional neural network(CNN)is deemed to be one of the best machine learning algorithms to achieve high-accuracy results in tumor identification and classification.In this paper,a CNN-based technique for brain tumor classification has been developed.The proposed CNN can distinguish between normal(no-cancer),astrocytoma tumors,gliomatosis cerebri tumors,and glioblastoma tumors.The implemented CNN was tested on MRI images that underwent a motion-correction procedure.The CNN was evaluated using two performance measurement procedures.The first one is a k-fold cross-validation testing method,in which we tested the dataset using k=8,10,12,and 14.The best accuracy for this procedure was 96.26%when k=10.To overcome the over-fitting problem that could be occurred in the k-fold testing method,we used a hold-out testing method as a second evaluation procedure.The results of this procedure succeeded in attaining 97.8%accuracy,with a specificity of 99.2%and a sensitivity of 97.32%.With this high accuracy,the developed CNN architecture could be considered an effective automated diagnosis method for the classification of brain tumors from MRI images.
基金Universiti Putra Malaysia for funding this project(GP-IPS/2017/9556800).
文摘Bio based nitrogen doped carbon dots(N-CDs)were obtained from empty fruit bunch carboxymethylcellulose and ethylenediamine(EDA)through one-pot hydrothermal carbonization route.The optimum as-formed NCDs were thoroughly characterized via Transmission electron microscopy(TEM),high-resolution TEM(HRTEM),Fourier transform infrared(FTIR),X-ray photoelectron spectra(XPS),UV–vis spectra(UV–Vis)and Fluorescence spectra(PL).Response surface methodology was statistically used to assess three independent variables that have major influence on the fluorescence quantum yield(QY),including temperature(230–270℃),time(2–6 h)and EDA mass(10%–23.3%).Based on analysis of variance(ANOVA)results,synthesis temperature was found to be the most influential factor on the QY,followed by time and EDA mass.Higher temperature,long synthesis time and high amount of EDA were satisfactorily enough for efficient carbonization conversion rate and obtaining highest QY of N-CDs.The obtained quadratic model(R^2=0.9991)shows a good correlation between the experimental data and predicted values.The optimum synthetic parameters are of 270℃temperature,6 h reaction time and 23.3%of EDA mass.The optimized as-made N-CDs exhibited blue photoluminescence with both excitation dependent/independent phenomena and high nitrogen content.The maximum emission intensity was 426 nm at a maximum excitation wavelength of 320 nm,with a QY of up to 22.9%.XPS and FTIR data confirmed the existence of polar containing groups,such as carbonyl,carboxyl,hydroxyl and amino groups over the surface of N-CDs whereas nitrogen species in the form of(pyridinic and graphitic-N)were introduced in the aromatic carbon domains,which imparts the hydrophilic and photostability of N-CDs.Taking into account the low-cost and sustainable production of N-CDs,this method considered a feasible route for converting low quality waste into value-added nanomaterials and utilizing for different functionalization processes and analytical applications.
文摘This study aimed to produce a prototype system for non-contact vital sign monitoring of the elderly using microwave radar with the intention of reducing the burdens on monitored individuals and nursing caregivers. In addition, we tested the ability of the proposed prototype system to measure the respiratory and heart rates of the elderly in a nursing home and discussed the systems effectiveness and problems by examining results of real-time monitoring. The prototype system consisted of two 24-GHz microwave radar antennas and an analysis system. The antennas were positioned below a mattress to monitor motion on the body surface for measuring cardiac and respiratory rates from the dorsal side of the subjects (23.3 ± 1.2 years) who would be lying on the mattress. The heart rates determined by the prototype system correlated significantly with those measured by electrocardiography (r = 0.92). Similarly, the respiratory rates determined by the prototype correlated with those obtained from respiration curves (r = 0.94). Next, we investigated the effectiveness of the prototype system with 7 elderly patients (93.3 ± 10.56 years) at a nursing home. The proposed system appears to be a promising tool for monitoring the vital signs of the elderly in a way that alleviates the need to attach electrodes overnight to confirm patient safety.
基金the financial support from the Universiti Kebangsaan Malaysia under the research grant UKM-DLP-2011-059
文摘An alternative method was introduced for voltage sag source location based on S and TT transformed disturbance powers. It is done to avoid the wrong and inconclusive detection of conventional disturbance power method proposed in the literature. Unlike in the case of the traditional method, the proposed method first transforms the recorded voltage and current during the sag event to some special features before calculating the new version of disturbance powers. The effectiveness of the proposed method has been verified through simulation and actual data from an industrial power system. The results show that the presented method can correctly detect the location of voltage sag source.
