Traditional chaotic maps struggle with narrow chaotic ranges and inefficiencies,limiting their use for lightweight,secure image encryption in resource-constrained Wireless Sensor Networks(WSNs).We propose the SPCM,a n...Traditional chaotic maps struggle with narrow chaotic ranges and inefficiencies,limiting their use for lightweight,secure image encryption in resource-constrained Wireless Sensor Networks(WSNs).We propose the SPCM,a novel one-dimensional discontinuous chaotic system integrating polynomial and sine functions,leveraging a piecewise function to achieve a broad chaotic range()and a high Lyapunov exponent(5.04).Validated through nine benchmarks,including standard randomness tests,Diehard tests,and Shannon entropy(3.883),SPCM demonstrates superior randomness and high sensitivity to initial conditions.Applied to image encryption,SPCM achieves 0.152582 s(39%faster than some techniques)and 433.42 KB/s throughput(134%higher than some techniques),setting new benchmarks for chaotic map-based methods in WSNs.Chaos-based permutation and exclusive or(XOR)diffusion yield near-zero correlation in encrypted images,ensuring strong resistance to Statistical Attacks(SA)and accurate recovery.SPCM also exhibits a strong avalanche effect(bit difference),making it an efficient,secure solution for WSNs in domains like healthcare and smart cities.展开更多
This paper designs and implements an image transmission algorithm applied to plant information collection based on the wireless sensor network. It can effectively reduce the volume of transmitted data, low-energy, hig...This paper designs and implements an image transmission algorithm applied to plant information collection based on the wireless sensor network. It can effectively reduce the volume of transmitted data, low-energy, high-availability image compression algorithm. This algorithm mainly has two aspects of improvement measures: the first is to reduce the number of pixels that transmit images, from interlaced scanning to interlaced neighbor scanning;the second is to use JPEG image compression algorithm [1], changing the value of the quantization table in the algorithm [2]. After image compression, the image data volume is greatly reduced;the transmission efficiency is improved;and the problem of excessive data volume during image transmission is effectively solved.展开更多
The presence of increased memory and computational power in imaging sensor networks attracts researchers to exploit image processing algorithms on distributed memory and computational power. In this paper, a typical p...The presence of increased memory and computational power in imaging sensor networks attracts researchers to exploit image processing algorithms on distributed memory and computational power. In this paper, a typical perimeter is investigated with a number of sensors placed to form an image sensor network for the purpose of content based distributed image search. Image search algorithm is used to enable distributed content based image search within each sensor node. The energy model is presented to calculate energy efficiency for various cases of image search and transmission. The simulations are carried out based on consideration of continuous monitoring or event driven activity on the perimeter. The simulation setups consider distributed image processing on sensor nodes and results show that energy saving is significant if search algorithms are embedded in image sensor nodes and image processing is distributed across sensor nodes. The tradeoff between sensor life time, distributed image search and network deployed cost is also investigated.展开更多
The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot ...The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention.Fortunately,digital watermarking provides an effective method to solve this problem.In order to improve the robustness of the medical image watermarking scheme,in this paper,we propose a novel zero-watermarking algorithm with the integer wavelet transform(IWT),Schur decomposition and image block energy.Specifically,we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,then we decompose the sub-blocks by Schur decomposition.After that,the feature matrix is constructed according to the relationship between the image block energy and the whole image energy.At the same time,we encrypt watermarking with the logistic chaotic position scrambling.Finally,the zero-watermarking is obtained by XOR operation with the encrypted watermarking.Three indexes of peak signal-to-noise ratio,normalization coefficient(NC)and the bit error rate(BER)are used to evaluate the robustness of the algorithm.According to the experimental results,most of the NC values are around 0.9 under various attacks,while the BER values are very close to 0.These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods,which indicates it is more suitable for medical image privacy and security protection.展开更多
To achieve much efficient multimedia transmission over an error-prone wireless network, there are still some problem must to be solved, especially in energy limited wireless sensor network. In this paper, we propose a...To achieve much efficient multimedia transmission over an error-prone wireless network, there are still some problem must to be solved, especially in energy limited wireless sensor network. In this paper, we propose a joint detection based on Schur Algorithm for image wireless transmission over wireless sensor network. To eliminate error transmissions and save transmission energy, we combine Schur algorithm with joint dynamic detection for wireless transmission of JPEG 2000 encoded image which we proposed in [1]. Schur algorithm is used to computing the decomposition of system matrix to decrease the computational complexity. We de-scribe our transmission protocol, and report on its performance evaluation using a simulation testbed we have designed for this purpose. Our results clearly indicate that our method could approach efficient images transmission in wireless sensor network and the transmission errors are significantly reduced when compared to regular transmissions.展开更多
The purpose of the article is to develop a methodology for automating the detection and selection of moving objects. The detection and separation of moving objects based on impulse and recurrence neural networks simul...The purpose of the article is to develop a methodology for automating the detection and selection of moving objects. The detection and separation of moving objects based on impulse and recurrence neural networks simulation. The result of the work is a developed motion detector based on impulse and recurrence neural networks and an automated system developed on the basis of this detector for detecting and separating moving objects and is ready for practical application. The feasibility of integrating the developed motion detector with Emgu CV (OpenCV) image processing package, multimedia framework functions, and DirectShow application programming interface were investigated. The proposed approach and software for the detection and separating of moving objects in video images using neural networks can be integrated into more sophisticated specialized computer-aided video surveillance systems, IoT (Internet of Things), IoV (Internet of Vehicles), etc.展开更多
Wyner-Ziv Video Coding （WZVC） is considered as a promising video coding scheme for Wireless Video Sensor Networks （WVSNs） due to its high compression efficiency and error resilience functionalities, as well as its...Wyner-Ziv Video Coding （WZVC） is considered as a promising video coding scheme for Wireless Video Sensor Networks （WVSNs） due to its high compression efficiency and error resilience functionalities, as well as its low encoding complex- ity. To achieve a good Rate-Distortion （R-D） per- formance, the current WZVC paradi^prls usually a- dopt an end-to-end rate control scheme in which the decoder repeatedly requests the additional deco- ding data from the encoder for decoding Wyner-Ziv frames. Therefore, the waiting time of the additional decoding data is especially long in multihop WVSNs. In this paper, we propose a novel pro- gressive in-network rate control scheme for WZVC. The proposed in-network puncturing-based rate control scheme transfers the partial channel codes puncturing task from the encoder to the relay nodes. Then, the decoder can request the addition- al decoding data from the relay nodes instead of the encoder, and the total waiting time for deco- ding Wyner-Ziv frames is reduced consequently. Simulation results validate the proposed rate con- trol scheme.展开更多
Neural image compression(NIC)has shown remarkable rate-distortion(R-D)efficiency.However,the considerable computational and spatial complexity of most NIC methods presents deployment challenges on resource-constrained...Neural image compression(NIC)has shown remarkable rate-distortion(R-D)efficiency.However,the considerable computational and spatial complexity of most NIC methods presents deployment challenges on resource-constrained devices.We introduce a lightweight neural image compression framework designed to efficiently process both local and global information.In this framework,the convolutional branch extracts local information,whereas the frequency domain branch extracts global information.To capture global information without the high computational costs of dense pixel operations,such as attention mechanisms,Fourier transform is employed.This approach allows for the manipulation of global information in the frequency domain.Additionally,we employ feature shift operations as a strategy to acquire large receptive fields without any computational cost,thus circumventing the need for large kernel convolution.Our framework achieves a superior balance between ratedistortion performance and complexity.On varying resolution sets,our method not only achieves rate-distortion(R-D)performance on par with versatile video coding(VVC)intra and other state-of-the-art(SOTA)NIC methods but also exhibits the lowest computational requirements,with approximately 200 KMACs/pixel.The code will be available at https://github.com/baoyu2020/SFNIC.展开更多
The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time.While traditional video surveillance ...The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time.While traditional video surveillance relies on human monitoring,this approach suffers from limitations such as fatigue and delayed response times.This study addresses these challenges by developing an automated detection system using advanced deep learning techniques to enhance public safety.Our approach leverages state-of-the-art convolutional neural networks(CNNs),specifically You Only Look Once version 4(YOLOv4)and EfficientDet,for real-time object detection.The system was trained on a comprehensive dataset of over 50,000 images,enhanced through data augmentation techniques to improve robustness across varying lighting conditions and viewing angles.Cloud-based deployment on Amazon Web Services(AWS)ensured scalability and efficient processing.Experimental evaluations demonstrated high performance,with YOLOv4 achieving 92%accuracy and processing images in 0.45 s,while EfficientDet reached 93%accuracy with a slightly longer processing time of 0.55 s per image.Field tests in high-traffic environments such as train stations and shopping malls confirmed the system’s reliability,with a false alarm rate of only 4.5%.The integration of automatic alerts enabled rapid security responses to potential threats.The proposed CNN-based system provides an effective solution for real-time detection of dangerous objects in video surveillance,significantly improving response times and public safety.While YOLOv4 proved more suitable for speed-critical applications,EfficientDet offered marginally better accuracy.Future work will focus on optimizing the system for low-light conditions and further reducing false positives.This research contributes to the advancement of AI-driven surveillance technologies,offering a scalable framework adaptable to various security scenarios.展开更多
This paper proposed a back propagation neural network model for predictive block-matching. Predictive block-matching is a way to significantly decrease the computational complexity of motion estimation, but the tradit...This paper proposed a back propagation neural network model for predictive block-matching. Predictive block-matching is a way to significantly decrease the computational complexity of motion estimation, but the traditional prediction model was proposed 26 years ago. It is straight forward but not accurate enough. The proposed back propagation neural network has 5 inputs, 5 neutrons and 1 output. Because of its simplicity, it requires very little calculation power which is negligible compared with existing computation complexity. The test results show 10% - 30% higher prediction accuracy and PSNR improvement up to 0.3 dB. The above advantages make it a feasible replacement of the current model.展开更多
The aggregation of data in recent years has been expanding at an exponential rate. There are various data generating sources that are responsible for such a tremendous data growth rate. Some of the data origins includ...The aggregation of data in recent years has been expanding at an exponential rate. There are various data generating sources that are responsible for such a tremendous data growth rate. Some of the data origins include data from the various social media, footages from video cameras, wireless and wired sensor network measurements, data from the stock markets and other financial transaction data, supermarket transaction data and so on. The aforementioned data may be high dimensional and big in Volume, Value, Velocity, Variety, and Veracity. Hence one of the crucial challenges is the storage, processing and extraction of relevant information from the data. In the special case of image data, the technique of image compressions may be employed in reducing the dimension and volume of the data to ensure it is convenient for processing and analysis. In this work, we examine a proof-of-concept multiresolution analytics that uses wavelet transforms, that is one popular mathematical and analytical framework employed in signal processing and representations, and we study its applications to the area of compressing image data in wireless sensor networks. The proposed approach consists of the applications of wavelet transforms, threshold detections, quantization data encoding and ultimately apply the inverse transforms. The work specifically focuses on multi-resolution analysis with wavelet transforms by comparing 3 wavelets at the 5 decomposition levels. Simulation results are provided to demonstrate the effectiveness of the methodology.展开更多
Established on the Intel Multi-Core Embedded platform, using 802.11 Wireless Network protocols as the communication medium, combining with Radio Frequency-Communication and Ultrasonic Ranging, imple-ment a mobile term...Established on the Intel Multi-Core Embedded platform, using 802.11 Wireless Network protocols as the communication medium, combining with Radio Frequency-Communication and Ultrasonic Ranging, imple-ment a mobile terminal system in an intellectualized building. It can provide its holder such functions: 1) Accurate Positioning 2) Intelligent Navigation 3) Video Monitoring 4) Wireless Communication. The inno-vative point for this paper is to apply the multi-core computing on the embedded system to promote its com-puting speed and give a real-time performance and apply this system into the indoor environment for the purpose of emergent event or rescuing.展开更多
Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration.Sensor data of all possible states of ...Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration.Sensor data of all possible states of a system are used for building machine learning models.These models are further used to predict the possible downtime for proactive action on the system condition.Aircraft engine data from run to failure is used in the current study.The run to failure data includes states like new installation,stable operation,first reported issue,erroneous operation,and final failure.In the present work,the non-linear multivariate sensor data is used to understand the health status and anomalous behavior.The methodology is based on different sampling sizes to obtain optimum results with great accuracy.The time series of each sensor is converted to a 2D image with a specific time window.Converted Images would represent the health of a system in higher-dimensional space.The created images were fed to Convolutional Neural Network,which includes both time variation and space variation of each sensed parameter.Using these created images,a model for estimating the remaining life of the aircraft is developed.Further,the proposed net is also used for predicting the number of engines that would fail in the given time window.The current methodology is useful in avoiding the health index generation for predicting the remaining useful life of the industrial components.Better accuracy in the classification of components is achieved using the TimeImagenet-based approach.展开更多
A method is established for measuring low energy γ-rays dose by using CMOS sensors without any X-/γ-ray converters. Gamma-ray source of241 Am and152Eu are used to test the system. Based on gray value, an analysis me...A method is established for measuring low energy γ-rays dose by using CMOS sensors without any X-/γ-ray converters. Gamma-ray source of241 Am and152Eu are used to test the system. Based on gray value, an analysis method is proposed to obtain the γ-ray dose. Cumulative dose is determined by correlating the gray value to the dose readings of standard dosimeters. The relationship between gray value and the cumulative dose of γ-rays are trained by using back propagation neural network with BFGS algorithm. After comparison, it shows that BFGS algorithm trainings are suitable for different γ-ray sources under higher error condition. These indicate the feasibility of measuring low energy γ-ray dose by using common CMOS image sensors.展开更多
In combination of the characteristic of the network architecture of wireless multimedia sensor networks （WMSNs）, a distributed multi-node cooperative network （DMCN） model is designed by using the concept of in-net...In combination of the characteristic of the network architecture of wireless multimedia sensor networks （WMSNs）, a distributed multi-node cooperative network （DMCN） model is designed by using the concept of in-network processing to improve their energy, memory and computational power. To balance the energy consumption of the network, according to roles division, camera nodes and common nodes are cooperated to accomplish the workload of image acquisition, compression and transmission. Camera nodes gather images and send blocking images to the common nodes in cluster. Common nodes adaptively compress the partitioned images by using a noise-tolerant distributed image compression （NDIC） algorithm based on principal component analysis （PCA） called NDIC-PCA algorithm and send the compressed data to the cluster head node. Then, the cluster head node sends the compressed image data to the station. Simulation results demonstrate that, DCNM can effectively balance the energy consumption of network and largely extend the network lifecycle. In addition, compared with previous algorithms, the proposed NDIC-PCA algorithm achieves higher peak signal to noise ratio without decreasing compression ratio.展开更多
Facial expression recognition(FER) in video has attracted the increasing interest and many approaches have been made.The crucial problem of classifying a given video sequence into several basic emotions is how to fuse...Facial expression recognition(FER) in video has attracted the increasing interest and many approaches have been made.The crucial problem of classifying a given video sequence into several basic emotions is how to fuse facial features of individual frames.In this paper, a frame-level attention module is integrated into an improved VGG-based frame work and a lightweight facial expression recognition method is proposed.The proposed network takes a sub video cut from an experimental video sequence as its input and generates a fixed-dimension representation.The VGG-based network with an enhanced branch embeds face images into feature vectors.The frame-level attention module learns weights which are used to adaptively aggregate the feature vectors to form a single discriminative video representation.Finally, a regression module outputs the classification results.The experimental results on CK+and AFEW databases show that the recognition rates of the proposed method can achieve the state-of-the-art performance.展开更多
Distributed video coding （DVC） is a new video coding approach based on Wyner-Ziv theorem. The novel uplink-friendly DVC, which offers low-complexity, low-power consuming, and low-cost video encoding, has aroused mor...Distributed video coding （DVC） is a new video coding approach based on Wyner-Ziv theorem. The novel uplink-friendly DVC, which offers low-complexity, low-power consuming, and low-cost video encoding, has aroused more and more research interests. In this paper a new method based on multiple view geometry is presented for spatial side information generation of uncalibrated video sensor network. Trifocal tensor encapsulates all the geometric relations among three views that are independent of scene structure; it can be computed from image correspondences alone without requiring knowledge of the motion or calibration. Simulation results show that trifocal tensor-based spatial side information improves the rate-distortion performance over motion compensation based interpolation side information by a maximum gap of around 2dB. Then fusion merges the different side information （temporal and spatial） in order to improve the quality of the final one. Simulation results show that the rate-distortion gains about 0.4 dB.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government Ministry of Science and ICT(MIST)(RS-2022-00165225).
文摘Traditional chaotic maps struggle with narrow chaotic ranges and inefficiencies,limiting their use for lightweight,secure image encryption in resource-constrained Wireless Sensor Networks(WSNs).We propose the SPCM,a novel one-dimensional discontinuous chaotic system integrating polynomial and sine functions,leveraging a piecewise function to achieve a broad chaotic range()and a high Lyapunov exponent(5.04).Validated through nine benchmarks,including standard randomness tests,Diehard tests,and Shannon entropy(3.883),SPCM demonstrates superior randomness and high sensitivity to initial conditions.Applied to image encryption,SPCM achieves 0.152582 s(39%faster than some techniques)and 433.42 KB/s throughput(134%higher than some techniques),setting new benchmarks for chaotic map-based methods in WSNs.Chaos-based permutation and exclusive or(XOR)diffusion yield near-zero correlation in encrypted images,ensuring strong resistance to Statistical Attacks(SA)and accurate recovery.SPCM also exhibits a strong avalanche effect(bit difference),making it an efficient,secure solution for WSNs in domains like healthcare and smart cities.
文摘This paper designs and implements an image transmission algorithm applied to plant information collection based on the wireless sensor network. It can effectively reduce the volume of transmitted data, low-energy, high-availability image compression algorithm. This algorithm mainly has two aspects of improvement measures: the first is to reduce the number of pixels that transmit images, from interlaced scanning to interlaced neighbor scanning;the second is to use JPEG image compression algorithm [1], changing the value of the quantization table in the algorithm [2]. After image compression, the image data volume is greatly reduced;the transmission efficiency is improved;and the problem of excessive data volume during image transmission is effectively solved.
文摘The presence of increased memory and computational power in imaging sensor networks attracts researchers to exploit image processing algorithms on distributed memory and computational power. In this paper, a typical perimeter is investigated with a number of sensors placed to form an image sensor network for the purpose of content based distributed image search. Image search algorithm is used to enable distributed content based image search within each sensor node. The energy model is presented to calculate energy efficiency for various cases of image search and transmission. The simulations are carried out based on consideration of continuous monitoring or event driven activity on the perimeter. The simulation setups consider distributed image processing on sensor nodes and results show that energy saving is significant if search algorithms are embedded in image sensor nodes and image processing is distributed across sensor nodes. The tradeoff between sensor life time, distributed image search and network deployed cost is also investigated.
基金supported in part by the Hainan Provincial Natural Science Foundation of China (No.620MS067)the Intelligent Medical Project of Chongqing Medical University (ZHYXQNRC202101)the Student Scientific Research and Innovation Experiment Project of the Medical Information College of Chongqing Medical University (No.2020C006).
文摘The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention.Fortunately,digital watermarking provides an effective method to solve this problem.In order to improve the robustness of the medical image watermarking scheme,in this paper,we propose a novel zero-watermarking algorithm with the integer wavelet transform(IWT),Schur decomposition and image block energy.Specifically,we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,then we decompose the sub-blocks by Schur decomposition.After that,the feature matrix is constructed according to the relationship between the image block energy and the whole image energy.At the same time,we encrypt watermarking with the logistic chaotic position scrambling.Finally,the zero-watermarking is obtained by XOR operation with the encrypted watermarking.Three indexes of peak signal-to-noise ratio,normalization coefficient(NC)and the bit error rate(BER)are used to evaluate the robustness of the algorithm.According to the experimental results,most of the NC values are around 0.9 under various attacks,while the BER values are very close to 0.These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods,which indicates it is more suitable for medical image privacy and security protection.
文摘To achieve much efficient multimedia transmission over an error-prone wireless network, there are still some problem must to be solved, especially in energy limited wireless sensor network. In this paper, we propose a joint detection based on Schur Algorithm for image wireless transmission over wireless sensor network. To eliminate error transmissions and save transmission energy, we combine Schur algorithm with joint dynamic detection for wireless transmission of JPEG 2000 encoded image which we proposed in [1]. Schur algorithm is used to computing the decomposition of system matrix to decrease the computational complexity. We de-scribe our transmission protocol, and report on its performance evaluation using a simulation testbed we have designed for this purpose. Our results clearly indicate that our method could approach efficient images transmission in wireless sensor network and the transmission errors are significantly reduced when compared to regular transmissions.
文摘The purpose of the article is to develop a methodology for automating the detection and selection of moving objects. The detection and separation of moving objects based on impulse and recurrence neural networks simulation. The result of the work is a developed motion detector based on impulse and recurrence neural networks and an automated system developed on the basis of this detector for detecting and separating moving objects and is ready for practical application. The feasibility of integrating the developed motion detector with Emgu CV (OpenCV) image processing package, multimedia framework functions, and DirectShow application programming interface were investigated. The proposed approach and software for the detection and separating of moving objects in video images using neural networks can be integrated into more sophisticated specialized computer-aided video surveillance systems, IoT (Internet of Things), IoV (Internet of Vehicles), etc.
基金This paper was supported by the National Key Basic Re- search Program of China under Grant No. 2011 CB302701 the National Natural Science Foundation of China under Grants No. 60833009, No. 61133015+2 种基金 the China National Funds for Distinguished Young Scientists under Grant No. 60925010 the Funds for Creative Research Groups of China under Grant No. 61121001 the Program for Changjiang Scholars and Innovative Research Team in University under Grant No. IRT1049.
文摘Wyner-Ziv Video Coding （WZVC） is considered as a promising video coding scheme for Wireless Video Sensor Networks （WVSNs） due to its high compression efficiency and error resilience functionalities, as well as its low encoding complex- ity. To achieve a good Rate-Distortion （R-D） per- formance, the current WZVC paradi^prls usually a- dopt an end-to-end rate control scheme in which the decoder repeatedly requests the additional deco- ding data from the encoder for decoding Wyner-Ziv frames. Therefore, the waiting time of the additional decoding data is especially long in multihop WVSNs. In this paper, we propose a novel pro- gressive in-network rate control scheme for WZVC. The proposed in-network puncturing-based rate control scheme transfers the partial channel codes puncturing task from the encoder to the relay nodes. Then, the decoder can request the addition- al decoding data from the relay nodes instead of the encoder, and the total waiting time for deco- ding Wyner-Ziv frames is reduced consequently. Simulation results validate the proposed rate con- trol scheme.
基金supported by the National Natural Science Foundation of China(Grants 62031013,62102339 and 62472124)the Guangdong Province Key Construction Discipline Scientific Research Capacity Improvement Project(Grant 2022ZDJS117)+1 种基金Shenzhen Colleges and Universities Stable Support Programme(Grant GXWD20220811170130002)Shenzhen Science and Technology Programme(Grant RCBS20221008093121052).
文摘Neural image compression(NIC)has shown remarkable rate-distortion(R-D)efficiency.However,the considerable computational and spatial complexity of most NIC methods presents deployment challenges on resource-constrained devices.We introduce a lightweight neural image compression framework designed to efficiently process both local and global information.In this framework,the convolutional branch extracts local information,whereas the frequency domain branch extracts global information.To capture global information without the high computational costs of dense pixel operations,such as attention mechanisms,Fourier transform is employed.This approach allows for the manipulation of global information in the frequency domain.Additionally,we employ feature shift operations as a strategy to acquire large receptive fields without any computational cost,thus circumventing the need for large kernel convolution.Our framework achieves a superior balance between ratedistortion performance and complexity.On varying resolution sets,our method not only achieves rate-distortion(R-D)performance on par with versatile video coding(VVC)intra and other state-of-the-art(SOTA)NIC methods but also exhibits the lowest computational requirements,with approximately 200 KMACs/pixel.The code will be available at https://github.com/baoyu2020/SFNIC.
文摘The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time.While traditional video surveillance relies on human monitoring,this approach suffers from limitations such as fatigue and delayed response times.This study addresses these challenges by developing an automated detection system using advanced deep learning techniques to enhance public safety.Our approach leverages state-of-the-art convolutional neural networks(CNNs),specifically You Only Look Once version 4(YOLOv4)and EfficientDet,for real-time object detection.The system was trained on a comprehensive dataset of over 50,000 images,enhanced through data augmentation techniques to improve robustness across varying lighting conditions and viewing angles.Cloud-based deployment on Amazon Web Services(AWS)ensured scalability and efficient processing.Experimental evaluations demonstrated high performance,with YOLOv4 achieving 92%accuracy and processing images in 0.45 s,while EfficientDet reached 93%accuracy with a slightly longer processing time of 0.55 s per image.Field tests in high-traffic environments such as train stations and shopping malls confirmed the system’s reliability,with a false alarm rate of only 4.5%.The integration of automatic alerts enabled rapid security responses to potential threats.The proposed CNN-based system provides an effective solution for real-time detection of dangerous objects in video surveillance,significantly improving response times and public safety.While YOLOv4 proved more suitable for speed-critical applications,EfficientDet offered marginally better accuracy.Future work will focus on optimizing the system for low-light conditions and further reducing false positives.This research contributes to the advancement of AI-driven surveillance technologies,offering a scalable framework adaptable to various security scenarios.
文摘This paper proposed a back propagation neural network model for predictive block-matching. Predictive block-matching is a way to significantly decrease the computational complexity of motion estimation, but the traditional prediction model was proposed 26 years ago. It is straight forward but not accurate enough. The proposed back propagation neural network has 5 inputs, 5 neutrons and 1 output. Because of its simplicity, it requires very little calculation power which is negligible compared with existing computation complexity. The test results show 10% - 30% higher prediction accuracy and PSNR improvement up to 0.3 dB. The above advantages make it a feasible replacement of the current model.
文摘The aggregation of data in recent years has been expanding at an exponential rate. There are various data generating sources that are responsible for such a tremendous data growth rate. Some of the data origins include data from the various social media, footages from video cameras, wireless and wired sensor network measurements, data from the stock markets and other financial transaction data, supermarket transaction data and so on. The aforementioned data may be high dimensional and big in Volume, Value, Velocity, Variety, and Veracity. Hence one of the crucial challenges is the storage, processing and extraction of relevant information from the data. In the special case of image data, the technique of image compressions may be employed in reducing the dimension and volume of the data to ensure it is convenient for processing and analysis. In this work, we examine a proof-of-concept multiresolution analytics that uses wavelet transforms, that is one popular mathematical and analytical framework employed in signal processing and representations, and we study its applications to the area of compressing image data in wireless sensor networks. The proposed approach consists of the applications of wavelet transforms, threshold detections, quantization data encoding and ultimately apply the inverse transforms. The work specifically focuses on multi-resolution analysis with wavelet transforms by comparing 3 wavelets at the 5 decomposition levels. Simulation results are provided to demonstrate the effectiveness of the methodology.
文摘Established on the Intel Multi-Core Embedded platform, using 802.11 Wireless Network protocols as the communication medium, combining with Radio Frequency-Communication and Ultrasonic Ranging, imple-ment a mobile terminal system in an intellectualized building. It can provide its holder such functions: 1) Accurate Positioning 2) Intelligent Navigation 3) Video Monitoring 4) Wireless Communication. The inno-vative point for this paper is to apply the multi-core computing on the embedded system to promote its com-puting speed and give a real-time performance and apply this system into the indoor environment for the purpose of emergent event or rescuing.
文摘Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration.Sensor data of all possible states of a system are used for building machine learning models.These models are further used to predict the possible downtime for proactive action on the system condition.Aircraft engine data from run to failure is used in the current study.The run to failure data includes states like new installation,stable operation,first reported issue,erroneous operation,and final failure.In the present work,the non-linear multivariate sensor data is used to understand the health status and anomalous behavior.The methodology is based on different sampling sizes to obtain optimum results with great accuracy.The time series of each sensor is converted to a 2D image with a specific time window.Converted Images would represent the health of a system in higher-dimensional space.The created images were fed to Convolutional Neural Network,which includes both time variation and space variation of each sensed parameter.Using these created images,a model for estimating the remaining life of the aircraft is developed.Further,the proposed net is also used for predicting the number of engines that would fail in the given time window.The current methodology is useful in avoiding the health index generation for predicting the remaining useful life of the industrial components.Better accuracy in the classification of components is achieved using the TimeImagenet-based approach.
基金Supported by National Natural Science Foundation of China(No.10905017)the Science and Technology Innovation Team Support Plan in Henan Province(No.13IRTSTHN016)
文摘A method is established for measuring low energy γ-rays dose by using CMOS sensors without any X-/γ-ray converters. Gamma-ray source of241 Am and152Eu are used to test the system. Based on gray value, an analysis method is proposed to obtain the γ-ray dose. Cumulative dose is determined by correlating the gray value to the dose readings of standard dosimeters. The relationship between gray value and the cumulative dose of γ-rays are trained by using back propagation neural network with BFGS algorithm. After comparison, it shows that BFGS algorithm trainings are suitable for different γ-ray sources under higher error condition. These indicate the feasibility of measuring low energy γ-ray dose by using common CMOS image sensors.
基金supported by the National Natural Science Foundation of China(61300239,61373139,61572261)China Postdoctoral Science Foundation(2014M551635)+1 种基金Postdoctoral Fund of Jiangsu Province(1302085B)Jiangsu Government Scholarship for Overseas Studies(JS-2014-085)
文摘In combination of the characteristic of the network architecture of wireless multimedia sensor networks （WMSNs）, a distributed multi-node cooperative network （DMCN） model is designed by using the concept of in-network processing to improve their energy, memory and computational power. To balance the energy consumption of the network, according to roles division, camera nodes and common nodes are cooperated to accomplish the workload of image acquisition, compression and transmission. Camera nodes gather images and send blocking images to the common nodes in cluster. Common nodes adaptively compress the partitioned images by using a noise-tolerant distributed image compression （NDIC） algorithm based on principal component analysis （PCA） called NDIC-PCA algorithm and send the compressed data to the cluster head node. Then, the cluster head node sends the compressed image data to the station. Simulation results demonstrate that, DCNM can effectively balance the energy consumption of network and largely extend the network lifecycle. In addition, compared with previous algorithms, the proposed NDIC-PCA algorithm achieves higher peak signal to noise ratio without decreasing compression ratio.
基金Supported by the Future Network Scientific Research Fund Project of Jiangsu Province (No. FNSRFP2021YB26)the Jiangsu Key R&D Fund on Social Development (No. BE2022789)the Science Foundation of Nanjing Institute of Technology (No. ZKJ202003)。
文摘Facial expression recognition(FER) in video has attracted the increasing interest and many approaches have been made.The crucial problem of classifying a given video sequence into several basic emotions is how to fuse facial features of individual frames.In this paper, a frame-level attention module is integrated into an improved VGG-based frame work and a lightweight facial expression recognition method is proposed.The proposed network takes a sub video cut from an experimental video sequence as its input and generates a fixed-dimension representation.The VGG-based network with an enhanced branch embeds face images into feature vectors.The frame-level attention module learns weights which are used to adaptively aggregate the feature vectors to form a single discriminative video representation.Finally, a regression module outputs the classification results.The experimental results on CK+and AFEW databases show that the recognition rates of the proposed method can achieve the state-of-the-art performance.
文摘Distributed video coding （DVC） is a new video coding approach based on Wyner-Ziv theorem. The novel uplink-friendly DVC, which offers low-complexity, low-power consuming, and low-cost video encoding, has aroused more and more research interests. In this paper a new method based on multiple view geometry is presented for spatial side information generation of uncalibrated video sensor network. Trifocal tensor encapsulates all the geometric relations among three views that are independent of scene structure; it can be computed from image correspondences alone without requiring knowledge of the motion or calibration. Simulation results show that trifocal tensor-based spatial side information improves the rate-distortion performance over motion compensation based interpolation side information by a maximum gap of around 2dB. Then fusion merges the different side information （temporal and spatial） in order to improve the quality of the final one. Simulation results show that the rate-distortion gains about 0.4 dB.
文摘现有的基于卷积神经网络(convolutional neural network,CNN)的环路滤波器倾向于将多个网络应用于不同的量化参数(quantization parameter,QP),消耗训练模型中的大量资源,并增加内存负担。针对这一问题,提出一种基于CNN的QP自适应环路滤波器。首先,设计一个轻量级分类网络,按照滤波难易程度将编码树单元(coding tree unit,CTU)划分为难、中、易3类;然后,构建3个融合了特征信息增强融合模块的基于CNN的滤波网络,以满足不同QP下的3类CTU滤波需求。将所提出的环路滤波器集成到多功能视频编码(versatile video coding,VVC)标准H.266/VVC的测试软件VTM 6.0中,替换原有的去块效应滤波器(deblocking filter,DBF)、样本自适应偏移(sample adaptive offset,SAO)滤波器和自适应环路滤波器。实验结果表明,该方法平均降低了3.14%的比特率差值(Bjøntegaard delta bit rate,BD-BR),与其他基于CNN的环路滤波器相比,显著提高了压缩效率,并减少了压缩伪影。
