We developed a fluorescent double network hydrogel with ionic responsiveness and high mechanical properties for visual detection.The nanocomposite hydrogel of laponite and polyacrylamide serves as the first network,wh...We developed a fluorescent double network hydrogel with ionic responsiveness and high mechanical properties for visual detection.The nanocomposite hydrogel of laponite and polyacrylamide serves as the first network,while the ionic cross-linked hydrogel of terbium ions and sodium alginate serves as the second network.The double-network structure,the introduction of nanoparticles and the reversible ionic crosslinked interactions confer high mechanical properties to the hydrogel.Terbium ions are not only used as the ionic cross-linked points,but also used as green emitters to endow hydrogels with fluorescent properties.On the basis of the “antenna effect” of terbium ions and the ion exchange interaction,the fluorescence of the hydrogels can make selective responses to various ions(such as organic acid radical ions,transition metal ions) in aqueous solutions,which enables a convenient strategy for visual detection toward ions.Consequently,the fluorescent double network hydrogel fabricated in this study is promising for use in the field of visual sensor detection.展开更多
In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible t...In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.展开更多
Ultra-wideband （UWB） microwave images are proposed for detecting small malignant breast tumors based on the large contrast of electric parameters between a malignant tumor and normal breast tissue. In this study, an...Ultra-wideband （UWB） microwave images are proposed for detecting small malignant breast tumors based on the large contrast of electric parameters between a malignant tumor and normal breast tissue. In this study, an antenna array composed of 9 antennas is applied to the detection. The double constrained robust capon beamforming （DCRCB） algorithm is used for reconstructing the breast image due to its better stability and high signal-to-interference-plus-noise ratio （SINR）. The successful detection of a tumor of 2 mm in diameter shown in the reconstruction demonstrates the robustness of the DCRCB beamforming algorithm. This study verifies the feasibility of detecting small breast tumors by using the DCRCB imaging algorithm.展开更多
In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to ...In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.展开更多
JPEG(Joint Image Experts Group)is currently the most widely used image format on the Internet.Existing cases show that many tampering operations occur on JPEG images.The basic process of the operation is that the JPEG...JPEG(Joint Image Experts Group)is currently the most widely used image format on the Internet.Existing cases show that many tampering operations occur on JPEG images.The basic process of the operation is that the JPEG file is first decompressed,modified in the null field,and then the tampered image is compressed and saved in JPEG format,so that the tampered image may be compressed several times.Therefore,the double compression detection of JPEG images can be an important part for determining whether an image has been tampered with,and the study of double JPEG compression anti-detection can further advance the progress of detection work.In this paper,we mainly review the literature in the field of double JPEG compression detection in recent years with two aspects,namely,the quantization table remains unchanged and the quantization table is inconsistent in the double JPEG compression process,Also,we will introduce some representative methods of double JPEG anti-detection in recent years.Finally,we analyze the problems existing in the field of double JPEG compression and give an outlook on the future development direction.展开更多
Echo canceller generally needs a double-talk detector which is used to keep the adaptive filter from diverging in the appearance of near-end speech. In this paper we adopt a new double-talk detection algorithm based o...Echo canceller generally needs a double-talk detector which is used to keep the adaptive filter from diverging in the appearance of near-end speech. In this paper we adopt a new double-talk detection algorithm based onl 2 norm to detect the existence of near-end speech in an acoustic echo canceller. We analyze this algorithm from the point of view of functional analysis and point out that the proposed double-talk detection algorithm has the same performance as the classic one in a finite Banach space. The remarkable feature of this algorithm is its higher accuracy and better computation complexity. The fine properties of this algorithm are confirmed by computer simulation and the application in a multimedia communication system. Key words acoustic echo cancellation - double-talk, detection - l 2 norm - adaptive FIR CLC number TN 911 Foundation item: Supported by the the National High Technology Development of China (863-306-ZT05)Biography: Wang Shao-wei (1975-) male, Ph. D candidate, research direction: multimedia communication.展开更多
This paper proposes a heading fault tolerance scheme for operation-level underwater robots subject to external interference.The scheme is based on a double-criterion fault detection method using a redundant structure ...This paper proposes a heading fault tolerance scheme for operation-level underwater robots subject to external interference.The scheme is based on a double-criterion fault detection method using a redundant structure of a dual electronic compass.First,two subexpansion Kalman filters are set up to fuse data with an inertial attitude measurement system.Then,fault detection can effectively identify the fault sensor and fault source.Finally,a fault-tolerant algorithm is used to isolate and alarm the faulty sensor.The program can effectively detect the constant magnetic field interference,change the magnetic field interference and small transient magnetic field interference,and conduct fault tolerance control in time to ensure the heading accuracy of the system.Test verification shows that the system is practical and effective.展开更多
The performance of classic Mel-frequency cepstral coefficients (MFCC) is unsatisfactory in noisy environment with different sound sources from nature. In this paper, a classification approach of the ecological environ...The performance of classic Mel-frequency cepstral coefficients (MFCC) is unsatisfactory in noisy environment with different sound sources from nature. In this paper, a classification approach of the ecological environmental sounds using the double-level energy detection (DED) was presented. The DED was used to detect the existence of the sound signals under noise conditions. In addition, MFCC features from the frames which were detected the presence of the sound signals by DED were extracted. Experimental results show that the proposed technology has better noise immunity than classic MFCC, and also outperforms time-domain energy detection (TED) and frequency-domain energy detection (FED) respectively.展开更多
Changepoint detection faces challenges when outlier data are present. This paper proposes a multivariate changepoint detection method which is based on the robust WPCA projection direction and the robust RFPOP method,...Changepoint detection faces challenges when outlier data are present. This paper proposes a multivariate changepoint detection method which is based on the robust WPCA projection direction and the robust RFPOP method, RWPCA-RFPOP method. Our method is double robust which is suitable for detecting mean changepoints in multivariate normal data with high correlations between variables that include outliers. Simulation results demonstrate that our method provides strong guarantees on both the number and location of changepoints in the presence of outliers. Finally, our method is well applied in an ACGH dataset.展开更多
A double-sided silicon strip detector(DSSD)with active area of 48 mm x 48 mm and thickness of300μm has been developed. Each side of DSSD consists of48 strips, each with width of 0.9 mm and inter-strip separation of 0...A double-sided silicon strip detector(DSSD)with active area of 48 mm x 48 mm and thickness of300μm has been developed. Each side of DSSD consists of48 strips, each with width of 0.9 mm and inter-strip separation of 0.1 mm. Electrical properties and detection performances including full depletion bias voltage, reverse leakage current, rise time, energy resolution and cross talk have been studied. At a bias of 80 V, leakage current in each strip is less than 15 nA, and rise time for alpha particle at 5157 keV is approximately 15 ns on both sides.Good energy resolutions have been achieved with0.65-0.80% for the junction strips and 0.85-1.00% for the ohmic strips. The cross talk is found to be negligible on both sides. The overall good performance of DSSD indicates its readiness for various nuclear physics experiments.展开更多
The double-stranded DNA (dsDNA) probe contains two different protein binding sites. One is for DNA- binding proteins to be detected and the other is for a DNA restriction enzyme. The two sites were arranged together w...The double-stranded DNA (dsDNA) probe contains two different protein binding sites. One is for DNA- binding proteins to be detected and the other is for a DNA restriction enzyme. The two sites were arranged together with no base interval. The working principle of the capturing dsDNA probe is described as follows: the capturing probe can be cut with the DNA restriction enzyme (such as EcoR I) to cause a sticky terminal, if the probe is not bound with a target protein, and the sticky terminal can be extended and labeled with Cy3-dUTP by DNA polymerase. When the probe is bound with a target protein, the probe is not capable to be cut by the restriction enzyme because of space obstruction. The amount of the target DNA binding proteins can be measured according to the variations of fluorescent signals of the corresponding probes.展开更多
Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompress...Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompression with different quantization matrices. However, the detectionof double JPEG color images with the same quantization matrix is stilla challenging task. An effective detection approach to extract features isproposed in this paper by combining traditional analysis with ConvolutionalNeural Networks (CNN). On the one hand, the number of nonzero pixels andthe sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the roundingerror, the truncation error and the quantization coefficient matrix are used togenerate a total of 128-dimensional features via a specially designed CNN. Insuch aCNN, convolutional layers with fixed kernel of 1×1 and Dropout layersare adopted to prevent overfitting of the model, and an average pooling layeris used to extract local characteristics. In this approach, the Support VectorMachine (SVM) classifier is applied to distinguishwhether a given color imageis primarily or secondarily compressed. The approach is also suitable for thecase when customized needs are considered. The experimental results showthat the proposed approach is more effective than some existing ones whenthe compression quality factors are low.展开更多
Fog computing paradigm extends computing,communication,storage,and network resources to the network’s edge.As the fog layer is located between cloud and end-users,it can provide more convenience and timely services t...Fog computing paradigm extends computing,communication,storage,and network resources to the network’s edge.As the fog layer is located between cloud and end-users,it can provide more convenience and timely services to end-users.However,in fog computing(FC),attackers can behave as real fog nodes or end-users to provide malicious services in the network.The attacker acts as an impersonator to impersonate other legitimate users.Therefore,in this work,we present a detection technique to secure the FC environment.First,we model a physical layer key generation based on wireless channel characteristics.To generate the secret keys between the legitimate users and avoid impersonators,we then consider a Double Sarsa technique to identify the impersonators at the receiver end.We compare our proposed Double Sarsa technique with the other two methods to validate our work,i.e.,Sarsa and Q-learning.The simulation results demonstrate that the method based on Double Sarsa outperforms Sarsa and Q-learning approaches in terms of false alarm rate(FAR),miss detection rate(MDR),and average error rate(AER).展开更多
Undoubtedly,uncooperative or malicious nodes threaten the safety of Internet of Vehicles(IoV)by destroying routing or data.To this end,some researchers have designed some node detection mechanisms and trust calculatin...Undoubtedly,uncooperative or malicious nodes threaten the safety of Internet of Vehicles(IoV)by destroying routing or data.To this end,some researchers have designed some node detection mechanisms and trust calculating algorithms based on some different feature parameters of IoV such as communication,data,energy,etc.,to detect and evaluate vehicle nodes.However,it is difficult to effectively assess the trust level of a vehicle node only by message forwarding,data consistency,and energy sufficiency.In order to resolve these problems,a novel mechanism and a new trust calculating model is proposed in this paper.First,the four tuple method is adopted,to qualitatively describing various types of nodes of IoV;Second,analyzing the behavioral features and correlation of various nodes based on route forwarding rate,data forwarding rate and physical location;third,designing double layer detection feature parameters with the ability to detect uncooperative nodes and malicious nodes;fourth,establishing a node correlative detection model with a double layer structure by combining the network layer and the perception layer.Accordingly,we conducted simulation experiments to verify the accuracy and time of this detection method under different speed-rate topological conditions of IoV.The results show that comparing with methods which only considers energy or communication parameters,the method proposed in this paper has obvious advantages in the detection of uncooperative and malicious nodes of IoV;especially,with the double detection feature parameters and node correlative detection model combined,detection accuracy is effectively improved,and the calculation time of node detection is largely reduced.展开更多
An important and challenging aspect of developing an intelligent transportation system is the identification of nighttime vehicles. Most accidents occur at night owing to the absence of night lighting conditions. Vehi...An important and challenging aspect of developing an intelligent transportation system is the identification of nighttime vehicles. Most accidents occur at night owing to the absence of night lighting conditions. Vehicle detection has become a vital subject for research to ensure safety and avoid accidents. New vision-based on-road nighttime vehicle detection and tracking system are suggested in this survey paper using taillight and headlight features. Using computer vision and some image processing techniques, the proposed system can identify vehicles based on taillight and headlight features. For vehicle tracking, a centroid tracking algorithm has been used. Euclidean Distance method has been used for measuring the distances between two neighboring objects and tracks the nearest neighbor. In the proposed system two flexible fixed Region of Interest (ROI) have been used, one is the Headlight ROI, and another is the Taillight ROI that could adapt to different resolutions of the images and videos. The achievement of this research work is that the proposed two ROIs can work simultaneously in a frame to identify oncoming and preceding vehicles at night. The segmentation techniques and double thresholding method have been used to extract the red and white components from the scene to identify the vehicle headlights and taillights. To evaluate the capability of the proposed process, two types of datasets have been used. Experimental findings indicate that the performance of the proposed technique is reliable and effective in distinct nighttime environments for detection and tracking of vehicles. The proposed method has been able to detect and track double lights as well as single light such as motorcycle light and achieved average accuracy and average processing time of vehicle detection about 97.22% and 0.01 s per frame respectively.展开更多
Owing to the serious potential side-effects on the environment and human health,the rapid detection and removal of antibiotics have become an important research focus.In this work,four zinc-based metal-organic framewo...Owing to the serious potential side-effects on the environment and human health,the rapid detection and removal of antibiotics have become an important research focus.In this work,four zinc-based metal-organic frameworks(MOFs)with different functional groups,i.e.,Zn-MOF,Zn-MOF-CH_(3),Zn-MOF-NO_(2),Zn-MOF-COOH,were utilized for the construction of LDO/MOF composite materials with a nickel-iron-cobalt-based layered double oxide,NiFeCo-LDO.The results showed that the LDO/MOF composites not only had high sensitivity in detecting sulfonamide and quinolone antibiotics,but also had an appreciable ability to adsorb them from wastewater.The maximum adsorption capacities of all the four types of LDO@Zn-MOFs to all antibiotics can at least reach 150 mg/g,and the limits of detection in relation to all four antibiotics were at least as low as 100μg/L.Our work suggested the dual-function extraction performance can be attributed to the synergistic effects between the LDO and the MOFs.Moreover,the strong ferromagnetism derived from the LDO provided great convenience for the separation and regeneration of the LDO/MOF composites.展开更多
基金Funded by the National Natural Science Foundation of China(No.51873167)the National Innovation and Entrepreneurship Training Program for College Students(No.226801001)。
文摘We developed a fluorescent double network hydrogel with ionic responsiveness and high mechanical properties for visual detection.The nanocomposite hydrogel of laponite and polyacrylamide serves as the first network,while the ionic cross-linked hydrogel of terbium ions and sodium alginate serves as the second network.The double-network structure,the introduction of nanoparticles and the reversible ionic crosslinked interactions confer high mechanical properties to the hydrogel.Terbium ions are not only used as the ionic cross-linked points,but also used as green emitters to endow hydrogels with fluorescent properties.On the basis of the “antenna effect” of terbium ions and the ion exchange interaction,the fluorescence of the hydrogels can make selective responses to various ions(such as organic acid radical ions,transition metal ions) in aqueous solutions,which enables a convenient strategy for visual detection toward ions.Consequently,the fluorescent double network hydrogel fabricated in this study is promising for use in the field of visual sensor detection.
基金supported by the National Science and Technology Council under grants NSTC 112-2221-E-320-002the Buddhist Tzu Chi Medical Foundation in Taiwan under Grant TCMMP 112-02-02.
文摘In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.
基金supported by the National Natural Science Foundation of China (Grant No. 61271323)the Open Project from State Key Laboratory of Millimeter Waves, China (Grant No. K200913)
文摘Ultra-wideband （UWB） microwave images are proposed for detecting small malignant breast tumors based on the large contrast of electric parameters between a malignant tumor and normal breast tissue. In this study, an antenna array composed of 9 antennas is applied to the detection. The double constrained robust capon beamforming （DCRCB） algorithm is used for reconstructing the breast image due to its better stability and high signal-to-interference-plus-noise ratio （SINR）. The successful detection of a tumor of 2 mm in diameter shown in the reconstruction demonstrates the robustness of the DCRCB beamforming algorithm. This study verifies the feasibility of detecting small breast tumors by using the DCRCB imaging algorithm.
基金This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R97),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.
文摘JPEG(Joint Image Experts Group)is currently the most widely used image format on the Internet.Existing cases show that many tampering operations occur on JPEG images.The basic process of the operation is that the JPEG file is first decompressed,modified in the null field,and then the tampered image is compressed and saved in JPEG format,so that the tampered image may be compressed several times.Therefore,the double compression detection of JPEG images can be an important part for determining whether an image has been tampered with,and the study of double JPEG compression anti-detection can further advance the progress of detection work.In this paper,we mainly review the literature in the field of double JPEG compression detection in recent years with two aspects,namely,the quantization table remains unchanged and the quantization table is inconsistent in the double JPEG compression process,Also,we will introduce some representative methods of double JPEG anti-detection in recent years.Finally,we analyze the problems existing in the field of double JPEG compression and give an outlook on the future development direction.
文摘Echo canceller generally needs a double-talk detector which is used to keep the adaptive filter from diverging in the appearance of near-end speech. In this paper we adopt a new double-talk detection algorithm based onl 2 norm to detect the existence of near-end speech in an acoustic echo canceller. We analyze this algorithm from the point of view of functional analysis and point out that the proposed double-talk detection algorithm has the same performance as the classic one in a finite Banach space. The remarkable feature of this algorithm is its higher accuracy and better computation complexity. The fine properties of this algorithm are confirmed by computer simulation and the application in a multimedia communication system. Key words acoustic echo cancellation - double-talk, detection - l 2 norm - adaptive FIR CLC number TN 911 Foundation item: Supported by the the National High Technology Development of China (863-306-ZT05)Biography: Wang Shao-wei (1975-) male, Ph. D candidate, research direction: multimedia communication.
基金supported by the Natural Science Foundation of Heilongjiang Province(E2017024)13th Five-Year Pre-Research(J040717005)+1 种基金National Defense Basic Research(A0420132202)China International Ministry of Science and Technology International Cooperation Project(2014DFR10010)
文摘This paper proposes a heading fault tolerance scheme for operation-level underwater robots subject to external interference.The scheme is based on a double-criterion fault detection method using a redundant structure of a dual electronic compass.First,two subexpansion Kalman filters are set up to fuse data with an inertial attitude measurement system.Then,fault detection can effectively identify the fault sensor and fault source.Finally,a fault-tolerant algorithm is used to isolate and alarm the faulty sensor.The program can effectively detect the constant magnetic field interference,change the magnetic field interference and small transient magnetic field interference,and conduct fault tolerance control in time to ensure the heading accuracy of the system.Test verification shows that the system is practical and effective.
文摘The performance of classic Mel-frequency cepstral coefficients (MFCC) is unsatisfactory in noisy environment with different sound sources from nature. In this paper, a classification approach of the ecological environmental sounds using the double-level energy detection (DED) was presented. The DED was used to detect the existence of the sound signals under noise conditions. In addition, MFCC features from the frames which were detected the presence of the sound signals by DED were extracted. Experimental results show that the proposed technology has better noise immunity than classic MFCC, and also outperforms time-domain energy detection (TED) and frequency-domain energy detection (FED) respectively.
文摘Changepoint detection faces challenges when outlier data are present. This paper proposes a multivariate changepoint detection method which is based on the robust WPCA projection direction and the robust RFPOP method, RWPCA-RFPOP method. Our method is double robust which is suitable for detecting mean changepoints in multivariate normal data with high correlations between variables that include outliers. Simulation results demonstrate that our method provides strong guarantees on both the number and location of changepoints in the presence of outliers. Finally, our method is well applied in an ACGH dataset.
基金supported by the National Natural Science Foundation of China(Nos.U1432246,U1632136,U1432127,11375268,11635015,and 11475263)the National Basic Research Program of China(No.2013CB834404)
文摘A double-sided silicon strip detector(DSSD)with active area of 48 mm x 48 mm and thickness of300μm has been developed. Each side of DSSD consists of48 strips, each with width of 0.9 mm and inter-strip separation of 0.1 mm. Electrical properties and detection performances including full depletion bias voltage, reverse leakage current, rise time, energy resolution and cross talk have been studied. At a bias of 80 V, leakage current in each strip is less than 15 nA, and rise time for alpha particle at 5157 keV is approximately 15 ns on both sides.Good energy resolutions have been achieved with0.65-0.80% for the junction strips and 0.85-1.00% for the ohmic strips. The cross talk is found to be negligible on both sides. The overall good performance of DSSD indicates its readiness for various nuclear physics experiments.
文摘The double-stranded DNA (dsDNA) probe contains two different protein binding sites. One is for DNA- binding proteins to be detected and the other is for a DNA restriction enzyme. The two sites were arranged together with no base interval. The working principle of the capturing dsDNA probe is described as follows: the capturing probe can be cut with the DNA restriction enzyme (such as EcoR I) to cause a sticky terminal, if the probe is not bound with a target protein, and the sticky terminal can be extended and labeled with Cy3-dUTP by DNA polymerase. When the probe is bound with a target protein, the probe is not capable to be cut by the restriction enzyme because of space obstruction. The amount of the target DNA binding proteins can be measured according to the variations of fluorescent signals of the corresponding probes.
基金Supported by the Fundamental Research Funds for the Central Universities (No.500421126)。
文摘Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompression with different quantization matrices. However, the detectionof double JPEG color images with the same quantization matrix is stilla challenging task. An effective detection approach to extract features isproposed in this paper by combining traditional analysis with ConvolutionalNeural Networks (CNN). On the one hand, the number of nonzero pixels andthe sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the roundingerror, the truncation error and the quantization coefficient matrix are used togenerate a total of 128-dimensional features via a specially designed CNN. Insuch aCNN, convolutional layers with fixed kernel of 1×1 and Dropout layersare adopted to prevent overfitting of the model, and an average pooling layeris used to extract local characteristics. In this approach, the Support VectorMachine (SVM) classifier is applied to distinguishwhether a given color imageis primarily or secondarily compressed. The approach is also suitable for thecase when customized needs are considered. The experimental results showthat the proposed approach is more effective than some existing ones whenthe compression quality factors are low.
基金supported by Natural Science Foundation of China(61801008)The China National Key R&D Program(No.2018YFB0803600)+1 种基金Scientific Research Common Program of Beijing Municipal Commission of Education(No.KM201910005025)Chinese Postdoctoral Science Foundation(No.2020M670074).
文摘Fog computing paradigm extends computing,communication,storage,and network resources to the network’s edge.As the fog layer is located between cloud and end-users,it can provide more convenience and timely services to end-users.However,in fog computing(FC),attackers can behave as real fog nodes or end-users to provide malicious services in the network.The attacker acts as an impersonator to impersonate other legitimate users.Therefore,in this work,we present a detection technique to secure the FC environment.First,we model a physical layer key generation based on wireless channel characteristics.To generate the secret keys between the legitimate users and avoid impersonators,we then consider a Double Sarsa technique to identify the impersonators at the receiver end.We compare our proposed Double Sarsa technique with the other two methods to validate our work,i.e.,Sarsa and Q-learning.The simulation results demonstrate that the method based on Double Sarsa outperforms Sarsa and Q-learning approaches in terms of false alarm rate(FAR),miss detection rate(MDR),and average error rate(AER).
基金This research is supported by the National Natural Science Foundations of China under Grants Nos.61862040,61762060 and 61762059The authors gratefully acknowledge the anonymous reviewers for their helpful comments and suggestions.
文摘Undoubtedly,uncooperative or malicious nodes threaten the safety of Internet of Vehicles(IoV)by destroying routing or data.To this end,some researchers have designed some node detection mechanisms and trust calculating algorithms based on some different feature parameters of IoV such as communication,data,energy,etc.,to detect and evaluate vehicle nodes.However,it is difficult to effectively assess the trust level of a vehicle node only by message forwarding,data consistency,and energy sufficiency.In order to resolve these problems,a novel mechanism and a new trust calculating model is proposed in this paper.First,the four tuple method is adopted,to qualitatively describing various types of nodes of IoV;Second,analyzing the behavioral features and correlation of various nodes based on route forwarding rate,data forwarding rate and physical location;third,designing double layer detection feature parameters with the ability to detect uncooperative nodes and malicious nodes;fourth,establishing a node correlative detection model with a double layer structure by combining the network layer and the perception layer.Accordingly,we conducted simulation experiments to verify the accuracy and time of this detection method under different speed-rate topological conditions of IoV.The results show that comparing with methods which only considers energy or communication parameters,the method proposed in this paper has obvious advantages in the detection of uncooperative and malicious nodes of IoV;especially,with the double detection feature parameters and node correlative detection model combined,detection accuracy is effectively improved,and the calculation time of node detection is largely reduced.
文摘An important and challenging aspect of developing an intelligent transportation system is the identification of nighttime vehicles. Most accidents occur at night owing to the absence of night lighting conditions. Vehicle detection has become a vital subject for research to ensure safety and avoid accidents. New vision-based on-road nighttime vehicle detection and tracking system are suggested in this survey paper using taillight and headlight features. Using computer vision and some image processing techniques, the proposed system can identify vehicles based on taillight and headlight features. For vehicle tracking, a centroid tracking algorithm has been used. Euclidean Distance method has been used for measuring the distances between two neighboring objects and tracks the nearest neighbor. In the proposed system two flexible fixed Region of Interest (ROI) have been used, one is the Headlight ROI, and another is the Taillight ROI that could adapt to different resolutions of the images and videos. The achievement of this research work is that the proposed two ROIs can work simultaneously in a frame to identify oncoming and preceding vehicles at night. The segmentation techniques and double thresholding method have been used to extract the red and white components from the scene to identify the vehicle headlights and taillights. To evaluate the capability of the proposed process, two types of datasets have been used. Experimental findings indicate that the performance of the proposed technique is reliable and effective in distinct nighttime environments for detection and tracking of vehicles. The proposed method has been able to detect and track double lights as well as single light such as motorcycle light and achieved average accuracy and average processing time of vehicle detection about 97.22% and 0.01 s per frame respectively.
基金support from the National Natural Science Foundation of China(Nos.22276080,21605105)the Foreign Expert Project,China(No.G2022014096L)+1 种基金the Natural Science Foundation of Jiangsu Province,China(No.BK20211340)Graduate Research and Practice Innovation Program of Jiangsu Province,China(No.KYCX22_3835).
文摘Owing to the serious potential side-effects on the environment and human health,the rapid detection and removal of antibiotics have become an important research focus.In this work,four zinc-based metal-organic frameworks(MOFs)with different functional groups,i.e.,Zn-MOF,Zn-MOF-CH_(3),Zn-MOF-NO_(2),Zn-MOF-COOH,were utilized for the construction of LDO/MOF composite materials with a nickel-iron-cobalt-based layered double oxide,NiFeCo-LDO.The results showed that the LDO/MOF composites not only had high sensitivity in detecting sulfonamide and quinolone antibiotics,but also had an appreciable ability to adsorb them from wastewater.The maximum adsorption capacities of all the four types of LDO@Zn-MOFs to all antibiotics can at least reach 150 mg/g,and the limits of detection in relation to all four antibiotics were at least as low as 100μg/L.Our work suggested the dual-function extraction performance can be attributed to the synergistic effects between the LDO and the MOFs.Moreover,the strong ferromagnetism derived from the LDO provided great convenience for the separation and regeneration of the LDO/MOF composites.
