Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embe...Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.展开更多
Retransmission avoidance is an essential need for any type of wireless communication.As retransmissions induce the unnecessary presence of redundant data in every accessible node.As storage capacity is symmetrical to ...Retransmission avoidance is an essential need for any type of wireless communication.As retransmissions induce the unnecessary presence of redundant data in every accessible node.As storage capacity is symmetrical to the size of the memory,less storage capacity is experienced due to the restricted size of the respective node.In this proposed work,we have discussed the integration of the Energy Proficient Reduced Coverage Set with Particle Swarm Optimization(PSO).PSO is a metaheuristic global search enhancement technique that promotes the searching of the best nodes in the search space.PSO is integrated with a Reduced Coverage Set,to obtain an optimal path with only high-power transmitting nodes.Energy Proficient Reduced Coverage Set with PSO constructs a set of only best nodes based on the fitness solution,to cover the whole network.The proposed algorithm has experimented with a different number of nodes.Comparison has been made between original and improved algorithm shows that improved algorithm performs better than the existing by reducing the redundant packet transmissions by 18%~40%,thereby increasing the network lifetime.展开更多
Sensors deployment optimization has become one of the most attractive fields in recent years. However, most of the previous work focused on the deployment problem in 2D space.Compared to the traditional form, sensors ...Sensors deployment optimization has become one of the most attractive fields in recent years. However, most of the previous work focused on the deployment problem in 2D space.Compared to the traditional form, sensors deployment in multidimensional space has greater research significance and practical potential to satisfy the detecting needs in complex environment.Aiming at solving this issue, a multi-dimensional space sensor network model is established, and the radar system is selected as an example. Considering the possible working mode of the radar system(e.g., searching and tracking), two distinctive deployment models are proposed based on maximum coverage area and maximum target detection probability in the attack direction respectively. The latter one is usually ignored in the previous literature.For uncovering the optimal deployment of the sensor network, the particle swarm optimization(PSO) algorithm is improved using the proposed weights determination scheme, in which the linear decreasing, the pooling strategy and the cloud theory are combined for weights updating. Experimental results illustrate the effectiveness of the proposed method.展开更多
This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchrono...This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchronous manner, and the delay probability of each sensor is assumed to be known or unknown. Firstly, a new model is constructed to describe the measurement process, based on which a new particle filter is developed with the ability to fuse multi-sensor information in the case of known delay probability.In addition, an online delay probability estimation module is introduced in the particle filtering framework, which leads to another new filter that can be implemented without the prior knowledge of delay probability. More importantly, since there is no complex iterative operation, the resulting filter can be implemented recursively and is suitable for many real-time applications. Simulation results show the effectiveness of the proposed filters.展开更多
We discuss blending sensor scheduling strategies with particle filtering (PF) methods to deal with the prob-lem of tracking a ‘smart’ target, that is, a target being able to be aware it is being tracked and act in a...We discuss blending sensor scheduling strategies with particle filtering (PF) methods to deal with the prob-lem of tracking a ‘smart’ target, that is, a target being able to be aware it is being tracked and act in a manner that makes the future track more difficult. We concern here how to accurately track the target with a care on concealing the observer to a possible extent. We propose a PF method, which is tailored to mix a sensor scheduling technique, called covariance control, within its framework. A Rao-blackwellised unscented Kal-man filter (UKF) is used to produce proposal distributions for the PF method, making it more robust and computationally efficient. We show that the proposed method can balance the tracking filter performance with the observer’s concealment.展开更多
A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian ...A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian and nonlinear models and non-stationary sources. Using some instantaneously mixed observations of several real-world vehicle acoustic signals, the proposed statistical method is compared with a conventional non-stationary Blind Source Separation algorithm and attractive simulation results are achieved. Moreover, considering the natural convenience to transmit particles between sensor nodes, the algorithm based on particle filtering is believed to have potential to enable the task of multiple vehicles recognition collaboratively performed by sensor nodes in distributed wireless sensor network.展开更多
In this paper we report on a study of the CMOS image sensor detection of DNA based on self-assembled nano- metallic particles, which are selectively deposited on the surface of the passive image sensor. The nano-metal...In this paper we report on a study of the CMOS image sensor detection of DNA based on self-assembled nano- metallic particles, which are selectively deposited on the surface of the passive image sensor. The nano-metallic particles effectively block the optical radiation in the visible spectrum of ordinary light source. When such a technical method is applied to DNA detection, the requirement for a special UV light source in the most popular fluorescence is eliminated. The DNA detection methodology is tested on a CMOS sensor chip fabricated using a standard 0.5 gm CMOS process. It is demonstrated that the approach is highly selective to detecting even a signal-base mismatched DNA target with an extremely-low-concentration DNA sample down to 10 pM under an ordinary light source.展开更多
Wireless sensor networks(WSN)are widely used in many situations,but the disordered and random deployment mode will waste a lot of sensor resources.This paper proposes a multi-topology hierarchical collaborative partic...Wireless sensor networks(WSN)are widely used in many situations,but the disordered and random deployment mode will waste a lot of sensor resources.This paper proposes a multi-topology hierarchical collaborative particle swarm optimization(MHCHPSO)to optimize sensor deployment location and improve the coverage of WSN.MHCHPSO divides the population into three types topology:diversity topology for global exploration,fast convergence topology for local development,and collaboration topology for exploration and development.All topologies are optimized in parallel to overcome the precocious convergence of PSO.This paper compares with various heuristic algorithms at CEC 2013,CEC 2015,and CEC 2017.The experimental results show that MHCHPSO outperforms the comparison algorithms.In addition,MHCHPSO is applied to the WSN localization optimization,and the experimental results confirm the optimization ability of MHCHPSO in practical engineering problems.展开更多
Energy efficient communication is a plenary issue in Wireless Sensor Networks (WSNs). Contemporary energy efficient optimization schemes are focused on reducing power consumption in various aspects of hardware design,...Energy efficient communication is a plenary issue in Wireless Sensor Networks (WSNs). Contemporary energy efficient optimization schemes are focused on reducing power consumption in various aspects of hardware design, data processing, network protocols and operating system. In this paper, optimization of network is formulated by Cuckoo Based Particle Approach (CBPA). Nodes are deployed randomly and organized as static clusters by Cuckoo Search (CS). After the cluster heads are selected, the information is collected, aggregated and forwarded to the base station using generalized particle approach algorithm. The Generalized Particle Model Algorithm (GPMA) transforms the network energy consumption problem into dynamics and kinematics of numerous particles in a force-field. The proposed approach can significantly lengthen the network lifetime when compared to traditional methods.展开更多
Seamless and reliable navigation for civilian/military application is possible by fusing prominent Global Positioning System (GPS) with Inertial Navigation System (INS). This integrated GPS/INS unit exhibits a continu...Seamless and reliable navigation for civilian/military application is possible by fusing prominent Global Positioning System (GPS) with Inertial Navigation System (INS). This integrated GPS/INS unit exhibits a continuous navigation solution with increased accuracy and reduced uncertainty or ambiguity. In this paper, we propose a novel approach of dynamically creating a Voronoi based Particle Filter (VPF) for integrating INS and GPS data. This filter is based on redistribution of the proposal distribution such that the redistributed particles lie in high likelihood region;thereby increasing the filter accuracy. The usual limitations like degeneracy, sample impoverishment that are seen in conventional particle filter are overcome using our VPF with minimum feasible particles. The small particle size in our methodology reduces the computational load of the filter and makes real-time implementation feasible. Our field test results clearly indicate that the proposed VPF algorithm effectively compensated and reduced positional inaccuracies when GPS data is available. We also present the preliminary results for cases with short GPS outages that occur for low-cost inertial sensors.展开更多
Optical nanoparticles are nowadays one of the key elements of photonics.They do not only allow optical imaging of a plethora of systems(from cells to microelectronics),but,in many cases,they also behave as highly sens...Optical nanoparticles are nowadays one of the key elements of photonics.They do not only allow optical imaging of a plethora of systems(from cells to microelectronics),but,in many cases,they also behave as highly sensitive remote sensors.In recent years,it has been demonstrated the success of optical tweezers in isolating and manipulating individual optical nanoparticles.This has opened the door to high resolution single particle scanning and sensing.In this quickly growing field,it is now necessary to sum up what has been achieved so far to identify the appropriate system and experimental set-up required for each application.In this review article we summarize the most relevant results in the field of optical trapping of individual optical nanoparticles.After systematic bibliographic research,we identify the main families of optical nanoparticles in which optical trapping has been demonstrated.For each case,the main advances and applications have been described.Finally,we also include our critical opinion about the future of the field,identifying the challenges that we are facing.展开更多
Target tracking is one of the main applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of the sensor nodes. A framework and ana...Target tracking is one of the main applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of the sensor nodes. A framework and analysis for collaborative tracking via particle filter are presented in this paper. Collaborative tracking is implemented through sensor selection, and results of tracking are propagated among sensor nodes. In order to save communication resources, a new Gaussian sum particle filter, called Gaussian sum quasi particle filter, to perform the target tracking is presented, in which only mean and covariance of mixands need to be communicated. Based on the Gaussian sum quasi particle filter, a sensor selection criterion is proposed, which is computationally much simpler than other sensor selection criterions. Simulation results show that the proposed method works well for target tracking.展开更多
Titania nano particles were synthesized at carbon paste electrode by cyclic voltammetry and then it was employed for the determination of acetaminophen in phosphate buffer at pH 7.4. Carbon paste electrode with titani...Titania nano particles were synthesized at carbon paste electrode by cyclic voltammetry and then it was employed for the determination of acetaminophen in phosphate buffer at pH 7.4. Carbon paste electrode with titania nano particle displayed excellent electrochemically catalytic activities by shifting the oxidation potential of acetaminophen towards the negative side. The mass transfer process at electrochemical interface was diffusion controlled. Electrochemical techniques such as, electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization methods were used to measure the resistance of the electrodes. The resistance of the titanium electrode decreased in two orders when compared to the bare carbon paste electrode;the decrease in the resistance of the electrode and increase in the surface area of the electrode are responsible for the negative shifting of the oxidation potential of acetaminophen. The present method was applied to the determination of actetaminophen in paracetamol tablet, urine and blood sample by using standard addition method and the obtained results were satisfactory with a good recovery of 98%.展开更多
In wireless sensor networks(WSNs) with single sink,the nodes close to the sink consume their energy too fast due to transferring a large number of data packages,resulting in the "energy hole" problem.Deployi...In wireless sensor networks(WSNs) with single sink,the nodes close to the sink consume their energy too fast due to transferring a large number of data packages,resulting in the "energy hole" problem.Deploying multiple sink nodes in WSNs is an effective strategy to solve this problem.A multi-sink deployment strategy based on improved particle swarm clustering optimization(IPSCO) algorithm for WSNs is proposed in this paper.The IPSCO algorithm is a combination of the improved particle swarm optimization(PSO) algorithm and K-means clustering algorithm.According to the sink nodes number K,the IPSCO algorithm divides the sensor nodes in the whole network area into K clusters based on the distance between them,making the total within-class scatter to minimum,and outputs the center of each cluster.Then,multiple sink nodes in the center of each cluster can be deployed,to achieve the effects of partition network reasonably and deploy multi-sink nodes optimally.The simulation results show that the deployment strategy can prolong the network lifetime.展开更多
Wireless Sensor Networks(WSNs)play an indispensable role in the lives of human beings in the fields of environment monitoring,manufacturing,education,agriculture etc.,However,the batteries in the sensor node under dep...Wireless Sensor Networks(WSNs)play an indispensable role in the lives of human beings in the fields of environment monitoring,manufacturing,education,agriculture etc.,However,the batteries in the sensor node under deployment in an unattended or remote area cannot be replaced because of their wireless existence.In this context,several researchers have contributed diversified number of cluster-based routing schemes that concentrate on the objective of extending node survival time.However,there still exists a room for improvement in Cluster Head(CH)selection based on the integration of critical parameters.The meta-heuristic methods that concentrate on guaranteeing both CH selection and data transmission for improving optimal network performance are predominant.In this paper,a hybrid Marine Predators Optimization and Improved Particle Swarm Optimizationbased Optimal Cluster Routing(MPO-IPSO-OCR)is proposed for ensuring both efficient CH selection and data transmission.The robust characteristic of MPOA is used in optimized CH selection,while improved PSO is used for determining the optimized route to ensure sink mobility.In specific,a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of MPOA.The high-speed ratio,unit speed rate and low speed rate strategy inherited by MPOA facilitate better exploitation by preventing solution from being struck into local optimality point.The simulation investigation and statistical results confirm that the proposed MPOIPSO-OCR is capable of improving the energy stability by 21.28%,prolonging network lifetime by 18.62%and offering maximum throughput by 16.79%when compared to the benchmarked cluster-based routing schemes.展开更多
The reasonable measuring of particle weight and effective sampling of particle state are considered as two important aspects to obtain better estimation precision in particle filter.Aiming at the comprehensive treatme...The reasonable measuring of particle weight and effective sampling of particle state are considered as two important aspects to obtain better estimation precision in particle filter.Aiming at the comprehensive treatment of above problems,a novel two-stage prediction and update particle filtering algorithm based on particle weight optimization in multi-sensor observation is proposed.Firstly,combined with the construction of multi-senor observation likelihood function and the weight fusion principle,a new particle weight optimization strategy in multi-sensor observation is presented,and the reliability and stability of particle weight are improved by decreasing weight variance.In addition,according to the prediction and update mechanism of particle filter and unscented Kalman filter,a new realization of particle filter with two-stage prediction and update is given.The filter gain containing the latest observation information is used to directly optimize state estimation in the framework,which avoids a large calculation amount and the lack of universality in proposal distribution optimization way.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.展开更多
The effective medium approximation (EMA) theory is the basis of a capacitance sensor used for concen-tration measurementof a particulate solid flow, its measurementresultis independenton particle size. In existence of...The effective medium approximation (EMA) theory is the basis of a capacitance sensor used for concen-tration measurementof a particulate solid flow, its measurementresultis independenton particle size. In existence ofparticle agglomeration or aggradation, however, it is found that the effective permittivity of a gas/solid mixture is de-pendent on particle size. In this paper, a parallel plate, differential capacitance sensor is utilized to investigate theinfluence of particle size on the effective permittivity of the mixture in such a case. Static experiments using threematerials including glass, limestone and quartz particles were carried out in an off-line manner. The volume fractionof particles being tested ranged from20×10-6to 600×10-6, while the particle size was between 3 and 100μm.Experimental results showthat the effective permittivity of a particle-gas mixture with particle agglomeration is largerthan that predicted by EMA and the smaller the particle size, the larger the effective permittivity. The experimentprocess and analysis results are discussed in detail in the paper.展开更多
One of the important research issues in wireless sensor networks(WSNs)is the optimal layout designing for the deployment of sensor nodes.It directly affects the quality of monitoring,cost,and detection capability of W...One of the important research issues in wireless sensor networks(WSNs)is the optimal layout designing for the deployment of sensor nodes.It directly affects the quality of monitoring,cost,and detection capability of WSNs.Layout optimization is an NP-hard combinatorial problem,which requires optimization of multiple competing objectives like cost,coverage,connectivity,lifetime,load balancing,and energy consumption of sensor nodes.In the last decade,several meta-heuristic optimization techniques have been proposed to solve this problem,such as genetic algorithms(GA)and particle swarm optimization(PSO).However,these approaches either provided computationally expensive solutions or covered a limited number of objectives,which are combinations of area coverage,the number of sensor nodes,energy consumption,and lifetime.In this study,a meta-heuristic multi-objective firefly algorithm(MOFA)is presented to solve the layout optimization problem.Here,the main goal is to cover a number of objectives related to optimal layouts of homogeneous WSNs,which includes coverage,connectivity,lifetime,energy consumption and the number of sensor nodes.Simulation results showed that MOFA created optimal Pareto front of non-dominated solutions with better hyper-volumes and spread of solutions,in comparison to multi-objective genetic algorithms(IBEA,NSGA-II)and particle swarm optimizers(OMOPSO,SMOPSO).Therefore,MOFA can be used in real-time deployment applications of large-scale WSNs to enhance their detection capability and quality of monitoring.展开更多
Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in...Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.展开更多
文摘Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.
文摘Retransmission avoidance is an essential need for any type of wireless communication.As retransmissions induce the unnecessary presence of redundant data in every accessible node.As storage capacity is symmetrical to the size of the memory,less storage capacity is experienced due to the restricted size of the respective node.In this proposed work,we have discussed the integration of the Energy Proficient Reduced Coverage Set with Particle Swarm Optimization(PSO).PSO is a metaheuristic global search enhancement technique that promotes the searching of the best nodes in the search space.PSO is integrated with a Reduced Coverage Set,to obtain an optimal path with only high-power transmitting nodes.Energy Proficient Reduced Coverage Set with PSO constructs a set of only best nodes based on the fitness solution,to cover the whole network.The proposed algorithm has experimented with a different number of nodes.Comparison has been made between original and improved algorithm shows that improved algorithm performs better than the existing by reducing the redundant packet transmissions by 18%~40%,thereby increasing the network lifetime.
文摘Sensors deployment optimization has become one of the most attractive fields in recent years. However, most of the previous work focused on the deployment problem in 2D space.Compared to the traditional form, sensors deployment in multidimensional space has greater research significance and practical potential to satisfy the detecting needs in complex environment.Aiming at solving this issue, a multi-dimensional space sensor network model is established, and the radar system is selected as an example. Considering the possible working mode of the radar system(e.g., searching and tracking), two distinctive deployment models are proposed based on maximum coverage area and maximum target detection probability in the attack direction respectively. The latter one is usually ignored in the previous literature.For uncovering the optimal deployment of the sensor network, the particle swarm optimization(PSO) algorithm is improved using the proposed weights determination scheme, in which the linear decreasing, the pooling strategy and the cloud theory are combined for weights updating. Experimental results illustrate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(6147322711472222)+3 种基金the Fundamental Research Funds for the Central Universities(3102015ZY001)the Aerospace Technology Support Fund of China(2014-HT-XGD)the Natural Science Foundation of Shaanxi Province(2015JM6304)the Aeronautical Science Foundation of China(20151353018)
文摘This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchronous manner, and the delay probability of each sensor is assumed to be known or unknown. Firstly, a new model is constructed to describe the measurement process, based on which a new particle filter is developed with the ability to fuse multi-sensor information in the case of known delay probability.In addition, an online delay probability estimation module is introduced in the particle filtering framework, which leads to another new filter that can be implemented without the prior knowledge of delay probability. More importantly, since there is no complex iterative operation, the resulting filter can be implemented recursively and is suitable for many real-time applications. Simulation results show the effectiveness of the proposed filters.
文摘We discuss blending sensor scheduling strategies with particle filtering (PF) methods to deal with the prob-lem of tracking a ‘smart’ target, that is, a target being able to be aware it is being tracked and act in a manner that makes the future track more difficult. We concern here how to accurately track the target with a care on concealing the observer to a possible extent. We propose a PF method, which is tailored to mix a sensor scheduling technique, called covariance control, within its framework. A Rao-blackwellised unscented Kal-man filter (UKF) is used to produce proposal distributions for the PF method, making it more robust and computationally efficient. We show that the proposed method can balance the tracking filter performance with the observer’s concealment.
基金the National "863" High Technology Development Program (2006AA01Z216)the MajorResearch Program of the Science and Technology Commission of Shanghai Municipality of China (054SGA1001).
文摘A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian and nonlinear models and non-stationary sources. Using some instantaneously mixed observations of several real-world vehicle acoustic signals, the proposed statistical method is compared with a conventional non-stationary Blind Source Separation algorithm and attractive simulation results are achieved. Moreover, considering the natural convenience to transmit particles between sensor nodes, the algorithm based on particle filtering is believed to have potential to enable the task of multiple vehicles recognition collaboratively performed by sensor nodes in distributed wireless sensor network.
基金Project supported by the Key Program of the National Natural Science Foundation of China (Grant No. 61036004)the Shenzhen Science & Technology Foundation, China (Grant No. CXB201005250031A)+1 种基金the Fundamental Research Project of Shenzhen Science & Technology Foundation, China (Grant No. JC201005280670A)the International Collaboration Project of Shenzhen Science & Technology Foundation, China (Grant No. ZYA2010006030006A)
文摘In this paper we report on a study of the CMOS image sensor detection of DNA based on self-assembled nano- metallic particles, which are selectively deposited on the surface of the passive image sensor. The nano-metallic particles effectively block the optical radiation in the visible spectrum of ordinary light source. When such a technical method is applied to DNA detection, the requirement for a special UV light source in the most popular fluorescence is eliminated. The DNA detection methodology is tested on a CMOS sensor chip fabricated using a standard 0.5 gm CMOS process. It is demonstrated that the approach is highly selective to detecting even a signal-base mismatched DNA target with an extremely-low-concentration DNA sample down to 10 pM under an ordinary light source.
基金supported by the National Key Research and Development Program Projects of China(No.2018YFC1504705)the National Natural Science Foundation of China(No.61731015)+1 种基金the Major instrument special project of National Natural Science Foundation of China(No.42027806)the Key Research and Development Program of Shaanxi(No.2022GY-331)。
文摘Wireless sensor networks(WSN)are widely used in many situations,but the disordered and random deployment mode will waste a lot of sensor resources.This paper proposes a multi-topology hierarchical collaborative particle swarm optimization(MHCHPSO)to optimize sensor deployment location and improve the coverage of WSN.MHCHPSO divides the population into three types topology:diversity topology for global exploration,fast convergence topology for local development,and collaboration topology for exploration and development.All topologies are optimized in parallel to overcome the precocious convergence of PSO.This paper compares with various heuristic algorithms at CEC 2013,CEC 2015,and CEC 2017.The experimental results show that MHCHPSO outperforms the comparison algorithms.In addition,MHCHPSO is applied to the WSN localization optimization,and the experimental results confirm the optimization ability of MHCHPSO in practical engineering problems.
文摘Energy efficient communication is a plenary issue in Wireless Sensor Networks (WSNs). Contemporary energy efficient optimization schemes are focused on reducing power consumption in various aspects of hardware design, data processing, network protocols and operating system. In this paper, optimization of network is formulated by Cuckoo Based Particle Approach (CBPA). Nodes are deployed randomly and organized as static clusters by Cuckoo Search (CS). After the cluster heads are selected, the information is collected, aggregated and forwarded to the base station using generalized particle approach algorithm. The Generalized Particle Model Algorithm (GPMA) transforms the network energy consumption problem into dynamics and kinematics of numerous particles in a force-field. The proposed approach can significantly lengthen the network lifetime when compared to traditional methods.
文摘Seamless and reliable navigation for civilian/military application is possible by fusing prominent Global Positioning System (GPS) with Inertial Navigation System (INS). This integrated GPS/INS unit exhibits a continuous navigation solution with increased accuracy and reduced uncertainty or ambiguity. In this paper, we propose a novel approach of dynamically creating a Voronoi based Particle Filter (VPF) for integrating INS and GPS data. This filter is based on redistribution of the proposal distribution such that the redistributed particles lie in high likelihood region;thereby increasing the filter accuracy. The usual limitations like degeneracy, sample impoverishment that are seen in conventional particle filter are overcome using our VPF with minimum feasible particles. The small particle size in our methodology reduces the computational load of the filter and makes real-time implementation feasible. Our field test results clearly indicate that the proposed VPF algorithm effectively compensated and reduced positional inaccuracies when GPS data is available. We also present the preliminary results for cases with short GPS outages that occur for low-cost inertial sensors.
基金supported by projects PID2019-106211RB-I00,PID2019-105195RA-I00,and PID2021-123228NB-I00funded by MCIN/AEI/10.13039/501100011033 and projects CNS2022-135495,and TED2021-129937B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGeneration EU/PRTR.P.Camarero thanks Comunidad de Madrid for Programa Investigo by Plan de Recuperación,Transformación y Resiliencia-Comunidad de Madrid-Ministerio de Trabajo y Economía Social de España-Unión Europea-NextGenerationEU.F.Zhang acknowledges the scholarship from the China Scholarship Council (202108440235)supported by Comunidad de Madrid (S2022/BMD-7403 RENIM-CM and REACT-UCM projects).
文摘Optical nanoparticles are nowadays one of the key elements of photonics.They do not only allow optical imaging of a plethora of systems(from cells to microelectronics),but,in many cases,they also behave as highly sensitive remote sensors.In recent years,it has been demonstrated the success of optical tweezers in isolating and manipulating individual optical nanoparticles.This has opened the door to high resolution single particle scanning and sensing.In this quickly growing field,it is now necessary to sum up what has been achieved so far to identify the appropriate system and experimental set-up required for each application.In this review article we summarize the most relevant results in the field of optical trapping of individual optical nanoparticles.After systematic bibliographic research,we identify the main families of optical nanoparticles in which optical trapping has been demonstrated.For each case,the main advances and applications have been described.Finally,we also include our critical opinion about the future of the field,identifying the challenges that we are facing.
基金Supported by the National Natural Science Foundation of China (No. 60372107)Ph.D. Innovation Program of Ji-angsu Province (No. 200670)+1 种基金Major Science Foundation of Jiangsu Province (BK2007729)Major Science Foundation of Jiangsu Universities (06KJ510001)
文摘Target tracking is one of the main applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of the sensor nodes. A framework and analysis for collaborative tracking via particle filter are presented in this paper. Collaborative tracking is implemented through sensor selection, and results of tracking are propagated among sensor nodes. In order to save communication resources, a new Gaussian sum particle filter, called Gaussian sum quasi particle filter, to perform the target tracking is presented, in which only mean and covariance of mixands need to be communicated. Based on the Gaussian sum quasi particle filter, a sensor selection criterion is proposed, which is computationally much simpler than other sensor selection criterions. Simulation results show that the proposed method works well for target tracking.
文摘Titania nano particles were synthesized at carbon paste electrode by cyclic voltammetry and then it was employed for the determination of acetaminophen in phosphate buffer at pH 7.4. Carbon paste electrode with titania nano particle displayed excellent electrochemically catalytic activities by shifting the oxidation potential of acetaminophen towards the negative side. The mass transfer process at electrochemical interface was diffusion controlled. Electrochemical techniques such as, electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization methods were used to measure the resistance of the electrodes. The resistance of the titanium electrode decreased in two orders when compared to the bare carbon paste electrode;the decrease in the resistance of the electrode and increase in the surface area of the electrode are responsible for the negative shifting of the oxidation potential of acetaminophen. The present method was applied to the determination of actetaminophen in paracetamol tablet, urine and blood sample by using standard addition method and the obtained results were satisfactory with a good recovery of 98%.
基金the Key Project of the National Natural Science Foundation of China(No.61134009)National Natural Science Foundations of China(Nos.61473077,61473078)+4 种基金Program for Changjiang Scholars from the Ministry of Education,ChinaSpecialized Research Fund for Shanghai Leading Talents,ChinaProject of the Shanghai Committee of Science and Technology,China(No.13JC1407500)Innovation Program of Shanghai Municipal Education Commission,China(No.14ZZ067)the Fundamental Research Funds for the Central Universities,China(No.15D110423)
文摘In wireless sensor networks(WSNs) with single sink,the nodes close to the sink consume their energy too fast due to transferring a large number of data packages,resulting in the "energy hole" problem.Deploying multiple sink nodes in WSNs is an effective strategy to solve this problem.A multi-sink deployment strategy based on improved particle swarm clustering optimization(IPSCO) algorithm for WSNs is proposed in this paper.The IPSCO algorithm is a combination of the improved particle swarm optimization(PSO) algorithm and K-means clustering algorithm.According to the sink nodes number K,the IPSCO algorithm divides the sensor nodes in the whole network area into K clusters based on the distance between them,making the total within-class scatter to minimum,and outputs the center of each cluster.Then,multiple sink nodes in the center of each cluster can be deployed,to achieve the effects of partition network reasonably and deploy multi-sink nodes optimally.The simulation results show that the deployment strategy can prolong the network lifetime.
文摘Wireless Sensor Networks(WSNs)play an indispensable role in the lives of human beings in the fields of environment monitoring,manufacturing,education,agriculture etc.,However,the batteries in the sensor node under deployment in an unattended or remote area cannot be replaced because of their wireless existence.In this context,several researchers have contributed diversified number of cluster-based routing schemes that concentrate on the objective of extending node survival time.However,there still exists a room for improvement in Cluster Head(CH)selection based on the integration of critical parameters.The meta-heuristic methods that concentrate on guaranteeing both CH selection and data transmission for improving optimal network performance are predominant.In this paper,a hybrid Marine Predators Optimization and Improved Particle Swarm Optimizationbased Optimal Cluster Routing(MPO-IPSO-OCR)is proposed for ensuring both efficient CH selection and data transmission.The robust characteristic of MPOA is used in optimized CH selection,while improved PSO is used for determining the optimized route to ensure sink mobility.In specific,a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of MPOA.The high-speed ratio,unit speed rate and low speed rate strategy inherited by MPOA facilitate better exploitation by preventing solution from being struck into local optimality point.The simulation investigation and statistical results confirm that the proposed MPOIPSO-OCR is capable of improving the energy stability by 21.28%,prolonging network lifetime by 18.62%and offering maximum throughput by 16.79%when compared to the benchmarked cluster-based routing schemes.
基金Supported by the National Natural Science Foundations of China(No.61300214,61170243)the Science and Technology Innovation Team Support Plan of Education Department of Henan Province(No.13IRTSTHN021)+2 种基金the Science and Technology Research Key Project of Education Department of Henan Province(No.13A413066)the Basic and Frontier Technology Research Plan of Henan Province(No.132300410148)the Funding Scheme of Young Key Teacher of Henan Province Universities,and the Key Project of Teaching Reform Research of Henan University(No.HDXJJG2013-07)
文摘The reasonable measuring of particle weight and effective sampling of particle state are considered as two important aspects to obtain better estimation precision in particle filter.Aiming at the comprehensive treatment of above problems,a novel two-stage prediction and update particle filtering algorithm based on particle weight optimization in multi-sensor observation is proposed.Firstly,combined with the construction of multi-senor observation likelihood function and the weight fusion principle,a new particle weight optimization strategy in multi-sensor observation is presented,and the reliability and stability of particle weight are improved by decreasing weight variance.In addition,according to the prediction and update mechanism of particle filter and unscented Kalman filter,a new realization of particle filter with two-stage prediction and update is given.The filter gain containing the latest observation information is used to directly optimize state estimation in the framework,which avoids a large calculation amount and the lack of universality in proposal distribution optimization way.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
文摘The effective medium approximation (EMA) theory is the basis of a capacitance sensor used for concen-tration measurementof a particulate solid flow, its measurementresultis independenton particle size. In existence ofparticle agglomeration or aggradation, however, it is found that the effective permittivity of a gas/solid mixture is de-pendent on particle size. In this paper, a parallel plate, differential capacitance sensor is utilized to investigate theinfluence of particle size on the effective permittivity of the mixture in such a case. Static experiments using threematerials including glass, limestone and quartz particles were carried out in an off-line manner. The volume fractionof particles being tested ranged from20×10-6to 600×10-6, while the particle size was between 3 and 100μm.Experimental results showthat the effective permittivity of a particle-gas mixture with particle agglomeration is largerthan that predicted by EMA and the smaller the particle size, the larger the effective permittivity. The experimentprocess and analysis results are discussed in detail in the paper.
基金This research has been funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University through Research Group No.RG-21-07-09.
文摘One of the important research issues in wireless sensor networks(WSNs)is the optimal layout designing for the deployment of sensor nodes.It directly affects the quality of monitoring,cost,and detection capability of WSNs.Layout optimization is an NP-hard combinatorial problem,which requires optimization of multiple competing objectives like cost,coverage,connectivity,lifetime,load balancing,and energy consumption of sensor nodes.In the last decade,several meta-heuristic optimization techniques have been proposed to solve this problem,such as genetic algorithms(GA)and particle swarm optimization(PSO).However,these approaches either provided computationally expensive solutions or covered a limited number of objectives,which are combinations of area coverage,the number of sensor nodes,energy consumption,and lifetime.In this study,a meta-heuristic multi-objective firefly algorithm(MOFA)is presented to solve the layout optimization problem.Here,the main goal is to cover a number of objectives related to optimal layouts of homogeneous WSNs,which includes coverage,connectivity,lifetime,energy consumption and the number of sensor nodes.Simulation results showed that MOFA created optimal Pareto front of non-dominated solutions with better hyper-volumes and spread of solutions,in comparison to multi-objective genetic algorithms(IBEA,NSGA-II)and particle swarm optimizers(OMOPSO,SMOPSO).Therefore,MOFA can be used in real-time deployment applications of large-scale WSNs to enhance their detection capability and quality of monitoring.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Larg Groups project Under Grant Number(71/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R238)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR20.
文摘Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.