We present a fractional-order three-dimensional chaotic system, which can generate four-wing chaotic attractor. Dynamics of the fractional-order system is investigated by numerical simulations. To rigorously verify th...We present a fractional-order three-dimensional chaotic system, which can generate four-wing chaotic attractor. Dynamics of the fractional-order system is investigated by numerical simulations. To rigorously verify the chaos properties of this system, the existence of horseshoe in the four-wing attractor is presented. Firstly, a Poincare′ section is selected properly, and a first-return Poincare′ map is established. Then, a one-dimensional tensile horseshoe is discovered, which verifies the chaos existence of the system in mathematical view. Finally, the fractional-order chaotic attractor is implemented physically with a field-programmable gate array(FPGA) chip, which is useful in further engineering applications of information encryption and secure communications.展开更多
Piezoelectric resonators are widely used in frequency reference devices, mass sensors, resonant sensors(such as gyros and accelerometers), etc. Piezoelectric resonators usually work in a special resonant mode. Obtaini...Piezoelectric resonators are widely used in frequency reference devices, mass sensors, resonant sensors(such as gyros and accelerometers), etc. Piezoelectric resonators usually work in a special resonant mode. Obtaining working resonant mode with high quality is key to improve the performance of piezoelectric resonators. In this paper, the resonance characteristics of a rectangular lead zirconium titanate(PZT) piezoelectric resonator are studied. On the basis of the field-programmable gate array(FPGA) embedded system, direct digital synthesizer(DDS) and automatic gain controller(AGC) are used to generate the driving signals with precisely adjustable frequency and amplitude. The driving signals are used to excite the piezoelectric resonator to the working vibration mode. The influence of the connection of driving electrodes and voltage amplitude on the vibration of the resonator is studied. The quality factor and vibration linearity of the resonator are studied with various driving methods mentioned in this paper. The resonator reaches resonant mode at 330 kHz by different driving methods.The relationship between resonant amplitude and driving signal amplitude is linear. The quality factor reaches over 150 by different driving methods. The results provide a theoretical reference for the efficient excitation of the piezoelectric resonator.展开更多
Neuromorphic computing is considered to be the future of machine learning,and it provides a new way of cognitive computing.Inspired by the excellent performance of spiking neural networks(SNNs)on the fields of low-pow...Neuromorphic computing is considered to be the future of machine learning,and it provides a new way of cognitive computing.Inspired by the excellent performance of spiking neural networks(SNNs)on the fields of low-power consumption and parallel computing,many groups tried to simulate the SNN with the hardware platform.However,the efficiency of training SNNs with neuromorphic algorithms is not ideal enough.Facing this,Michael et al.proposed a method which can solve the problem with the help of DNN(deep neural network).With this method,we can easily convert a well-trained DNN into an SCNN(spiking convolutional neural network).So far,there is a little of work focusing on the hardware accelerating of SCNN.The motivation of this paper is to design an SNN processor to accelerate SNN inference for SNNs obtained by this DNN-to-SNN method.We propose SIES(Spiking Neural Network Inference Engine for SCNN Accelerating).It uses a systolic array to accomplish the task of membrane potential increments computation.It integrates an optional hardware module of max-pooling to reduce additional data moving between the host and the SIES.We also design a hardware data setup mechanism for the convolutional layer on the SIES with which we can minimize the time of input spikes preparing.We implement the SIES on FPGA XCVU440.The number of neurons it supports is up to 4000 while the synapses are 256000.The SIES can run with the working frequency of 200 MHz,and its peak performance is 1.5625 TOPS.展开更多
Emerging applications widely use field-programmable gate array(FPGA)prototypes as a tool to verify modern very-large-scale integration(VLSI)circuits,imposing many problems,including routing failure caused by the limit...Emerging applications widely use field-programmable gate array(FPGA)prototypes as a tool to verify modern very-large-scale integration(VLSI)circuits,imposing many problems,including routing failure caused by the limited number of connections among blocks of FPGAs therein.Such a shortage of connections can be alleviated through time-division multiplexing(TDM),by which multiple signals sharing an identical routing channel can be transmitted.In this context,the routing quality dominantly decides the performance of such systems,proposing the requirement of minimizing the signal delay between FPGA pairs.This paper proposes algorithms for the routing problem in a multi-FPGA system with TDM support,aiming to minimize the maximum TDM ratio.The algorithm consists of two major stages:(1)A method is proposed to set the weight of an edge according to how many times it is shared by the routing requirements and consequently to compute a set of approximate minimum Steiner trees.(2)A ratio assignment method based on the edge-demand framework is devised for assigning ratios to the edges respecting the TDM ratio constraints.Experiments were conducted against the public benchmarks to evaluate our proposed approach as compared with all published works,and the results manifest that our method achieves a better TDM ratio in comparison.展开更多
With the development of silicon photomultiplier(SiPM)technology,front-end electronics for SiPM signal processing have been highly sought after in various fields.A compact 64-channel front-end electronics(FEE)system ac...With the development of silicon photomultiplier(SiPM)technology,front-end electronics for SiPM signal processing have been highly sought after in various fields.A compact 64-channel front-end electronics(FEE)system achieved by fieldprogrammable gate array-based charge-to-digital converter(FPGA-QDC)technology was built and developed.The FEE consists of an analog board and FPGA board.The analog board incorporates commercial amplifiers,resistors,and capacitors.The FPGA board is composed of a low-cost FPGA.The electronics performance of the FEE was evaluated in terms of noise,linearity,and uniformity.A positron emission tomography(PET)detector with three different readout configurations was designed to validate the readout capability of the FEE for SiPM-based detectors.The PET detector was made of a 15×15 lutetium–yttrium oxyorthosilicate(LYSO)crystal array directly coupled with a SiPM array detector.The experimental results show that FEE can process dual-polarity charge signals from the SiPM detectors.In addition,it shows a good energy resolution for 511-keV gamma photons under the dual-end readout for the LYSO crystal array irradiated by a Na-22 source.Overall,the FEE based on FPGA-QDC shows promise for application in SiPM-based radiation detectors.展开更多
As a core component in intelligent edge computing,deep neural networks(DNNs)will increasingly play a critically important role in addressing the intelligence-related issues in the industry domain,like smart factories ...As a core component in intelligent edge computing,deep neural networks(DNNs)will increasingly play a critically important role in addressing the intelligence-related issues in the industry domain,like smart factories and autonomous driving.Due to the requirement for a large amount of storage space and computing resources,DNNs are unfavorable for resource-constrained edge computing devices,especially for mobile terminals with scarce energy supply.Binarization of DNN has become a promising technology to achieve a high performance with low resource consumption in edge computing.Field-programmable gate array(FPGA)-based acceleration can further improve the computation efficiency to several times higher compared with the central processing unit(CPU)and graphics processing unit(GPU).This paper gives a brief overview of binary neural networks(BNNs)and the corresponding hardware accelerator designs on edge computing environments,and analyzes some significant studies in detail.The performances of some methods are evaluated through the experiment results,and the latest binarization technologies and hardware acceleration methods are tracked.We first give the background of designing BNNs and present the typical types of BNNs.The FPGA implementation technologies of BNNs are then reviewed.Detailed comparison with experimental evaluation on typical BNNs and their FPGA implementation is further conducted.Finally,certain interesting directions are also illustrated as future work.展开更多
Satellite signal simulator for global navigation satellite system(GNSS)can evaluate the accuracy of capturing,tracing and positioning of GNSS receiver.It has significant use-value in the military and civil fields.The ...Satellite signal simulator for global navigation satellite system(GNSS)can evaluate the accuracy of capturing,tracing and positioning of GNSS receiver.It has significant use-value in the military and civil fields.The system adopts the overall design scheme of digital signal processor(DSP)and field-programmable gate array(FPGA).It consists of four modules:industrial control computer simulation software,mid-frequency signal generator,digital-to-analog(D/A)module and radio frequency(RF)module.In this paper,we test the dynamic performance of simulator using the dynamic scenes testing method,and the signal generated by the designed simulator is primarily validated.展开更多
This paper proposes the fractal patterns classifier for multiple cardiac arrhythmias on field-programmable gate array (FPGA) device. Fractal dimension transformation (FDT) is employed to adjoin the fractal features of...This paper proposes the fractal patterns classifier for multiple cardiac arrhythmias on field-programmable gate array (FPGA) device. Fractal dimension transformation (FDT) is employed to adjoin the fractal features of QRS-complex, including the supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. FDT with fractal dimension (FD) is addressed for constructing various symptomatic patterns, which can produce family functions and enhance features, making clear differences between normal and unhealthy subjects. The probabilistic neural network (PNN) is proposed for recognizing multiple cardiac arrhythmias. Numerical experiments verify the efficiency and higher accuracy with the software simulation in order to formulate the mathematical model logical circuits. FDT results in data self-similarity for the same arrhythmia category, the number of dataset requirement and PNN architecture can be reduced. Its simplified model can be easily embedded in the FPGA chip. The prototype classifier is tested using the MIT-BIH arrhythmia database, and the tests reveal its practicality for monitoring ECG signals.展开更多
Aiming at high requirements of temperature measurement system in high temperature,high pressure,highly corrosive and other special environments,a temperature acquisition system based on field-programmable gate array(F...Aiming at high requirements of temperature measurement system in high temperature,high pressure,highly corrosive and other special environments,a temperature acquisition system based on field-programmable gate array(FPGA) which is the controller of the system is designed.Also a Flash memory is used as the memory and an erosion thermocouple is used as sensor of the system.Compared with the traditional system using complex programmable logic device(CPLD)and microcontroller unit(MCU)as the main body,this system has some advantages,such as short response time,small volume,no loss of data once power is off,high precision,stability and reliability.And the sensor of the system can be reused.In this paper,boiling water experiment is used to verify accuracy of the system.The millisecond level signal from firecrackers is for verifying the stability and fast response characteristics of the system.The results of experiment indicate that the temperature measurement system is more suitable for the field of explosion and other environments which have high requirements for the system.展开更多
High performance hardware architecture for depth measurement by using binocular-camera is proposed.In the system,at first,video streams of the target are captured by left and right charge-coupled device(CCD)cameras to...High performance hardware architecture for depth measurement by using binocular-camera is proposed.In the system,at first,video streams of the target are captured by left and right charge-coupled device(CCD)cameras to obtain an image including the target.Then,two different images with two different view points are obtained,and they are used in calculating the position deviation of the image's pixels based on triangular measurement.Finally,the three-dimensional coordinate of the object is reconstructed.All the video data is processed by using field-programmable gate array(FPGA)in real-time.Hardware implementation speeds up the performance and reduces the power,thus,this hardware architecture can be applied in the portable environment.展开更多
In the applications of water regime monitoring, incompleteness, and inaccuracy of sensor data may directly affect the reliability of acquired monitoring information. Based on the spatial and temporal correlation of wa...In the applications of water regime monitoring, incompleteness, and inaccuracy of sensor data may directly affect the reliability of acquired monitoring information. Based on the spatial and temporal correlation of water regime monitoring information, this paper addresses this issue and proposes an information fusion method to implement data rectification. An improved Back Propagation (BP) neural network is used to perform data fusion on the hardware platform of a stantion unit, which takes Field-Programmable Gate Array (FPGA) as the core component. In order to verify the effectiveness, five measurements including water level, discharge and velocity are selected from three different points in a water regime monitoring station. The simulation results show that this method can recitify random errors as well as gross errors significantly.展开更多
To facilitate the application of support vector machines (SVMs) in embedded systems,we propose and test a parallel and scalable digital architecture based on the sequential minimal optimization (SMO) algorithm for tra...To facilitate the application of support vector machines (SVMs) in embedded systems,we propose and test a parallel and scalable digital architecture based on the sequential minimal optimization (SMO) algorithm for training SVMs.By taking advantage of the mature and popular SMO algorithm,the numerical instability issues that may exist in traditional numerical algorithms are avoided.The error cache updating task,which dominates the computation time of the algorithm,is mapped into multiple processing units working in parallel.Experiment results show that using the proposed architecture,SVM training problems can be solved effectively with inexpensive fixed-point arithmetic and good scalability can be achieved.This architecture overcomes the drawbacks of the previously proposed SVM hardware that lacks the necessary flexibility for embedded applications,and thus is more suitable for embedded use,where scalability is an important concern.展开更多
This paper focuses on the design process for reconfigurable architecture. Our contribution focuses on introducing a new temporal partitioning algorithm. Our algorithm is based on typical mathematic flow to solve the t...This paper focuses on the design process for reconfigurable architecture. Our contribution focuses on introducing a new temporal partitioning algorithm. Our algorithm is based on typical mathematic flow to solve the temporal partitioning problem. This algorithm optimizes the transfer of data required between design partitions and the reconfiguration overhead. Results show that our algorithm considerably decreases the communication cost and the latency compared with other well known algorithms.展开更多
In this era of pervasive computing, low-resource devices have been deployed in various fields. PRINCE is a lightweight block cipher designed for low latency, and is suitable for pervasive computing applications. In th...In this era of pervasive computing, low-resource devices have been deployed in various fields. PRINCE is a lightweight block cipher designed for low latency, and is suitable for pervasive computing applications. In this paper, we propose new circuit structures for PRINCE components by sharing and simplifying logic circuits, to achieve the goal of using a smaller number of logic gates to obtain the same result. Based on the new circuit structures of components and the best sharing among components,we propose three new hardware architectures for PRINCE. The architectures are simulated and synthesized on different programmable gate array devices. The results on Virtex-6 show that compared with existing architectures, the resource consumption of the unrolled, low-cost, and two-cycle architectures is reduced by 73, 119, and 380 slices, respectively. The low-cost architecture costs only 137 slices. The unrolled architecture costs 409 slices and has a throughput of 5.34 Gb/s. To our knowledge, for the hardware implementation of PRINCE, the new low-cost architecture sets new area records, and the new unrolled architecture sets new throughput records. Therefore, the newly proposed architectures are more resource-efficient and suitable for lightweight,latency-critical applications.展开更多
Chaotic systems are an effective tool for various applications, including information security and internet of things. Many chaotic systems may have the weaknesses of incomplete output distributions, discontinuous cha...Chaotic systems are an effective tool for various applications, including information security and internet of things. Many chaotic systems may have the weaknesses of incomplete output distributions, discontinuous chaotic regions, and simple chaotic behaviors.These may result in many negative influences in practical applications utilizing chaos. To deal with these issues, this study introduces a modular chaotification model(MCM) to increase the dynamic properties of current one-dimensional(1 D) chaotic maps. To exhibit the effect of the MCM, three 1 D chaotic maps are improved using the MCM as examples. Studies of the resulting properties show the robust and complex dynamics of these improved chaotic maps. Moreover, we implement these improved chaotic maps of MCM in a field-programmable gate array hardware platform and apply them to the application of PRNG. Performance analyses verify that these chaotic maps improved by the MCM have more complicated chaotic behaviors and wider chaotic ranges than the existing and several new chaotic maps.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61502340 and 61374169)the Application Base and Frontier Technology Research Project of Tianjin,China(Grant No.15JCYBJC51800)the South African National Research Foundation Incentive Grants(Grant No.81705)
文摘We present a fractional-order three-dimensional chaotic system, which can generate four-wing chaotic attractor. Dynamics of the fractional-order system is investigated by numerical simulations. To rigorously verify the chaos properties of this system, the existence of horseshoe in the four-wing attractor is presented. Firstly, a Poincare′ section is selected properly, and a first-return Poincare′ map is established. Then, a one-dimensional tensile horseshoe is discovered, which verifies the chaos existence of the system in mathematical view. Finally, the fractional-order chaotic attractor is implemented physically with a field-programmable gate array(FPGA) chip, which is useful in further engineering applications of information encryption and secure communications.
文摘Piezoelectric resonators are widely used in frequency reference devices, mass sensors, resonant sensors(such as gyros and accelerometers), etc. Piezoelectric resonators usually work in a special resonant mode. Obtaining working resonant mode with high quality is key to improve the performance of piezoelectric resonators. In this paper, the resonance characteristics of a rectangular lead zirconium titanate(PZT) piezoelectric resonator are studied. On the basis of the field-programmable gate array(FPGA) embedded system, direct digital synthesizer(DDS) and automatic gain controller(AGC) are used to generate the driving signals with precisely adjustable frequency and amplitude. The driving signals are used to excite the piezoelectric resonator to the working vibration mode. The influence of the connection of driving electrodes and voltage amplitude on the vibration of the resonator is studied. The quality factor and vibration linearity of the resonator are studied with various driving methods mentioned in this paper. The resonator reaches resonant mode at 330 kHz by different driving methods.The relationship between resonant amplitude and driving signal amplitude is linear. The quality factor reaches over 150 by different driving methods. The results provide a theoretical reference for the efficient excitation of the piezoelectric resonator.
基金The work was supported by the HeGaoJi Program of China under Grant Nos.2017ZX01028103-002 and 2017ZX01038104-002the National Natural Science Foundation of China under Grant No.61472432.
文摘Neuromorphic computing is considered to be the future of machine learning,and it provides a new way of cognitive computing.Inspired by the excellent performance of spiking neural networks(SNNs)on the fields of low-power consumption and parallel computing,many groups tried to simulate the SNN with the hardware platform.However,the efficiency of training SNNs with neuromorphic algorithms is not ideal enough.Facing this,Michael et al.proposed a method which can solve the problem with the help of DNN(deep neural network).With this method,we can easily convert a well-trained DNN into an SCNN(spiking convolutional neural network).So far,there is a little of work focusing on the hardware accelerating of SCNN.The motivation of this paper is to design an SNN processor to accelerate SNN inference for SNNs obtained by this DNN-to-SNN method.We propose SIES(Spiking Neural Network Inference Engine for SCNN Accelerating).It uses a systolic array to accomplish the task of membrane potential increments computation.It integrates an optional hardware module of max-pooling to reduce additional data moving between the host and the SIES.We also design a hardware data setup mechanism for the convolutional layer on the SIES with which we can minimize the time of input spikes preparing.We implement the SIES on FPGA XCVU440.The number of neurons it supports is up to 4000 while the synapses are 256000.The SIES can run with the working frequency of 200 MHz,and its peak performance is 1.5625 TOPS.
基金supported by the Natural Science Foundation of Fujian Province(No.2020J01845)the National Natural Science Foundation of China(Nos.61772005 and 11871280)+1 种基金the Outstanding Youth Innovation Team Project for Universities of Shandong Province(No.2020KJN008)Qinglan Project.
文摘Emerging applications widely use field-programmable gate array(FPGA)prototypes as a tool to verify modern very-large-scale integration(VLSI)circuits,imposing many problems,including routing failure caused by the limited number of connections among blocks of FPGAs therein.Such a shortage of connections can be alleviated through time-division multiplexing(TDM),by which multiple signals sharing an identical routing channel can be transmitted.In this context,the routing quality dominantly decides the performance of such systems,proposing the requirement of minimizing the signal delay between FPGA pairs.This paper proposes algorithms for the routing problem in a multi-FPGA system with TDM support,aiming to minimize the maximum TDM ratio.The algorithm consists of two major stages:(1)A method is proposed to set the weight of an edge according to how many times it is shared by the routing requirements and consequently to compute a set of approximate minimum Steiner trees.(2)A ratio assignment method based on the edge-demand framework is devised for assigning ratios to the edges respecting the TDM ratio constraints.Experiments were conducted against the public benchmarks to evaluate our proposed approach as compared with all published works,and the results manifest that our method achieves a better TDM ratio in comparison.
基金supported by the Natural Science Foundation of Shandong Province (No. ZR2022QA039)the Program of Qilu Young Scholars of Shandong University
文摘With the development of silicon photomultiplier(SiPM)technology,front-end electronics for SiPM signal processing have been highly sought after in various fields.A compact 64-channel front-end electronics(FEE)system achieved by fieldprogrammable gate array-based charge-to-digital converter(FPGA-QDC)technology was built and developed.The FEE consists of an analog board and FPGA board.The analog board incorporates commercial amplifiers,resistors,and capacitors.The FPGA board is composed of a low-cost FPGA.The electronics performance of the FEE was evaluated in terms of noise,linearity,and uniformity.A positron emission tomography(PET)detector with three different readout configurations was designed to validate the readout capability of the FEE for SiPM-based detectors.The PET detector was made of a 15×15 lutetium–yttrium oxyorthosilicate(LYSO)crystal array directly coupled with a SiPM array detector.The experimental results show that FEE can process dual-polarity charge signals from the SiPM detectors.In addition,it shows a good energy resolution for 511-keV gamma photons under the dual-end readout for the LYSO crystal array irradiated by a Na-22 source.Overall,the FEE based on FPGA-QDC shows promise for application in SiPM-based radiation detectors.
基金supported by the Natural Science Foundation of Sichuan Province of China under Grant No.2022NSFSC0500the National Natural Science Foundation of China under Grant No.62072076.
文摘As a core component in intelligent edge computing,deep neural networks(DNNs)will increasingly play a critically important role in addressing the intelligence-related issues in the industry domain,like smart factories and autonomous driving.Due to the requirement for a large amount of storage space and computing resources,DNNs are unfavorable for resource-constrained edge computing devices,especially for mobile terminals with scarce energy supply.Binarization of DNN has become a promising technology to achieve a high performance with low resource consumption in edge computing.Field-programmable gate array(FPGA)-based acceleration can further improve the computation efficiency to several times higher compared with the central processing unit(CPU)and graphics processing unit(GPU).This paper gives a brief overview of binary neural networks(BNNs)and the corresponding hardware accelerator designs on edge computing environments,and analyzes some significant studies in detail.The performances of some methods are evaluated through the experiment results,and the latest binarization technologies and hardware acceleration methods are tracked.We first give the background of designing BNNs and present the typical types of BNNs.The FPGA implementation technologies of BNNs are then reviewed.Detailed comparison with experimental evaluation on typical BNNs and their FPGA implementation is further conducted.Finally,certain interesting directions are also illustrated as future work.
基金Shanxi Provincial Science and Technology Research Fund(No.2012021013-6)
文摘Satellite signal simulator for global navigation satellite system(GNSS)can evaluate the accuracy of capturing,tracing and positioning of GNSS receiver.It has significant use-value in the military and civil fields.The system adopts the overall design scheme of digital signal processor(DSP)and field-programmable gate array(FPGA).It consists of four modules:industrial control computer simulation software,mid-frequency signal generator,digital-to-analog(D/A)module and radio frequency(RF)module.In this paper,we test the dynamic performance of simulator using the dynamic scenes testing method,and the signal generated by the designed simulator is primarily validated.
文摘This paper proposes the fractal patterns classifier for multiple cardiac arrhythmias on field-programmable gate array (FPGA) device. Fractal dimension transformation (FDT) is employed to adjoin the fractal features of QRS-complex, including the supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. FDT with fractal dimension (FD) is addressed for constructing various symptomatic patterns, which can produce family functions and enhance features, making clear differences between normal and unhealthy subjects. The probabilistic neural network (PNN) is proposed for recognizing multiple cardiac arrhythmias. Numerical experiments verify the efficiency and higher accuracy with the software simulation in order to formulate the mathematical model logical circuits. FDT results in data self-similarity for the same arrhythmia category, the number of dataset requirement and PNN architecture can be reduced. Its simplified model can be easily embedded in the FPGA chip. The prototype classifier is tested using the MIT-BIH arrhythmia database, and the tests reveal its practicality for monitoring ECG signals.
基金Natural Science Foundation of Shanxi Province(No. 2009011023)
文摘Aiming at high requirements of temperature measurement system in high temperature,high pressure,highly corrosive and other special environments,a temperature acquisition system based on field-programmable gate array(FPGA) which is the controller of the system is designed.Also a Flash memory is used as the memory and an erosion thermocouple is used as sensor of the system.Compared with the traditional system using complex programmable logic device(CPLD)and microcontroller unit(MCU)as the main body,this system has some advantages,such as short response time,small volume,no loss of data once power is off,high precision,stability and reliability.And the sensor of the system can be reused.In this paper,boiling water experiment is used to verify accuracy of the system.The millisecond level signal from firecrackers is for verifying the stability and fast response characteristics of the system.The results of experiment indicate that the temperature measurement system is more suitable for the field of explosion and other environments which have high requirements for the system.
文摘High performance hardware architecture for depth measurement by using binocular-camera is proposed.In the system,at first,video streams of the target are captured by left and right charge-coupled device(CCD)cameras to obtain an image including the target.Then,two different images with two different view points are obtained,and they are used in calculating the position deviation of the image's pixels based on triangular measurement.Finally,the three-dimensional coordinate of the object is reconstructed.All the video data is processed by using field-programmable gate array(FPGA)in real-time.Hardware implementation speeds up the performance and reduces the power,thus,this hardware architecture can be applied in the portable environment.
基金Supported by the National Natural Science Foundation of China (No. 60774092, No. 60901003)the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20070294027)
文摘In the applications of water regime monitoring, incompleteness, and inaccuracy of sensor data may directly affect the reliability of acquired monitoring information. Based on the spatial and temporal correlation of water regime monitoring information, this paper addresses this issue and proposes an information fusion method to implement data rectification. An improved Back Propagation (BP) neural network is used to perform data fusion on the hardware platform of a stantion unit, which takes Field-Programmable Gate Array (FPGA) as the core component. In order to verify the effectiveness, five measurements including water level, discharge and velocity are selected from three different points in a water regime monitoring station. The simulation results show that this method can recitify random errors as well as gross errors significantly.
基金Project (No.60720106003) supported by the National Natural Science Foundation of China
文摘To facilitate the application of support vector machines (SVMs) in embedded systems,we propose and test a parallel and scalable digital architecture based on the sequential minimal optimization (SMO) algorithm for training SVMs.By taking advantage of the mature and popular SMO algorithm,the numerical instability issues that may exist in traditional numerical algorithms are avoided.The error cache updating task,which dominates the computation time of the algorithm,is mapped into multiple processing units working in parallel.Experiment results show that using the proposed architecture,SVM training problems can be solved effectively with inexpensive fixed-point arithmetic and good scalability can be achieved.This architecture overcomes the drawbacks of the previously proposed SVM hardware that lacks the necessary flexibility for embedded applications,and thus is more suitable for embedded use,where scalability is an important concern.
文摘This paper focuses on the design process for reconfigurable architecture. Our contribution focuses on introducing a new temporal partitioning algorithm. Our algorithm is based on typical mathematic flow to solve the temporal partitioning problem. This algorithm optimizes the transfer of data required between design partitions and the reconfiguration overhead. Results show that our algorithm considerably decreases the communication cost and the latency compared with other well known algorithms.
基金Project supported by the Scientific Research Fund of Hunan Provincial Education Department,China (Nos. 19A072 and 20C0268)the Science and Technology Innovation Program of Hunan Province,China (No. 2016TP1020)+2 种基金the Application-Oriented Special Disciplines,Double First-Class University Project of Hunan Province,China (No. Xiangjiaotong [2018] 469)the Scienceof Hengyang Normal University,China (No. 18D23)the Postgraduate Scientific Research Innovation Project of Hunan Province,China (No. CX20190980)。
文摘In this era of pervasive computing, low-resource devices have been deployed in various fields. PRINCE is a lightweight block cipher designed for low latency, and is suitable for pervasive computing applications. In this paper, we propose new circuit structures for PRINCE components by sharing and simplifying logic circuits, to achieve the goal of using a smaller number of logic gates to obtain the same result. Based on the new circuit structures of components and the best sharing among components,we propose three new hardware architectures for PRINCE. The architectures are simulated and synthesized on different programmable gate array devices. The results on Virtex-6 show that compared with existing architectures, the resource consumption of the unrolled, low-cost, and two-cycle architectures is reduced by 73, 119, and 380 slices, respectively. The low-cost architecture costs only 137 slices. The unrolled architecture costs 409 slices and has a throughput of 5.34 Gb/s. To our knowledge, for the hardware implementation of PRINCE, the new low-cost architecture sets new area records, and the new unrolled architecture sets new throughput records. Therefore, the newly proposed architectures are more resource-efficient and suitable for lightweight,latency-critical applications.
基金supported by the National Natural Science Foundation of China (Grant No. 62071142)the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology (Grant No. HIT.NSRIF.2020077)。
文摘Chaotic systems are an effective tool for various applications, including information security and internet of things. Many chaotic systems may have the weaknesses of incomplete output distributions, discontinuous chaotic regions, and simple chaotic behaviors.These may result in many negative influences in practical applications utilizing chaos. To deal with these issues, this study introduces a modular chaotification model(MCM) to increase the dynamic properties of current one-dimensional(1 D) chaotic maps. To exhibit the effect of the MCM, three 1 D chaotic maps are improved using the MCM as examples. Studies of the resulting properties show the robust and complex dynamics of these improved chaotic maps. Moreover, we implement these improved chaotic maps of MCM in a field-programmable gate array hardware platform and apply them to the application of PRNG. Performance analyses verify that these chaotic maps improved by the MCM have more complicated chaotic behaviors and wider chaotic ranges than the existing and several new chaotic maps.