Design extraction and reduction have been extensively used in modern VLSI design process. The extracted and reduced design can be efficiently processed by various applications, such as formal verification, simulation,...Design extraction and reduction have been extensively used in modern VLSI design process. The extracted and reduced design can be efficiently processed by various applications, such as formal verification, simulation, automatic test pattern generation (ATPG), etc. This paper presents a new circuit extraction method using program slicing technique, and develops an elegant theoretical basis based on program slicing for circuit extraction from Verilog description. The technique can obtain a chaining slice for given signals of interest. Compared with related researches, the main advantages of the method include that it is fine grain, it has no hardware description language (HDL) coding style limitation; it is precise and is capable of dealing with various Verilog constructions. The technique has been integrated with a commercial simulation environment and incorporated into a design process. The results of practical designs show the significant benefits of the approach.展开更多
The real-time monitoring and prediction system for quality attributes of jujube slices during the drying process was designed to solve the problem of destructive and inconvenient of the traditional quality detection m...The real-time monitoring and prediction system for quality attributes of jujube slices during the drying process was designed to solve the problem of destructive and inconvenient of the traditional quality detection method and realize quality online monitoring.Firstly,machine vision and automatic weighing were employed to monitor the color and moisture content changes of jujube slices in real-time.Secondly,correlation models between color parameter(a^(*)value)and nutritional quality attributes(vitamin C,reducing sugar)were established to predict vitamin C and reducing sugar content of jujube slices during the drying process.Finally,the upper computer monitoring software was integrated and designed based on LABVIEW virtual instrument,and the real-time monitoring system was tested and validated.Results showed that:the changing trends of color(L^(*),a^(*),and b^(*)values)monitored by the system were basically the same as the results detected by color difference meter,and the average errors of L^(*),a^(*),and b^(*)values were 0.93,0.52,and 0.73,respectively.The average relative error of moisture content between the system monitoring and manual static detection was 0.18%.The average error of vitamin C and reducing sugar content between the system prediction and manual detection were 50 mg/100 g in dry basis and 0.71g/100 g in dry basis,respectively.The current work can provide a useful reference for real-time monitoring of quality attributes of fruits and vegetables during the drying process.展开更多
Network slicing is envisioned as one of the key techniques to meet the extremely diversified service requirements of the Internet of Things(IoT)as it provides an enhanced user experience and elastic resource configura...Network slicing is envisioned as one of the key techniques to meet the extremely diversified service requirements of the Internet of Things(IoT)as it provides an enhanced user experience and elastic resource configuration.In the context of slicing enhanced IoT networks,both the Service Provider(SP)and Infrastructure Provider(InP)face challenges of ensuring efficient slice construction and high profit in dynamic environments.These challenges arise from randomly generated and departed slice requests from end-users,uncertain resource availability,and multidimensional resource allocation.Admission and resource allocation for distinct demands of slice requests are the key issues in addressing these challenges and should be handled effectively in dynamic environments.To this end,we propose an Opportunistic Admission and Resource allocation(OAR)policy to deal with the issues of random slicing requests,uncertain resource availability,and heterogeneous multi-resources.The key idea of OAR is to allow the SP to decide whether to accept slice requests immediately or defer them according to the load and price of resources.To cope with the random slice requests and uncertain resource availability,we formulated this issue as a Markov Decision Process(MDP)to obtain the optimal admission policy,with the aim of maximizing the system reward.Furthermore,the buyer-seller game theory approach was adopted to realize the optimal resource allocation,while motivating each SP and InP to maximize their rewards.Our numerical results show that the proposed OAR policy can make reasonable decisions effectively and steadily,and outperforms the baseline schemes in terms of the system reward.展开更多
文摘Design extraction and reduction have been extensively used in modern VLSI design process. The extracted and reduced design can be efficiently processed by various applications, such as formal verification, simulation, automatic test pattern generation (ATPG), etc. This paper presents a new circuit extraction method using program slicing technique, and develops an elegant theoretical basis based on program slicing for circuit extraction from Verilog description. The technique can obtain a chaining slice for given signals of interest. Compared with related researches, the main advantages of the method include that it is fine grain, it has no hardware description language (HDL) coding style limitation; it is precise and is capable of dealing with various Verilog constructions. The technique has been integrated with a commercial simulation environment and incorporated into a design process. The results of practical designs show the significant benefits of the approach.
基金This work was financially supported by the Natural Science Fund of China(Grant No.31960488)the Shihezi University Achievement Transformation and Technology Promotion Project(Grant No.CGZH201808).
文摘The real-time monitoring and prediction system for quality attributes of jujube slices during the drying process was designed to solve the problem of destructive and inconvenient of the traditional quality detection method and realize quality online monitoring.Firstly,machine vision and automatic weighing were employed to monitor the color and moisture content changes of jujube slices in real-time.Secondly,correlation models between color parameter(a^(*)value)and nutritional quality attributes(vitamin C,reducing sugar)were established to predict vitamin C and reducing sugar content of jujube slices during the drying process.Finally,the upper computer monitoring software was integrated and designed based on LABVIEW virtual instrument,and the real-time monitoring system was tested and validated.Results showed that:the changing trends of color(L^(*),a^(*),and b^(*)values)monitored by the system were basically the same as the results detected by color difference meter,and the average errors of L^(*),a^(*),and b^(*)values were 0.93,0.52,and 0.73,respectively.The average relative error of moisture content between the system monitoring and manual static detection was 0.18%.The average error of vitamin C and reducing sugar content between the system prediction and manual detection were 50 mg/100 g in dry basis and 0.71g/100 g in dry basis,respectively.The current work can provide a useful reference for real-time monitoring of quality attributes of fruits and vegetables during the drying process.
基金This work was supported in part by the Chongqing Technological Innovation and Application Development Projects under Grant cstc2019jscx-msxm1322,in part by the Zhejiang Lab under Grant 2021KF0AB03in part by the National Natural Science Foundation of China under Grant 62071091.
文摘Network slicing is envisioned as one of the key techniques to meet the extremely diversified service requirements of the Internet of Things(IoT)as it provides an enhanced user experience and elastic resource configuration.In the context of slicing enhanced IoT networks,both the Service Provider(SP)and Infrastructure Provider(InP)face challenges of ensuring efficient slice construction and high profit in dynamic environments.These challenges arise from randomly generated and departed slice requests from end-users,uncertain resource availability,and multidimensional resource allocation.Admission and resource allocation for distinct demands of slice requests are the key issues in addressing these challenges and should be handled effectively in dynamic environments.To this end,we propose an Opportunistic Admission and Resource allocation(OAR)policy to deal with the issues of random slicing requests,uncertain resource availability,and heterogeneous multi-resources.The key idea of OAR is to allow the SP to decide whether to accept slice requests immediately or defer them according to the load and price of resources.To cope with the random slice requests and uncertain resource availability,we formulated this issue as a Markov Decision Process(MDP)to obtain the optimal admission policy,with the aim of maximizing the system reward.Furthermore,the buyer-seller game theory approach was adopted to realize the optimal resource allocation,while motivating each SP and InP to maximize their rewards.Our numerical results show that the proposed OAR policy can make reasonable decisions effectively and steadily,and outperforms the baseline schemes in terms of the system reward.
