In many real-time resource-constrained embedded systems, highly-predictable system behavior is a key design requirement. The “time-triggered co-operative” (TTC) scheduling algorithm provides a good match for a wide ...In many real-time resource-constrained embedded systems, highly-predictable system behavior is a key design requirement. The “time-triggered co-operative” (TTC) scheduling algorithm provides a good match for a wide range of low-cost embedded applications. As a consequence of the resource, timing, and power constraints, the implementation of such algorithm is often far from trivial. Thus, basic implementation of TTC algorithm can result in excessive levels of task jitter which may jeopardize the predictability of many time-critical applications using this algorithm. This paper discusses the main sources of jitter in earlier TTC implementations and develops two alternative implementations – based on the employment of “sandwich delay” (SD) mechanisms – to reduce task jitter in TTC system significantly. In addition to jitter levels at task release times, we also assess the CPU, memory and power requirements involved in practical implementations of the proposed schedulers. The paper concludes that the TTC scheduler implementation using “multiple timer interrupt” (MTI) technique achieves better performance in terms of timing behavior and resource utilization as opposed to the other implementation which is based on a simple SD mechanism. Use of MTI technique is also found to provide a simple solution to “task overrun” problem which may degrade the performance of many TTC systems.展开更多
以某型声探测设备软件设计为例,介绍一种以双片数字信号处理器(Digital Signal Process,DSP)(FT-C6713J400)、微控制单元(Micro Control Unit,MCU)和现场可编程逻辑门阵列(Field Programmable Gate Array,FPGA)为核心的全国产芯片信号...以某型声探测设备软件设计为例,介绍一种以双片数字信号处理器(Digital Signal Process,DSP)(FT-C6713J400)、微控制单元(Micro Control Unit,MCU)和现场可编程逻辑门阵列(Field Programmable Gate Array,FPGA)为核心的全国产芯片信号采集和处理系统。详细描述FT-C6713J400的软件时序设计、软件状态转换设计、软件中断设计及其编程实现,并列出核心代码。结果表明,以国产芯片FT-C6713J400为核心的信号处理软件不仅能够满足声探测算法较大的计算量,而且通过处理时序的设计实现了双片DSP在工作时保持配合同步,满足探测系统的实时性要求。该软件还设计了数据库更新功能,通过上位机分包下发数据库信息,能够实现嵌入式软件接收和在线更新数据库功能。展开更多
Purpose–The task of internet intrusion detection is to detect anomalous network connections caused by intrusive activities.There have been many intrusion detection schemes proposed,most of which apply both normal and...Purpose–The task of internet intrusion detection is to detect anomalous network connections caused by intrusive activities.There have been many intrusion detection schemes proposed,most of which apply both normal and intrusion data to construct classifiers.However,normal data and intrusion data are often seriously imbalanced because intrusive connection data are usually difficult to collect.Internet intrusion detection can be considered as a novelty detection problem,which is the identification of new or unknown data,to which a learning system has not been exposed during training.This paper aims to address this issue.Design/methodology/approach–In this paper,a novelty detection-based intrusion detection system is proposed by combining the self-organizing map(SOM)and the kernel auto-associator(KAA)model proposed earlier by the first author.The KAA model is a generalization of auto-associative networks by training to recall the inputs through kernel subspace.For anomaly detection,the SOM organizes the prototypes of samples while the KAA provides data description for the normal connection patterns.The hybrid SOM/KAA model can also be applied to classify different types of attacks.Findings–Using the KDD CUP,1999 dataset,the performance of the proposed scheme in separating normal connection patterns from intrusive connection patterns was compared with some state-of-art novelty detection methods,showing marked improvements in terms of the high intrusion detection accuracy and low false positives.Simulations on the classification of attack categories also demonstrate favorable results of the accuracy,which are comparable to the entries from the KDD CUP,1999 data mining competition.Originality/value–The hybrid model of SOM and the KAA model can achieve significant results for intrusion detection.展开更多
文摘In many real-time resource-constrained embedded systems, highly-predictable system behavior is a key design requirement. The “time-triggered co-operative” (TTC) scheduling algorithm provides a good match for a wide range of low-cost embedded applications. As a consequence of the resource, timing, and power constraints, the implementation of such algorithm is often far from trivial. Thus, basic implementation of TTC algorithm can result in excessive levels of task jitter which may jeopardize the predictability of many time-critical applications using this algorithm. This paper discusses the main sources of jitter in earlier TTC implementations and develops two alternative implementations – based on the employment of “sandwich delay” (SD) mechanisms – to reduce task jitter in TTC system significantly. In addition to jitter levels at task release times, we also assess the CPU, memory and power requirements involved in practical implementations of the proposed schedulers. The paper concludes that the TTC scheduler implementation using “multiple timer interrupt” (MTI) technique achieves better performance in terms of timing behavior and resource utilization as opposed to the other implementation which is based on a simple SD mechanism. Use of MTI technique is also found to provide a simple solution to “task overrun” problem which may degrade the performance of many TTC systems.
文摘以某型声探测设备软件设计为例,介绍一种以双片数字信号处理器(Digital Signal Process,DSP)(FT-C6713J400)、微控制单元(Micro Control Unit,MCU)和现场可编程逻辑门阵列(Field Programmable Gate Array,FPGA)为核心的全国产芯片信号采集和处理系统。详细描述FT-C6713J400的软件时序设计、软件状态转换设计、软件中断设计及其编程实现,并列出核心代码。结果表明,以国产芯片FT-C6713J400为核心的信号处理软件不仅能够满足声探测算法较大的计算量,而且通过处理时序的设计实现了双片DSP在工作时保持配合同步,满足探测系统的实时性要求。该软件还设计了数据库更新功能,通过上位机分包下发数据库信息,能够实现嵌入式软件接收和在线更新数据库功能。
基金Suzhou Municipal Science and Technology Foundation Key Technologies for Video Objects Intelligent Analysis for Criminal Investigation(SS201109).
文摘Purpose–The task of internet intrusion detection is to detect anomalous network connections caused by intrusive activities.There have been many intrusion detection schemes proposed,most of which apply both normal and intrusion data to construct classifiers.However,normal data and intrusion data are often seriously imbalanced because intrusive connection data are usually difficult to collect.Internet intrusion detection can be considered as a novelty detection problem,which is the identification of new or unknown data,to which a learning system has not been exposed during training.This paper aims to address this issue.Design/methodology/approach–In this paper,a novelty detection-based intrusion detection system is proposed by combining the self-organizing map(SOM)and the kernel auto-associator(KAA)model proposed earlier by the first author.The KAA model is a generalization of auto-associative networks by training to recall the inputs through kernel subspace.For anomaly detection,the SOM organizes the prototypes of samples while the KAA provides data description for the normal connection patterns.The hybrid SOM/KAA model can also be applied to classify different types of attacks.Findings–Using the KDD CUP,1999 dataset,the performance of the proposed scheme in separating normal connection patterns from intrusive connection patterns was compared with some state-of-art novelty detection methods,showing marked improvements in terms of the high intrusion detection accuracy and low false positives.Simulations on the classification of attack categories also demonstrate favorable results of the accuracy,which are comparable to the entries from the KDD CUP,1999 data mining competition.Originality/value–The hybrid model of SOM and the KAA model can achieve significant results for intrusion detection.
