Task assignment using a problem‐space genetic algorithm

10 - Hardware and software development tools

This paper introduces a technique, called resynchronization, for reducing synchronization overhead in multiprocessor implementations of digital signal processing (DSP) systems. The technique applies to arbitrary collections of dedicated, programmable or configurable processors, such as combinations of programmable DSP's, ASICs, and FPGA subsystems. Thus, it is particularly well-suited to the evolving trend toward heterogeneous single-chip multiprocessors in DSP systems. Resynchronization exploits the well-known observation that in a given multiprocessor implementation, certain synchronization operations may be redundant in the sense that their associated sequencing requirements are ensured by other synchronizations in the system. The goal of resynchronization is to introduce new synchronizations in such a way that the number of original synchronizations that become redundant exceeds the number of new synchronizations that are added, and thus, the net synchronization cost is reduced. Our study is based on the context of self-timed execution for iterative dataflow specifications of DSP applications. An iterative dataflow specification consists of a dataflow representation of the body of a loop that is to be iterated indefinitely; dataflow programming in this form has been employed extensively in the DSP domain.

This paper describes the methodology that utilises some of the ATM traffic analysis techniques in the performance analysis of multichannel digital signal processing (DSP) systems. The analysed multichannel DSP systems employ statistical multiplexing in order to improve the DSP resource utilisation. A case study is used to illustrate the application of the proposed methodology. Performance of the studied system was estimated and it was shown that a significant efficiency gain may be achieved over the DSP systems that do not utilise statistical multiplexing of their resources.

Hereafter, we present an approach aiming to improve the reliability of digital signal processing (DSP) systems operating in real noisy (electromagnetic interference—EMI) environments. The approach is based on the coupling of two techniques: the “DSP-oriented signal integrity improvement” technique deals with increasing the signal-to-noise ratio (SNR) and is essentially a modification of the classic Recovery Blocks Scheme. The second technique, named “SW-based fault handling” aims to detect in real-time data- and control-flow faults throughout modifications of the processor code. When compared to conventional approaches using Fast Fourier Transform (FFT) and Hamming Code, the primary benefit of such an approach is to improve system reliability by means of a considerably low complexity, reasonably low performance degradation and, when implemented in hardware, with reduced area overhead. Aiming to illustrate the proposed approach, we implemented a HW/SW prototype to operate as a speech recognition system (SRS). This prototype was tested under a home-tailored EMI environment according to the IEC 61000-4-29 International Standard Normative. The obtained results indicate that the proposed approach can effectively improve the reliability of DSP systems operating in real noise (EMI) environments.

This paper considers the problem of task assignment in heterogeneous distributed computing systems with the goal of minimizing the total execution and communication costs. An iterated local search algorithm is proposed for finding the suboptimal task assignment in a reasonable amount of computation time. We study the performance of the proposed algorithm over a wide range of parameters such as the problem scales, the ratio of average communication time to average computation time, and task interaction density of applications. The effectiveness of the algorithm is manifested by comparing it with other competing algorithms in the relevant literature.

This paper presents a high-level synthesis methodology which may be applied to the synthesis of bit-serial, digit-serial or bit-parallel digital signal processing (DSP) systems. The methodology accepts as input a DSP system behavioral specification. The methodology provides for the optimization of DSP systems subject to constraints on throughput, IC area, data path width, and resource sharing strategies. The optimization technique is based on a generalized hill climbing algorithm known as simulated annealing. The output is control logic and data path specifications in a device independent hardware description language. Design examples of bit-serial and digit-serial filter realizations synthesized using the methodology are included.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The intense requirements of high-speed implementations of MultiDimensional (MD) Digital Signal Processing (DSP) systems justify the Application Specific Integrated Circuits (ASIC) designs and/or multiprocessor implementations. MD retiming has been recently proposed to improve the circuitry performance in high-level synthesis of single-rate MD DSP systems. This paper has conducted new theoretical analysis of MD multirate DSP systems modeled in data-flow graphs, and proposes intercalation of MD multirate systems so that unified MD retiming operations can be applied on multidimensional multirate DSP systems. By retiming and intercalation, full intra-iteration parallelism is achieved and functional elements can be executed simultaneously on circuits for the generic class of MD multirate DSP systems.KeywordsMultirate Signal ProcessingMD Data Flow GraphMultidimenional Retiming

This paper deals with problems arising in the scope of implementing distributed system self-diagnosis algorithms by employing simulation models. Two algorithms developed by Kuhl, Reddy and Hosseini, SELF 2 [1] and NEWSELF [2], were implemented and embedded into a simulated distributed computing system in order to analyse their usefulness, overhead and performance [3], Special attention was laid on synchronization of the diagnosis process executed by the system components. Dependence of system availability on some system self-diagnosis parameters was investigated as well. It became visible that the ratio of user available processing time highly depends on choosing proper test rates.

Honeypot is a recent developed computer security concept which uses active offense to against attacks from blackhats (people with evil or illegal intents like hackers and virus-producers). It lures hackers to attack a seemly vulnerable but well observed computer system in order to learn about the tactics and tools used by the attackers so that we may improve the system security accordingly. However, a system developed with noble intentions may sometimes be used in evil applications. We discuss the consequences and implications of honeypots being used with evil intents. A honeypot system may be set up by blackhats to lure common Internet users to use a fake system in order to gain valuable information, like credit card numbers, from these users. Implications of such possible usages may damage the credibility of services provided in the Internet, especially those involving payment transactions and sensitive information exchanges. Unless we find ways to avoid such misusages, the consumer trust of e-commerce, e-health and e-government depending on the use of Internet services may be severely hampered.

The authors introduce a parallel DSP (digital signal processing) system called NOVI, consisting of 36 processor nodes. NOVI has a multicomputer architecture, which provides high programmability and effective scheduling capability. Its program-development-assist system facilitates powerful debugging functions to observe all states of NOVI and to control its execution. Each processor node comprises a transputer, a floating-point ALU (arithmetic logic unit), and 1 Mbyte of local memory, which can deal with even rather large tasks within a single node. An interconnection board allows easy reconfiguration into various network topologies.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cloud Computing is a strong, large-scale and complex Computing technology. It lessens the need to maintain an expensive, specialized computer hardware area, as well as expensive software and software. Cloud computing has shown a significant increase in data quality or the production of large amounts of data. Big data processing is a difficult and time-consuming operation that necessitates the use of a large computer system in order to ensure performance. Knowledge creation and analysis are two intertwined activities and this paper investigates the rise of Big Data and Artificial Intelligence (AI) in Cloud Computing research. As data storage and mining methods advance, the preservation of expanding data quantities is characterized by a change in the core of structured results. This shift is reflected in the evolution of structured results. However, one major barrier is that this rate of growth surpasses the ability of data gathering systems and cloud infrastructure platforms to be upgraded. Workloads are really heavy and it is possible that certain cloud computing disputes will be created, which will include the description, characteristics, and categorization of huge data. In addition, analysis problems based on data integrity, heterogeneity and protection.

The cognitive state of a person affects their task performance and learning success. This holds particularly for immersed states like flow and immersion. Hence, estimating immersed user states appears useful for adapting computer systems in order to maintain or achieve user flow or immersion. For practical use, the estimation method must provide continuously quantitative measurements in real-time in order to allow capturing short-term user state changes. In addition, the method should work as unobtrusive as possible in order to impose as less additional load on the user as possible. Tracking eye movement behavior using a remote eye-tracking device meets both requirements. Eye movement parameters are related to various cognitive processes and might therefore be useful for the estimation of immersed user states. This contribution gives an overview on the potential of the eye movement parameters fixation duration, pupil dilation, and spontaneous eye blink rate. As immersed states are complex cognitive states and as the three parameters provide complementary information it appears appropriate to capture all three parameters for the estimation. However, all three parameters are affected by multiple other factors besides the characteristics of the task. Hence, even the estimation using the combination appears to be a challenging issue.

&lt;abstract&gt; &lt;p&gt;In this article, I discuss the problem of automatic detection of coarse measurements (outliers) in the time series of measurement data generated by technical devices. Solving this problem is of great importance to improve the accuracy of estimates of various physical quantities obtained in solving many applications in which the input data is observations. Since outliers adversely affect the accuracy of final results, they must be detected and removed from further calculations at the stage of data preprocessing and analysis. This can be done in various ways, since the concept of outliers does not have a strict definition in statistics. The author of the article previously formulated the problem of finding the optimal solution that satisfies the condition of maximizing the amount of measuring data that remained after removal of outliers and proposed a robust algorithm for finding such a solution. The complexity of this algorithm is estimated of the order of magnitude $ (N+{N}_{out}^{2}) $, where N is the number of source data and N&lt;sub&gt;out&lt;/sub&gt; is the number of outliers detected. For highly noisy data, the number of outliers can be extremely large, for example, comparable to N. In this case, it will take about N&lt;sup&gt;2&lt;/sup&gt; arithmetic operations to find the optimal solution using the algorithm developed earlier. I propose a new algorithm for finding the optimal solution, requiring the order of NlogN arithmetic operations, regardless of the number of outliers detected. The efficiency of the algorithm is manifested when cleaning from outliers large amounts of highly noisy measuring data containing a great many of outliers. The algorithm can be used for automated cleaning from outliers of observation data in information and measuring systems, in systems with artificial intelligence, as well as when solving various scientific, applied managerial and other problems using modern computer systems in order to obtain promptly the most accurate final result.&lt;/p&gt; &lt;/abstract&gt;

Machine learning (ML) is a scientific study of algorithms and statistical models used by computer systems in order to perform specific tasks effectively without using clear instructions and relying on patterns and reasoning. It is regarded as a subset of artificial intelligence. The base of machine learning is support vector regression. In contemporary China, with the in-depth development of the sinicization and popularization of Marxism, further strengthening the education of Marxist theory has become its inherent inevitable requirement. Teaching quality is the lifeline of colleges and universities. Effective management of teaching depends not only on the control of teaching process, but also on the establishment of a complete and standardized teaching quality monitoring and evaluation system. This paper puts forward an evaluation model of teaching quality of Marxist theory in massive open online course based on support vector regression, and establishes an evaluation index system of teaching quality of Marxist theory in massive open online course according to the specific needs and the construction principle of evaluation index system. Theoretical and experimental results show that the model has better evaluation effect. Compared with other methods, it has the characteristics of high evaluation accuracy, fast implementation and strong operability, and is suitable for evaluating the teaching quality of massive open online course Marxist theory in colleges and universities.

The characteristics of the linear computing elements of the general-purpose electronic differential analyzer are discussed. Equivalent circuits are given for these elements. Criteria for optimization of the linear computing system in order to obtain maximum computational accuracy are given. Examples of the effects of optimization in improving system stability, transient response, and increasing bandwidth of high and medium accuracy computation are shown.