WI 64-2,3 ToC

2012

2012

Zenodo (CERN European Organization for Nuclear Research), 2013

science.uva.nl

This paper targets heterogeneous low power communication circuits and Systems that will be used in the future generations' hand-held devices (PDA's, mobile phones). Those platforms will probably contain a few studies have emerged and considerable amount of on-chip memories. An optimized communication architecture will be required to interconnect them efficiently. Many communication architectures have been proposed in the literature: shared buses, bridged buses, segmented buses and more recently, Networks-on-Chip. Being battery-powered devices, the energy consumption of the platform is a critical issue. However, with the exception of buses, power consumption has been mostly neglected in interconnection networks. Only very recently have a few studies emerged in that domain. The Power Aware Wireless Sensors (PAWiS) simulation framework becomes an essential tool to evaluate design and simulation of Wireless Sensor Networks (WSNs) models including SoftWare (SW) and HardWare (HW) platforms. PAWiS is an OMNeT++ based discrete event simulator written in C++. It captures the node internals (modules) as well as the node surroundings (network, environment) and provides specific features critical to WSNs like capturing power consumption at various levels of granularity, support for mobility, and environmental dynamics as well as the simulation of timing effects. The design of integrated low-power wireless sensor nodes involves the convergence of many technologies and disciplines. Submicron complementary metal oxide semiconductor (CMOS) devices, micro-electro-mechanical system filters, on and off chip electromagnetic elements, sensors and dc-dc converters are some of the technologies that will enable pervasive systems such as wireless sensor networks. High system complexity requires the use of many simulation environments during design: algorithm simulators, behavioural and transistor-level circuit simulators, electromagnetic (EM) simulators and network simulators. It is shown that highly integrated, self-contained systems require multiple-domain simulations to uncover complex interactions between domains. In this paper we present a flexible and extensible simulation framework to estimate power consumption of sensor network applications for arbitrary HW platforms. Specific examples of block and system level design methodologies used in low-power wireless systems are presented here.

This paper targets heterogeneous low power communication circuits and Systems that will be used in the future generations' hand-held devices (PDA's, mobile phones). Those platforms will probably contain a few studies have emerged and considerable amount of on-chip memories. An optimized communication architecture will be required to interconnect them efficiently. Many communication architectures have been proposed in the literature: shared buses, bridged buses, segmented buses and more recently, Networks-on-Chip. Being battery-powered devices, the energy consumption of the platform is a critical issue. However, with the exception of buses, power consumption has been mostly neglected in interconnection networks. Only very recently have a few studies emerged in that domain. The Power Aware Wireless Sensors (PAWiS) simulation framework becomes an essential tool to evaluate design and simulation of Wireless Sensor Networks (WSNs) models including SoftWare (SW) and HardWare (HW) platforms. PAWiS is an OMNeT++ based discrete event simulator written in C++. It captures the node internals (modules) as well as the node surroundings (network, environment) and provides specific features critical to WSNs like capturing power consumption at various levels of granularity, support for mobility, and environmental dynamics as well as the simulation of timing effects. The design of integrated low-power wireless sensor nodes involves the convergence of many technologies and disciplines. Submicron complementary metal oxide semiconductor (CMOS) devices, micro-electro-mechanical system filters, on and off chip electromagnetic elements, sensors and dc-dc converters are some of the technologies that will enable pervasive systems such as wireless sensor networks. High system complexity requires the use of many simulation environments during design: algorithm simulators, behavioural and transistor-level circuit simulators, electromagnetic (EM) simulators and network simulators. It is shown that highly integrated, self-contained systems require multiple-domain simulations to uncover complex interactions between domains. In this paper we present a flexible and extensible simulation framework to estimate power consumption of sensor network applications for arbitrary HW platforms. Specific examples of block and system level design methodologies used in low-power wireless systems are presented here.

This paper targets heterogeneous low power communication circuits and Systems that will be used in the future generations' hand-held devices (PDA's, mobile phones). Those platforms will probably contain a few studies have emerged and considerable amount of on-chip memories. An optimized communication architecture will be required to interconnect them efficiently. Many communication architectures have been proposed in the literature: shared buses, bridged buses, segmented buses and more recently, Networks-on-Chip. Being battery-powered devices, the energy consumption of the platform is a critical issue. However, with the exception of buses, power consumption has been mostly neglected in interconnection networks. Only very recently have a few studies emerged in that domain. The Power Aware Wireless Sensors (PAWiS) simulation framework becomes an essential tool to evaluate design and simulation of Wireless Sensor Networks (WSNs) models including SoftWare (SW) and HardWare (HW) platforms. PAWiS is an OMNeT++ based discrete event simulator written in C++. It captures the node internals (modules) as well as the node surroundings (network, environment) and provides specific features critical to WSNs like capturing power consumption at various levels of granularity, support for mobility, and environmental dynamics as well as the simulation of timing effects. The design of integrated low-power wireless sensor nodes involves the convergence of many technologies and disciplines. Submicron complementary metal oxide semiconductor (CMOS) devices, micro-electro-mechanical system filters, on and off chip electromagnetic elements, sensors and dc-dc converters are some of the technologies that will enable pervasive systems such as wireless sensor networks. High system complexity requires the use of many simulation environments during design: algorithm simulators, behavioural and transistor-level circuit simulators, electromagnetic (EM) simulators and network simulators. It is shown that highly integrated, self-contained systems require multiple-domain simulations to uncover complex interactions between domains. In this paper we present a flexible and extensible simulation framework to estimate power consumption of sensor network applications for arbitrary HW platforms. Specific examples of block and system level design methodologies used in low-power wireless systems are presented here.

International Association of Engineers, 2011

This paper describes NUSA, a newly developed programming-language which is based on orthogonality and modular programming. Compared to OOPLs (Object- Oriented Programming Languages) that lack orthogonality, NUSA is designed with orthogonality in mind. Issues found during the creation of highly orthogonal language were the inorthogonality in OOPLs, the real semantic of class, encapsulation of code and data through module, and the unbundling of operators from record-type. The result is NUSA, a highly orthogonal programming-language. NUSA provides input-output, user-defined types, and user-defined (infix and prefix) polymorphic operators for user-defined types. Quantitative comparison with C# in terms of number of source-code lines is presented from the perspective of pragmatic advantage.

Journal of Advances in Mathematical & Computational Science. Vol 10, No.3. Pp 1 – 14., 2022

Machine learning and associated algorithms occupies a pride of place in the execution of automation in the field of computing and its application to addressing contemporary and human-centred problems such as predictions, evaluations, deductions, analytics and analysis. This paper presents types of data and machine learning algorithms in a broader sense. We briefly discuss and explain different machine learning algorithms and real-world application areas based on machine learning. We highlight several research issues and potential future directions