Bassem Alhalabi

VLSI parallel architecture for Kalman filter: an algorithm specific approach

An algorithm specific architecture for Kalman filter is presented. It is based on systolic arrays. Parallelism has been exploited on both algorithm and architecture levels. Faddeevs algorithm has been employed. The involved computation tasks, triangularization and nullification are performed in parallel which leads to a speedup of about 40%. Throughput has been increased by using bi-trapezoidal arrays. Techniques have been employed for data storage and skewing which enables fast data transfer rates. A VLSI implementation of a prototype of matrix of size 4×4 has been discussed.

A novel low power multiplexer-based full adder cell

A novel low-power multiplexer-based 1-bit full adder cell that uses 12 transistors (MBA-12T) is presented here. MBA-12T is tested along with four other low-power 10-transistor 1-bit full adders that were shown to have more than 26% in power-savings over the conventional 28-transistor CMOS cell. The testing consists of simulating using HSpice under 6 frequencies, and 6 different loads. The testing result shows that the MBA-12T exhibits at least 23% in power-savings over the least power-consuming 10-transistor cell and a minimum of 64% in speed improvement.

Software and communications architecture for Prognosis and Health Monitoring of ocean-based power generator

This paper presents a communications and software architecture in support of Prognosis and Health Monitoring (PHM) applications for renewable ocean-based power generation. The generator/turbine platform is instrumented with various sensors (e.g. vibration, temperature) that generate periodic measurements used to assess the current system health and to project its future performance. The power generator platform is anchored miles offshore and uses a pair of wireless data links for monitoring and control. Since the link is expected to be variable and unreliable, being subject to challenging environmental conditions, the main functions of the PHM system are performed on a computing system located on the surface platform. The PHM system architecture is implemented using web services technologies following MIMOSA OSA-CBM standards. To provide sufficient Quality of Service for mission-critical traffic, the communications system employs application-level queue management with semantic-based filtering for the XML PHM messages, combined with IP packet traffic control and link quality monitoring at the network layer.

An integrated component selection framework for system-level design

The increasing system design complexity is negatively impacting the overall system design productivity by increasing the cost and time of product development. One key to overcoming these challenges is exploiting Component Based Engineering practices. Components are designed for various specifications and usually variants of each component exist with different performance and quality of service parameters. Therefore it is a challenge to select the optimum components from a component library that will satisfy all functional and non-functional system requirements. If these designs and architectural decisions are delayed, they may lead to design re-spins thereby, negatively impacting the product development cost and time. In this paper we propose an integrated framework for component selection.

Universal Physical Access Control System

With the recent rapid increase in the number of physical facilities and structures that need to be protected by restricting physical access to them, there has been an explosion in the number and type of physical access control systems being deployed to protect them. However, these systems are quite different from each other and there is no common standard that provides for interoperability between the various systems. The number and types of access devices being employed has grown steadily, but the systems in which they are being used are physically and technologically incompatible with each other. Consequently, there is renewed interest within the research community in developing a common universal system providing physical resource access protection regardless of the type of physical resource and where it is located. In this article we propose the Universal Physical Access Control System (UPACS) which provides a universal framework for controlling access to physical resources. It provides for the use of a wide variety of access devices and allows for both onsite and remote access. We show how it can be used to control access to any type of resource, including homes, vehicles and public infrastructure such as street lights and traffic lights and industrial infrastructure such as power plants. We also show how it can be implemented regardless of the location of the owner of the physical resource and the location of the resource relative to its users.

Validation of Object Recognition Framework on Android Mobile Platform

In recent years there has been great interest in implementing object recognition frame work on mobile phones. This has stemmed from the fact the advances in object recognition algorithm and mobile phone capabilities have built a congenial ecosystem. Application developers on mobile platforms are trying to utilize the object recognition technology to build better human computer interfaces. This approach is in the nascent phase and proper application framework is required. In this paper, we propose a framework to overcome design challenges and provide an evaluation methodology to assess the system performance. We use the emerging Android mobile platform to implement and test the framework. We performed a case study using the proposal and reported the test result. This assessment will help developers make wise decisions about their application design. Furthermore, the Android API developers could use this information to provide better interfaces to the third party developers. The design and evaluation methodology could be extended to other mobile platforms for a wider consumer base.

A Dynamometer for an Ocean Turbine Prototype: Reliability through Automated Monitoring

An ocean turbine extracts the kinetic energy from ocean currents to generate electricity. Machine Condition Monitoring(MCM) / Prognostic Health Monitoring (PHM) systems allow for self-checking and automated fault detection, and are integral in the construction of a highly reliable ocean turbine. This paper presents an onshore test platform for an ocean turbine as well as a case study showing how machine learning can be used to detect changes in the operational state of this plant based on its vibration signals. In the case study, seven widely used machine learners a retrained on experimental data gathered from the test platform, a dynamometer, to detect changes in the machinesstate. The classification models generated by these classifiers are being considered as possible components of the state detection module of an MCM/PHM system for ocean turbines, and would be used for fault prediction. Experimental results presented here show the effectiveness of decision tree and random forest learners on distinguishing between faulty and normal states based on vibration data preprocessed by a wavelet transform.

Remote Labs: The Next High-Tech Step Beyond Simulation for Distance Education

SUMMARY As computer and networking technology has improved, computer-mediated communication and distance learning (DL) have become more prevalent. Software is used in a DL environment to replace traditional laboratory exercises. Software simulation provides wide-scale access to laboratory exercises at a low cost. However, simulations do not adequately replace all aspects of the real laboratory environment. This paper reviews DL alternatives to real laboratories and discusses whether the present alternatives successfully give students the experience of real laboratory experiments without physically attending a laboratory. The review found that present alternatives to real laboratories use software simulation, which do not accomplish the desired exposure to real lab environments in some disciplines, such as engineering. The paper presents an alternative to simulation, presently under development by the authors, in which real laboratory facilities can be accessed remotely via the Internet. These real physical laboratories provide the one dimension that simulation cannot: real response of real physical elements to real inputs.

Five new high-performance multiplexer-based 1-bit full adder cells

Five new multiplexer-based architectures for 1-bit full adder cell design are presented. Implementing with the pass-gate CMOS multiplexer, results in five distinct adders. Those adder cells along with the conventional 28-transistor CMOS adder are tested using H-Spice under 6 different frequencies and 6 different loads. Testing results shows the new cells exhibit on average 21.7% increase in sum signal speed, and 19.9% increase in carry out signal speed over the conventional 28-transistor CMOS adder, with power-delay product savings reaching up to 18.4%.

Mixed-Mode Programmable and Scalable Architecture for On-Chip Learning

Typical analog VLSI architectures for on-chip learning are limited in functionality, and scale poorly under variable problem size. We present a scalable hybrid analog-digital architecture for backpropagation learning in multilayer feedforward neural networks, which integrates the flexible functionality and programmability of digital control functions with the efficiency of analog parallel neural computation. The architecture is fully scalable, both in the parallel analog functions of forward and backward signal propagation through synaptic and neural functional units (SynMod and NeuMod), and in the global and local digital functions controlling recall, learning, initialization, monitoring and built-in test. The architecture includes local provisions for long-term weight storage using refresh, which is transparent to the functional operation both during recall and learning. “Refresh While Learning” (RWL) provides a means to compensate for the finite precision of the quantized analog weights during learning. We include simulation results for a network of 32×32 neurons, mapped in parallel onto a MassPar computational engine, which validate the functionality of the architecture on simple character recognition tasks, and demonstrate robust operation of the trained network under 4-bit quantization of the weights owing to the RWL technique.