Yahya E. Osais

Test vector decomposition-based static compaction algorithms for combinational circuits

Testing system-on-chips involves applying huge amounts of test data, which is stored in the tester memory and then transferred to the chip under test during test application. Therefore, practical techniques, such as test compression and compaction, are required to reduce the amount of test data in order to reduce both the total testing time and memory requirements for the tester. In this article, a new approach to static compaction for combinational circuits, referred to as test vector decomposition (TVD), is proposed. In addition, two new TVD based static compaction algorithms are presented. Experimental results for benchmark circuits demonstrate the effectiveness of the two new static compaction algorithms.

On Sensor Placement for Directional Wireless Sensor Networks

A directional sensor network is formed by directional sensors which may be oriented toward different directions. The sensing region of a directional sensor can be viewed as a sector in a two-dimensional plane. Therefore, a directional sensor can only choose one sector (or direction) at any time instant. Planning of directional sensor networks has received very little attention in the literature. In this paper, we discuss directional sensor placement which is a critical task in the planning of directional sensor networks. We also present an integer linear programming model whose goal is to minimize the number of directional sensors that need to be deployed to monitor a set of discrete targets in a sensor field. Numerical results demonstrate the viability and effectiveness of the model.

The Minimum Cost Sensor Placement Problem for Directional Wireless Sensor Networks

Unlike isotropic sensors, directional sensors have a finite angle of view and thus cannot sense the whole circular area around them. The sensing region of a directional sensor can be viewed as a sector in a 2D plane. A directional sensor network is formed by directional sensors which may be oriented toward different directions. In this paper, we present an integer linear programming model for the minimum cost sensor placement problem in directional sensor networks. The objective is to minimize the total cost of directional sensors by properly choosing the type and direction for each sensor to be installed in the sensor field. The model guarantees that all the targets are covered and sensor nodes can deliver their data to a sink node. Numerical results demonstrate the viability and effectiveness of the proposed model.

Directional Sensor Placement with Optimal Sensing Range, Field of View and Orientation

The directional sensor placement problem is an essential part of any planning model for directional wireless sensor networks. Its goal is to find an optimal subset of locations where directional sensors should be placed so that the total network cost is minimized while the requirements of coverage and connectivity are satisfied. Directional sensors are characterized by three important parameters: sensing range, field of view and orientation. These parameters have significant impact on the overall cost of the directional sensor network. In this paper, we present an integer linear programming formulation for the directional sensor placement problem in which the goal is to minimize the network cost by appropriately choosing the values of the above parameters for each sensor to be installed in the sensor field. We show the viability and effectiveness of the proposed model through numerical results.

A static test compaction technique for combinational circuits based on independent fault clustering

Testing system-on-chip involves applying huge amounts of test data, which is stored in the tester memory and then transferred to the circuit under test during test application. Therefore, practical techniques, such as test compression and compaction, are required to reduce the amount of test data in order to reduce both the total testing time and the memory requirements for the tester. In this paper, a new static compaction algorithm for combinational circuits is presented. The algorithm is referred to as independent fault clustering. It is based on a new concept called test vector decomposition. Experimental results for benchmark circuits demonstrate the effectiveness of the new static compaction algorithm.

Optimal Management of Rechargeable Biosensors in Temperature-Sensitive Environments

Biosensors are tiny wireless medical devices which are attached or implanted into the body of a human being or animal to monitor and control biological processes. They are distinguished from conventional sensors by their biologically derived sensing elements. Biosensors generate heat when they transmit their measurements and their temperature rises when recharged by electromagnetic energy. These phenomena translate to a temperature increase in the tissues surrounding the biosensors. If the temperature increase exceeds a certain threshold, the tissues might be damaged. In this paper, we discuss the problem of finding an optimal operating policy for a rechargable biosensor under a strict maximum temperature increase constraint. This problem can be formulated as a Markov decision process with an average reward criterion. The solution is an optimal policy that maximizes the average number of samples which can be generated by the biosensor while observing the constraint on the maximum safe temperature level. Due to the exponential nature of the problem, a heuristic policy is proposed. The performance of the policies is studied through simulation. A greedy policy is used as a baseline for comparison.

Dynamic Sensor Scheduling for Thermal Management in Biological Wireless Sensor Networks

Biological sensors are a very promising technology that will take healthcare to the next level. However, there are obstacles that must be overcome before the full potential of this technology can be realized. One such obstacle is that the heat generated by biological sensors implanted into a human body might damage the tissues around them. Dynamic sensor scheduling is one way to manage and evenly distribute the generated heat. In this paper, the dynamic sensor scheduling problem is formulated as a Markov decision process (MDP). Unlike previous works, the temperature increase in the tissues caused by the generated heat is incorporated into the model. The solution of the model gives an optimal policy that when executed will result in the maximum possible network lifetime under a constraint on the maximum temperature level tolerable by the patients body. In order to obtain the optimal policy in a lesser amount of time, two specific types of states are aggregated to produce a considerably smaller MDP model equivalent to the original one. Numerical and simulation results are presented to show the validity of the model and superiority of the optimal policy produced by it when compared with two policies one of which is specifically designed for biological wireless sensor networks.

Thermal Management of Biosensor Networks

Biosensors are a very promlsmg technology that will take health care to the next level. However, there are obstacles that must be overcome before the full potential of this technology can be realized. One such obstacle is that the heat generated by implanted biosensors may damage the tissues around them. Dynamic sensor scheduling is one way to manage the heat generated by implanted biosensors. In this paper, the dynamic sensor scheduling problem is formulated as a Markov decision process. Not like previous works, the temperature increase in the tissues caused by heat is incorporated into the model. The solution of the model gives an optimal policy that when executed, it will result in the maximum possible network lifetime under a constraint on the maximum temperature tolerable by the patients body. The optimal policy is compared with two policies one of which is specifically designed for biosensor networks. Numerical and simulation results show the validity of the model and superiority of the optimal policy produced by the model in terms of both network lifetime and temperature increase.

A retiming-based test pattern generator design for built-in self test of data path architectures

Recently, a new Built-In Self Test (BIST) methodology based on balanced bistable sequential kernels has been proposed that reduces the area overhead and performance degradation associated with the conventional BILBO-oriented BIST methodology. This new methodology guarantees high fault coverage but requires special test sequences and test pattern generator (TPG) designs. In this paper, we demonstrate the use of the retiming technique in designing TPGs for balanced bistable sequential kernels. Experimental results on ISCAS benchmark circuits demonstrate the effectiveness of the designed TPGs in achieving higher fault coverage than the conventional maximal-length LFSR TPGs.

On test vector reordering for combinational circuits

The cost of testing is a major factor in the cost of digital system design. In order to reduce the test application time, it is required to order the test vectors in such a way that it reduces the time a defective chip spends on a tester until the defect is detected. In this paper, we propose an efficient test vector reordering technique that significantly reduces both the time and memory complexities of reordering procedures based on fault simulation without dropping. Experimental results demonstrate both the efficiency and effectiveness of our proposed technique.