A novel Built-In Self-Test (BIST) algorithm is proposed in this paper, which is used for testing low-voltage SRAM. The algorithm is the improvement of March C+ algorithm, which integrates the continuous write 0 and write 1 operations to cover more fault models like Read Destructive Coupling Fault (CFrd), Write Destructive Coupling Fault (CFwd) and Write Disturb Fault. Consequently, higher fault coverage is achieved than traditional March algorithm. The proposed algorithm is implemented by user defined algorithm (UDA) of Mentor tools. In order to verify the effectiveness of the algorithm, a low-voltage 8T SRAM chip is designed and tested based on SMIC 40nm LL CMOS process. Simulation results show that the proposed BIST algorithm named March-LV for low-voltage SRAM arrays covers 100% target faults. The test results further verify the feasibility of the algorithm.

Abstract An examination is .an important approach to comprehensively evaluate students' abilities. An examination paper is composed of test items. Most studies assume that test items are independent, but there may, in fact, be associations among test items. How to select test items with low correlations into a test paper is our concern. Meanwhile, for diagnostic learning , students must perform highly relevant exercises to help them efficiently understand the concepts included in the test items. People usually find the associations among test items based on properties of individual test items. This paper proposes a test item association model to represent the correlations of test item pairs by mining the response data of examinees. Then, a hypothesis test and an empirical analysis were used to verify and analyse the relationship between the test item correlation and similarity. Next, the effects of test item parameters on the correlation are investigated by applying the model to the simulated response data which are produced by Item Response Theory (IRT) simulation data generation software. Finally, three applications of this test item association model are listed. After verification, the proposed model can be used for precise teaching.

This paper focuses on scheduling in multihop wireless networks where flows are associated with fixed routes. The well-known back-pressure scheduling algorithm is throughput-optimal, but requires constant exchange of queue length information among neighboring nodes for calculating the “back-pressure.” Moreover, previous research shows that the total queue length along a route increases quadratically as the route length under the back-pressure algorithm, resulting in poor delay performance. In this paper, we propose a self-regulated MaxWeight scheduling, which does not require back-pressure calculation. We prove that the self-regulated MaxWeight scheduling is throughput-optimal (an algorithm is said to be throughput-optimal if it can stabilize any traffic that can be stabilized by any other algorithm). In the simulation part, we show that the self-regulated MaxWeight scheduling has a much better delay performance than the back-pressure algorithm.

We consider multiuser scheduling in wireless networks with channel variations and flow-level dynamics. Recently, it has been shown that the MaxWeight algorithm, which is throughput-optimal in networks with a fixed number of users, fails to achieve the maximum throughput in the presence of flow-level dynamics. In this paper, we propose a new algorithm, called Workload-based Scheduling with Learning, which is provably throughput-optimal, requires no prior knowledge of channels and user demands, and performs significantly better than previously suggested algorithms.

Back-pressure-type algorithms based on the algorithm by Tassiulas and Ephremides have recently received much attention for jointly routing and scheduling over multihop wireless networks. However, this approach has a significant weakness in routing because the traditional back-pressure algorithm explores and exploits all feasible paths between each source and destination. While this extensive exploration is essential in order to maintain stability when the network is heavily loaded, under light or moderate loads, packets may be sent over unnecessarily long routes, and the algorithm could be very inefficient in terms of end-to-end delay and routing convergence times. This paper proposes a new routing/scheduling back-pressure algorithm that not only guarantees network stability (throughput optimality), but also adaptively selects a set of optimal routes based on shortest-path information in order to minimize average path lengths between each source and destination pair. Our results indicate that under the traditional back-pressure algorithm, the end-to-end packet delay first decreases and then increases as a function of the network load (arrival rate). This surprising low-load behavior is explained due to the fact that the traditional back-pressure algorithm exploits all paths (including very long ones) even when the traffic load is light. On the other-hand, the proposed algorithm adaptively selects a set of routes according to the traffic load so that long paths are used only when necessary, thus resulting in much smaller end-to-end packet delays as compared to the traditional back-pressure algorithm .

The back-pressure algorithm introduced in 1992 by Tassiulas and Ephremides is a well-known distributed and adaptive routing/scheduling algorithm where nodes only need the queue-length information of neighboring nodes to make routing decisions. Packets are adaptively routed in the network according to congestion information, which makes the algorithm resilient to traffic and topology changes. However, the back-pressure algorithm requires routers to maintain a separate queue for each destination, which precludes its implementation in large-scale networks. In this paper, we propose a distributed cluster-based back-pressure routing algorithm that retains the adaptability of back-pressure routing while significantly reducing the number of queues that have to be maintained at each node.

We consider multiuser scheduling in wireless networks with channel variations and flow-level dynamics. Recently, it has been shown that the MaxWeight algorithm, which is throughput-optimal in networks with a fixed number users, fails to achieve the maximum throughput in the presence of flow-level dynamics. In this paper, we propose a new algorithm, calledworkload-based scheduling with learning, which is provably throughput-optimal, requires no prior knowledge of channels and user demands, and performs significantly better than previously suggested algorithms.

This paper studies scheduling in multichannel wireless networks with flow-level dynamics. We consider a downlink network with a single base station, M channels (frequency bands), and multiple mobile users (flows). We also assume mobiles dynamically join the network to receive finite-size files and leave after downloading the complete files. A recent study [16] has shown that the MaxWeight algorithm fails to be throughput-optimal under this flow-level dynamics. The main contribution of this paper is the development of joint channel-assignment and workload-based scheduling algorithms for multichannel downlink networks with dynamic flow arrivals/departures. We prove that these algorithms are throughput-optimal. Our simulations further demonstrate that a hybrid channel-assignment and workload-based scheduling algorithm significantly improves the network performance (in terms of both file-transfer delay and blocking probability) compared to the existing algorithms.