This paper introduces and uses spider monkey optimization (SMO) for synthesis sparse linear arrays, which are composed of a uniformly spaced core subarray and an extended sparse subarray. The amplitudes of all the elements and the locations of elements in the extended sparse subarray are optimized by the SMO algorithm to reduce the side lobe levels of the whole array, under a set of practical constraints. To show the efficiency of SMO, different examples are presented and solved. Simulation results of the sparse arrays designed by SMO are compared with published results to verify the effectiveness of the SMO method.

Three dimensional integration using Through-Silicon Vias (TSVs) offers short inter-layer interconnects and higher packing density. In order to take advantage of these attributes, a novel hybrid 3D NoC-Bus architecture is proposed in the paper. For vertical link, a Fake Token Bus architecture is elaborated, which utilizes the bandwidth efficiently by updating token synchronously. Based on this bus architecture, a methodology of hybrid 3D NoC-Bus design is introduced. The network hybridizes with the bus in vertical link and distributes long links of the full connected network into different layers, which achieves a network with a diameter of only 3 hops and limited radix. In addition, a congestion-aware routing algorithm applied to the hybrid network is proposed. The algorithm routes packets in horizontal firstly when the bus is busy, which balances the communication and reduces the possibility of congestion. Experimental results show that our network can achieve a 34.4% reduction in latency and a 43% reduction in power consumption under uniform random traffic and a 36.9% reduction in latency and a 48% reduction in power consumption under hotspot traffic over regular 3D mesh implementations on average.

Software defect prediction (SDP) plays a significant part in allocating testing resources reasonably, reducing testing costs, and ensuring software quality. One of the most widely used algorithms of SDP models is Naive Bayes (NB) because of its simplicity, effectiveness and robustness. In NB, when a data set has continuous or numeric attributes, they are generally assumed to follow normal distributions and incorporate the probability density function of normal distribution into their conditional probabilities estimates. However, after conducting a Kolmogorov-Smirnov test, we find that the 21 main software metrics follow non-normal distribution at the 5% significance level. Therefore, this paper proposes an improved NB approach, which estimates the conditional probabilities of NB with kernel density estimation of training data sets, to help improve the prediction accuracy of NB for SDP. To evaluate the proposed method, we carry out experiments on 34 software releases obtained from 10 open source projects provided by PROMISE repository. Four well-known classification algorithms are included for comparison, namely Naive Bayes, Support Vector Machine, Logistic Regression and Random Tree. The obtained results show that this new method is more successful than the four well-known classification algorithms in the most software releases.

This paper presents a synchronization mechanism to effectively implement the lock and barrier protocols in a decentralized manner through explicit message passing. In the proposed solution, a simple and efficient synchronization control mechanism is proposed to support queued synchronization without contention. By using state-of-the-art Application-Specific Instruction-set Processor (ASIP) technology, we embed the synchronization functionality into a baseline processor, making the proposed mechanism feature ultra-low overhead. Experimental results show the proposed synchronization achieves ultra-low latency and almost ideal scalability when the number of processors increases.