Emerging non-volatile memory technologies, especially flash-based solid state drives (SSDs), have increasingly been adopted in the storage stack. They provide numerous advantages over traditional mechanically rotating hard disk drives (HDDs) and have a tendency to replace HDDs. Due to the long existence of HDDs as primary building blocks for storage systems, however, much of the system software has been specially designed for HDD and may not be optimal for non-volatile memory media. Therefore, in order to realistically leverage its superior raw performance to the maximum, the existing upper layer software has to be re-evaluated or re-designed. To this end, in this paper, we propose PASS, an optimized I/O scheduler at the Linux block layer to accommodate the changing trend of underlying storage devices toward flash-based SSDs. PASS takes the rich internal parallelism in SSDs into account when dispatching requests to the device driver in order to achieve high performance. Specifically, it partitions the logical storage space into fixed-size regions (preferably the component package sizes) as scheduling units. These scheduling units are serviced in a round-robin manner and for every chance that the chosen dispatching unit issues only a batch of either read or write requests to suppress the excessive mutual interference. Additionally, the requests are sorted according to their visiting addresses while waiting in the dispatching queues to exploit high sequential performance of SSD. The experimental results with a variety of workloads have shown that PASS outperforms the four Linux off-the-shelf I/O schedulers by a degree of 3% up to 41%, while at the same time it improves the lifetime significantly, due to reducing the internal write amplification.

The cross-layer resource allocation problem in wireless multi-hop networks (WMHNs) has been extensively studied in the past few years. Most of these studies assume that every node has the perfect channel state information (CSI) of other nodes. In practical settings, however, the networks are generally dynamic and CSI usually becomes outdated when it is used, due to the time-variant channel and feedback delay. To deal with this issue, we study the cross-layer resource allocation problem in dynamic WMHNs with outdated CSI under channel conditions where there is correlation between the outdated CSI and current CSI. Two major contributions are made in this work: (1) a closed-form expression of conditional average capacity is derived under the signal-to-interference-plus-noise ratio (SINR) model; (2) a joint optimization problem of congestion control, power control, and channel allocation in the context of outdated CSI is formulated and solved in both centralized and distributed manners. Simulation results show that the network utility can be improved significantly using our proposed algorithm.

We propose a robust blind watermarking algorithm for three-dimensional (3D) mesh models based on vertex curvature to maintain good robustness and improve visual masking in 3D mesh models. In the embedding process, by using the local window of vertex, the root mean square curvature is calculated for every vertex of the 3D mesh model and an ordered set of fluctuation values is obtained. According to the ordered fluctuation values, the vertices are separated into bins. In each bin the fluctuation values are normalized. Finally, the mean of the root mean square curvature fluctuation values of the vertices in each bin is modulated to embed watermark information. In watermark detection, the algorithm uses a blind watermark extraction technique to extract the watermark information. The experimental results show that the algorithm has a very good performance for visual masking of the embedded model and that it can resist a variety of common attacks such as vertex rearrangement, rotation, translating, uniform scaling, noise, smoothing, quantization, and simplification.

Despite the success of the imperialist competitive algorithm (ICA) in solving optimization problems, it still suffers from frequently falling into local minima and low convergence speed. In this paper, a fuzzy version of this algorithm is proposed to address these issues. In contrast to the standard version of ICA, in the proposed algorithm, powerful countries are chosen as imperialists in each step; according to a fuzzy membership function, other countries become colonies of all the empires. In absorption policy, based on the fuzzy membership function, colonies move toward the resulting vector of all imperialists. In this algorithm, no empire will be eliminated; instead, during the execution of the algorithm, empires move toward one point. Other steps of the algorithm are similar to the standard ICA. In experiments, the proposed algorithm has been used to solve the real world optimization problems presented for IEEE-CEC 2011 evolutionary algorithm competition. Results of experiments confirm the performance of the algorithm.

Magnetic sensors can be applied in vehicle recognition. Most of the existing vehicle recognition algorithms use one sensor node to measure a vehicle’s signature. However, vehicle speed variation and environmental disturbances usually cause errors during such a process. In this paper we propose a method using multiple sensor nodes to accomplish vehicle recognition. Based on the matching result of one vehicle’s signature obtained by different nodes, this method determines vehicle status and corrects signature segmentation. The co-relationship between signatures is also obtained, and the time offset is corrected by such a co-relationship. The corrected signatures are fused via maximum likelihood estimation, so as to obtain more accurate vehicle signatures. Examples show that the proposed algorithm can provide input parameters with higher accuracy. It improves the average accuracy of vehicle recognition from 94.0% to 96.1%, and especially the bus recognition accuracy from 77.6% to 92.8%.

Increasing awareness concerning food safety problems has been driving the search for simple and efficient biochemical analytical methods. In this paper, we develop a portable electro-acoustic biosensor based on a film bulk acoustic resonator for the detection of pesticide residues in agricultural products. A shear mode ZnO film bulk acoustic resonator with a micro-machining structure was fabricated as a mass-sensitive transducer for the real-time detection of antibody-antigen reactions in liquids. In order to obtain an ultra-low detection level, the artificial antigens were immobilized on the sensing surface of the resonator to employ a competitive format for the immunoassays. The competitive immunoreactions can be observed clearly through monitoring the frequency changes. The presence of pesticides was detected through the diminution of the frequency shift compared with the level without pesticides. The limit of detection for carbaryl (a widely used pesticide for vegetables and crops) is 2×10⁻¹⁰ M. The proposed device represents a potential alternative to the complex optical systems and electrochemical methods that are currently being used, and represents a significant opportunity in terms of simplicity of use and portability for on-site food safety testing.

Inverse lithography technology (ILT), also known as pixel-based optical proximity correction (PB-OPC), has shown promising capability in pushing the current 193 nm lithography to its limit. By treating the mask optimization process as an inverse problem in lithography, ILT provides a more complete exploration of the solution space and better pattern fidelity than the traditional edge-based OPC. However, the existing methods of ILT are extremely time-consuming due to the slow convergence of the optimization process. To address this issue, in this paper we propose a support vector machine (SVM) based layout retargeting method for ILT, which is designed to generate a good initial input mask for the optimization process and promote the convergence speed. Supervised by optimized masks of training layouts generated by conventional ILT, SVM models are learned and used to predict the initial pixel values in the ‘undefined areas’ of the new layout. By this process, an initial input mask close to the final optimized mask of the new layout is generated, which reduces iterations needed in the following optimization process. Manufacturability is another critical issue in ILT; however, the mask generated by our layout retargeting method is quite irregular due to the prediction inaccuracy of the SVM models. To compensate for this drawback, a spatial filter is employed to regularize the retargeted mask for complexity reduction. We implemented our layout retargeting method with a regularized level-set based ILT (LSB-ILT) algorithm under partially coherent illumination conditions. Experimental results show that with an initial input mask generated by our layout retargeting method, the number of iterations needed in the optimization process and runtime of the whole process in ILT are reduced by 70.8% and 69.0%, respectively.