This paper proposes a formal approach of verifying ubiquitous computing application scenarios. Ubiquitous computing application scenarios assume that there are a lot of devices and physical things with computation and communication capabilities, which are called smart objects, and these are interacted with each other. Each of these interactions among smart objects is called “federation”, and these federations form a ubiquitous computing application scenario. Previously, Yuzuru Tanaka proposed “a proximity-based federation model among smart objects”, which is intended for liberating ubiquitous computing from stereotyped application scenarios. However, there are still challenges to establish the verification method of this model. This paper proposes a verification method of this model through model checking. Model checking is one of the most popular formal verification approach and it is often used in various fields of industry. Model checking is conducted using a Kripke structure which is a formal state transition model. We introduce a context catalytic reaction network (CCRN) to handle this federation model as a formal state transition model. We also give an algorithm to transform a CCRN into a Kripke structure and we conduct a case study of ubiquitous computing scenario verification, using this algorithm and the model checking. Finally, we discuss the advantages of our formal approach by showing the difficulties of our target problem experimentally.

Regression testing is essential for assuring the quality of a software product. Because rerunning all test cases in regression testing may be impractical under limited resources, test case prioritization is a feasible solution to optimize regression testing by reordering test cases for the current testing version. In this paper, we propose a novel test case prioritization approach that combines the clustering algorithm and the scheduling algorithm for improving the effectiveness of regression testing. By using the clustering algorithm, test cases with same or similar properties are merged into a cluster, and the scheduling algorithm helps allocate an execution priority for each test case by incorporating fault detection rates with the waiting time of test cases in candidate set. We have conducted several experiments on 12 C programs to validate the effectiveness of our proposed approach. Experimental results show that our approach is more effective than some well studied test case prioritization techniques in terms of average percentage of fault detected (APFD) values.

Second-order sampling of 2-D frequency distributions is examined in this paper. When a figure in the frequency space can fill up the entire frequency space by tiling, we call this figure a tiling cluster. We also introduce the concept of pair regions. The results obtained for the second-order sampling of 1-D and 2-D frequency distributions are arranged using these two concepts. The sampling functions and sampling positions of second-order sampling of a 2-D rectangular-complement highpass frequency distribution, which have not been solved until now, are explicitly presented by using these two concepts.

Linked Open Data (LOD) at Schema-Level and knowledge described in Chinese is an important part of the LOD project. Previous work generally ignored the rules of word-order sensitivity and polysemy in Chinese or could not deal with the out-of-vocabulary (OOV) mapping task. There is still no efficient system for large-scale Chinese ontology mapping. In order to solve the problem, this study proposes a novel TongYiCiCiLin (TYCCL) and Sequence Alignment-based Chinese Ontology Mapping model, which is called TongSACOM, to evaluate Chinese concept similarity in LOD environment. Firstly, an improved TYCCL-based similarity algorithm is proposed to compute the similarity between atomic Chinese concepts that have been included in TYCCL. Secondly, a global sequence-alignment and improved TYCCL-based combined algorithm is proposed to evaluate the similarity between Chinese OOV. Finally, comparing the TongSACOM to other typical similarity computing algorithms, and the results prove that it has higher overall performance and usability. This study may have important practical significance for promoting Chinese knowledge sharing, reusing, interoperation and it can be widely applied in the related area of Chinese information processing.

This study focuses on the design of electrical stimulator for retinal prosthesis. The stimulator must be designed such that the occurrence of electrolysis or any irreversible process in the electrodes and flexible lead is prevented in order to achieve safe stimulation over long periods using the large number of electrodes. Some types of biphasic current pulse circuits, charge balance circuits, and AC power delivery circuits were developed to address this issue. Electronic circuitry must be introduced in the stimulator to achieve the large number of electrodes required to obtain high quality of vision. The concept of a smart electrode, in which a microchip is embedded inside an electrode, is presented for future retinal prostheses with over 1000 electrodes.

In this paper, we present sub-1-V CMOS-based electrophoresis method for small-form-factor biomolecule manipulation that is contained in a microchip. This is the first time this type of device has been presented in the literature. By combining CMOS technology with electroless gold plating, the electrode pitch can be reduced and the required input voltage can be decreased to less than 1 V. We fabricated the CMOS electrophoresis chip in a cost-competitive 0.6 µm standard CMOS process. A sample/hold circuit in each cell is used to generate a constant output from an analog input. After forming gold electrodes using an electroless gold plating technique, we were able to manipulate red food coloring with a 0-0.7 V input voltage range. The results shows that the proposed CMOS chip is effective for electrophoresis-based manipulation.

This paper proposes an effective continuous super-resolution (CSR) algorithm for the multipath channel estimation. By designing a preamble including up-chirp and down-chirp symbols, the Doppler shift and multipath delay are estimated jointly by using convex programming. Simulation results show that the proposed CSR can achieve better detection probability of the number of multipaths than the eigenvalue based methods. Moreover, compared with conventional super-resolution techniques, such as MUSIC and ESPRIT methods, the proposed CSR algorithm demonstrates its advantage in root mean square error of the Doppler shift and multipath delay, especially for the closely located paths within low SNR.

Attaining extremely low latency service in 5G cellular networks is an important challenge in the communication research field. A higher QoS in the next-generation network could enable several unprecedented services, such as Tactile Internet, Augmented Reality, and Virtual Reality. However, these services will all need support from powerful computational resources provided through cloud computing. Unfortunately, the geolocation of cloud data centers could be insufficient to satisfy the latency aimed for in 5G networks. The physical distance between servers and users will sometimes be too great to enable quick reaction within the service time boundary. The problem of long latency resulting from long communication distances can be solved by Mobile Edge Computing (MEC), though, which places many servers along the edges of networks. MEC can provide shorter communication latency, but total latency consists of both the transmission and the processing times. Always selecting the closest edge server will lead to a longer computing latency in many cases, especially when there is a mass of users around particular edge servers. Therefore, the research studies the effects of both latencies. The communication latency is represented by hop count, and the computation latency is modeled by processor sharing (PS). An optimization model and selection policies are also proposed. Quantitative evaluations using simulations show that selecting a server according to the lowest total latency leads to the best performance, and permitting an over-latency barrier would further improve results.

Sorting is an extremely important computation kernel that has been accelerated in a lot of fields such as databases, image processing, and genome analysis. Given that advent of Internet of Things (IoT) era due to mobile technology progressions, the future needs a sorting method that is available on any environment, such as not only high performance systems like servers but also low computational performance machines like embedded systems. In this paper, we present an FPGA-based sorting accelerator combining Sorting Network and Merge Sorter Tree, which is customizable by means of tuning design parameters. The proposed FPGA accelerator sorts data sent from a host PC via the PCIe bus, and sends back the fully sorted data sequence to it. We also present a detailed analytical model that accurately estimates the sorting performance. Due to these characteristics, designers can know how fast a developed sorting hardware is in advance and can implement the best one to fulfill the cost and performance constraints. Our experiments show that the proposed hardware achieves up to 19.5x sorting performance, compared with Intel Core i7-3770K operating at 3.50GHz, when sorting 256M 32-bits integer elements. However, this result is limited because of insufficient memory bandwidth. To overcome this problem, we propose a data compression mechanism and the experimental result shows that the sorting hardware with it achieves almost 90% of the estimated performance, while the hardware without it does about 60%. In order to allow every designer to easily and freely use this accelerator, the RTL source code is released as open-source hardware.

Transfer boosting, a branch of instance-based transfer learning, is a commonly adopted transfer learning method. However, currently popular transfer boosting methods focus on binary classification problems even though there are many multi-classification tasks in practice. In this paper, we developed a new algorithm called MultiTransferBoost on the basis of TransferBoost for multi-classification. MultiTransferBoost firstly separated the multi-classification problem into several orthogonal binary classification problems. During each iteration, MultiTransferBoost boosted weighted instances from different source domains while each instance's weight was assigned and updated by evaluating the difficulty of the instance being correctly classified and the “transferability” of the instance's corresponding source domain to the target. The updating process repeated until it reached the predefined training error or iteration number. The weight update factors, which were analyzed and adjusted to minimize the Hamming loss of the output coding, strengthened the connections among the sub binary problems during each iteration. Experimental results demonstrated that MultiTransferBoost had better classification performance and less computational burden than existing instance-based algorithms using the One-Against-One (OAO) strategy.

Focused on network reconnaissance, eavesdropping, and DoS attacks caused by static routing policies, this paper designs a random routing mutation architecture based on the OpenFlow protocol, which takes advantages of the global network view and centralized control in a software-defined network. An entropy matrix of network traffic characteristics is constructed by using volume measurements and characteristic measurements of network traffic. Random routing mutation is triggered according to the result of network anomaly detection, which using a wavelet transform and principal component analysis to handle the above entropy matrix for both spatial and temporal correlations. The generation of a random routing path is specified as a 0-1 knapsack problem, which is calculated using an improved ant colony algorithm. Theoretical analysis and simulation results show that the proposed method not only increases the difficulty of network reconnaissance and eavesdropping but also reduces the impact of DoS attacks on the normal communication in an SDN network.

In this letter, we propose a Hadoop-based Video Transcoding System (HVTS), which is designed to run on all major cloud computing services. HVTS is highly adapted to the structure and policies of Hadoop, thus it has additional capacities for transcoding, task distribution, load balancing, and content replication and distribution. To evaluate, our proposed system, we carry out two performance tests on our local testbed, transcoding and robustness to data node and task failures. The results confirmed that our system delivers satisfactory performance in facilitating seamless streaming services in cloud computing environments.

In this paper, the virtual antenna technique is applied to a single input multiple output (SIMO) radar system to enhance the performance of the conventional beamforming direction of arrival (DOA) estimation method. Combining the virtual array generated by the interpolated array technique and the real array, the angular resolution of the DOA estimation algorithm is improved owing to the extended number of antennas and aperture size. Based on the proposed interpolation technique, we transform the position of the antenna elements in a uniform linear array (ULA) to the arbitrary positions to suppress the grating lobe and side lobe levels. In simulations, the pseudo spectrum of the Bartlett algorithm and the root mean square error (RMSE) of the DOA estimation with the signal-to-noise ratio (SNR) are analyzed for the real array and the proposed virtually extended array. Simulation results show that the angular resolution of the proposed array is better than that of the real array using the same aperture size of array and the number of antennas. The proposed technique is verified with the practical data from commercialized radar system.

We present a lifting-based lapped transform (L-LT) and a reversible symmetric extension (RSE) in the boundary processing for more effective lossy-to-lossless image coding of data with various qualities from only one piece of lossless compressed data. The proposed dual-DCT-lifting-based LT (D2L-LT) parallel processes two identical LTs and consists of 1-D and 2-D DCT-liftings which allow the direct use of a DCT matrix in each lifting coefficient. Since the DCT-lifting can utilize any existing DCT software or hardware, it has great potential for elegant implementations that are dependent on the architecture and DCT algorithm used. In addition, we present an improved RSE (IRSE) that works by recalculating the boundary processing and solves the boundary problem that the DCT-lifting-based L-LT (DL-LT) has. We show that D2L-LT with IRSE mostly outperforms conventional L-LTs in lossy-to-lossless image coding.

An output voltage-current equation of charge pump DC-DC voltage multiplier using diodes is provided to cover wide clock frequency and output current ranges for designing energy harvester operating at a near-threshold voltage or in sub-threshold region. Equivalent circuits in slow and fast switching limits are extracted. The effective threshold voltage of the diode in slow switching limit is also derived as a function of electrical characteristics of the diodes, such as the saturation current and voltage slope parameter, and design parameters such as the number of stages, capacitance per stage, parasitic capacitance at the top plate of the main boosting capacitor, and the clock frequency. The model is verified compared with SPICE simulation.

Fuzzy techniques can implement the fine-grained access control of encrypted data in the Cloud because they support error-tolerance. In this system, using biometric attributes such as fingerprints, faces and irises as pubic parameters is advantageous over those systems based on Public Key Infrastructure (PKI). This is because biometric information is unique, unforgettable and non-transferable. However the biometric-attribute measurements are noisy and most of the existing encryption systems can not support the biometric-attribute encryption. Additionally, the previous fuzzy encryption schemes only achieve the selective security which is a weak security model. To overcome these drawbacks, we propose a new fuzzy encryption scheme based on the lattice in this letter. The proposed scheme is based on a hierarchical identity-based encryption with fixed-dimensional private keys space and thus has short public parameters and short private keys, which results in high computation efficiency. Furthermore, it achieves the strong security, i.e., adaptive security. Lastly, the security is reduced to the learning with errors (LWE) problem in the standard model.

Recently, several methods to process continuous queries for mobile objects in broadcast environments have been proposed. We propose a new indexing method for processing continuous queries that uses vector information in broadcast environments. We separate the index structure according to the velocities of the objects to avoid unnecessary accesses. The index structure consists of the index files for the slow moving objects and the fast moving objects. By avoiding unnecessary accesses, we reduce the tuning time to process a query in broadcast environments. To show the superiority of the proposed method, we evaluate its performance from various perspectives.

Conventional image retargeting methods fail to avoid distortion in the case where visually important regions are distributed all over the image. To reduce distortions, this paper proposes a novel image retargeting method that incorporates letterboxing into an image warping framework. Letterboxing has the advantage of producing results without distortion or content loss although being unable to use the entire display area. Therefore, it is preferable to combine a retargeting method with a letterboxing operator when displaying images in full screen. Experimental results show that the proposed method is superior to conventional methods in terms of visual quality measured by an objective metric.

Adaptive and flexible network control technology is considered essential for efficient network resource utilization. Moreover, such technology is becoming a key to cost-effectively meet diverse service requirements and accommodate heavier traffic with limited network resources; demands that conventional static operation cannot satisfy. To address this issue, we previously studied dynamic network control technology for large-capacity network services including on-demand broad bandwidth provisioning services and layer-one VPN. Our previous study introduced a simple weighting function for achieving fairness in terms of path length and proposed two dynamic Make Before Break Routing algorithms for reducing blocking probability. These algorithms enhance network utilization by rerouting existing paths to alternative routes while completely avoiding disruption for highly reliable services. However, the impact of this avoidance of service disruption on blocking probability has not been clarified. In this paper, we propose modified versions of the algorithms that enhance network utilization while slightly increasing disruption by rerouting, which enable us to elucidate the effectiveness of hitless rerouting. We also provide extensive evaluations including a comparison of original and modified algorithms. Numerical examples demonstrate that they achieve not only a high degree of fairness but also low service blocking probability. Hitless rerouting is achieved with a small increase in blocking probability.

In conventional spread spectrum (SS) watermarking schemes, random sequences are used for the modulation of watermark information. However, because of the mutual interference among those sequences, it requires complicated removal operation to improve the performance. In this paper, we propose an efficient spread spectrum watermarking scheme by introducing the orthogonal frequency divisiion multiplexing (OFDM) technique at the modulation of watermark information. The SS sequences in the proposed method are the DCT basic vectors modulated by a pseudo-random number (PN) sequence. We investigate the SS-based method considering the host interference at the blind detection scenario and analyze the noise caused by attacks. Because every operation is invertible, the quantization index modulation (QIM)-based method is applicable for the OFDM modulated signals. We also consider the property of watermark extracting operation in SS-based and QIM-based method and formalize their models of noisy channel in order to employ an error correcting code. The performance of their methods with error correcting code is numerically evaluated under the constraints of same distortion level in watermarked content. The experimental results indicated a criteria for the selection of SS-based and QIM-based methods for given content, which is determined by the amount of host interference. In case that the host interference is 0.8 times smaller than a watermark signal, the SS-based method is suitable. When it is 1.0 times larger, the QIM-based method should be selected.