We have developed a portable NIRS-based optical BCI system that features a non-invasive, facile probe attachment and does not require muscle movement to control the target devices. The system consists of a 2-channel probe, a signal-processing unit, and an infrared-emission device, which measures the blood volume change in the participant's prefrontal cortex in a real time. We use the threshold logic as a switching technology, which transmits a control signal to a target device when the electrical waveforms exceed the pre-defined threshold. Eight healthy volunteers participated in the experiments and they could change the television channel or control the movement of a toy robot with average switching times of 11.5 ± 5.3 s and the hit rate was 83.3%. These trials suggest that this system provides a novel communication aid for people with motor disabilities.

To reconstruct low-resolution facial photographs which are in focus and without motion blur, a novel algorithm based on local similarity preserving is proposed. It is based on the theories of local manifold learning. The innovations of the new method include mixing point-based entropy and Euclidian distance to search for the nearest points, adding point-to-patch degradation model to restrict the linear weights and compensating the fusing patch to keep energy coherence. The compensation reduces the algorithm dependence on training sets and keeps the luminance of reconstruction constant. Experiments show that our method can effectively reconstruct 1612 images with the magnification of 88 and the 3224 facial photographs in focus and without motion blur.

Service providers should monitor the quality of experience of a communication service in real time to confirm its status. To do this, we previously proposed a packet-layer model that can be used for monitoring the average video quality of typical Internet protocol television content using parameters derived from transmitted packet headers. However, it is difficult to monitor the video quality per user using the average video quality because video quality depends on the video content. To accurately monitor the video quality per user, a model that can be used for estimating the video quality per video content rather than the average video quality should be developed. Therefore, to take into account the impact of video content on video quality, we propose a model that calculates the difference in video quality between the video quality of the estimation-target video and the average video quality estimated using a packet-layer model. We first conducted extensive subjective quality assessments for different codecs and video sequences. We then model their characteristics based on parameters related to compression and packet loss. Finally, we verify the performance of the proposed model by applying it to unknown data sets different from the training data sets used for developing the model.

This article introduces some improvements to the already proposed custom instruction candidates selection for the automatic ISA customisation problem targeting reconfigurable processors. It introduces new opportunities to prune the search space, and a technique based on dynamic programming to check the independence between groups. The proposed new algorithm yields one order less measured number of convexity checks than the related work for the same inputs and outputs.

A novel dual-band magnetic loop antenna is proposed using slot-loaded composite right/left-handed (SL-CRLH) structures. Since each radiating element consists of a symmetrically-array of unit-cells, a dual-band magnetic loop source is obtained with unchanged beam patterns. Simulations and measurements show its good radiation performance with monopole-like radiation patterns in both operating bands.

A method for efficiently representing the state equation in a class of max-plus linear systems is proposed. We introduce a construct referred to as 'cell' in which the list of possible longest paths is stored. By imposing interval constraints on the system parameters, we can reduce the complexity of the state equation. The proposed method would be useful in scheduling applications for systems with adjustable system parameters.

We describe a cell-based connected component labeling algorithm to calculate the 0th and 1st moment features as the attributes for labeled regions. These can be used to indicate their sizes and positions for multi-object extraction. Based on the additivity in moment features, the cell-based labeling algorithm can label divided cells of a certain size in an image by scanning the image only once to obtain the moment features of the labeled regions with remarkably reduced computational complexity and memory consumption for labeling. Our algorithm is a simple-one-time-scan cell-based labeling algorithm, which is suitable for hardware and parallel implementation. We also compared it with conventional labeling algorithms. The experimental results showed that our algorithm is faster than conventional raster-scan labeling algorithms.

This paper proposes a diversity scheme for Multi-Input Multi-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) based on Radio Frequency (RF) signal processing. Although a 22 MIMO-OFDM system can double the capacity without expanding the occupied frequency bandwidth, we cannot get additional diversity gain using the linear MIMO decomposition method. The proposed method improves the bit error rate (BER) performance by making efficient use of RF signal processing. Computer simulation results show that the proposed scheme gives additional diversity gain.

Obtaining a compact representation of a large-size feature map built by mapper robots is a critical issue in recent mobile robotics. This “map compression” problem is explored from a novel perspective of dictionary-based data compression techniques in the paper. The primary contribution of the paper is the proposal of the dictionary-based map compression approach. A map compression system is presented by employing RANSAC map matching and sparse coding as building blocks. The effectiveness levels of the proposed techniques is investigated in terms of map compression ratio, compression speed, the retrieval performance of compressed/decompressed maps, as well as applications to the Kolmogorov complexity.

This paper analyzes a pseudo-differential dynamic comparator with a dynamic pre-amplifier. The transient gain of a dynamic pre-amplifier is derived and applied to equations of the thermal noise and the regeneration time of a comparator. This analysis enhances understanding of the roles of transistor's parameters in pre-amplifier's gain. Based on the calculated gain, two calibration methods are also analyzed. One is calibration of a load capacitance and the other is calibration of a bypass current. The analysis helps designers' estimation for the accuracy of calibration, dead-zone of a comparator with a calibration circuit, and the influence of PVT variation. The analyzed comparator uses 90-nm CMOS technology as an example and each estimation is compared with simulation results.

Coarse-grained reconfigurable architecture (CGRA) combines the performance of application-specific integrated circuits (ASICs) and the flexibility of general-purpose processors (GPPs), which is a promising solution for embedded systems. With the increasing complexity of reconfigurable resources (processing elements, routing cells, I/O blocks, etc.), the reconfiguration cost is becoming the performance bottleneck. The major reconfiguration cost comes from the frequent memory-read/write operations for transferring the configuration context from main memory to context buffer. To improve the overall performance, it is critical to reduce the amount of configuration context. In this paper, we propose a configuration context reduction method for CGRA. The proposed method exploits the structure correlation of computation tasks that are mapped onto CGRA and reduce the redundancies in configuration context. Experimental results show that the proposed method can averagely reduce the configuration context size up to 71% and speed up the execution up to 68%. The proposed method does not depend on any architectural feature and can be applied to CGRA with an arbitrary architecture.

The 2-adic complexity of binary periodic sequences plays an important role in cryptology. In this paper, by means of the usual Fourier transform, we give a simpler form of the upper bound for 2-adic complexity than related result before. For pn-periodic sequences, we discuss the relation between sequences and their Fourier coefficients. Furthermore, based on the relation, we get the lower bound for the number of pn-periodic sequences with given 2-adic complexity.

A new optical fan-out adapter is proposed and fabricated by applying the jacket removing system with a CO2 laser. The fan-out adapter has both the multi-fiber connection function and the fiber separating function for single-fiber connections. In order to remove the jacket of a fiber ribbon to connectorize and fabricate the fan-out adapter, the optimum conditions of the laser power P and the scan speed V are clarified for the jacket removing. Based on the optimum conditions, the fan-out adapter was fabricated successfully. Individual fibers could be taken out from the MT connector of the fan-out adapter. The connection losses of the fabricated fan-out adapter were comparable with the values of commercially available MT connectors and SC connectors. The length of the fabricated fan-out adapter was 27 mm, including 2 MT connectors. This result clarifies that the size of the connection with a fan-out can be reduced dramatically by the proposed fan-out adapter.

In multimedia communication, due to the limited computational capability of the personal information machine, a coder with low computational complexity is needed to integrate services from several media sources. This paper presents two efficient candidate schemes to simplify the most computationally demanding operation, the excitation codebook search procedure. For fast adaptive codebook search, we propose an algorithm that uses residual signals to predict the candidate gain-vectors of the adaptive codebook. For the fixed codebook, we propose a fast search algorithm using an energy function to predict the candidate pulses, and we redesign the codebook structure to twin multi-track positions architecture. Overall simulation results indicate that the average perceptual evaluation of speech quality (PESQ) score is degraded slightly, by 0.049, and our proposed methods can reduce total computational complexity by about 67% relative to the original G.723.1 encoder computation load, and with perceptually negligible degradation. Objective and subjective evaluations verify that the more efficient candidate schemes we propose can provide speech quality comparable to that using the original coder approach.

A kernel based asymmetric learning method is developed for software defect prediction. This method improves the performance of the predictor on class imbalanced data, since it is based on kernel principal component analysis. An experiment validates its effectiveness.

We propose a novel metamaterial antenna that is based on loading a single complementary split ring resonator (CSRR) onto a substrate integrated waveguide (SIW) structure. Negative order and zeroth-order resonance can be observed in the proposed structure. These resonance modes are used to reduce the antenna size. In addition, a high quality (Q) factor of the CSRR-loaded SIW structure can minimize the radiation loss. The -1st, 0th, and 1st resonances are experimentally observed at 6.63, 13.68, and 20.31 GHz with maximum gains of 1.59, 3.97, 6.83 dBi, respectively. The electrical size of the antenna at the -1st resonance is only 42% of the resonance of a square microstrip patch antenna.

Tomo-SAR imaging with sparse baselines can be formulated as a sparse signal recovery problem, which suggests the use of the Compressive Sensing (CS) method. In this paper, a novel Tomo-SAR imaging approach based on Sparse Bayesian Learning (SBL) is presented to obtain super-resolution in elevation direction and is validated by simulation results.

Network structure has a great impact both on hazard spread and network immunization. The vulnerability of the network node is associated with each other, assortative or disassortative. Firstly, an algorithm for vulnerability relevance clustering is proposed to show that the vulnerability community phenomenon is obviously existent in complex networks. On this basis, next, a new indicator called network “hyper-betweenness” is given for evaluating the vulnerability of network node. Network hyper-betweenness can reflect the importance of network node in hazard spread better. Finally, the dynamic stochastic process of hazard spread is simulated based on Monte-Carlo sampling method and a two-player, non-cooperative, constant-sum game model is designed to obtain an equilibrated network immunization strategy.

This paper proposes a combinatorial auction-based marketplace mechanism for cloud computing services, which allows users to reserve arbitrary combination of services at requested timeslots, prices and quality of service. The proposed mechanism helps enterprise users build workflow applications in a cloud computing environment, specifically on the platform-as-a-service, where the users need to compose multiple types of services at different timeslots. The proposed marketplace mechanism consists of a forward market for an advance reservation and a spot market for immediate allocation of services. Each market employs mixed integer programming to enforce a Pareto optimum allocation with maximized social economic welfare, as well as double-sided auction design to encourage both users and providers to compete for buying and selling the services. The evaluation results show that (1) the proposed forward/combinatorial mechanism outperforms other non-combinatorial and/or non-reservation (spot) mechanisms in both user-centric rationality and global efficiency, and (2) running both a forward market and a spot market improves utilization without disturbing advance reservations depending on the provider's policy.

This paper presents a new non-interactive string-commitment scheme that achieves universally composable security. Security is proven under the decisional composite residuosity (DCR) assumption (or the decisional Diffie-Hellman (DDH) assumption) in the common reference string (CRS) model. The universal composability (UC) is a very strong security notion. If cryptographic protocols are proven secure in the UC framework, then they remain secure even if they are composed with arbitrary protocols and polynomially many copies of the protocols are run concurrently. Many UC commitment schemes in the CRS model have been proposed, but they are either interactive commitment or bit-commitment (not string-commitment) schemes. We note, however, that although our scheme is the first non-interactive UC string-commitment scheme, a CRS is not reusable. We use an extension of all-but-one trapdoor functions (ABO-TDFs) proposed by Peikert and Waters at STOC 2008 as an essential building block. Our main idea is to extend (original deterministic) ABO-TDFs to probabilistic ones by using the homomorphic properties of their function indices. The function indices of ABO-TDFs consist of ciphertexts of homomorphic encryption schemes (such as ElGamal, and Damgåd-Jurik encryption). Therefore we can re-randomize the output of ABO-TDFs by re-randomization of ciphertexts. This is a new application of ABO-TDFs.