Robustness is one of the significant properties in wireless sensor networks because sensor nodes and wireless links are subjected to frequent failures. Once these failures occur, system performance falls into critical condition due to increases in traffic and losses of connectivity and reachability. Most of the existing studies on sensor networks, however, do not conduct quantitative evaluation on robustness and do not discuss what brings in robustness. In this paper, we define and evaluate robustness of wireless sensor networks and show how to improve them. By computer simulation, we show that receiver-initiated MAC protocols are more robust than sender-initiated ones and a simple detour-routing algorithm has more than tripled robustness than the simple minimum-hop routing algorithm.

We consider wireless secure communications between a source and a destination aided by a multi-antenna relay, in the presence of an eavesdropper. In particular, two cooperation schemes of the relay are explored: cooperative relaying (CR) and cooperative jamming (CJ). We first investigate the transmit weight optimization of CR and CJ, for both cases with and without the eavesdropper's channel state information (ECSI). Then, for the case with ECSI, we derive the conditions under which CR achieves a higher secrecy rate than CJ; for the case without ECSI, we compare the secrecy rates of CR and CJ in high transmit power regimes. Building on this, we propose a novel hybrid scheme in which the relay utilizes both CR and CJ, and study the power allocation of the relay between CR and CJ for maximizing the secrecy rate under individual power constraints. Further, we study the case with imperfect channel state information (CSI) for both CR and CJ. At last, extensive numerical results are provided.

Network-on-chip (NoC) architectures have emerged as a promising solution to the lack of scalability in multi-processor systems-on-chips (MPSoCs). With the explosive growth in the usage of multimedia applications, it is expected that NoC serves as a multimedia server supporting multi-class services. In this paper, we propose a configuration algorithm for a hybrid bus-NoC architecture together with simulation results. Our target architecture is a hybrid bus-NoC architecture, called busmesh NoC, which is a generalized version of a hybrid NoC with local buses. In our BMNoC configuration algorithm, cores which have a heavy communication volume between them are mapped in a cluster node (CN) and connected by a local bus. CNs can have communication with each other via edge switches (ESes) and mesh routers (MRs). With this hierarchical communication network, our proposed algorithm can improve the latency as compared with conventional methods. Several realistic applications applied to our algorithm illustrate the better performance than earlier studies and feasibility of our proposed algorithm.

Mel Frequency Cepstral Coefficients (MFCC) are the most popular acoustic features used in automatic speech recognition (ASR), mainly because the coefficients capture the most useful information of the speech and fit well with the assumptions used in hidden Markov models. As is well known, MFCCs already employ several principles which have known counterparts in the peripheral properties of human hearing: decoupling across frequency, mel-warping of the frequency axis, log-compression of energy, etc. It is natural to introduce more mechanisms in the auditory periphery to improve the noise robustness of MFCC. In this paper, a k-nearest neighbors based frequency masking filter is proposed to reduce the audibility of spectra valleys which are sensitive to noise. Besides, Moore and Glasberg's critical band equivalent rectangular bandwidth (ERB) expression is utilized to determine the filter bandwidth. Furthermore, a new bandpass infinite impulse response (IIR) filter is proposed to imitate the temporal masking phenomenon of the human auditory system. These three auditory perceptual mechanisms are combined with the standard MFCC algorithm in order to investigate their effects on ASR performance, and a revised MFCC extraction scheme is presented. Recognition performances with the standard MFCC, RASTA perceptual linear prediction (RASTA-PLP) and the proposed feature extraction scheme are evaluated on a medium-vocabulary isolated-word recognition task and a more complex large vocabulary continuous speech recognition (LVCSR) task. Experimental results show that consistent robustness against background noise is achieved on these two tasks, and the proposed method outperforms both the standard MFCC and RASTA-PLP.

Conventional features for Automatic Speech Recognition and Sound Event Recognition such as Mel-Frequency Cepstral Coefficients (MFCCs) have been shown to perform poorly in noisy conditions. We introduce an auditory feature based on the gammatone filterbank, the Selective Gammatone Envelope Feature (SGEF), for Robust Sound Event Recognition where channel selection and the filterbank envelope is used to reduce the effect of noise for specific noise environments. In the experiments with Hidden Markov Model (HMM) recognizers, we shall show that our feature outperforms MFCCs significantly in four different noisy environments at various signal-to-noise ratios.

It has been an important issue to deal with risks in business processes for achieving companies' goals. This paper introduces a method for applying a formal method to analysis of risks and control activities in business processes in order to evaluate control activities consistently, exhaustively, and to give us potential to have scientific discussion on the result of the evaluation. We focus on document flows in business activities and control activities and risks related to documents because documents play important roles in business. In our method, document flows including control activities are modeled and it is verified by OTS/CafeOBJ Method that risks about falsification of documents are avoided by control activities in the model. The verification is done by interaction between humans and CafeOBJ system with theorem proving, and it raises potential to discuss the result scientifically because the interaction gives us rigorous reasons why the result is derived from the verification.

A new approach to speech feature estimation under noise circumstances is proposed in this paper. It is used in noise-robust continuous speech recognition (CSR). As the noise robust techniques in isolated word speech recognition, the running spectrum analysis (RSA), the running spectrum filtering (RSF) and the dynamic range adjustment (DRA) methods have been developed. Among them, only RSA has been applied to a CSR system. This paper proposes an extended DRA for a noise-robust CSR system. In the stage of speech recognition, a continuous speech waveform is automatically assigned to a block defined by a short time length. The extended DRA is applied to these estimated blocks. The average recognition rate of the proposed method has been improved under several different noise conditions. As a result, the recognition rates are improved up to 15% in various noises with 10 dB SNR.

Many researchers have proposed ultrasound imaging techniques for product inspection; however, most of these techniques are aimed at detecting the existence of flaws in products. The acquisition of an accurate three-dimensional image using ultrasound has the potential to be a useful product inspection tool. In this paper we apply the Envelope algorithm, which was originally proposed for accurate UWB (Ultra Wide-Band) radar imaging systems, to ultrasound imaging. We show that the Envelope algorithm results in image deterioration, because it is difficult for ultrasound measurements to achieve high signal to noise (S/N) ratio values as a result of a high level of noise and interference from the environment. To reduce errors, we propose two adaptive smoothing techniques that effectively stabilize the estimated image produced by the Envelope algorithm. An experimental study verifies that the proposed imaging algorithm has accurate 3-D imaging capability with a mean error of 6.1 µm, where the transmit center frequency is 2.0 MHz and the S/N ratio is 23 dB. These results demonstrate the robustness of the proposed imaging algorithm compared with a conventional Envelope algorithm.

Telecommunications networks have become an important social infrastructure, and their robustness is considered to be a matter of social significance. Conventional network planning methods are generally based on the maximum volume of ordinary traffic and only assume explicitly specified failure scenarios. Therefore, present networks have marginal survivability against multiple failures induced by an extraordinarily high volume of traffic generated during times of natural disasters or popular social events. This paper proposes a telecommunications network planning method based on probabilistic risk assessment. In this method, risk criterion reflecting the degree of risk due to extraordinarily large traffic loads is predefined and estimated using probabilistic risk assessment. The probabilistic risk assessment can efficiently calculate the small but non-negligible probability that a series of multiple failures will occur in the considered network. Detailed procedures for the proposed planning method are explained using a district mobile network in terms of the extraordinarily large traffic volume resulting from earthquakes. As an application example of the proposed method, capacity dimensioning for the local session servers within the district mobile network is executed to reduce the risk criterion most effectively. Moreover, the optimum traffic-rerouting scheme that minimizes the estimated risk criterion is ascertained simultaneously. From the application example, the proposed planning method is verified to realize a telecommunications network with sufficient robustness against the extraordinarily high volume of traffic caused by the earthquakes.

The impact degree is a measure of the robustness of a metabolic network against deletion of single or multiple reaction(s). Although such a measure is useful for mining important enzymes/genes, it was defined only for networks without cycles. In this paper, we extend the impact degree for metabolic networks containing cycles and develop a simple algorithm to calculate the impact degree. Furthermore we improve this algorithm to reduce computation time for the impact degree by deletions of multiple reactions. We applied our method to the metabolic network of E. coli, that includes reference pathways, consisting of 3281 reaction nodes and 2444 compound nodes, downloaded from KEGG database, and calculate the distribution of the impact degree. The results of our computational experiments show that the improved algorithm is 18.4 times faster than the simple algorithm for deletion of reaction-pairs and 11.4 times faster for deletion of reaction-triplets. We also enumerate genes with high impact degrees for single and multiple reaction deletions.

In multiuser cognitive radio (CR) networks, we address the problem of joint transmit beamforming (BF) and power control (PC) for secondary users (SUs) when they are allowed to transmit simultaneously with primary users (PUs). The objective is to optimize the network sum rate under the interference constraints of PUs, which is a nonconvex problem. Iterative dual subgradient (IDuSuG) algorithm is proposed by iteratively performing BF and PC to optimize the sum rate, among which minimum mean square error (MMSE) or virtual power-weighed projection (VIP2) is used to design beamformers and subgradient method is used to control the power. VIP2 algorithm is devised for the case in which the interference caused by MMSE beamformer exceeds the threshold. Moreover, channel uncertainty due to lack of cooperation is considered. A closed-form worst-case expression is derived, with which the uncertainty optimization problem is transformed into a certain one. A robust algorithm based on IDuSuG is provided by modifying updates in iterative process. Furthermore, second-order cone programming approximation (SOCPA) method is proposed as another robust algorithm. Typical network models are approximated to SOCP problems and solved by interior-point method. Finally the network sum rates for different PU and SU numbers are assessed for both certainty and uncertainty channel models by simulation.

The doubly constrained robust Capon beamformer (DCRCB), which employs a spherical uncertainty set of the steering vector together with the constant norm constraint, can provide robustness against arbitrary array imperfections. However, its performance can be greatly degraded when the uncertainty bound of the spherical set is not properly selected. In this paper, combining the DCRCB and the weight-vector-norm-constrained beamformer (WVNCB), we suggest a new robust adaptive beamforming method which allows us to overcome the performance degradation due to improper selection of the uncertainty bound. In WVNCB, its weight vector norm is limited not to be larger than a threshold. Both WVNCB and DCRCB belong to a class of diagonal loading methods. The diagonal loading range of WVNCB, which dose not consider negative loading, is extended to match that of DCRCB which can have a negative loading level as well as a positive one. In contrast to the conventional DCRCB with a fixed uncertainty bound, the bound in the proposed method varies such that the weight vector norm constraint is satisfied. Simulation results show that the proposed beamformer outperforms both DCRCB and WVNCB, being far less sensitive to the uncertainty bound than DCRCB.

Hayabusa returned to Earth on June 13, 2010, becoming the world's first explorer to complete a round-trip voyage to an asteroid. After being released from the spacecraft, the sample return capsule landed in the Woomera Prohibited Area in the desert of South Australia. The capsule recovery team from JAXA found the capsule within 1 h of its landing. The beacon tracking system that was developed by JAXA played an important role in the tracking and discovery of the sample return capsule. The system has flexibility regarding the landing position of the capsule, because it does not rely on primary radar. In this paper, we describe the beacon tracking system and evaluate the system by discussing the results of preliminary examination and of operation on the day of re-entry.

Cognitive radio is a wireless technology aimed at improving the efficient use of the radio-electric spectrum, thus facilitating a reduction in the load on the free frequency bands. Cognitive radio networks can scan the spectrum and adapt their parameters to operate in the unoccupied bands. To avoid interfering with licensed users operating on a given channel, the networks need to be highly sensitive, which is achieved by using cooperative sensing methods. Current cooperative sensing methods are not robust enough against occasional or continuous attacks. This article outlines a Group Fusion method that takes into account the behaviour of users over the short and long term. On fusing the data, the method is based on giving more weight to user groups that are more unanimous in their decisions. Simulations of a dynamic environment with interference are performed. Results prove that when attackers are present (both reiterative or sporadic), the proposed Group Fusion method has superior sensing capability than other methods.

Complex-valued Associative Memory (CAM) is an advanced model of Hopfield Associative Memory. The CAM is based on multi-state neurons and has the high ability of representation. Lee proposed gradient descent learning for the CAM to improve the storage capacity. It is based on only the phases of input signals. In this paper, we propose another type of gradient descent learning based on both the phases and the amplitude. The proposed learning method improves the noise robustness and accelerates the learning speed.

In this paper, we propose a robust state estimation method using a particle filter (PF) for a class of nonlinear systems which have stochastic parameter uncertainties. A robust PF was designed using prediction and correction structure. The proposed PF draws particles from a simple proposal density function and corrects the particles with particle-wise correction gains. We present a method to obtain an error variance of each particle and its upper bound, which is minimized to determine the correction gain. The proposed method is less restrictive on system nonlinearities and noise statistics; moreover, it can be applied regardless of system stability. The effectiveness of the proposed robust PF is illustrated via an example based on Chua's circuit.

Robust local image features have become crucial components of many state-of-the-art computer vision algorithms. Due to limited hardware resources, computing local features on embedded system is not an easy task. In this paper, we propose an efficient parallel computing framework for speeded-up robust features with an orientation towards multi-DSP based embedded system. We optimize modules in SURF to better utilize the capability of DSP chips. We also design a compact data layout to adapt to the limited memory resource and to increase data access bandwidth. A data-driven barrier and workload balance schemes are presented to synchronize parallel working chips and reduce overall cost. The experiment shows our implementation achieves competitive time efficiency compared with related works.

In nearest neighbor distance ratio (NNDR) matching the fixed distance ratio threshold sometimes results in an insufficient number of inliers or a huge number of outliers, which is not good for robust tracking. In this letter, we propose adjusting the distance ratio threshold based on maximizing the number of inliers while maintaining the ratio of the number of outliers to that of inliers. By applying the proposed method to a model-based camera tracking system, its effectiveness is verified.

This letter presents an experiment for estimating accurate state in distributed power systems. This letter employs a technique that combines a projected Jacobi method with a parallel dual-type method to solve the distributed state estimation with constraints problems. Via numerous tests, this letter demonstrates the efficiency of the proposed method on the IEEE 118-bus with four subsystems in a PC network.

This survey summarizes the state-of-the-art research on network coding, mainly focusing on its applications to computer networking. Network coding generalizes traditional store-and-forward routing techniques by allowing intermediate nodes in networks to encode several received packets into a single coded packet before forwarding. Network coding was proposed in 2000, and since then, it has been studied extensively in the field of computer networking. In this survey, we first summarize linear network coding and provide a taxonomy of network coding research, i.e., the network coding design problem and network coding applications. Moreover, the latter is subdivided into throughput/capacity enhancement, robustness enhancement, network tomography, and security. We then discuss the fundamental characteristics of network coding and diverse applications of network coding in details, following the above taxonomy.