We present a round addition differential fault analysis (DFA) for some lightweight 80-bit block ciphers. It is shown that only one correct ciphertext and two faulty ciphertexts are required to reconstruct secret keys in 80-bit Piccolo and TWINE, and the reconstructions are easier than 128-bit CLEFIA.

In this paper, for an odd prime p such that p≡3 mod 4, odd n, and d=(pn+1)/(pk+1)+(pn-1)/2 with k|n, the value distribution of the exponential sum S(a,b) is calculated as a and b run through $mathbb{F}_{p^n}$. The sequence family $mathcal{G}$ in which each sequence has the period of N=pn-1 is also constructed. The family size of $mathcal{G}$ is pn and the correlation magnitude is roughly upper bounded by $(p^k+1)sqrt{N}/2$. The weight distribution of the relevant cyclic code C over $mathbb{F}_p$ with the length N and the dimension ${ m dim}_{mathbb{F}_p}mathcal{C}=2n$ is also derived.

The conventional hybrid STBC schemes can achieve less BER performance for STBC detection schemes than conventional STBC schemes since SM symbols interfere with STBC symbols. Therefore, this letter proposes the improved scheme for hybrid STBC systems. STBC and SM schemes are combined for the hybrid space-time block code system. Our approach effectively obtains both diversity gain and spectral efficiency gain. The proposed scheme offers improved BER performance since it uses iterative detection. Moreover, it increases the data rate effectively with a little performance loss.

A method to estimate sound source orientation in a reverberant room using a microphone array is proposed. We extend the conventional modeling of a room transfer function based on the image method in order to take into account the directivity of a sound source. With this extension, a transfer function between a sound source and a listener (or a microphone) is described by the superposition of transfer functions from each image source to the listener multiplied by the source directivity; thus, the sound source orientation can be estimated by analyzing how the image sources are distributed (power distribution of image sources) from observed signals. We applied eigenvalue analysis to the spatial correlation matrix of the microphone array observation to obtain the power distribution of image sources. Bsed on the assumption that the spatial correlation matrix for each set of source position and orientation is known a priori, the variation of the eigenspace can be modeled. By comparing the eigenspace of observed signals and that of pre-learned models, we estimated the sound source orientation. Through experiments using seven microphones, the sound source orientation was estimated with high accuracy by increasing the reverberation time of a room.

Information security has been seriously threatened by the differential power analysis (DPA). Delay-based dual-rail precharge logic (DDPL) is an effective solution to resist these attacks. However, conventional DDPL convertors have some shortcomings. In this paper, we propose improved convertor pairs based on dynamic logic and a sense amplifier (SA). Compared with the reference CMOS-to-DDPL convertor, our scheme could save 69% power consumption. As to the comparison of DDPL-to-CMOS convertor, the speed and power performances could be improved by 39% and 54%, respectively.

In this paper, we propose a hybrid fuzzy geometric active contour method, which embeds the spatial fuzzy clustering into the evolution of geometric active contour. In every iteration, the evolving curve works as a spatial constraint on the fuzzy clustering, and the clustering result is utilized to construct the fuzzy region force. On one hand, the fuzzy region force provides a powerful capability to avoid the leakages at weak boundaries and enhances the robustness to various noises. On the other hand, the local information obtained from the gradient feature map contributes to locating the object boundaries accurately and improves the performance on the images with heterogeneous foreground or background. Experimental results on synthetic and real images have shown that our model can precisely extract object boundaries and perform better than the existing representative hybrid active contour approaches.

In this paper, a stochastic asymptotic stabilization method is proposed for deterministic input-affine control systems, which are randomized by including Gaussian white noises in control inputs. The sufficient condition is derived for the diffusion coefficients so that there exist stochastic control Lyapunov functions for the systems. To illustrate the usefulness of the sufficient condition, the authors propose the stochastic continuous feedback law, which makes the origin of the Brockett integrator become globally asymptotically stable in probability.

A method of calculating the top event probability of a fault tree, where dynamic gates and repeated events are included and the occurrences of basic events follow nonexponential distributions, is proposed. The method is on the basis of the Bayesian network formulation for a DFT proposed by Yuge and Yanagi [1]. The formulation had a difficulty in calculating a sequence probability if components have nonexponential failure distributions. We propose an alternative method to obtain the sequence probability in this paper. First, a method in the case of the Erlang distribution is discussed. Then, Tijms's fitting procedure is applied to deal with a general distribution. The procedure gives a mixture of two Erlang distributions as an approximate distribution for a general distribution given the mean and standard deviation. A numerical example shows that our method works well for complex systems.

Clustering methods are divided into hierarchical clustering, partitioning clustering, and more. K-Means is a method of partitioning clustering. We improve the performance of a K-Means, selecting the initial centers of a cluster through a calculation rather than using random selecting. This method maximizes the distance among the initial centers of clusters. Subsequently, the centers are distributed evenly and the results are more accurate than for initial cluster centers selected at random. This is time-consuming, but it can reduce the total clustering time by minimizing allocation and recalculation. Compared with the standard algorithm, F-Measure is more accurate by 5.1%.

The mining problem over data streams has recently been attracting considerable attention thanks to the usefulness of data mining in various application fields of information science, and sequence data streams are so common in daily life. Therefore, a study on mining sequential patterns over sequence data streams can give valuable results for wide use in various application fields. This paper proposes a new framework for mining novel interesting sequential patterns over a sequence data stream and a mining method based on the framework. Assuming that a sequence with small time-intervals between its data elements is more valuable than others with large time-intervals, the novel interesting sequential pattern is defined and found by analyzing the time-intervals of data elements in a sequence as well as their orders. The proposed framework is capable of obtaining more interesting sequential patterns over sequence data streams whose data elements are highly correlated in terms of generation time.

This paper investigates the transmission capacity of open-loop spatial multiplexing with zero-forcing receivers in overlaid ad hoc networks. We first derive asymptotic closed-form expressions for the transmission capacity of two coexisting networks (a primary network vs. a secondary network). We then address a special case with equal numbers of transmit and receive antennas through exact analysis. Numerical results validate the accuracy of our expressions. Our findings show that the overall transmission capacity of coexisting networks will improve significantly over that of a single network if the primary network can tolerate a slight outage probability increase. This improvement can be further boosted if more streams are configured in the spatial multiplexing scheme; less improvement is achieved by placing more antennas at the receive side than the transmit side. However, when the stream number exceeds a certain limit, spatial multiplexing will produce negative effect for the overlaid network.

A new path loss model of interference between mobile terminals in a residential area is proposed. The model uses invertible formulas and considers the effects on path loss characteristics produced by paths having many corners or corners with various angles. Angular profile and height pattern measurements clarify three paths that are dominant in terms of their effect on the accurate modeling of path loss characteristics in residential areas: paths along a road, paths between houses, and over-roof propagation paths. Measurements taken in a residential area to verify the model's validity show that the model is able to predict path loss with greater accuracy than conventional models.

In the sensor networks for surveillance, the requirements of providing energy efficiency and service differentiation, which is to deliver high-priority packets preferentially, while maintaining high goodput, which is to deliver many packets within their deadline are increasing. However, previous works have difficulties in satisfying the requirements simultaneously. Thus, we propose GES-MAC, which satisfies the requirements simultaneously. GES-MAC reduces idle listening energy consumption by using a duty cycle, periodic listen (i.e., turn on radio module) and sleep (i.e. turn off radio module) of sensor nodes. Cluster-based multi-hop scheduling provides high goodput in a duty-cycled environment by scheduling clusters of nodes in the listen period and opportunistically forwarding data packets in the sleep period. Priority-aware schedule switching makes more high-priority packets reach the sink node by letting high-priority packets preempt the schedules of low-priority packets. In experiments with MICA2 based sensor nodes and in simulations, the energy consumption of the radio module is reduced by 70% compared to the approaches without a duty cycle, while providing 80% 100% goodput of the approaches that provide high goodput. Service differentiation is also supported with little overhead.

This paper provides a novel normalized sign least-mean square (NSLMS) algorithm which updates only a part of the filter coefficients and simultaneously performs sparse updates with the goal of reducing computational complexity. A combination of the partial-update scheme and the set-membership framework is incorporated into the context of L∞-norm adaptive filtering, thus yielding computational efficiency. For the stabilized convergence, we formulate a robust update recursion by imposing an upper bound of a step size. Furthermore, we analyzed a mean-square stability of the proposed algorithm for white input signals. Experimental results show that the proposed low-complexity NSLMS algorithm has similar convergence performance with greatly reduced computational complexity compared to the partial-update NSLMS, and is comparable to the set-membership partial-update NLMS.

This paper presents a method of designing transmission line couplers (TLCs) and a mixer-based receiver for dicode partial response communications. The channel design method results in the optimum TLC design. The receiver with mixers and DC balancing circuits reduces the threshold control circuits and digital circuits to decode dicode partial response signals. Our techniques enable low inter-symbol interference (ISI) dicode partial response communications without three level decision circuits and complex threshold control circuits. The techniques were evaluated in a simulation with an EM solver and a transistor level simulation. The circuit was designed in the 90-nm CMOS process. The simulation results show 12-Gb/s operation and 52mW power consumption at 1.2V.

The average Hamming correlation is an important performance indicator of frequency-hopping sequences (FHSs). In this letter, the average partial Hamming correlation (APHC) properties of FHSs are discussed. Firstly, the theoretical bound on the average partial Hamming correlation of FHSs is established. It works for any correlation window with length 1≤ω≤υ, where υ is the sequence period, and generalizes the bound developed by Peng et al which is valid only when ω=υ. A sufficient and necessary condition for a set of FHSs having optimal APHC for any correlation window is then given. Finally, sets of FHSs with optimal APHC are presented.

In forthcoming sensor networks, a multitude of sensor nodes deployed over a large geographical area for monitoring traffic, climate, etc. are expected to become an inevitable infrastructure. Clustering algorithms play an important role in aggregating a large volume of data that are produced continuously by the huge number of sensor nodes. In such networks, equal-sized multi-hop clusters which include an equal number of nodes are useful for efficiency and resiliency. In addition, scalability is important in such large-scale networks. In this paper, we mathematically design a decentralized equal-sized clustering algorithm using a partial differential equation based on the Fourier transform technique, and then design its protocol by discretizing the equation. We evaluated through simulations the equality of cluster sizes and the resiliency against packet loss and node failure in two-dimensional perturbed grid topologies.

We propose a retrieval method using scale invariant visual phrases (SIVPs). Our method encodes spatial information into the SIVPs which capture translation, rotation and scale invariance, and employs the SIVPs to determine the spatial correspondences between query image and database image. To compute the spatial correspondences efficiently, the SIVPs are introduced into the inverted index, and SIVP verification is investigated to refine the candidate images returned from inverted index. Experimental results demonstrate that our method improves the retrieval accuracy while increasing the retrieval efficiency.

This work proposes an exponential computation with low-computational complexity and applies this technique to the expectation-maximization (EM) algorithm for Gaussian mixture model (GMM). For certain machine-learning techniques, such as the EM algorithm for the GMM, fast and low-cost implementations are preferred over high precision ones. Since the exponential function is frequently used in machine-learning algorithms, this work proposes reducing computational complexity by transforming the function into powers of two and introducing a look-up table. Moreover, to improve efficiency the look-up table is scaled. To verify the validity of the proposed technique, this work obtains simulation results for the EM algorithm used for parameter estimation and evaluates the performances of the results in terms of the mean absolute error and computational time. This work compares our proposed method against the Taylor expansion and the exp( ) function in a standard C library, and shows that the computational time of the EM algorithm is reduced while maintaining comparable precision in the estimation results.

Design of acoustic modem becomes increasingly important in underwater sensor networks' development. This paper presents the design of a reconfigurable acoustic modem, by defining modulation and demodulation as reconfigurable modules, the proposed modem changes its modulation scheme and data rate to provide reliable and energy efficient communication. The digital system, responsible for signal processing and control, is implemented on Xilinx Virtex5 FPGA. Hardware and software co-verification shows that the modem works correctly and can self-configure to BFSK and BPSK mode. Partial reconfiguration design method improves flexibility of algorithm design, and slice, LUT, register, DSP, RAMB are saved by 17%, 25%, 22%, 25%, 25% respectively.