Spatial stochastic models have been much used for performance analysis of wireless communication networks. This is due to the fact that the performance of wireless networks depends on the spatial configuration of wireless nodes and the irregularity of node locations in a real wireless network can be captured by a spatial point process. Most works on such spatial stochastic models of wireless networks have adopted homogeneous Poisson point processes as the models of wireless node locations. While this adoption makes the models analytically tractable, it assumes that the wireless nodes are located independently of each other and their spatial correlation is ignored. Recently, the authors have proposed to adopt the Ginibre point process — one of the determinantal point processes — as the deployment models of base stations (BSs) in cellular networks. The determinantal point processes constitute a class of repulsive point processes and have been attracting attention due to their mathematically interesting properties and efficient simulation methods. In this tutorial, we provide a brief guide to the Ginibre point process and its variant, α-Ginibre point process, as the models of BS deployments in cellular networks and show some existing results on the performance analysis of cellular network models with α-Ginibre deployed BSs. The authors hope the readers to use such point processes as a tool for analyzing various problems arising in future cellular networks.

Kiasu-BC is a recently proposed tweakable variant of the AES-128 block cipher. The designers of Kiasu-BC claim that no more than 7-round Meet-in-the-Middle (MitM) attack can be launched against it. In this letter, we present a MitM attack, utilizing the differential enumeration technique, on the 8-round reduced cipher. The attack has time complexity of 2116 encryptions, memory complexity of 286 128-bit blocks, and data complexity of 2116 plaintext-tweak combinations.

The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter has emerged as a promising tool for tracking a time-varying number of targets. However, the standard CBMeMBer filter may perform poorly when measurements are coupled with sensor biases. This paper extends the CBMeMBer filter for simultaneous target tracking and sensor biases estimation by introducing the sensor translational biases into the multi-Bernoulli distribution. In the extended CBMeMBer filter, the biases are modeled as the first order Gauss-Markov process and assumed to be uncorrelated with target states. Furthermore, the sequential Monte Carlo (SMC) method is adopted to handle the non-linearity and the non-Gaussian conditions. Simulations are carried out to examine the performance of the proposed filter.

In this paper, we first analyze the discriminative power in the Best Match (BM) 25 formula and provide its calculation method from the Bayesian point of view. The resulting, derived discriminative power is quite similar to the exponential inverse document frequency (EIDF) that we have previously proposed [1] but retains more preferable theoretical advantages. In our previous paper [1], we proposed the EIDF in the framework of the probabilistic information retrieval (IR) method BM25 to address the instance search task, which is a specific object search for videos using an image query. Although the effectiveness of our EIDF was experimentally demonstrated, we did not consider its theoretical justification and interpretation. We also did not describe the use of region-of-interest (ROI) information, which is supposed to be input to the instance search system together with the original image query showing the instance. Therefore, here, we justify the EIDF by calculating the discriminative power in the BM25 from the Bayesian viewpoint. We also investigate the effect of the ROI information for improving the instance search accuracy and propose two search methods incorporating the ROI effect into the BM25 video ranking function. We validated the proposed methods through a series of experiments using the TREC Video Retrieval Evaluation instance search task dataset.

In this paper, we consider a two-hop relay network with a decode-and-forward (DF) relaying protocol where a multi-input/multi-output (MIMO) relay station (RS) is deployed in a cell edge to extend cell coverage of a base station (BS). We propose two MIMO relaying schemes to improve the quality of the BS-RS link, which is a key to improve data rates in the DF relaying: 1) spatial multiplexed MIMO antenna relaying (SM-MAR) with a uniform channel decomposition (UCD) precoder, and 2) MIMO relaying with section diversity (SD-MAR). In the SM-MAR, we greatly simplify user allocation by the UCD precoder and propose a sophisticated rate maximization technique to resolve the non-convexity of rate maximization problems. Through simulations, we show that the proposed UCD based power allocation exhibits up to two times higher achievable throughput than other techniques. In addition, the proposed SD-MAR supports the BS with a single transmit antenna and increases the signal quality of the BS-RS link with the selection diversity at the RS, which is much simpler to be implemented. For the SD-MAR, we derive a closed form expression for the achievable throughput and show that the selection diversity plays more important role on the achievable throughput than the multiuser diversity.

In a scenario-based software development, a lot of scenarios should be described in order to clarify the whole behaviors of the target software. By reusing scenarios of similar software systems, it becomes more efficient to newly describe scenarios of the target software. A differential scenario includes the difference between sequences of events of the two scenarios and the difference between nouns in the scenarios. If the nouns of the two scenarios are commonly used in the two scenarios, we regard the two scenarios specify the same or similar system. If the sequences of the events of the two scenarios are corresponding each other, we regard behavior of the two scenarios are similar. In this paper, we derive differential information including different words and events from two scenarios. Then, we propose a method of scenario retrieval using differential information between two scenarios. This method enables to detect similar scenarios for a given scenario. The proposed retrieval method and a prototype system for creating and visualizing differential scenario will be illustrated with examples.

This paper proposes a blind, inaudible, robust digital-audio watermarking scheme based on singular-spectrum analysis, which relates to watermarking techniques based on singular value decomposition. We decompose a host signal into its oscillatory components and modify amplitudes of some of those components with respect to a watermark bit and embedding rule. To improve the sound quality of a watermarked signal and still maintain robustness, differential evolution is introduced to find optimal parameters of the proposed scheme. Test results show that, although a trade-off between inaudibility and robustness still persists, the difference in sound quality between the original and the watermarked one is considerably smaller. This improved scheme is robust against many attacks, such as MP3 and MP4 compression, and band-pass filtering. However, there is a drawback, i.e., some music-dependent parameters need to be shared between embedding and extraction processes. To overcome this drawback, we propose a method for automatic parameter estimation. By incorporating the estimation method into the framework, those parameters need not to be shared, and the test results show that it can blindly decode watermark bits with an accuracy of 99.99%. This paper not only proposes a new technique and scheme but also discusses the singular value and its physical interpretation.

The purpose of this paper is to show that a new type of information-theoretic learning method called “potential learning” can be used to detect and extract important tweets among a great number of redundant ones. In the experiment, we used a dataset of 10,000 tweets, among which there existed only a few important ones. The experimental results showed that the new method improved overall classification accuracy by correctly identifying the important tweets.

Side channel attacks (SCAs) on security devices have become a major concern for system security. Existing SCA countermeasures are costly in terms of area and power consumption. This paper presents a novel differential power analysis (DPA) countermeasure referred to as short-time three-phase single-rail precharge logic (STSPL). The proposed logic is based on a single-rail three-phase operation scheme providing effective DPA-resistance with low cost. In the scheme, a controller is inserted to discharge logic gates by reusing evaluation paths to achieve more balanced power consumption. This reduces the latency between different phases, increasing the difficult of the adversary to conduct DPA, compared with the state-of-the-art DPA-resistance logics. To verify the chip's power consumption in practice, a 4-bit ripple carry adder and a 4-bit inverter of AES-SBOX were implemented. The testing and simulation results of DPA attacks prove the security and efficiency of the proposed logic.

In this paper, we study the problem of a Boolean function can be represented as the sum of two bent functions. This problem was recently presented by N. Tokareva when studying the number of bent functions [27]. Firstly, several classes of functions, such as quadratic Boolean functions, Maiorana-MacFarland bent functions, many partial spread functions etc, are proved to be able to be represented as the sum of two bent functions. Secondly, methods to construct such functions from low dimension ones are also introduced. N. Tokareva's main hypothesis is proved for n≤6. Moreover, two hypotheses which are equivalent to N. Tokareva's main hypothesis are presented. These hypotheses may lead to new ideas or methods to solve this problem. Finally, necessary and sufficient conditions on the problem when the sum of several bent functions is again a bent function are given.

To provide basic considerations for the realization of method for suppressing the EMI from differential-paired lines on flexible printed circuits (FPC), the characteristics of the SI performance and shielding effectiveness (SE) of shielded-flexible printed circuits for differential-signaling are investigated in this paper experimentally and by a numerical modeling. Firstly, transmission characteristics of TDR measurement and frequency response of |Sdd21| are discussed, from view point of signal integrity. Secondly, as the characteristics of the SE performance for EMI, frequency responses of magnetic field are investigated. Although placement of conductive shield near the paired-lines decreases characteristics impedance, |Sdd21| for the “with Cu 5.5 µm-thickness copper shield” is not deteriorated compared with “without shield” and sufficient SE performance for magnetic field can be established. But, thin-shield deteriorates SI as well as SE performances. The frequency response of |Sdd21| at higher frequencies for the “Ag 0.1 µm” case has the steep loss roll off. A reflection loss resulted from impedance-mismatching is not dominant factor of the losses. The dominant factor may be magnetic field leakage due to very thin-conductive shield.

In interactive audio services, users can render audio objects rather freely to match their desires and the spatial audio object coding (SAOC) scheme is fairly good both in the sense of bitrate and audio quality. But rather perceptible audio quality degradation can occur when an object is suppressed or played alone. To complement this, the SAOC scheme with Two-Step Coding (SAOC-TSC) was proposed. But the bitrate of the side information increases two times compared to that of the original SAOC due to the bitrate needed for the residual coding used to enhance the audio quality. In this paper, an efficient residual coding method of the SAOC-TSC is proposed to reduce the side information bitrate without audio quality degradation or complexity increase.

In lighting control systems, accurate data of artificial light (lighting coefficients) are essential for the illumination control accuracy and energy saving efficiency. This research proposes a novel Lambertian-Radial Basis Function Neural Network (L-RBFNN) to realize modeling of both lighting coefficients and the illumination environment for an office. By adding a Lambertian neuron to represent the rough theoretical illuminance distribution of the lamp and modifying RBF neurons to regulate the distribution shape, L-RBFNN successfully solves the instability problem of conventional RBFNN and achieves higher modeling accuracy. Simulations of both single-light modeling and multiple-light modeling are made and compared with other methods such as Lambertian function, cubic spline interpolation and conventional RBFNN. The results prove that: 1) L-RBFNN is a successful modeling method for artificial light with imperceptible modeling error; 2) Compared with other existing methods, L-RBFNN can provide better performance with lower modeling error; 3) The number of training sensors can be reduced to be the same with the number of lamps, thus making the modeling method easier to apply in real-world lighting systems.

Synthetic aperture radar (SAR) imagery is significantly deteriorated by the random phase noises which are generated by the frequency jitter of the transmit signal and atmospheric turbulence. In this paper, we recast the SAR imaging problem via the phase-corrupted data as for a special case of quadratic compressed sensing (QCS). Although the quadratic measurement model has potential to mitigate the effects of the phase noises, it also leads to a nonconvex and quartic optimization problem. In order to overcome these challenges and increase reconstruction robustness to the phase noises, we proposed a QCS-based SAR imaging algorithm by greedy local search to exploit the spatial sparsity of scatterers. Our proposed imaging algorithm can not only avoid the process of precise random phase noise estimation but also acquire a sparse representation of the SAR target with high accuracy from the phase-corrupted data. Experiments are conducted by the synthetic scene and the moving and stationary target recognition Sandia laboratories implementation of cylinders (MSTAR SLICY) target. Simulation results are provided to demonstrate the effectiveness and robustness of our proposed SAR imaging algorithm.

A method for increasing alignment tolerance in simple multiple-stream transmission is described. Its use of π-shifted antenna directivity phase enables it to cancel interference even when antenna placement deviations occur. The interference cancellation by using π-shifted directivities provides higher alignment tolerance than that with conventional fixed weight methods. It also provides smaller channel gain variation than can be obtained using fixed weights even when antenna displacement occurs. An objective function is described that is determined by the alignment tolerance. The function is defined to maximize the alignment tolerance. The method's validity is confirmed by an experimental analysis of two-stream transmission in which the alignment tolerance of the proposed method is compared to that of conventional fixed weight methods.

In this paper a new method for noise removal from single-trial event-related potentials recorded with a multi-channel electroencephalogram is addressed. An observed signal is separated into multiple signals with a multi-channel Wiener filter whose coefficients are estimated based on parameter estimation of a probabilistic generative model that locally models the amplitude of each separated signal in the time-frequency domain. Effectiveness of using prior information about covariance matrices to estimate model parameters and frequency dependent covariance matrices were shown through an experiment with a simulated event-related potential data set.

Given a set of positive-weighted points and a query rectangle r (specified by a client) of given extents, the goal of a maximizing range sum (MaxRS) query is to find the optimal location of r such that the total weights of all points covered by r is maximized. In this paper, we address the problem of processing MaxRS queries over road network databases and propose two new external memory methods. Through a set of simulations, we evaluate the performance of the proposed methods.

Despite logic families based on floating-gate MOS (FGMOS) transistors achieve significant reductions in terms of power and transistor count, these logics have had little impact on VLSI design due to their sensitivity to noise. In order to attain robustness to this phenomenon, Positive-Feedback Floating-Gate logic (PFFGL) uses a differential architecture and positive feedback; data obtained from a 0.5µm ON Semiconductors test chip and from SPICE simulations shows PFFGL to be immune to noise from parasitic couplings as well as to leakage even when minimum device size is used.

Electroencephalography (EEG) and magnetoencephalography (MEG) measure the brain signal from spatially-distributed electrodes. In order to detect event-related synchronization and desynchronization (ERS/ERD), which are utilized for brain-computer/machine interfaces (BCI/BMI), spatial filtering techniques are often used. Common spatial potential (CSP) filtering and its extensions which are the spatial filtering methods have been widely used for BCIs. CSP transforms brain signals that have a spatial and temporal index into vectors via a covariance representation. However, the variance-covariance structure is essentially different from the vector space, and not all the information can be transformed into an element of the vector structure. Grassmannian embedding methods, therefore, have been proposed to utilize the variance-covariance structure of variational patterns. In this paper, we propose a metric learning method to classify the brain signal utilizing the covariance structure. We embed the brain signal in the extended Grassmann manifold, and classify it on the manifold using the proposed metric. Due to this embedding, the pattern structure is fully utilized for the classification. We conducted an experiment using an open benchmark dataset and found that the proposed method exhibited a better performance than CSP and its extensions.

Recently, a high dimensional classification framework has been proposed to introduce spatial and anatomical priors in classical single kernel support vector machine optimization scheme, wherein the sequential minimal optimization (SMO) training algorithm is adopted, for brain image analysis. However, to satisfy the optimization conditions required in the single kernel case, it is unreasonably assumed that the spatial regularization parameter is equal to the anatomical one. In this letter, this approach is improved by combining SMO algorithm with multiple kernel learning to avoid that assumption and optimally estimate two parameters. The improvement is comparably demonstrated by experimental results on classification of Alzheimer patients and elderly controls.