Hough-based voting approaches have been widely used to solve many detection problems such as object and action detection. These approaches for action detection cast votes for action classes and positions based on the local spatio-temporal features of given videos. The voting process of each local feature is performed independently of the other local features. This independence enables the method to be robust to occlusions because votes based on visible local features are not influenced by occluded local features. However, such independence makes discrimination of similar motions between different classes difficult and causes the method to cast many false votes. We propose a novel Hough-based action detection method to overcome the problem of false votes. The false votes do not occur randomly such that they depend on relevant action classes. We introduce vote distributions, which represent the number of votes for each action class. We assume that the distribution of false votes include important information necessary to improving action detection. These distributions are used to build a model that represents the characteristics of Hough voting that include false votes. The method estimates the likelihood using the model and reduces the influence of false votes. In experiments, we confirmed that the proposed method reduces false positive detection and improves action detection accuracy when using the IXMAS dataset and the UT-Interaction dataset.

In general, in-focus images are used in visual object detection because image blur is considered as a factor reducing detection accuracy. However, in-focus images make it difficult to separate target objects from background images, because of that, visual object detection becomes a hard task. Background subtraction and inter-frame difference are famous schemes for separating target objects from background but they have a critical disadvantage that they cannot be used if illumination changes or the point of view moves. Considering these problems, the authors aim to improve detection accuracy by using images with out-of-focus blur obtained from a camera with a shallow depth of field. In these images, it is expected that target objects become in-focus and other regions are blurred. To enable visual object detection based on such image blur, this paper proposes a novel scheme using DFT-based feature extraction. The experimental results using synthetic images including, circle, star, and square objects as targets showed that a classifier constructed by the proposed scheme showed 2.40% miss rate at 0.1 FPPI and perfect detection has been achieved for detection of star and square objects. In addition, the proposed scheme achieved perfect detection of humans in natural images when the upper half of the human body was trained. The accuracy of the proposed scheme is better than the Filtered Channel Features, one of the state-of-the-art schemes for visual object detection. Analyzing the result, it is convincing that the proposed scheme is very feasible for visual object detection based on image blur.

Wireless channel emulators are used for the performance evaluation of wireless systems when actual wireless environment test is infeasible. The main contribution of this paper is the design of a MU-MIMO channel emulator capable of sending channel feedback automatically to the access point from the generated channel coefficients after the programmable time duration. This function is used for MU beamforming features of IEEE 802.11ac. The second contribution is the low complexity design of MIMO channel emulator with a single path implementation for all MIMO channel taps. A single path design allows all elements of the MIMO channel matrix to use only one Gaussian noise generator, Doppler filter, spatial correlation channel and Rician fading emulator to minimize the hardware complexity. In addition, single path implementation allows the addition of the feedback channel output with only a few additional non-sequential elements which would otherwise double in a parallel implementation. To demonstrate the functionality of our MU-MIMO channel emulator, we present actual hardware emulator results of MU-BF receive signal constellation on oscilloscope.

As one of the techniques enabling individual singers to produce the varieties of voice timbre beyond their own physical constraints, a statistical voice timbre control technique based on the perceived age has been developed. In this technique, the perceived age of a singing voice, which is the age of the singer as perceived by the listener, is used as one of the intuitively understandable measures to describe voice characteristics of the singing voice. The use of statistical voice conversion (SVC) with a singer-dependent multiple-regression Gaussian mixture model (MR-GMM), which effectively models the voice timbre variations caused by a change of the perceived age, makes it possible for individual singers to manipulate the perceived ages of their own singing voices while retaining their own singer identities. However, there still remain several issues; e.g., 1) a controllable range of the perceived age is limited; 2) quality of the converted singing voice is significantly degraded compared to that of a natural singing voice; and 3) each singer needs to sing the same phrase set as sung by a reference singer to develop the singer-dependent MR-GMM. To address these issues, we propose the following three methods; 1) a method using gender-dependent modeling to expand the controllable range of the perceived age; 2) a method using direct waveform modification based on spectrum differential to improve quality of the converted singing voice; and 3) a rapid unsupervised adaptation method based on maximum a posteriori (MAP) estimation to easily develop the singer-dependent MR-GMM. The experimental results show that the proposed methods achieve a wider controllable range of the perceived age, a significant quality improvement of the converted singing voice, and the development of the singer-dependnet MR-GMM using only a few arbitrary phrases as adaptation data.

A number of parallel applications run on a high-performance computing (HPC) system simultaneously. Job mapping and scheduling become crucial to improve system utilization, because fragmentation prevents an incoming job from being assigned even if there are enough compute nodes unused. Wireless supercomputers and datacenters with free-space optical (FSO) terminals have been proposed to replace the conventional wired interconnection so that a diverse application workload can be better supported by changing their network topologies. In this study we firstly present an efficient job mapping by swapping the endpoints of FSO links in a wireless HPC system. Our evaluation shows that an FSO-equipped wireless HPC system can achieve shorter average queuing length and queuing time for all the dispatched user jobs. Secondly, we consider the use of a more complicated and enhanced scheduling algorithm, which can further improve the system utilization over different host networks, as well as the average response time for all the dispatched user jobs. Finally, we present the performance advantages of the proposed wireless HPC system under more practical assumptions such as different cabinet capacities and diverse subtopology packings.

In this paper, an iterative robust minimum-mean square error (MMSE) receiver for space-time block coding (STBC) is proposed to mitigate the performance degradations caused by channel state information (CSI) errors. The proposed scheme estimates an instantaneous covariance matrix of the effective noise, which includes additive white Gaussian noise and the effect of CSI errors. For this estimation, multiple solution candidate vectors are selected based on the distances between the MMSE estimate of the solution and the constellation points, and their a-posteriori probabilities are utilized to execute the estimation of the covariance matrix. To improve the estimation accuracy, the estimated covariance matrix is updated iteratively. Simulation results show that proposed robust receiver achieves substantial performance gains in terms of bit error rates as compared to conventional receiver schemes under CSI errors.

Internal power losses in lumped-element impedance matching circuits are formulated by means of Q factors of the elements and port impedances to be matched. Assuming that Q factors are relatively high, the above mentioned loss is expressed by a simple formula containing only the tangents of the impedances. The formula is a powerful tool for such applications that put emphasis on power efficiency as wireless power transfer. As well as the formulation, we illustrate some design examples with the derived formula: design of the least lossy L-section circuit and two-stage low-pass ladder. The examples provide ready-to-use knowledge for low-loss matching design.

The most commonly used scattering parameters (S parameters) are normalized to a real reference resistance, typically 50Ω. In some cases, the use of S parameters normalized to some complex reference impedance is essential or convenient. But there are different definitions of complex-referenced S parameters that are incompatible with each other and serve different purposes. To make matters worse, different simulators implement different ones and which ones are implemented is rarely properly documented. What are possible scenarios in which using the right one matters? This tutorial-style paper is meant as an informal and not overly technical exposition of some such confusing aspects of S parameters, for those who have a basic familiarity with the ordinary, real-referenced S parameters.

We propose a speech analysis method based on the source-filter model using multivariate empirical mode decomposition (MEMD). The proposed method takes multiple adjacent frames of a speech signal into account by combining their log spectra into multivariate signals. The multivariate signals are then decomposed into intrinsic mode functions (IMFs). The IMFs are divided into two groups using the peak of the autocorrelation function (ACF) of an IMF. The first group characterized by a spectral fine structure is used to estimate the fundamental frequency F0 by using the ACF, whereas the second group characterized by the frequency response of the vocal-tract filter is used to estimate formant frequencies by using a peak picking technique. There are two advantages of using MEMD: (i) the variation in the number of IMFs is eliminated in contrast with single-frame based empirical mode decomposition and (ii) the common information of the adjacent frames aligns in the same order of IMFs because of the common mode alignment property of MEMD. These advantages make the analysis more accurate than with other methods. As opposed to the conventional linear prediction (LP) and cepstrum methods, which rely on the LP order and cut-off frequency, respectively, the proposed method automatically separates the glottal-source and vocal-tract filter. The results showed that the proposed method exhibits the highest accuracy of F0 estimation and correctly estimates the formant frequencies of the vocal-tract filter.

Sensor networks are often used to understand underlying phenomena that are reflected through sensing data. In real world applications, this understanding supports decision makers attempting to access a disaster area or monitor a certain event regularly and thus necessary actions can be triggered in response to the problems. Practitioners designing such systems must overcome difficulties due to the practical limitations of the data and the fidelity of a network condition. This paper explores the design of a network solution for the data acquisition domain with the goal of increasing the efficiency of data gathering efforts. An unmanned aerial vehicle (UAV) is introduced to address various real-world sensor network challenges such as limited resources, lack of real-time representative data, and mobility of a relay station. Towards this goal, we introduce a novel cooperative path selection framework to effectively collect data from multiple sensor sources. The framework consists of six main parts ranging from the system initialization to the UAV data acquisition. The UAV data acquisition is useful to increase situational awareness or used as inputs for data manipulation that support response efforts. We develop a system-based simulation that creates the representative sensor networks and uses the UAV for collecting data packets. Results using our proposed framework are analyzed and compared to existing approaches to show the efficiency of the scheme.

Considering that existing clutter cancellation methods process information either in the time domain or in the spatial domain, this paper proposes a new clutter cancellation method that utilizes joint multi-domain information for passive radar. Assuming that there is a receiving array at the surveillance channel, firstly we propose a multi-domain information clutter cancellation model by constructing a time domain weighted matrix and a spatial weighted vector. Secondly the weighted matrix and vector can be updated adaptively utilizing the constant modulus constraint. Finally the weighted matrix is derived from the principle of optimal filtering and the recursion formula of weighted vector is obtained utilizing the Gauss-Newton method. Making use of the information in both time and spatial domain, the proposed method attenuates the noise and residual clutter whose directions are different from that of the target echo. Simulation results prove that the proposed method has higher clutter attenuation (CA) compared with the traditional methods in the low signal to noise ratio condition, and it also improves the detection performance of weak targets.

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, a new non-uniform weight-updating scheme for adaptive digital beamforming (DBF) is proposed. The unique feature of the letter is that the effective working range of the beamformer is extended and the computational complexity is reduced by introducing the robust DBF based on worst-case performance optimization. The robust parameter for each weight updating is chosen by analyzing the changing rate of the Direction of Arrival (DOA) of desired signal in LEO satellite communication. Simulation results demonstrate the improved performance of the new Non-Uniform Weight-Updating Beamformer (NUWUB).

A vehicular ad hoc network (VANET) consists of vehicles (mobile nodes) and road side units which are equipped with the wireless devices such as wireless LANs. Mobile nodes exchange information messages with each other so that VANETs are configured in a self-organized manner. As one of network service scenarios in VANETs, there is a network service to provide the parking spaces in the city central to vehicles (mobile nodes). In this scenario, the road side units (source nodes) which are deployed at the parking spaces periodically disseminate the number of available parking spaces to mobile nodes. Therefore, in this paper, we propose a mobile agent-based information dissemination scheme using location information of mobile nodes and source nodes in the VANET environment. In addition, we conduct simulation experiments in the VANET environment to evaluate the proposed mobile agent-based information dissemination scheme. We confirmed that it could disseminate information messages with lower overhead because mobile agents migrate among mobile nodes by using the location information.

This paper proposes a method of incrementally constructing semantic representations. Our method is based on Steedman's Combinatory Categorial Grammar (CCG), which has a transparent correspondence between syntax and semantics. In our method, a derivation for a sentence is constructed in an incremental fashion and the corresponding semantic representation is derived synchronously. Our method uses normal form CCG derivation. This is the difference between our approach and previous ones. Previous approaches use most left-branching derivation called incremental derivation, but they cannot process coordinate structures incrementally. Our method overcomes this problem.

It is qualitatively considered that emergency information processing by using UTM grids is effective in generating COP (Common Operational Pictures). Here, we conducted a numerical evaluation based on emergency information-processing training to examine the efficiency of the use of UTM grid maps by staff at the Tagajo City Government office. The results of the demonstration experiment were as follows: 1) The time required for information propagation and mapping with UTM coordinates was less than that with address text consisting of area name and block number. 2) There was no measurable difference in subjective estimates of the training performance of participants with or without the use of UTM grids. 3) Fear of real emergency responses decreased among training participants using UTM grids. 4) Many of the negative free answers on a questionnaire evaluation of participants involved requests regarding the reliability and operability of UTM tools.

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.

Information floating delivers information to mobile nodes in specific areas without meaningless spreading of information by permitting mobile nodes to directly transfer information to other nodes by wireless links in designated areas called transmittable areas. In this paper, we assume that mobile nodes change direction at intersections after receiving such information as warnings and local advertisements and that an information source remains in some place away from the transmittable area and continuously broadcasts information. We analyze performance of information floating under these assumptions to explore effects of the behavior changes of mobile nodes, decision deadline of the behavior change, and existence of a fixed source on information floating. We theoretically analyze the probability that a node cannot receive information and also derive the size of each transmittable area so that this probability is close to desired values.

Analyzing the schedulability of hierarchical real-time systems is difficult because of the systems' complex behavior. It gets more complicated when shared resources or dependencies among tasks are included. This paper introduces a framework based on UPPAAL that can analyze the schedulability of hierarchical real-time systems.

A network-based remote host clock skew measurement involves collecting the offsets, the differences between sending and receiving times, of packets from the host within a period of time. Although the variant and immeasurable delay in each packet prevents the measurer from getting the real clock offset, the local minimum delays and the majority of delays delineate the clock offset shifts, and are used by existing approaches to estimate the skew. However, events during skew measurement like time synchronization and rerouting caused by switching network interface or base transceiver station may break the trend into multi-segment patterns. Although the skew in each segment is theoretically of the same value, the skew derived from the whole offset-set usually differs with an error of unpredictable scale. In this work, a method called dynamic region of offset majority locating (DROML) is developed to detect multi-segment cases, and to precisely estimate the skew. DROML is designed to work in real-time, and it uses a modified version of the HT-based method [8] both to measure the skew of one segment and to detect the break between adjacent segments. In the evaluation section, the modified HT-based method is compared with the original method and with a linear programming algorithm (LPA) on accumulated-time and short-term measurement. The fluctuation of the modified method in the short-term experiment is 0.6 ppm (parts per million), which is obviously less than the 1.23 ppm and 1.45 ppm from the other two methods. DROML, when estimating a four-segment case, is able to output a skew of only 0.22 ppm error, compared with the result of the normal case.