This paper proposes an efficient lossless coding scheme for color video in RGB 4:4:4 format. For the R signal that is encoded before the other signals at each frame, we employ a block-adaptive prediction technique originally developed for monochrome video. The prediction technique used for the remaining G and B signals is extended to exploit inter-color correlations as well as inter- and intra-frame ones. In both cases, multiple predictors are adaptively selected on a block-by-block basis. For the purpose of designing a set of predictors well suited to the local properties of video signals, we also explore an appropriate setting for the spatiotemporal partitioning of a video volume.

In the present paper we show how to obtain rapidly the spectral BRDF and BTDF of different display components or transparent displays using Fourier optics system under different illumination configurations. Results can be used to simulate the entire structure of a LCD display or to predict transparent display performances under various illuminations.

When using a wireless capsule endoscope (WCE), it is important to know WCE location. In this paper, we focus on a time of arrival (TOA)-based localization technique, as it has better location estimation performance than other radio frequency-based techniques. However, the propagation speed of signals transmitted from inside of a human body varies depending on which biological tissues they pass through. For this reason, almost all of conventional TOA-based methods have to obtain the relative permittivity of the passed biological tissues or the propagation speed beforehand through another measurement system, i.e., magnetic resonance imaging (MRI) or computational tomography (CT). To avoid such troublesome pre-measurement, we propose a hybrid TOA/received signal strength indicator (RSSI)-based method, which can simultaneously estimate the WCE location and the averaged relative permittivity of the human body. First, we derive the principle of RSSI-based relative permittivity estimation from an finite difference time domain (FDTD) simulation. Second, we combine the TOA-based localization and the proposed RSSI-based relative permittivity estimation, and add them to the particle filter tracking technique. Finally, we perform computer simulations to evaluate the estimation accuracy of the proposed method. The simulation results show that the proposed method can accomplish good localization performance, 1.3mm, without pre-measurement of the human body structure information.

In this letter, we propose a method for obtaining a clear and natural output image by tuning the illumination component in an input image. The proposed method is based on the retinex process and it is suitable for the image quality improvement of images of which illumination is insufficient.

How to manage the video streaming in future networks is becoming a more and more challenging issue. Recent studies on vehicular networks depict a new picture of the next generation Intelligent Transport System (ITS), with high level road safety and more comfortable driving experience. To cope with the heterogeneous network development for the next generation cellular network, centralized medium control is promising to be employed upon Road Side Unit (RSU). To accommodate the QoS constraints posed by video services in vehicular networks, the scalable video coding (SVC) scheme in H.264/AVC standard family offers spatial and temporal scalabilities in the video dissemination. In this paper, we target the resource allocation and layer selection problem for the multi-user video streaming over highway scenario, by employing SVC coding scheme for the video contents. We propose a Resource Allocation and Layer Selection (RALS) algorithm, which explicitly takes account of the utility value of each Group Of Picture (GOP) among all the vehicular users. Simulation results show that our proposed RALS algorithm outperforms the comparison schemes in typical scenarios.

Diabetes mellitus is a group of metabolic diseases that cause high blood sugar due to functional problems with the pancreas or metabolism. Diabetic patients have few subjective symptoms and may experience decreased sensation without being aware of it. The commonly performed tests for sensory disorders are qualitative in nature. The authors pay attention to the decline of the sensitivity of tactile sensations, and develop a non-invasive method to detect the level of tactile sensation using a novel micro-vibration actuator that employs shape-memory alloy wires. Previously, we performed a pilot study that applied the device to 15 diabetic patients and confirmed a significant reduction in the tactile sensation in diabetic patients when compared to healthy subjects. In this study, we focus on the asymptomatic development of decreased sensation associated with diabetes mellitus. The objectives are to examine diabetic patients who are unaware of abnormal or decreased sensation using the quantitative tactile sensation measurement device and to determine whether tactile sensation is decreased in patients compared to healthy controls. The finger method is used to measure the Tactile Sensation Threshold (TST) score of the index and middle fingers using the new device and the following three procedures: TST-1, TST-4, and TST-8. TST scores ranged from 1 to 30 were compared between the two groups. The TST scores were significantly higher for the diabetic patients (P<0.05). The TST scores for the left fingers of diabetic patients and healthy controls were 5.9±6.2 and 2.7±2.9 for TST-1, 15.3±7.0 and 8.7±6.4 for TST-4, and 19.3±7.8 and 12.7±9.1 for TST-8. Our data suggest that the use of the new quantitative tactile sensation measurement device enables the detection of decreased tactile sensation in diabetic patients who are unaware of abnormal or decreased sensation compared to controls.

In this paper, we study a novel method to avoid a local minimum stagnation in the design problem of IIR (Infinite Impulse Response) filters using PSO (Particle Swarm Optimization). Although PSO is appropriate to solve nonlinear optimization problems, it is reported that a local minimum stagnation occurs due to a strong intensification of particles during the search. Then, multi-swarm PSO based on the particle reallocation strategy is proposed to avoid the local minimum stagnation. In this method, a reallocation space is determined by using some global bests. In this paper, the relationship between the number of swarms and the best value of design error is shown and the effectiveness of the proposed method is shown through several design examples.

In a network with dense deployment of multiple-input multiple-output (MIMO) small cells, coverage overlap between the small cells produces intercell-interference, which degrades system capacity. This paper proposes an intercell-interference management (IIM) scheme that aims to maximize system capacity by using both power control for intercell-interference coordination (ICIC) on the transmitter side and interference cancellation (IC) on the receiver side. The power control determines transmit power levels at the base stations (BSs) by employing a neural network (NN) algorithm over the backhaul. To further improve the signal to interference plus noise ratio (SINR), every user terminal (UT) employs a multiuser detector (MUD) as IC. The MUD detects not only the desired signals, but also some interfering signals to be cancelled from received signals. The receiver structure consists of branch metric generators (BMGs) and MUD. BMGs suppress residual interference and noise in the received signals by whitening matched filters (WMFs), and then generate metrices by using the WMFs' outputs and symbol candidates that the MUD provides. On the basis of the metrices, the MUD detects both the selected interfering signals and the desired signals. In addition, the MUD determines which interfering signals are detected by an SINR based replica selection algorithm. Computer simulations demonstrate that the SINR based replica selection algorithm, which is combined with channel encoders and packet interleavers, can significantly improve the system bit error rate (BER) and that combining IC at the receiver with NN power control at the transmitter can considerably increase the system capacity. Furthermore, it is shown that choosing the detected interfering signals by the replica selection algorithm can obtain system capacity with comparable loss and less computational complexity compared to the conventional greedy algorithm.

In this paper, we aim to investigate the potential usefulness of machine learning in image quality assessment (IQA). Most previous studies have focused on designing effective image quality metrics (IQMs), and significant advances have been made in the development of IQMs over the last decade. Here, our goal is to improve prediction outcomes of “any” given image quality metric. We call this the “IQM's Outcome Improvement” problem, in order to distinguish the proposed approach from the existing IQA approaches. We propose a method that focuses on the underlying IQM and improves its prediction results by using machine learning techniques. Extensive experiments have been conducted on three different publicly available image databases. Particularly, through both 1) in-database and 2) cross-database validations, the generality and technological feasibility (in real-world applications) of our machine-learning-based algorithm have been evaluated. Our results demonstrate that the proposed framework improves prediction outcomes of various existing commonly used IQMs (e.g., MSE, PSNR, SSIM-based IQMs, etc.) in terms of not only prediction accuracy, but also prediction monotonicity.

The previous researches on the chaotic CDMA have theoretically derived the chaotic sequences having the minimum asynchronous cross-correlation. To minimize the asynchronous cross-correlation, autocorrelation of each sequence have to be C(τ)≈C×rτ, r=-2+√3, dumped oscillation with increase of the lag τ. There are several methods to generate such sequences, using a chaotic map, using the Lebesgue spectrum filter (LSF) and so on. In this paper, such lowest cross-correlation found in the chaotic CDMA researches is applied to solution search algorithms for combinatorial optimization problems. In combinatorial optimization, effectiveness of the chaotic search has already been clarified. First, an importance of chaos and autocorrelation with dumped oscillation for combinatorial optimization is shown. Next, in order to realize ideal solution search, the LSF is applied to the Hopfield-Tank neural network, the 2-opt method and the 2-exchange method. Effectiveness of the LSF is clarified even for the large problems for the traveling salesman problems and the quadratic assignment problems.

In this paper, a built-in test circuit for an electrical interconnect test method is proposed to detect an open defect occurring at an interconnect between an IC and a printed circuit board. The test method is based on measuring the supply current of an inverter gate in the test circuit. A time-varying signal is provided to an interconnect as a test signal by the built-in test circuit. In this paper, the test circuit is evaluated by SPICE simulation and by experiments with a prototyping IC. The experimental results reveal that a hard open defect is detectable by the test method in addition to a resistive open defect and a capacitive open one at a test speed of 400 kHz.

Wireless body area networks (BANs) are attracting much attention due to their suitable for healthcare and medical applications. Unfortunately, electrostatic discharge (ESD) is a major electromagnetic (EM) noise source that can degrade wireless communication performance. In this study, we measure EM noise power in the 2.4GHz and 30MHz bands for indirect ESD testing specified in IEC 61000-4-2 standard, and derived a statistical ESD noise model from the measurement results. The ESD noise power was found to follow a lognormal distribution in both 2.4GHz and 30MHz bands. We use this ESD noise model to conduct bit error rate (BER) simulations in a communication channel with additive white Gaussian noise (AWGN) plus ESD noise at 2.4GHz and 30MHz bands. The result is that the BER performance is virtually the same in both bands, and decreases with the signal to noise power ratio (SNR). It is also shown that an error floor exists in the BER performances at both frequencies, which, if the ESD noise power is larger than the Gaussian noise, cannot be improved by increasing the SNR. Although the ESD noise power at 2.4GHz band is nearly 30dB smaller than that at 30MHz band, the signal attenuation along the human body at 2.4GHz band is much larger compared to 30MHz band. This may yield a similar SNR level at 30MHz and 2.4GHz bands in an ESD-dominated environment, so that the 2.4GHz band does not have an obvious merit for BAN applications. Since there are so many in-band interference sources at 2.4GHz band, the 30MHz band seems more promising for vital data transmission in a BAN scenario even in an ESD-dominated environment.

Extending the very popular tori interconnection networks[1]-[3], Torus-Connected Cycles (TCC) have been proposed as a novel network topology for massively parallel systems [5]. Here, the set-to-set disjoint paths routing problem in a TCC is solved. In a TCC(k,n), it is proved that paths of lengths at most kn2+2n can be selected in O(kn2) time.

Kernel discriminant analysis (KDA) is the mainstream approach of nonlinear discriminant analysis (NDA). Since it uses the kernel trick, KDA does not consider its nonlinear discriminant mapping explicitly. In this paper, another NDA approach where the nonlinear discriminant mapping is analytically given is developed. This study is based on the theory of optimal nonlinear discriminant analysis (ONDA) of which the nonlinear mapping is exactly expressed by using the Bayesian posterior probability. This theory indicates that various NDA can be derived by estimating the Bayesian posterior probability in ONDA with various estimation methods. Also, ONDA brings an insight about novel kernel functions, called discriminant kernel (DK), which is defined by also using the posterior probabilities. In this paper, several NDA and DK derived from ONDA with several posterior probability estimators are developed and evaluated. Given fine estimation methods of the Bayesian posterior probability, they give good discriminant spaces for visualization or classification.

This paper proposes a multiband automatic tunable antenna system for wide frequency bands of 704-2690MHz for cellular wireless communication systems. The proposed system controls variable capacitors connected between the antenna and a transmitter based on the received power of a probe. Locating the probe near the tip of the antenna enables frequency-a operation. The antenna is a multiband two-arm monopole antenna printed on a 60mm × 10mm area of a 60mm × 100mm FR-4 printed circuit board (PCB). The probe is a small dipole antenna capacitively coupled with the antenna. Fine-tuning based on simple hill-climbing optimization compensates the mismatch due to the surroundings, e.g., a user's hand/head or desk assuming channel-informed rough-tuning beforehand. A prototype consisting of varicap diodes and some other devices demonstrates automatic tunability.

We investigate the influence of launching conditions on misalignment tolerance of pluggable ballpoint-pen interconnects, where graded-index plastic optical fibers (GI POFs) are coupled with ball lenses mounted on their end faces. The lateral-misalignment tolerance of the ballpoint-pen connector decreased with an increase in the driving current of a vertical cavity surface emitting laser (VCSEL) under the center launching condition. This was attributed to the VCSEL multimode oscillation, which increased the connector coupling loss through the higher-order guided mode launching in the GI POF and the resulting output beam expansion in the ballpoint-pen connector. The driving-current dependence of the connector coupling loss could be decreased using offset launchings. For a radial launching offset of 20µm, we could obtain coupling losses below 1dB for lateral coupling offsets of ±50µm with little dependence on the driving current. This suggests that data transmission quality for misaligned connection of the GI POFs can be improved further by optimizing launching systems for the ballpoint-pen interconnects.

Sound localization systems are widely studied and have several potential applications, including hearing aid devices, surveillance and robotics. However, few proposed solutions target portable systems, such as wearable devices, which require a small unnoticeable platform, or unmanned aerial vehicles, in which weight and low power consumption are critical aspects. The main objective of this research is to achieve real-time sound localization capability in a small, self-contained device, without having to rely on large shaped platforms or complex microphone arrays. The proposed device has two surface-mount microphones spaced only 20 mm apart. Such reduced dimensions present challenges for the implementation, as differences in level and spectra become negligible, and only time-difference of arrival (TDoA) can be used as a localization cue. Three main issues have to be addressed in order to accomplish these objectives. To achieve real-time processing, the TDoA is calculated using zero-crossing spikes applied to the hardware-friendly Jeffers model. In order to make up for the reduction in resolution due to the small dimensions, the signal is upsampled several-fold within the system. Finally, a coherence-based spectral masking is used to select only frequency components with relevant TDoA information. The proposed system was implemented on a field-programmable gate array (FPGA) based platform, due to the large amount of concurrent and independent tasks, which can be efficiently parallelized in reconfigurable hardware devices. Experimental results with white-noise and environmental sounds show high accuracies for both anechoic and reverberant conditions.

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.

This paper describes an equivalent circuit analysis of a meta-surface using a double-layered patch-type frequency-selective surface (FSS); the analysis considers the coupling between FSSs. Two types of double-layered structures are examined. One is a stacked structure and the other is an alternated structure. The results calculated using the equivalent circuit are in agreement with the results of the FDTD analysis. In addition, it is clarified that the stacked and alternated structures exhibit the common mode and the differential mode coupling, respectively. Moreover, experiments support analysis results for both stacked and alternated structures.

This paper puts forward the concept of cellular network location with less information which can overcome the weaknesses of the cellular location technology in practical applications. After a systematic introduction of less-information location model, this paper presents a location algorithm based on AGA (Adaptive Genetic Algorithm) and an optimized RBF (Radical Basis Function) neural network. The virtues of this algorithm are that it has high location accuracy, reduces the location measurement parameters and effectively enhances the robustness. The simulation results show that under the condition of less information, the optimized location algorithm can effectively solve the fuzzy points in the location model and satisfy the FCC's (Federal Communications Commission) requirements on location accuracy.