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.

With the increase of network components connected to the Internet, the need to ensure secure connectivity is becoming increasingly vital. Intrusion Detection Systems (IDSs) are one of the common security components that identify security violations. This paper proposes a novel multilevel hybrid classifier that uses different feature sets on each classifier. It presents the Discernibility Function based Feature Selection method and two classifiers involving multilayer perceptron (MLP) and decision tree (C4.5). Experiments are conducted on the KDD'99 Cup and ISCX datasets, and the proposal demonstrates better performance than individual classifiers and other proposed hybrid classifiers. The proposed method provides significant improvement in the detection rates of attack classes and Cost Per Example (CPE) which was the primary evaluation method in the KDD'99 Cup competition.

In order to tackle a process-variation problem, we can define several scenarios, each of which corresponds to a particular LSI behavior, such as a typical-case scenario and a worst-case scenario. By designing a single LSI chip which realizes multiple scenarios simultaneously, we can have a process-variation-tolerant LSI chip. In this paper, we propose a multi-scenario high-level synthesis algorithm for variation-tolerant floorplan-driven design targeting new distributed-register architectures, called HDR architectures. We assume two scenarios, a typical-case scenario and a worst-case scenario, and realize them onto a single chip. We first schedule/bind each of the scenarios independently. After that, we commonize the scheduling/binding results for the typical-case and worst-case scenarios and thus generate a commonized area-minimized floorplan result. At that time, we can explicitly take into account interconnection delays by using distributed-register architectures. Experimental results show that our algorithm reduces the latency of the typical-case scenario by up to 50% without increasing the latency of the worst-case scenario, compared with several existing methods.

In the development of intrabody communication systems, it is important to understand the effects of user's posture on the communication channels. In this study, dynamic measurements of intrabody communication channels were made and their dependences on the grounding conditions were investigated. Furthermore, the physical mechanism of the dynamic communication channels was discussed based on electrostatic simulations. According to the measured and the simulated results, the variations in the signal transmission characteristics depend not only on the distance between the Tx and the Rx but also on the shadowing by body parts.

Maneuvering target tracking under mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions has received considerable interest in the last decades. In this paper, a hierarchical interacting multiple model (HIMM) method is proposed for estimating target position under mixed LOS/NLOS conditions. The proposed HIMM is composed of two layers with Markov switching model. The purpose of the upper layer, which is composed of two interacting multiple model (IMM) filters in parallel, is to handle the switching between the LOS and the NLOS environments. To estimate the target kinetic variables (position, speed and acceleration), the unscented Kalman filter (UKF) with the current statistical (CS) model is used in the lower-layer. Simulation results demonstrate the effectiveness and superiority of the proposed method, which obtains better tracking accuracy than the traditional IMM.

This paper introduces a simple but effective way to boost the performance of scene classification through a novel approach to the LLC coding process. In our proposed method, a local descriptor is encoded not only with k-nearest visual words but also with k-farthest visual words to produce more discriminative code. Since the proposed method is a simple modification of the image classification model, it can be easily integrated into various existing BoF models proposed in various areas, such as coding, pooling, to boost their scene classification performance. The results of experiments conducted with three scene datasets: 15-Scenes, MIT-Indoor67, and Sun367 show that adding k-farthest visual words better enhances scene classification performance than increasing the number of k-nearest visual words.

We developed an electrically driven near-infrared broadband light source based on self-assembled InAs quantum dots (QDs). By combining emissions from four InAs QD ensembles with controlled emission center wavelengths, electro-luminescence (EL) with a Gaussian-like spectral shape and approximately 85-nm bandwidth was obtained. The peak wavelength of the EL was blue-shifted from approximately 1230 to 1200 nm with increased injection current density (J). This was due to the state-filling effect: sequential filling of the discrete QD electron/hole states by supplied carriers from lower (ground state; GS) to higher (excited state; ES) energy states. The EL intensities of the ES and GS emissions exhibited different J dependence, also because of the state-filling effect. The point-spread function (PSF) deduced from the Fourier-transformed EL spectrum exhibited a peak without apparent side lobes. The half width at half maximum of the PSF was 6.5 µm, which corresponds to the estimated axial resolution of the optical coherence tomography (OCT) image obtained with this light source. These results demonstrate the effectiveness of the QD-based device for realizing noise-reduced high-resolution OCT.

High structural perfection, wafer uniformity, and reproducibility are key parameters for high-volume, low cost manufacture of resonant tunnelling diode (RTD) terahertz (THz) devices. Low-cost, rapid, and non-destructive techniques are required for the development of such devices. In this paper, we report photoluminescence (PL) spectroscopy as a non-destructive characterisation technique for high current densityInGaAs/AlAs/InP RTD structures grown by metal-organic vapour phase epitaxy (MOVPE) for THz applications. By using a PL line scanning technique across the edge of the sample, we identify characteristic luminescence from the quantum well (QW) and the undoped/n+ InGaAs layers. By using the Moss-Burstein effect, we are able to measure the free-electron concentration of the emitter/collector and contact layers in the RTD structure. Whilst the n+ InGaAs luminescence provides information on the doping concentration, information on the alloy composition and compositional variation is extracted from the InGaAs buffer layer. The QW luminescence provides information on the average well width and provides a monitor of the structural perfection with regard to interface and alloy disorder.

This paper proposes an efficient video object segmentation approach that is tolerant to complex scene dynamics. Unlike existing approaches that rely on estimating object-like proposals on an intra-frame basis, the proposed approach employs temporally consistent foreground hypothesis using nonlinear regression of saliency guided proposals across a video sequence. For this purpose, we first generate salient foreground proposals at superpixel level by leveraging a saliency signature in the discrete cosine transform domain. We propose to use a random forest based nonlinear regression scheme to learn both appearance and shape features from salient foreground regions in all frames of a sequence. Availability of such features can help rank every foreground proposals of a sequence, and we show that the regions with high ranking scores are well correlated with semantic foreground objects in dynamic scenes. Subsequently, we utilize a Markov Random Field to integrate both appearance and motion coherence of the top-ranked object proposals. A temporal nonlinear regressor for generating salient object support regions significantly improves the segmentation performance compared to using only per-frame objectness cues. Extensive experiments on challenging real-world video sequences are performed to validate the feasibility and superiority of the proposed approach for addressing dynamic scene segmentation.

A low-complexity time-frequency multiplex estimator and low-complexity equalizer transceiver design are proposed to combat the problems of RF impairment associated with zero-IF transceiver of multi-carrier systems. Moreover, the proposed preambles can estimate the transmitter (TX) in-phase and quadrature-phase (IQ) imbalance, carrier frequency offset (CFO), and channel impulse response parameters. The proposed system has two parts. First, all parameters of the impairments are estimated by the designed time-frequency multiplex estimator. Second, the estimated parameters are used to compensate the above problems and detect the transmitted signal with low complexity. Simulation results confirm that the proposed estimator performs reliably with respect to IQ imbalance, CFO, and multipath fading channel effects.

In this paper, we consider a distributed power control scheme that can maximize overall capacity of an interference-limited wireless system in which the same radio resource is spatially reused among different transmitter-receiver pairs. This power control scheme employs a gradient-descent method in each transmitter, which adapts its own transmit power to co-channel interference dynamically to maximize the total weighted sum rate (WSR) of the system over a given interval. The key contribution in this paper is to propose a common feedback channel, over which a backward physical signal is accumulated for computing the gradient of the transmit power in each transmitter, thereby significantly reducing signaling overhead for the distributed power control. We show that the proposed power control scheme can achieve almost 95% of its theoretical upper WSR bound, while outperforming the non-power-controlled system by roughly 63% on average.

We present a method for recognition of acoustic events in conversation scenarios where speech usually overlaps with other acoustic events. While speech is usually considered the most informative acoustic event in a conversation scene, it does not always contain all the information. Non-speech events, such as a door knock, steps, or a keyboard typing can reveal aspects of the scene that speakers miss or avoid to mention. Moreover, being able to robustly detect these events could further support speech enhancement and recognition systems by providing useful information cues about the surrounding scenarios and noise. In acoustic event detection, state-of-the-art techniques are typically based on derived features (e.g. MFCC, or Mel-filter-banks) which have successfully parameterized the spectrogram of speech but reduce resolution and detail when we are targeting other kinds of events. In this paper, we propose a method that learns features in an unsupervised manner from high-resolution spectrogram patches (considering a patch as a certain number of consecutive frame features stacked together), and integrates within the deep neural network framework to detect and classify acoustic events. Superiority over both previous works in the field, and similar approaches based on derived features, has been assessed by statical measures and evaluation with CHIL2007 corpus, an annotated database of seminar recordings.

A highly efficient sky-blue organic light-emitting diode (OLED) based on a thermally-activated delayed fluorescence (TADF) molecule, 1,2-bis(carbazol-9-yl)-4,5-dicyanobenzene (2CzPN), was studied. The sky-blue OLED exhibited a maximum external electroluminescence quantum efficiency (ηEQE) of over 24.0%. In addition, a white OLED using 2CzPN combined with green and orange TADF emitters showed a high ηEQE of 17.3% with a maximum power efficiency of 52.3 lm/W and Commission Internationale de l'Eclairage coordinates of (0.32, 0.43).

This paper gives an outline of key technologies necessary for science-based agriculture. In order to design future agriculture, present agriculture should be redesigned based on the context of smart agriculture that indicates the overall form of agriculture including a social system while the present precision agriculture shows a technical form of agriculture only. Wireless Sensor Network (WSN) and the various type of optical sensors are assumed to be a basic technology of smart agriculture which intends the harmony with the economic development and sustainable agro-ecosystem. In this paper, the current state and development for the optical sensing for environment and plant are introduced.

Recently, Intelligent Transport Systems (ITS) are being researched and developed briskly. As a part of ITS, detecting vehicles from images taken by a camera loaded on a vehicle are conducted. From such backgrounds, authors have been conducting vehicle detection in nighttime. To evaluate the accuracy of this detection, gold standards of the detection are required. At present, gold standards are created manually, but manually detecting vehicles take time. Accordingly, a system which detects vehicles accurately without human help is needed to evaluate the accuracy of the vehicle detection in real time. Therefore the purpose of this study is to automatically detect vehicles in nighttime images, taken by an in-vehicle camera, with high accuracy in offline processing. To detect vehicles we focused on the brightness of the headlights and taillights, because it is difficult to detect vehicles from their shape in nighttime driving scenes. The method we propose uses Center Surround Extremas, called CenSurE for short, to detect blobs. CenSurE is a method that uses the difference in brightness between the lights and the surroundings. However, blobs obtained by CenSurE will also include objects other than headlights and taillights. For example, streetlights and delineators would be detected. To distinguish such blobs, they are tracked in inverse time and vehicles are detected using tags based on the characteristics of each object. Although every object appears from the same point in forward time process, each object appears from different places in images in inverse time processing, allowing it to track and tag blobs easily. To evaluate the effectiveness of this proposed method, experiment of detecting vehicles was conducted using nighttime driving scenes taken by a camera loaded on a vehicle. Experimental results of the proposed method were nearly equivalent to manual detection.

This paper provides a method based on electromagnetic (EM) analysis to predict conducted currents in the bulk current injection (BCI) test system for automotive components. The BCI test system is comprised of an injection probe, equipment under test (EUT), line impedance stabilization networks (LISNs), wires and an electric load. All components are modeled in full-wave EM analysis. The EM model of the injection probe enables us to handle multi wires. By using the transmission line theory, the BCI setup model is divided into several parts in order to reduce the calculation time. The proposed method is applied to an actual BCI setup of an automotive component and the simulated common mode currents at the input terminals of EUT have a good accuracy in the frequency range of 1-400MHz. The model separation reduces the calculation time to only several hours.

With the rise of component-based software development, its reliability has attracted much attention from both academic and industry communities. Component-based software development focuses on architecture design, and thus it is important for reliability analysis to emphasize software architecture. Existing approaches to architecture-based software reliability analysis don't model the usage profile explicitly, and they ignore the difference between the testing profile and the practical profile of components, which limits their applicability and accuracy. In response to these issues, a new reliability modeling and prediction approach is introduced. The approach considers reliability-related architecture factors by explicitly modeling the system usage profile, and transforms the testing profile into the practical usage profile of components by representing the profile with input sub-domains. Finally, the evaluation experiment shows the potential of the approach.

We report a real time method to monitor the chemical reaction in microdroplets, which contain an organic dye, 5(6)-carboxynaphthofluorescein and a CdSe/ZnS quantum dot using fluorescence spectra. Especially, the relationship between the droplet size and the reaction rate of the two reagents was investigated by changing an injection speed.

We propose an unsharp-masking technique which preserves the hue of colors in images. This method magnifies the contrast of colors and spatially sharpens textures in images. The contrast magnification ratio is adaptively controlled. We show by experiments that this method enhances the color tone of photographs while keeping their perceptual scene depth.

Rate control that is required to regulate the bitrate of video coding is critical to time-sensitive video applications used over networks. However, the H.264/AVC standard does not respond to scene changes, and this causes the transmission quality to deteriorate as a scene change occurs. In this work, a scene change is detected by comparing the ratio of the sum of absolute difference (SAD) between two consecutive frames. As the scene change is detected, the proposed method, which is modified from the reference software of H.264/AVC, re-assigns a quantization parameter (QP) value to regulate the bitrate. Because the inter-prediction works poorly for the scene-changed frame, the proposed method estimates its frame complexity based on the content, and further creates another Q-R model to assign QP. The adaptive rate control mechanism presented in this study can quickly respond to the heavy bitrate increment caused by a change of scene. Simulation results show that the proposed method improves the average peak signal noise ratio (PSNR) to approximately 1.1dB, with a smaller buffer size compared with the performance of the reference software JM version 17.2.