Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, good generalization ability, and ease of implementation. However, it has a linear output layer which may limit the capability of exploring the available information, since higher-order statistics of the signals are not taken into account. To address this, we propose a novel ELM architecture in which the linear output layer is replaced by a Volterra filter structure. Additionally, the principal component analysis (PCA) technique is used to reduce the number of effective signals transmitted to the output layer. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. Then we carry out performance evaluation and application analysis for the proposed architecture in the context of supervised classification and unsupervised equalization respectively, and the obtained results either on publicly available datasets or various channels, when compared to those produced by already proposed ELM versions and a state-of-the-art algorithm: support vector machine (SVM), highlight the adequacy and the advantages of the proposed architecture and characterize it as a promising tool to deal with signal processing tasks.

Heart disease is one of the major causes of death in many advanced countries. For prevention or treatment of heart disease, getting an early diagnosis from a long time period of electrocardiogram (ECG) examination is necessary. However, it could be a large burden on medical experts to analyze this large amount of data. To reduce the burden and support the analysis, this paper proposes an arrhythmia detection method based on a deformable part model, which absorbs individual variation of ECG waveform and enables the detection of various arrhythmias. Moreover, to detect the arrhythmia in low processing delay, the proposed method only utilizes time domain features. In an experimental result, the proposed method achieved 0.91 F-measure for arrhythmia detection.

The aim of this research is realizing a high resolution and a fast color switching of electronic papers. In this report, we realized basis of electric papers comprised on magnetic Janus particles was established. Colored and magnetic Janus particles were successfully prepared, and magnetic Janus particles were introduced into honeycomb matrices. Introduced magnetic Janus particles quickly respond to an external magnetic field.

We examined the novel aggregation control of the LC and monomer during formation of the polymer walls from a LC/monomer mixture in order to suppress the presence of the residual monomers and polymer networks in the pixel areas. The method is utilization of the differing wettabilities among LC and monomer molecules on a substrate surface. We patterned a substrate surface with a fluororesin and a polyimide film, and promoted phase separation of the LC and monomer by cooling process. This resulted in the LC and monomer aggregates primarily existing in the pixel areas and non-pixel areas, respectively. Moreover, the polymer-walls structure which was formed in this method partitioned into individual pixels in a lattice region and prevented the LC from flowing. This polymer-walls formation technique will be useful for developing high-quality flexible LCDs.

Establishing local visual correspondences between images taken under different conditions is an important and challenging task in computer vision. A common solution for this task is detecting keypoints in images and then matching the keypoints with a feature descriptor. This paper proposes a robust and low-dimensional local feature descriptor named Adaptively Integrated Gradient and Intensity Feature (AIGIF). The proposed AIGIF descriptor partitions the support region surrounding each keypoint into sub-regions, and classifies the sub-regions into two categories: edge-dominated ones and smoothness-dominated ones. For edge-dominated sub-regions, gradient magnitude and orientation features are extracted; for smoothness-dominated sub-regions, intensity feature is extracted. The gradient and intensity features are integrated to generate the descriptor. Experiments on image matching were conducted to evaluate performances of the proposed AIGIF. Compared with SIFT, the proposed AIGIF achieves 75% reduction of feature dimension (from 128 bytes to 32 bytes); compared with SURF, the proposed AIGIF achieves 87.5% reduction of feature dimension (from 256 bytes to 32 bytes); compared with the state-of-the-art ORB descriptor which has the same feature dimension with AIGIF, AIGIF achieves higher accuracy and robustness. In summary, the AIGIF combines the advantages of gradient feature and intensity feature, and achieves relatively high accuracy and robustness with low feature dimension.

The ball state tracking and detection technology plays a significant role in volleyball game analysis, whose performance is limited due to the challenges include: 1) the inaccurate ball trajectory; 2) multiple numbers of the ball event category; 3) the large intra-class difference of one event. With the goal of broadcasting supporting for volleyball games which requires a real time system, this paper proposes a ball state based parallel ball tracking and event detection method based on a sequential estimation method such as particle filter. This method employs a parallel process of the 3D ball tracking and the event detection so that it is friendly for real time system implementation. The 3D ball tracking process uses the same models with the past work [8]. For event detection process, a ball event change estimation based multiple system model, a past trajectory referred hit point likelihood and a court-line distance feature based event type detection are proposed. First, the multiple system model transits the ball event state, which consists the event starting time and the event type, through three models dealing with different ball motion situations in the volleyball game, such as the motion keeping and changing. The mixture of these models is decided by estimation of the ball event change estimation. Secondly, the past trajectory referred hit point likelihood avoids the processing time delay between the ball tracking and the event detection process by evaluating the probability of the ball being hit at certain time without using future ball trajectories. Third, the feature of the distance between the ball and the specific court line are extracted to detect the ball event type. Experimental results based on multi-view HDTV video sequences (2014 Inter High School Men's Volleyball Games, Japan), which contains 606 events in total, show that the detection rate reaches 88.61% while the success rate of 3D ball tracking keeps more than 99%.

This paper describes the background noise estimation technique of the tensor product expansion with absolute error (TPE-AE) to estimate multiple sources. The electroencephalogram (EEG) signal produced by the saccadic eye movement is adopted to analyze relationship between a brain function and a human activity. The electrooculogram (EOG) generated by eye movements yields significant problems for the EEG analysis. The denoising of EOG artifacts is important task to perform an accurate analysis. In this paper, the two types of TPE-AE are proposed to estimates EOG and other components in EEG during eye movement. One technique estimates two outer products using median filter based TPE-AE. The another technique uses a reference signal to separate the two sources. We show that the proposed method is effective to estimate and separate two sources in EEG.

Mobility management is very important in mobile cellular networks, since to connect incoming calls, the network must maintain the locations of the mobiles. This study considers the zone-based registration methods that most mobile cellular networks have adopted. We focus on two special zone-based registration methods, called two-zone registration (2Z), and two-zone registration with implicit registration by outgoing calls (2Zi). Although some mathematical models for their performances have been presented, they still cannot accurately estimate 2Zi performance. We provide a new and simple mathematical model based on Markov chain theory that can accurately analyze the performances of 2Z and 2Zi. We also explain the propositions underlying the explicit expressions adopted by our model. We finally present various numerical results, to compare the performance of 2Zi with those of 2Z and one-zone registration (1Z), and show that in every case, 2Zi is superior to 2Z, and in most practical cases, to 1Z.

This study involves proposing a high-speed computer-generated hologram playback by using a digital micromirror device for high-definition spatiotemporal division multiplexing electroholography. Consequently, the results indicated that the study successfully reconstructed a high-definition 3-D movie of 3-D objects that was comprised of approximately 900,000 points at 60 fps when each frame was divided into twelve parts.

This paper aims to support quick and easy page access in digital documents. We tried to use a paper book as a device to navigate pages for digital documents. Our proposed system allows the users to perform the same interaction as a paper book such as inserting fingers among pages or folding an edge of the page as a dog-ear. Three experiments were conducted to confirm the effectiveness of the proposed system. As a result, we confirmed our proposed system was superior to conventional navigation methods especially in moving back and forth among pages.

This paper proposes a component ranking method to identify important components which have great impact on the system reliability. This method, which is opposite to an existing method, believes components which frequently invoke other components have more impact than others and employs component invocation structures and invocation frequencies for making important component ranking. It can strongly support for improving the reliability of software systems, especially large-scale systems. Extensive experiments are provided to validate this method and draw performance comparison.

We demonstrate on the basis of ab initio calculations that metal-oxide-nitride-oxide-semiconductor (MONOS) memory is one of the most promising future high-density archive memories. We find that O related defects in a MONOS memory cause irreversible structural changes to the SiO2/Si3N4 interface at the atomistic level during program/erase (P/E) cycles. Carrier injection during the programming operation makes the structure energetically very stable, because all the O atoms in this structure take on three-fold-coordination. The estimated lifespan of the programmed state is of the order of a thousand years.

Emerging digital payment services, also known as FinTech, have enabled various types of advanced payment transactions (such as Google Wallet, Apple Pay, Samsung Pay, etc.). However, offline peer-to-peer cash transactions still make up about 25.6% of the overall financial transactions in everyday life. By investigating existing online and offline payment systems, we identify three key challenges for building a digital cash transaction system with core features of the offline cash transactions: self-verifiability of digital cash; user anonymity; atomic cash transfer for double spending/depositing protection. In this paper, we propose OPERA, an offline peer-to-peer digital cash transaction system that addresses the three challenges. It newly introduces a concept of ‘one-time-readable memory(ORM)’ and ‘digital token’ which is a unit of self-verifiable digital cash. The one-time readability from ORM and three-stage token exchange protocol enable OPERA to provide uniqueness to digital cash and to allow a complete offline digital payment. OPERA devices are enhanced with TCPA technology to ensure the integrity of the physical device package. To evaluate the feasibility and resilience of the OPERA design, we built a prototype on a customized embedded board.

This paper presents a programmable wideband low pass filter (LPF) with Continuous-Time (CT)/Discrete-Time (DT) hybrid architecture. Unlike the conventional DT LPF, the proposed LPF eliminates sample & hold circuits, enabling to expand available bandwidth. The transfer function and the influence of the circuit imperfection are derived from CT/DT hybrid analysis. A prototype has been fabricated in 40 nm CMOS process. The proposed LPF achieves 2.5 GHz bandwidth by wideband equalization, which offers capacitance ratio (Cratio) and clock frequency (fCK) programmability. The proposed LPF occupies only 0.048 mm2 of active area.

A single-dimensional interface which enables users to obtain diverse localizations of audio sources is proposed. In many conventional interfaces for arranging audio sources, there are multiple arrangement parameters, some of which allow users to control positions of audio sources. However, it is difficult for users who are unfamiliar with these systems to optimize the arrangement parameters since the number of possible settings is huge. We propose a simple, single-dimensional interface for adjusting arrangement parameters, allowing users to sample several diverse audio source arrangements and easily find their preferred auditory localizations. To select subsets of arrangement parameters from all of the possible choices, auditory-localization space vectors (ASVs) are defined to represent the auditory localization of each arrangement parameter. By selecting subsets of ASVs which are approximately orthogonal, we can choose arrangement parameters which will produce diverse auditory localizations. Experimental evaluations were conducted using music composed of three audio sources. Subjective evaluations confirmed that novice users can obtain diverse localizations using the proposed interface.

Web browsing services are expanding as smartphones are becoming increasingly popular worldwide. To provide customers with appropriate quality of web-browsing services, quality design and in-service quality management on the basis of quality of experience (QoE) is important. We propose a web-browsing QoE estimation model. The most important QoE factor for web-browsing is the waiting time for a web page to load. Next, the variation in the communication quality based on a mobile network should be considered. We developed a subjective quality assessment test to clarify QoE characteristics in terms of waiting time using 20 different types of web pages and constructed a web-page QoE estimation model. We then conducted a subjective quality assessment test of web-browsing to clarify the relationship between web-page QoE and web-browsing QoE for three web sites. We obtained the following two QoE characteristics. First, the main factor influencing web-browsing QoE is the average web-page QoE. Second, when web-page QoE variation occurs, a decrease in web-page QoE with a huge amplitude causes the web-browsing QoE to decrease. We used these characteristics in constructing our web-browsing QoE estimation model. The verification test results using non-training data indicate the accuracy of the model. We also show that our findings are applicable to web-browsing quality design and solving management issues on the basis of QoE.

Locally repairable code (LRC) can recover any codeword symbol failure by accessing a small number of other symbols, which can increase the efficiency during the repair process. In a distributed storage system with locally repairable codes, any node failure can be rebuilt by accessing other fixed nodes. It is a promising prospect for the application of LRC. In this paper, some methods of constructing matrices which can generate codes with small locality will be proposed firstly. By analyzing the parameters, we construct the generator matrices of the best-known ternary linear codes of dimension 6, using methods such as shortening, puncturing and expansion. After analyzing the linear dependence of the column vectors in the generator matrices above, we find out the locality of the codes they generate. Many codes with small locality have been found.

This paper proposes a new methodology to design optimal antennas for MIMO (Multi-Input Multi-Output) communication systems by using spherical mode expansion. Given spatial channel properties of a MIMO channel, such as the angular profile at both sides, the optimal MIMO antennas should provide the largest channel capacity with a constraint of the limited implementation space (volume). In designing a conventional MIMO antenna, first the antenna structure (current distribution) is determined, second antenna directivity is calculated based on the current distribution, and thirdly MIMO channel capacity is calculated by using given angular profiles and obtained antenna directivity. This process is repeated by adjusting the antenna structure until the performance satisfies a predefined threshold. To the contrary, this paper solves the optimization problem analytically and finally gives near optimal antenna structure (current distribution) without any greedy search. In the proposed process, first the optimal directivity of MIMO antennas is derived by applying spherical mode expansion to the angular profiles, and second a far-near field conversion is applied on the derived optimal directivity to achieve near optimal current distributions on a limited surface. The effectiveness of the proposed design methodology is validated via numerical calculation of MIMO channel capacity as in the conventional design method while giving near optimal current distribution with constraint of an antenna structure derived from proposed methodology.

As a next-generation CAN (Controller Area Network), CAN FD (CAN with flexible data rate) has attracted much attention recently. However, how to use the improved bus bandwidth efficiently in CAN FD is still an issue. Contrasting with existing methods using greedy approximate algorithms, this paper proposes a genetic algorithm for CAN FD frame packing. It tries to minimize the bandwidth utilization by considering the different periods of signals when packing them in the same frame. Moreover, it also checks the schedulability of packed frames to guarantee the real-time constraints of each frame and proposed a merging algorithm to improve the schedulability for signal set with high bus load. Experimental results validate that the proposed algorithm can achieve significantly less bandwidth utilization and improved schedulability than existing methods for a given set of signals.

This paper presents a novel identification method for hybrid dynamical system models, where parameters have stochastic and time-varying characteristics. The proposed parameter identification scheme is based on a modified implementation of particle filtering, together with a time-smoothing technique. Parameters of the identified model are considered as time-varying random variables. Parameters are identified independently at each time step, using the Bayesian inference implemented as an iterative particle filtering method. Parameters time dynamics are smoothed using a distribution based moving average technique. Modes of the hybrid system model are handled independently, allowing any type of nonlinear piecewise model to be identified. The proposed identification scheme has low computation burden, and it can be implemented for online use. Effectiveness of the scheme is verified by numerical experiments, and an application of the method is proposed: analysis of driving behavior through identified time-varying parameters.