This letter proposes a novel speech enhancement system based on the ‘L’ shaped triple-microphone. The modified coherence-based algorithm and the first-order differential beamforming are combined to filter the spatial distributed noise. The experimental results reveal that the proposed algorithm achieves significant performance in spatial filtering under different noise scenarios.

Finding widespread events in a distributed network is crucial when detecting cyber-attacks or network malfunctions. We propose a new detection scheme for widespread events based on bitmaps that can succinctly record and deliver event information between monitoring agents and a central coordinator. Our proposed scheme reduces communication overhead as well as total number of rounds, and achieves even higher accuracy, compared with the current state of the art.

A phase locking value (PLV) in electrocorticography is an essential indicator for analysis of cognitive activities and detection of severe diseases such as seizure of epilepsy. The PLV computation requires a simultaneous pursuit of high-throughput and low-cost implementation in hardware acceleration. The PLV computation consists of bandpass filtering, Hilbert transform, and mean phase coherence (MPC) calculation. The MPC calculation includes trigonometric functions and divisions, and these calculations require a lot of computational amounts. This paper proposes an MPC calculation method that removes high-cost operations from the original MPC with mathematically identical derivations while the conventional methods sacrifice either computational accuracy or throughput. This paper also proposes a hardware implementation of MPC calculator whose latency is 21 cycles and pipeline interval is five cycles. Compared with the conventional implementation with the same standard cell library, the proposed implementation marks 2.8 times better hardware implementation efficiency that is defined as throughput per gate counts.

This paper considers a joint channel coding and random number generation from the channel output. Specifically, we want to transmit a message to a receiver reliably and at the same time the receiver extracts pure random bits independent of the channel input. We call this problem as the joint channel coding and intrinsic randomness problem. For general channels, we clarify the trade-off between the coding rate and the random bit rate extracted from the channel output by using the achievable rate region, where both the probability of decoding error and the approximation error of random bits asymptotically vanish. We also reveal the achievable rate regions for stationary memoryless channels, additive channels, symmetric channels, and mixed channels.

Indoor fingerprint location based on WiFi in large-scale indoor parking lots is more and more widely employed for vehicle lookup. However, the challenge is to ensure the location functionality because of the particularity and complexities of the indoor parking lot environment. To reduce the need to deploy of reference points (RPs) and the offline sampling workload, a partition-fitting fingerprint algorithm (P-FP) is proposed. To improve the location accuracy of the target, the PS-FP algorithm, a sampling importance resampling (SIR) particle filter with threshold based on P-FP, is further proposed. Firstly, the entire indoor parking lot is partitioned and the environmental coefficients of each partitioned section are gained by using the polynomial fitting model. To improve the quality of the offline fingerprint database, an error characteristic matrix is established using the difference between the fitting values and the actual measured values. Thus, the virtual RPs are deployed and C-means clustering is utilized to reduce the amount of online computation. To decrease the fluctuation of location coordinates, the SIR particle filter with a threshold setting is adopted to optimize the location coordinates. Finally, the optimal threshold value is obtained by comparing the mean location error. Test results demonstrated that PS-FP could achieve high location accuracy with few RPs and the mean location error is only about 0.7m. The cumulative distribution function (CDF) show that, using PS-FP, 98% of location errors are within 2m. Compared with the weighted K-nearest neighbors (WKNN) algorithm, the location accuracy by PS-FP exhibit an 84% improvement.

Adaptive noise cancellation using adaptive filters is a known method for removing noise that interferes with signal measurements. The adaptive noise canceller performs filtering based on the current situation through a windowing process. The shape of the window function determines the tracking performance of the adaptive noise canceller with respect to the fluctuation of the property of the unknown system that noise (reference signal) passes. However, the shape of the window function in the field of adaptive filtering has not yet been considered in detail. This study mathematically treats the effect of the window function on the adaptive noise canceller and proposes an optimization method for the window function in situations where offline processing can be performed, such as biomedical signal measurements. We also demonstrate the validity of the optimized window function through numerical experiments.

Integer codes are defined by error-correcting codes over integers modulo a fixed positive integer. In this paper, we show that the construction of integer codes can be reduced into the cases of prime-power moduli. We can efficiently search integer codes with small prime-power moduli and can construct target integer codes with a large composite-number modulus. Moreover, we also show that this prime-factorization reduction is useful for the construction of self-orthogonal and self-dual integer codes, i.e., these properties in the prime-power moduli are preserved in the composite-number modulus. Numerical examples of integer codes and generator matrices demonstrate these facts and processes.

Amorphous indium-gallium-zinc-oxide (a-IGZO) thin film transistor (TFT) was fabricated by an advanced self-aligned imprint lithography (ASAIL) method with a hybrid etching process. The SAIL is a top-down method to fabricate a TFT using a three-dimensional multilayer etch mask having all pattern information for the TFT. The hybrid etching process was newly applied in the original SAIL process for the purpose of reducing plasma damage of a-IGZO channel layer during plasma etching in the ASAIL process. This research demonstrated that the a-IGZO TFT could be successfully fabricated by the ASAIL process. In particular, the hybrid etching process applied in this paper can be utilized for the back-channel-etch type a-IGZO TFT and further extended for the roll-to-roll backplane process.

Recently, a control technique of light distribution pattern has become important to improve the functionality and the light utilization efficiency of electronic displays, illumination devices and so on. As a light control technique, polymer-dispersed liquid crystals (PDLCs) have been commonly used so far. However, a precise control of the light diffusion distribution of conventional PDLC has been difficult due to the random polymer network structure, which results in the low light utilization efficiency. On the other hand, reverse-mode PDLCs with homogeneously aligned molecules can anisotropically diffuse light. The reverse-mode PDLC, however, has polarization dependency in the haze value due to homogeneously aligned molecules, which also results in the low light utilization efficiency. Therefore, it is necessary to establish the optimization method of light diffusion distribution without the molecules alignment treatment, and we have proposed a novel PDLC with structure-controlled polymer network which was fabricated by the irradiation with uni-directionally diffused UV light. In this paper, we investigated the effect of the process temperature during UV irradiation on the internal structure and light diffusion distribution of the proposed PDLC. As a result, in case that the mixture during UV irradiation was in isotropic phase, we clarified that the structure-controlled PDLCs with alternating striped LCs/polymer pattern could be obtained because the mixture was sufficiently irradiated with uni-directionally diffused UV light. For the high haze, this structure-controlled PDLC should be fabricated as low temperature as possible with maintaining the mixture in isotropic phase so that the mixture was not a nano-scaled molecular mixing state. Also, this PDLC had no polarization dependency in the haze value and could electrically switch the light distribution pattern between anisotropic light diffusion and light transmission. From the above results, we concluded that the proposed PDLC could precisely control the light diffusion distribution, and realize the high light utilization efficiency.

This paper presents an efficient acceleration algorithm for Lloyd-type k-means clustering, which is suitable to a large-scale and high-dimensional data set with potentially numerous classes. The algorithm employs a novel projection-based filter (PRJ) to avoid unnecessary distance calculations, resulting in high-speed performance keeping the same results as a standard Lloyd's algorithm. The PRJ exploits a summable lower bound on a squared distance defined in a lower-dimensional space to which data points are projected. The summable lower bound can make the bound tighter dynamically by incremental addition of components in the lower-dimensional space within each iteration although the existing lower bounds used in other acceleration algorithms work only once as a fixed filter. Experimental results on large-scale and high-dimensional real image data sets demonstrate that the proposed algorithm works at high speed and with low memory consumption when large k values are given, compared with the state-of-the-art algorithms.

Automatic monitoring of food intake in free living conditions is still an open problem to solve. This paper presents a novel necklace-type wearable system embedded with a piezoelectric sensor to monitor ingestive behavior by detecting skin motion from the lower trachea. Detected events are incorporated for food classification. Unlike the previous state-of-the-art piezoelectric sensor based system that employs spectrogram features, we have tried to fully exploit time-domain based signals for optimal features. Through numerous evaluations on the length of a frame, we have found the best performance with a frame length of 70 samples (3.5 seconds). This demonstrates that the chewing sequence carries important information for food classification. Experimental results show the validity of the proposed algorithm for food intake detection and food classification in real-life scenarios. Our system yields an accuracy of 89.2% for food intake detection and 80.3% for food classification over 17 food categories. Additionally, our system is based on a smartphone app, which helps users live healthy by providing them with real-time feedback about their ingested food episodes and types.

A hexagonal waveguide branch line coupler suitable for additive manufacturing is proposed in this study, and its design method is elucidated. The additive manufactured Ka-band coupler exhibits characteristics similar to those of a machined coupler, but its weight and cost are reduced by 40% and 60%, respectively. Its effectiveness is also confirmed in this study.

This paper proposes a novel environmental monitoring strategy, incremental environmental monitoring, that enables scientists to reveal the ecology of wild animals in the field. We applied this strategy to the habitat of endangered freshwater fish. Specifically, we designed and implemented a network-based system using distributed sensors to continuously monitor and record the habitat of endangered fish. Moreover, we developed a set of analytical tools to exploit a variety of sensor data, including environmental time-series data such as amount of dissolved oxygen, as well as underwater video capturing the interaction of fish and their environment. We also describe the current state of monitoring the behavior and habitat of endangered fish and discuss solutions for making such environmental monitoring more efficient in the field.

Location information is essential to varieties of applications. It is one of the most important context to be detected by wireless distributed sensors, which is a key technology in Internet-of-Things. Fingerprint-based methods, which compare location unique fingerprints collected beforehand with the fingerprint measured from the target, have attracted much attention recently in both of academia and industry. They have been successfully used for many location-based applications. From the viewpoint of practical applications, in this paper, four different typical approaches of fingerprint-based radio emitter localization system are introduced with four different representative applications: localization of LTE smart phone used for anti-cheating in exams, indoor localization of Wi-Fi terminals, localized light control in BEMS using location information of occupants, and illegal radio localization in outdoor environments. Based on the different practical application scenarios, different solutions, which are designed to enhance the localization performance, are discussed in detail. To the best of the authors' knowledge, this is the first paper to give a guideline for readers about fingerprint-based localization system in terms of fingerprint selection, hardware architecture design and algorithm enhancement.

Spectrum databases are required to assist the process of radio propagation estimation for spectrum sharing. Especially, a measurement-based spectrum database achieves highly efficient spectrum sharing by storing the observed radio environment information such as the signal power transmitted from a primary user. However, when the average received signal power is calculated in a given square mesh, the bias of the observation locations within the mesh strongly degrades the accuracy of the statistics because of the influence of terrain and buildings. This paper proposes a method for determining the statistics by using mesh clustering. The proposed method clusters the feature vectors of the measured data by using the k-means and Gaussian mixture model methods. Simulation results show that the proposed method can decrease the error between the measured value and the statistically processed value even if only a small amount of data is available in the spectrum database.

A light field, which is often understood as a set of dense multi-view images, has been utilized in various 2D/3D applications. Efficient light field acquisition using a coded aperture camera is the target problem considered in this paper. Specifically, the entire light field, which consists of many images, should be reconstructed from only a few images that are captured through different aperture patterns. In previous work, this problem has often been discussed from the context of compressed sensing (CS), where sparse representations on a pre-trained dictionary or basis are explored to reconstruct the light field. In contrast, we formulated this problem from the perspective of principal component analysis (PCA) and non-negative matrix factorization (NMF), where only a small number of basis vectors are selected in advance based on the analysis of the training dataset. From this formulation, we derived optimal non-negative aperture patterns and a straight-forward reconstruction algorithm. Even though our method is based on conventional techniques, it has proven to be more accurate and much faster than a state-of-the-art CS-based method.

Wireless power transfer (WPT) via coupled magnetic resonances has more than ten years history of development. However, it appears frequency splitting phenomenon in the over-coupled region, thus, the output power of the two-coil WPT system achieves the maximum output power at the two splitting angular frequencies and not at the natural resonant angular frequency. By investigating the relationship between the impedances of the transmitter side and receiver side, we found that WPT system is a power superposition system, and the reasons were given to explaining how to appear the frequency splitting and impact on the maximum output power of the system in details. First, the circuit model was established and transfer characteristics of the two-coil WPT system were studied by utilizing circuit theories. Second, the mechanism of the power superposition of the WPT system was carefully researched. Third, the relationship between the impedances of the transmitter side and receiver side was obtained by investigating the impedance characteristics of a two-coil WPT system, and also the impact factors of the maximum output power of the system were obtained by using a power superposition mechanism. Finally, the experimental circuit was designed and experimental results are well consistent with the theoretical analysis.

Managing SaaS systems requires administrators to monitor and analyze diverse types of log data collected from a variety of components such as applications and IT resources. Integrated monitoring systems, enabled with datastore capable of storing and query-based processing of semi-structured data (e.g., NOSQL - some specific document database), is a promising solution that can store and query any type of log data with a single unified set of management panes. However, due to the increasing scale of SaaS systems and their long service lives, integrated monitoring systems have faced the problems in response times of log analysis and storage consumption for logs. In this present work, we solve the problems by developing an efficient log management method for SaaS systems. Our empirical observation is that the problems are primarily derived from the unselective log processing of datastore, whereas there should be heterogeneities in log data that we can take advantage of for efficient log management. Based on this observation, we first confirm this insight by investigating the usage patterns of log data in a quantitative manner with an actual dataset of log access histories obtained from a SaaS system serving tens of thousands of enterprise users over the course of more than 1.5 years. We show that there are heterogeneities in required retention period of logs, response time of log analysis, and amount of data, and the heterogeneities depend on log data category and its analysis scenario. Armed with the evidence of the heterogeneities in log data and the usage patterns found from the investigation, we design a methodology of context-aware log data management, key features of which are to speculatively pre-cache the result of log analysis and to proactively archive log data, depending on log data category and analysis scenario. Evaluation with a prototype implementation shows that the proposed method reduces the response time by 47% compared to a conventional method and the storage consumption by approximately 40% compared to the original log data.

We show a construction of a quantum ramp secret sharing scheme from a nested pair of linear codes. Necessary and sufficient conditions for qualified sets and forbidden sets are given in terms of combinatorial properties of nested linear codes. An algebraic geometric construction for quantum secret sharing is also given.

We have proposed a fish farm monitoring system for achieving efficient fish farming. In our system, sensor nodes are attached at fish to monitor its health status. In this letter, we propose a method for gathering sensor data from sensor nodes to sink nodes when the transmission range of sensor node is shorter than the size of fish cage. In our proposed method, a part of sensor nodes become leader nodes and they forward gathered sensor data to the sink nodes. Through simulation evaluations, we show that the data gathering performance of our proposed method is higher than that of traditional methods.