Synthetic aperture interferometric radiometer (SAIR) is a powerful sensors for high-resolution imaging. However, because of the observation errors and small number of visibility sampling points, the accuracy of reconstructed images is usually low. To overcome this deficiency, a novel super-resolution imaging (SrI) method based on super-resolution reconstruction idea is proposed in this paper. In SrI method, sparse visibility functions are first measured at different observation locations. Then the sparse visibility functions are utilized to simultaneously construct the fusion visibility function and the fusion imaging model. Finally, the high-resolution image is reconstructed by solving the sparse optimization of fusion imaging model. The simulation results demonstrate that the proposed SrI method has higher reconstruction accuracy and can improve the imaging quality of SAIR effectively.

Rehabilitation and evaluation of motor function are important for motor disabled patients. In stride length estimation using an IMU attached to the foot, it is necessary to detect the time of the movement state, in which acceleration should be integrated. In our previous study, acceleration thresholds were used to determine the integration section, so it was necessary to adjust the threshold values for each subject. The purpose of this study was to develop a method for estimating stride length automatically using an artificial neural network (ANN). In this paper, a 4-layer ANN with feature extraction layers trained by autoencoder was tested. In addition, the methods of searching for the local minimum of acceleration or ANN output after detecting the movement state section by ANN were examined. The proposed method estimated the stride length for healthy subjects with error of -1.88 ± 2.36%, which was almost the same as the previous threshold based method (-0.97 ± 2.68%). The correlation coefficients between the estimated stride length and the reference value were 0.981 and 0.976 for the proposed and previous methods, respectively. The error ranges excluding outliers were between -7.03% and 3.23%, between -7.13% and 5.09% for the proposed and previous methods, respectively. The proposed method would be effective because the error range was smaller than the conventional method and no threshold adjustment was required.

The brain processes numerous information related to traffic scenes for appropriate perception, judgment, and operation in vehicle driving. Here, the strategy for perception, judgment, and operation is individually different for each driver, and this difference is thought to be arise from experience of driving. In the present work, we measure and analyze human brain activity (EEG: Electroencephalogram) related to visual perception during vehicle driving to clarify the relationship between experience of driving and brain activity. As a result, more experts generate α activities than beginners, and also confirm that the β activities is reduced than beginners. These results firstly indicate that experience of driving is reflected into the activation pattern of EEG.

Matching circuits using LC elements are widely applied to high-frequency circuits such as power amplifier (PA) and low-noise amplifier (LNA). For determining matching condition of multi-stage matching circuits, this paper shows that any multi-stage LC-Ladder matching circuit with resistive termination can be decomposed to the extended L-type matching circuits with resistive termination containing negative elements where the analytical solution exists. The matching conditions of each extended L-type matching circuit are obtained easily from the termination resistances and the design frequency. By synthesizing these simple analysis solutions, it is possible to systematically determine the solution even in a large number of stages (high order) matching circuits.

In the statistic en-route filtering, each report generation node must collect a certain number of endorsements from its neighboring nodes. However, at some point, a node may fail to collect an insufficient number of endorsements since some of its neighboring nodes may have dead batteries. This letter presents a report generation method that can enhance the generation process of sensing reports under such a situation. Simulation results show the effectiveness of the proposed method.

In this paper, we study a radio frequency (RF)-powered backscatter assisted cognitive radio network (CRN), where an eavesdropper exists. This network includes a primary transmitter, a pair of secondary transmitter and receiver, a friendly jammer and an eavesdropper. We assume that the secondary transmitter works in ambient backscatter (AmBack) mode and the friendly jammer works in harvest-then-transmit (HTT) mode, where the primary transmitter serves as energy source. To enhance the physical layer security of the secondary user, the friendly jammer uses its harvested energy from the primary transmitter to transmit jamming noise to the eavesdropper. Furthermore, for maximizing the secrecy rate of secondary user, the optimal time allocation including the energy harvesting and jamming noise transmission phases is obtained. Simulation results verify the superiority of the proposed scheme.

Fast computation of singular value decomposition (SVD) is of great interest in various machine learning tasks. Recently, SVD methods based on randomized linear algebra have shown significant speedup in this regime. For processing large-scale data, computing systems with accelerators like GPUs have become the mainstream approach. In those systems, access to the input data dominates the overall process time; therefore, it is needed to design an out-of-core algorithm to dispatch the computation into accelerators. This paper proposes an accurate two-pass randomized SVD, named block randomized SVD (BRSVD), designed for matrices with a slow-decay singular spectrum that is often observed in image data. BRSVD fully utilizes the power of modern computing system architectures and efficiently processes large-scale data in a parallel and out-of-core fashion. Our experiments show that BRSVD effectively moves the performance bottleneck from data transfer to computation, so that outperforms existing randomized SVD methods in terms of speed with retaining similar accuracy.

We propose a new framework named ROICF based on reinforcement learning orienting reliable compilation optimization sequence generation. On the foundation of the LLVM standard compilation optimization passes, we can obtain specific effective phase ordering for different programs to improve program reliability.

A planar electromagnetic field stirrer with periodically arranged metal patterns and diode switches is proposed for improving uneven heating of a heated object placed in a microwave oven. The reflection phase of the proposed stirrer changes by switching the states of diodes mounted on the stirrer and the electromagnetic field in the microwave oven is stirred. The temperature distribution of a heated object located in a microwave oven was simulated and measured using the stirrer in order to evaluate the improving effect of the uneven heating. As the result, the heated parts of the objects were changed with the diode states and the improving effect of the uneven heating was experimentally indicated.

In wireless sensor networks (WSN), communication interference and the energy limitation of sensor nodes seriously hamper the network performance such as throughput and network lifetime. In this paper, we focus on the Successive Interference Cancellation (SIC) and Wireless Energy Transmission (WET) technology aiming to design a heuristic power control algorithm and an efficient cross-layer strategy to realize concurrency communication and improve the network throughput, channel utilization ratio and network lifetime. We realize that the challenge of this problem is that joint consideration of communication interference and energy shortage makes the problem model more complicated. To solve the problem efficiently, we adopt link scheduling strategy, time-slice scheduling scheme and energy consumption optimization protocol to construct a cross-layer optimization problem, then use an approximate linearization method to transform it into a linear problem which yields identical optimal value and solve it to obtain the optimal work strategy of wireless charging equipment (WCE). Simulation results show that adopting SIC and WCE can greatly improve communication capability and channel utilization ratio, and increase throughput by 200% to 500% while prolonging the network lifetime.

A limited number of types of sound event occur in an acoustic scene and some sound events tend to co-occur in the scene; for example, the sound events “dishes” and “glass jingling” are likely to co-occur in the acoustic scene “cooking.” In this paper, we propose a method of sound event detection using graph Laplacian regularization with sound event co-occurrence taken into account. In the proposed method, the occurrences of sound events are expressed as a graph whose nodes indicate the frequencies of event occurrence and whose edges indicate the sound event co-occurrences. This graph representation is then utilized for the model training of sound event detection, which is optimized under an objective function with a regularization term considering the graph structure of sound event occurrence and co-occurrence. Evaluation experiments using the TUT Sound Events 2016 and 2017 detasets, and the TUT Acoustic Scenes 2016 dataset show that the proposed method improves the performance of sound event detection by 7.9 percentage points compared with the conventional CNN-BiGRU-based detection method in terms of the segment-based F1 score. In particular, the experimental results indicate that the proposed method enables the detection of co-occurring sound events more accurately than the conventional method.

Some non-acoustic modalities have the ability to reveal certain speech attributes that can be used for synthesizing speech signals without acoustic signals. This study validated the use of ultrasonic Doppler frequency shifts caused by facial movements to implement a silent speech interface system. A 40kHz ultrasonic beam is incident to a speaker's mouth region. The features derived from the demodulated received signals were used to estimate the speech parameters. A nonlinear regression approach was employed in this estimation where the relationship between ultrasonic features and corresponding speech is represented by deep neural networks (DNN). In this study, we investigated the discrepancies between the ultrasonic signals of audible and silent speech to validate the possibility for totally silent communication. Since reference speech signals are not available in silently mouthed ultrasonic signals, a nearest-neighbor search and alignment method was proposed, wherein alignment was achieved by determining the optimal pair of ultrasonic and audible features in the sense of a minimum mean square error criterion. The experimental results showed that the performance of the ultrasonic Doppler-based method was superior to that of EMG-based speech estimation, and was comparable to an image-based method.

In IoT systems, data acquired by many sensors are required. However, since sensor operation depends on the actual environment, it is important to ensure sensor redundancy to improve system reliability in IoT systems. To evaluate the safety of the system, it is important to estimate the achievement probability of the function based on the sensing probability. In this research, we proposed a method to automatically generate a PRISM model from the sensor configuration of the target system and calculate and verify the function achievement probability in the assumed environment. By designing and evaluating iteratively until the target achievement probability is reached, the reliability of the system can be estimated at the initial design phase. This method reduces the possibility that the lack of reliability will be found after implementation and the redesign accompanying it will occur.

The choreography realization problem is a design challenge for systems based on service-oriented architecture. In our previous studies, we studied the problem on a case where choreography was given by one or two scenarios and was expressed by an acyclic relation of events; we introduced the notion of re-constructibility as a property of acyclic relations to be satisfied. However, when choreography is defined by multiple scenarios, the resulting behavior cannot be expressed by an acyclic relation. An event structure is composed of an acyclic relation and a conflict relation. Because event structures are a generalization of acyclic relations, a wider class of systems can be expressed by event structures. In this paper, we propose the use of event structures to express choreography, introduce the re-constructibility of event structures, and show a necessary condition for an event structure to be re-constructible.

Boolean networks (BNs) are considered as popular formal models for the dynamics of gene regulatory networks. There are many different types of BNs, depending on their updating scheme (synchronous, asynchronous, deterministic, or non-deterministic), such as Classical Random Boolean Networks (CRBNs), Asynchronous Random Boolean Networks (ARBNs), Generalized Asynchronous Random Boolean Networks (GARBNs), Deterministic Asynchronous Random Boolean Networks (DARBNs), and Deterministic Generalized Asynchronous Random Boolean Networks (DGARBNs). An important long-term behavior of BNs, so-called attractor, can provide valuable insights into systems biology (e.g., the origins of cancer). In the previous paper [1], we have studied properties of attractors of GARBNs, their relations with attractors of CRBNs, also proposed different algorithms for attractor detection. In this paper, we propose a new algorithm based on SAT-based bounded model checking to overcome inherent problems in these algorithms. Experimental results prove the effectiveness of the new algorithm. We also show that studying attractors of GARBNs can pave potential ways to study attractors of ARBNs.

The accurate calculation of the inductance is the most basic problem of the inductor design. In this paper, the core flux density distribution and leakage flux in core window and winding of core-type inductor are analyzed by finite element analysis (FEA) firstly. Based on it, an improved magnetic equivalent circuit with high accuracy flux density distribution (iMEC) is proposed for a single-phase core-type inductor. Depend on the geometric structure, two leakage paths of the core window are modeled. Furthermore, the iMEC divides the magnetomotive force of the winding into the corresponding core branch. It makes the core flux density distribution consistent with the FEA distribution to improve the accuracy of the inductance. In the iMEC, flux density of the core leg has an error less than 5.6% compared to FEA simulation at 150A. The maximum relative error of the inductance is less than 8.5% and the average relative error is less than 6% compared to the physical prototype test data. At the same time, due to the high computational efficiency of iMEC, it is very suitable for the population-based optimization design.

This study focuses on computer based foreign language vocabulary learning systems. Our objective is to automatically build vocabulary decks with desired levels of relative difficulty relations. To realize this goal, we exploit the fact that word frequency is a good indicator of vocabulary difficulty. Subsequently, for composing the decks, we pose two requirements as uniformity and diversity. Namely, the difficulty level of the cards in the same deck needs to be uniform enough so that they can be grouped together and difficulty levels of the cards in different decks need to be diverse enough so that they can be grouped in different decks. To assess uniformity and diversity, we use rank-biserial correlation and propose an iterative algorithm, which helps in attaining desired levels of uniformity and diversity based on word frequency in daily use of language. In experiments, we employed a spaced repetition flashcard software and presented users various decks built with the proposed algorithm, which contain cards from different content types. From users' activity logs, we derived several behavioral variables and examined the polyserial correlation between these variables and difficulty levels across different word classes. This analysis confirmed that the decks compiled with the proposed algorithm induce an effect on behavioral variables in line with the expectations. In addition, a series of experiments with decks involving varying content types confirmed that this relation is independent of word class.

This paper describes graph-based Wave Function Collapse algorithm for procedural content generation. The goal of this system is to enable a game designer to procedurally create key content elements in the game level through simple association rule input. To do this, we propose a graph-based data structure that can be easily integrated with a navigation mesh data structure in a three-dimensional world. With our system, if the user inputs the minimum association rule, it is possible to effectively perform procedural content generation in the three-dimensional world. The experimental results show that the Wave Function Collapse algorithm, which is a texture synthesis algorithm, can be extended to non-grid shape content with high controllability and scalability.

In this letter, the principle of LLR-based successive-cancellation (SC) polar decoding algorithm is explored. In order to simplify the logarithm and exponential operations in the updating rules for polar codes, we further utilize a piece-wise linear algorithm to approximate the transcendental functions, where the piece-wise linear algorithm only consists of multiplication and addition operations. It is demonstrated that with one properly allowable maximum error δ chosen for success-failure algorithm, performances approach to that of the standard SC algorithm can be achieved. Besides, the complexity reduction is realized by calculating a linear function instead of nonlinear function. Simulation results show that our proposed piece-wise SC decoder greatly reduces the complexity of the SC-based decoders with no loss in error correcting performance.