This study evaluates the effects of some non-learning blind bandwidth extension (BWE) methods on state-of-the-art automatic speaker verification (ASV) systems. Recently, a non-linear bandwidth extension (N-BWE) method has been proposed as a blind, non-learning, and light-weight BWE approach. Other non-learning BWEs have also been developed in recent years. For ASV evaluations, most data available to train ASV systems is narrowband (NB) telephone speech. Meanwhile, wideband (WB) data have been used to train the state-of-the-art ASV systems, such as i-vector, d-vector, and x-vector. This can cause sampling rate mismatches when all datasets are used. In this paper, we investigate the influence of sampling rate mismatches in the x-vector-based ASV systems and how non-learning BWE methods perform against them. The results showed that the N-BWE method improved the equal error rate (EER) on ASV systems based on the x-vector when the mismatches were present. We researched the relationship between objective measurements and EERs. Consequently, the N-BWE method produced the lowest EERs on both ASV systems and obtained the lower RMS-LSD value and the higher STOI score.

Because of its high throughput potentiality on short-range communications and inherent superiority of high precision on ranging and localization, ultra-wideband (UWB) technology has been attracting attention continuously in research and development (R&D) as well as in commercialization. The first domestic regulation admitting indoor UWB in Japan was released by the Ministry of Internal Affairs and Communications (MIC) in 2006. Since then, several revisions have been made in conjunction with UWB commercial penetration, emerging new trends of industrial demands, and coexistence evaluation with other wireless systems. However, it was not until May 2019 that MIC released a new revision to admit outdoor UWB. Meanwhile, the IEEE 802 LAN/MAN Standards Committee has been developing several UWB related standards or amendments accordingly for supporting different use cases. At the time when this paper is submitted, a new amendment known as IEEE 802.15.4z is undergoing drafting procedure which is expected to enhance ranging ability for impulse radio UWB (IR-UWB). In this paper, we first review the domestic UWB regulation and some of its revisions to get a picture of the domestic regulation transition from indoor to outdoor. We also foresee some anticipating changes in future revisions. Then, we overview several published IEEE 802 standards or amendments that are related to IR-UWB. Some features of IEEE 802.15.4z in drafting are also extracted from open materials. Finally, we show with our recent research results that time bias internal a transceiver becomes important for increasing localization accuracy.

With massive utilization of video in every aspect of our daily lives, managing videos is crucial and demanding. The rich literature of data embedding has proven its viability in managing as well as enriching videos and other multimedia contents, but conventional methods are designed to operate in the media/compression layer. In this work, the synchronization between the audio-video and subtitle tracks within an MP4 format container is manipulated to insert data. Specifically, the data are derived from the statistics of the audio samples and video frames, and it serves as the authentication data for verification purpose. When needed, the inserted authentication data can be extracted and compared against the information computed from the received audio samples and video frames. The proposed method is lightweight because simple statistics, i.e., ‘0’ and ‘1’ at the bit stream level, are treated as the authentication data. Furthermore, unlike conventional MP4 container format-based data insertion technique, the bit stream size remains unchanged before and after data insertion using the proposed method. The proposed authentication method can also be deployed for joint utilization with any existing authentication technique for audio / video as long as these media can be multiplexed into a single bit stream and contained within an MP4 container. Experiments are carried out to verify the basic functionality of the proposed technique as an authentication method.

As Internet-connected service is emerged, there has been a need for use cases where a lightweight cryptographic primitive meets both of a constrained hardware implementation requirement and a constrained embedded software requirement. One of the examples of these use cases is the PKES (Passive Keyless Entry and Start) system in an automotive domain. From the perspective on these use cases, one interesting direction is to investigate how small the memory (RAM/ROM) requirement of ARM-implementations of hardware-oriented stream ciphers can be. In this paper, we propose implementation techniques for memory-optimized implementations of lightweight hardware-oriented stream ciphers including Grain-128a specified in ISO/IEC 29167-13 for RFID protocols. Our techniques include data-dependency analysis to take a close look at how and in which timing certain variables are updated and also the way taking into account the structure of registers on the target micro-controller. In order to minimize RAM size, we reduce the number of general purpose registers for computation of Grain-128a's update and pre-output values. We present results of our memory-optimized implementations of Grain-128a, one of which requires 84 RAM bytes on ARM Cortex-M3.

The major weakness of global navigation satellite system receivers is their vulnerability to intentional and unintentional interference. Frequency domain interference suppression (FDIS) technology is one of the most useful countermeasures. The pseudo-range measurement is unbiased after FDIS filtering given an ideal analog channel. However, with the influence of the analog modules used in RF front-end, the amplitude response and phase response of the channel equivalent filter are non-ideal, which bias the pseudo-range measurement after FDIS filtering and the bias varies along with the frequency of the interference. This paper proposes an unbiased interference suppression method based on signal estimation and spectrum compensation. The core idea is to use the parameters calculated from the tracking loop to estimate and reconstruct the desired signal. The estimated signal is filtered by the equivalent filter of actual channel, then it is used for compensating the spectrum loss caused by the FDIS method in the frequency domain. Simulations show that the proposed algorithm can reduce the pseudo-range measurement bias significantly, even for channels with asymmetrical group delay and multiple interference sources at any location.

For the frequency-division-duplex (FDD)-based massive multiple-input multiple-output (MIMO) systems, channel state information (CSI) feedback plays a critical role. Although deep learning has been used to compress the CSI feedback, some issues like truncation and noise still need further investigation. Facing these practical concerns, we propose an improved model (called CsiNet-Plus), which includes a truncation process and a channel noise process. Simulation results demonstrate that the CsiNet-Plus outperforms the existing CsiNet. The performance interchangeability between truncated decimal digits and the signal-to-noise-ratio helps support flexible configuration.

Multi-modal semantic trajectory prediction has become a new challenge due to the rapid growth of multi-modal semantic trajectories with text message. Traditional RNN trajectory prediction methods have the following problems to process multi-modal semantic trajectory. The distribution of multi-modal trajectory samples shifts gradually with training. It leads to difficult convergency and long training time. Moreover, each modal feature shifts in different directions, which produces multiple distributions of dataset. To solve the above problems, MNERM (Mode Normalization Enhanced Recurrent Model) for multi-modal semantic trajectory is proposed. MNERM embeds multiple modal features together and combines the LSTM network to capture long-term dependency of trajectory. In addition, it designs Mode Normalization mechanism to normalize samples with multiple means and variances, and each distribution normalized falls into the action area of the activation function, so as to improve the prediction efficiency while improving greatly the training speed. Experiments on real dataset show that, compared with SERM, MNERM reduces the sensitivity of learning rate, improves the training speed by 9.120 times, increases HR@1 by 0.03, and reduces the ADE by 120 meters.

Fluctuations in nonlinear systems can enhance the synchronization with weak input signals. These nonlinear synchronization phenomena are classified as stochastic resonance and chaotic resonance. Many applications of stochastic resonance have been realized, utilizing its enhancing effect for the signal sensitivity. However, although some studies showed that the sensitivity of chaotic resonance is higher than that of stochastic resonance, only few studies have investigated the engineering application of chaotic resonance. A possible reason is that, in chaotic resonance, the chaotic state must be adjusted through internal parameters to reach the state that allows resonance. In many cases and especially in biological systems, such adjustments are difficult to perform externally. To overcome this difficulty, we developed a method to control the chaotic state for an appropriate state of chaotic resonance by using an external feedback signal. The method is called reducing the range of orbit (RRO) feedback method. Previously, we have developed the RRO feedback method for discrete chaotic systems. However, for applying the RRO feedback method to actual chaotic systems including biological systems, development of the RRO feedback signals in continuous chaotic systems must be considered. Therefore, in this study, we extended the RRO feedback method to continuous chaotic systems by focusing on the map function on the Poincaré section. We applied the extended RRO feedback method to Chua's circuit as a continuous chaotic system. The results confirmed that the RRO feedback signal can induce chaotic resonance. This study is the first to report the application of RRO feedback to a continuous chaotic system. The results of this study will facilitate further device development based on chaotic resonance.

Let us consider a situation where someone wants to encrypt his/her will on an existing blockchain, e.g. Bitcoin, and allow an encrypted will to be decryptable only if designated members work together. At a first glance, such a property seems to be easily provided by using conventional threshold encryption. However, this idea cannot be straightforwardly implemented since key pairs for an encryption mechanism is additionally required. In this paper, we propose a new threshold encryption scheme in which key pairs for ECDSA that are already used in the Bitcoin protocol can be directly used as they are. Namely, a unique key pair can be simultaneously used for both ECDSA and our threshold encryption scheme without losing security. Furthermore, we implemented our scheme on the Bitcoin regtest network, and show that it is fairly practical. For example, the execution time of the encryption algorithm Enc (resp., the threshold decryption algorithm Dec) is 0.2sec. (resp., 0.3sec.), and the total time is just only 3sec. including all the cryptographic processes and network communications for a typical parameter setting. Also, we discuss several applications of our threshold encryption scheme in detail: Claiming priority of intellectual property, sealed-bid auction, lottery, and coin tossing service.

A low-complexity time-invariant angle-range dependent directional modulation (DM) based on time-modulated frequency diverse array (TM-FDA-DM) is proposed to achieve point-to-point physical layer security communications. The principle of TM-FDA is elaborated and the vector synthesis method is utilized to realize the proposal, TM-FDA-DM, where normalization and orthogonal matrices are designed to modulate the useful baseband symbols and inserted artificial noise, respectively. Since the two designed matrices are time-invariant fixed values, which avoid real-time calculation, the proposed TM-FDA-DM is much easier to implement than time-invariant DMs based on conventional linear FDA or logarithmical FDA, and it also outperforms the time-invariant angle-range dependent DM that utilizes genetic algorithm (GA) to optimize phase shifters on radio frequency (RF) frontend. Additionally, a robust synthesis method for TM-FDA-DM with imperfect angle and range estimations is proposed by optimizing normalization matrix. Simulations demonstrate that the proposed TM-FDA-DM exhibits time-invariant and angle-range dependent characteristics, and the proposed robust TM-FDA-DM can achieve better BER performance than the non-robust method when the maximum range error is larger than 7km and the maximum angle error is larger than 4°.

As one of the most popular intelligent optimization algorithms, Simulated Annealing (SA) faces two key problems, the generation of perturbation solutions and the control strategy of the outer loop (cooling schedule). In this paper, we introduce the Gaussian Cloud model to solve both problems and propose a novel cloud annealing algorithm. Its basic idea is to use the Gaussian Cloud model with decreasing numerical character He (Hyper-entropy) to generate new solutions in the inner loop, while He essentially indicates a heuristic control strategy to combine global random search of the outer loop and local tuning search of the inner loop. Experimental results in function optimization problems (i.e. single-peak, multi-peak and high dimensional functions) show that, compared with the simple SA algorithm, the proposed cloud annealing algorithm will lead to significant improvement on convergence and the average value of obtained solutions is usually closer to the optimal solution.

Herein, we analytically derive the effective index and field distribution of the LP01 mode of a step-index N-sided regular-polygonal-core fiber. To do this, we utilize the lowest-order non-anomalous approximation of the π/N expansion. These properties are also calculated numerically and the results are compared the with approximations.

Power line communications (PLC) is a communication technology that uses a power-line as a transmission medium. Previous studies have shown that connecting an AC adapter such as a mobile phone charger to the power-line affects signal quality. Therefore, in this paper, the authors analyze the influence of chargers on inter-computer communications using packet capture to evaluate communications quality. The analysis results indicate the occurrence of a short duration in which packets are not detected once in a half period of the power-line supply: named communication forbidden time. For visualizing the communication forbidden time and for evaluating the communications quality of the inter-computer communications using PLC, the authors propose an instantaneous power-line frequency synchronized superimposed chart and its plotting algorithm. Further, in order to analyze accurately, the position of the communication forbidden time can be changed by altering the initial burst signal plotting position. The difference in the chart, which occurs when the plotting start position changes, is also discussed. We show analysis examples using the chart for a test bed data assumed an ideal environment, and show the effectiveness of the chart for analyzing PLC inter-computer communications.

Indexing is one of the fields where the non-volatile memory (NVM) has the advantages of byte-addressable characteristics and fast read/write speed. The existing index structures for NVM have been developed based on the fact that the size of cache line and the atomicity guarantee unit of NVM are different and they tried to overcome the weakness of consistency from the difference. To overcome the weakness, an expensive flush operation is required which results in a lower performance than a basic B+tree index. Recent studies have shown that the I/O units of the NVM can be matched with the atomicity guarantee units under limited circumstances. In this paper, we propose a Cache line sized Atomic Write B+tree (CAWBT), which is a minimal B+tree structure that shows higher performance than a basic b+ tree and designed for NVM. CAWBT has almost same performance compared to basic B+tree without consistency guarantee and shows remarkable performance improvement compared to other B+tree indexes for NVM.

Spoofing attacks are one of the biggest concerns for most biometric recognition systems. This will be also the case with silhouette-based gait recognition in the near future. So far, gait recognition has been fortunately out of the scope of spoofing attacks. However, it is becoming a real threat with the rapid growth and spread of deep neural network-based multimedia generation techniques, which will allow attackers to generate a fake video of gait silhouettes resembling a target person's walking motion. We refer to such computer-generated fake silhouettes as gait silhouette clones (GSCs). To deal with the future threat caused by GSCs, in this paper, we propose a supervised method for discriminating GSCs from genuine gait silhouettes (GGSs) that are observed from actual walking people. For training a good discriminator, it is important to collect training datasets of both GGSs and GSCs which do not differ from each other in any aspect other than genuineness. To this end, we propose to generate a training set of GSCs from GGSs by transforming them using multiple autoencoders. The generated GSCs are used together with their original GGSs for training the discriminator. In our experiments, the proposed method achieved the recognition accuracy of up to 94% for several test datasets, which demonstrates the effectiveness and the generality of the proposed method.

Free/libre open source software (FLOSS) are being rapidly employed in several companies and organizations, because it can be modified and used for free. Hence, the use of FLOSS could contribute to its originally intended benefits and to the competence of its users. In this study, we analyzed the effect of using FLOSS on related competences. We investigated the change in the competences through an empirical study before and after the use of FLOSS among project participants. Consequently, it was confirmed that the competences of the participants improved after utilizing FLOSS.

The natural gradient descent is an optimization method for real-valued neural networks that was proposed from the viewpoint of information geometry. Here, we present an extension of the natural gradient descent to complex-valued neural networks. Our idea is to use the Hermitian extension of the Fisher information matrix. Moreover, we generalize the projected natural gradient (PRONG), which is a fast natural gradient descent algorithm, to complex-valued neural networks. We also consider the advantage of complex-valued neural networks over real-valued neural networks. A useful property of complex numbers in the complex plane is that the rotation is simply expressed by the multiplication. By focusing on this property, we construct the output function of complex-valued neural networks, which is invariant even if the input is changed to its rotated value. Then, our complex-valued neural network can learn rotated data without data augmentation. Finally, through simulation of online character recognition, we demonstrate the effectiveness of the proposed approach.

In this letter, we propose a novel wireless power transfer (WPT) scheme in the radiative near-field (Fresnel) region, which based on machine vision and dynamically reconfigurable holographic metasurface aperture capable of focusing power to multiple spots simultaneously without any information feedback. The states of metamaterial elements, formed by tunable meander line resonators, is determined using holographic design principles, in which the interference pattern of reference mode and the desired radiated field pattern leads to the required phase distribution over the surface of the aperture. The three-dimensional position information of mobile point sources is determined by machine visual localization, which can be used to obtain the aperture field. In contrast to the existing research studies, the proposed scheme is not only designed to achieve free multi-focuses, but also with machine vision, low-dimensionality, high transmission efficiency, real-time continuous reconfigurability and so on. The accuracy of the analysis is confirmed using numerical simulation.

In order to obtain high-performance circuits in advanced technology nodes, design methodology has to take the existence of large delay variations into account. Clock scheduling and speculative execution have overheads to realize them, but have potential to improve the performance by averaging the imbalance of maximum delay among paths and by utilizing valid data available earlier than worst-case scenarios, respectively. In this paper, we propose a high-performance digital circuit design method with speculative executions with less overhead by utilizing clock scheduling with delay insertions effectively. The necessity of speculations that cause overheads is effectively reduced by clock scheduling with delay insertion. Experiments show that a generated circuit achieves 26% performance improvement with 1.3% area overhead compared to a circuit without clock scheduling and without speculative execution.

A privacy-preserving support vector machine (SVM) computing scheme is proposed in this paper. Cloud computing has been spreading in many fields. However, the cloud computing has some serious issues for end users, such as the unauthorized use of cloud services, data leaks, and privacy being compromised. Accordingly, we consider privacy-preserving SVM computing. We focus on protecting visual information of images by using a random unitary transformation. Some properties of the protected images are discussed. The proposed scheme enables us not only to protect images, but also to have the same performance as that of unprotected images even when using typical kernel functions such as the linear kernel, radial basis function (RBF) kernel and polynomial kernel. Moreover, it can be directly carried out by using well-known SVM algorithms, without preparing any algorithms specialized for secure SVM computing. In an experiment, the proposed scheme is applied to a face-based authentication algorithm with SVM classifiers to confirm the effectiveness.