This paper discusses the parallelization of boost and buck converters. Passivity-based control is applied to each converter to achieve the asymptotic stability of the system. The ripple characteristics, error characteristics, and time constants of the parallelized converters are discussed with considering the dependency on the feedback gains. The numerical results are confirmed to coincide with the results in the experiment for certain feedback gains. The stability of the system is also discussed in simulation and experiment. The results will be a step to achieve the design of parallel converters.

Let m, k be positive integers with m=2k and k≥3. Let C(u, ν) is a class of cyclic codes of length 2m-1 whose parity-check polynomial is mu(x)mν(x), where mu(x) and mν(x) are the minimal polynomials of α-u and α-ν over GF(2). For the case $(u, u)=(1,rac{1}{3}(2^m-1))$, the weight distributions of binary cyclic codes C(u, ν) was determined in 2017. This paper determines the weight distributions of the binary cyclic codes C(u, ν) for the case of (u, ν)=(3, 2k-1+1). The application of these cyclic codes in secret sharing is also considered.

In this letter, we adopt two multi-carrier relay selections, i.e., bulk and per-subcarrier (PS), to the multi-hop decode-and-forward relaying orthogonal frequency-division multiplexing with index modulation (OFDM-IM) system. Particularly, in the form of average outage probability (AOP), the influence of joint selection and non-joint selection acting on the last two hops on the system is analyzed. The closed-form expressions of AOPs and the asymptotic AOPs expressions at high signal-to-noise ratio are given and verified by numerical simulations. The results show that both bulk and PS can achieve full diversity order and that PS can provide additional power gain compared to bulk when JS is used. The theoretical analyses in this letter provide an insight into the combination of OFDM-IM and cooperative communication.

Wireless networked control systems (WNCSs) are control systems whose components are connected through wireless networks. In WNCSs, a controlled object (CO) could become unstable due to bursty packet losses in addition to random packet losses and round-trip delays on wireless networks. In this paper, to reduce these network-induced effects, we propose a new design for multihop TDMA-based WNCSs with two-disjoint-path switching, where two disjoint paths are established between a controller and a CO, and they are switched if bursty packet losses are detected. In this system, we face the following two difficulties: (i) link scheduling in TDMA should be done in such a way that two paths can be switched without rescheduling, taking into account of the constraint of control systems. (ii) the conventional cross-layer design method of control systems is not directly applicable because round-trip delays may vary according to the path being used. Therefore, to overcome the difficulties raised by the two-path approach, we reformulate link scheduling in multihop TDMA and cross-layer design for control systems. Simulation results confirm that the proposed WNCS achieves better performance in terms of the 2-norm of CO's states.

In this article, we propose a method to identify the link layer home network topology, motivated by applications to cost reduction of support centers. If the topology of home networks can be identified automatically and efficiently, it is easier for operators of support centers to identify fault points. We use MAC address forwarding tables (AFTs) which can be collected from network devices. There are a couple of existing methods for identifying a network topology using AFTs, but they are insufficient for our purpose; they are not applicable to some specific network topologies that are typical in home networks. The advantage of our method is that it can handle such topologies. We also implemented these three methods and compared their running times. The result showed that, despite its wide applicability, our method is the fastest among the three.

This paper presents a two-dimensional (2D) DOA algorithm for double L-shaped arrays. The algorithm is applied to the underwater environment for eliminating the performance error caused by the sound speed uncertainty factor. By introducing the third dimensional array, the algorithm eliminates the sound velocity variable in the depression angle expression, so that the DOA estimation no longer considering the true value of unknown sound velocity. In order to determine the parameters of a three-dimensional array, a parameter matching method with the double L-shaped array is also proposed. Simulations show that the proposed algorithm outperforms the conventional 2D-DOA estimation algorithm in unknown sound velocity environment.

The degradation of a SiC-MOSFET-based DC-AC converter-circuit efficiency due to aging of the electrically active devices is investigated. The newly developed compact aging model HiSIM_HSiC for high-voltage SiC-MOSFETs is used in the investigation. The model considers explicitly the carrier-trap-density increase in the solution of the Poisson equation. Measured converter characteristics during a 3-phase line-to-ground (3LG) fault is correctly reproduced by the model. It is verified that the MOSFETs experience additional stress due to the high biases occurring during the fault event, which translates to severe MOSFET aging. Simulation results predict a 0.5% reduction of converter efficiency due to a single 70ms-3LG, which is equivalent to a year of operation under normal conditions, where no additional stress is applied. With the developed compact model, prediction of the efficiency degradation of the converter circuit under prolonged stress, for which measurements are difficult to obtain and typically not available, is also feasible.

Here, we present a novel spectroscopic imaging method based on the boundary-extraction scheme for wide-beam terahertz (THz) three-dimensional imaging. Optical-lens-focusing systems for THz subsurface imaging generally require the depth of the object from the surface to be input beforehand to achieve the desired azimuth resolution. This limitation can be alleviated by incorporating a wide-beam THz transmitter into the synthetic aperture to automatically change the focusing depth in the post-signal processing. The range point migration (RPM) method has been demonstrated to have significant advantages in terms of imaging accuracy over the synthetic-aperture method. Moreover, in the RPM scheme, spectroscopic information can be easily associated with each scattering center. Thus, we propose an RPM-based terahertz spectroscopic imaging method. The finite-difference time-domain-based numerical analysis shows that the proposed algorithm provides accurate target boundary imaging associated with each frequency-dependent characteristic.

The multislope ski-rental problem is an online optimization problem that generalizes the classical ski-rental problem. The player is offered not only a buy and a rent options but also other options that charge both initial and per-time fees. The competitive ratio of the classical ski-rental problem is known to be 2. In contrast, the best known so far on the competitive ratio of the multislope ski-rental problem is an upper bound of 4 and a lower bound of 3.62. In this paper we consider a parametric version of the multislope ski-rental problem, regarding the number of options as a parameter. We prove an upper bound for the parametric problem which is strictly less than 4. Moreover, we give a simple recurrence relation that yields an equation having a lower bound value as its root.

A regular pattern is a string consisting of constant symbols and distinct variable symbols. The language of a regular pattern is the set of all constant strings obtained by replacing all variable symbols in the regular pattern with non-empty strings. The present paper deals with the learning problem of languages of regular patterns within Angluin's query learning model, which is an established mathematical model of learning via queries in computational learning theory. The class of languages of regular patterns was known to be identifiable from one positive example using a polynomial number of membership queries, in the query learning model. In present paper, we show that the class of languages of regular patterns is identifiable from one positive example using a linear number of membership queries, with respect to the length of the positive example.

Recently, criminals frequently utilize logical attacks to Automated Teller Machines (ATMs) and financial institutes' (FIs') networks to steal cash. We proposed a security measure utilizing peripheral devices in an ATM for smart card transactions to prevent “unauthorized cash withdrawals” of logical attacks, and the fundamental framework as a generalized model of the measure in other paper. As the measure can prevent those logical attacks with tamper-proof hardware, it is quite difficult for criminals to compromise the measure. However, criminals can still carry out different types of logical attacks to ATMs, such as “unauthorized deposit”, to steal cash. In this paper, we propose a security measure utilizing peripheral devices to prevent unauthorized deposits with a smart card. The measure needs to protect multiple transaction sub-processes in a deposit transaction from multiple types of logical attacks and to be harmonized with existing ATM system/operations. A suitable implementation of the fundamental framework is required for the measure and such implementation design is confusing due to many items to be considered. Thus, the measure also provides an implementation model analysis of the fundamental framework to derive suitable implementation for each defense point in a deposit transaction. Two types of measure implementation are derived as the result of the analysis.

Three-dimensional (3D) reconstruction and scene depth estimation from 2-dimensional (2D) images are major tasks in computer vision. However, using conventional 3D reconstruction techniques gets challenging in participating media such as murky water, fog, or smoke. We have developed a method that uses a continuous-wave time-of-flight (ToF) camera to estimate an object region and depth in participating media simultaneously. The scattered light observed by the camera is saturated, so it does not depend on the scene depth. In addition, received signals bouncing off distant points are negligible due to light attenuation, and thus the observation of such a point contains only a scattering component. These phenomena enable us to estimate the scattering component in an object region from a background that only contains the scattering component. The problem is formulated as robust estimation where the object region is regarded as outliers, and it enables the simultaneous estimation of an object region and depth on the basis of an iteratively reweighted least squares (IRLS) optimization scheme. We demonstrate the effectiveness of the proposed method using captured images from a ToF camera in real foggy scenes and evaluate the applicability with synthesized data.

Since a concept can be represented by different vocabularies, styles, and levels of detail, a translation task resembles a many-to-many mapping task from a distribution of sentences in the source language into a distribution of sentences in the target language. This viewpoint, however, is not fully implemented in current neural machine translation (NMT), which is one-to-one sentence mapping. In this study, we represent the distribution itself as multiple paraphrase sentences, which will enrich the model context understanding and trigger it to produce numerous hypotheses. We use a visually grounded paraphrase (VGP), which uses images as a constraint of the concept in paraphrasing, to guarantee that the created paraphrases are within the intended distribution. In this way, our method can also be considered as incorporating image information into NMT without using the image itself. We implement this idea by crowdsourcing a paraphrasing corpus that realizes VGP and construct neural paraphrasing that behaves as expert models in a NMT. Our experimental results reveal that our proposed VGP augmentation strategies showed improvement against a vanilla NMT baseline.

This letter presents a novel technique to achieve a fast inference of the binarized convolutional neural networks (BCNN). The proposed technique modifies the structure of the constituent blocks of the BCNN model so that the input elements for the max-pooling operation are binary. In this structure, if any of the input elements is +1, the result of the pooling can be produced immediately; the proposed technique eliminates such computations that are involved to obtain the remaining input elements, so as to reduce the inference time effectively. The proposed technique reduces the inference time by up to 34.11%, while maintaining the classification accuracy.

More and more attacks are found due to the insecure channel between different network domains in legacy mobile network. In this letter, we discover an attack exploiting SUCI to track a subscriber in 5G network, which is directly caused by the insecure air channel. To cover this issue, a secure authentication scheme is proposed utilizing the existing PKI mechanism. Not only dose our protocol ensure the authentication signalling security in the channel between UE and SN, but also SN and HN. Further, formal methods are adopted to prove the security of the proposed protocol.

This paper presents a deep learning-based non-intrusive speech intelligibility estimation method using bottleneck features of autoencoder. The conventional standard non-intrusive speech intelligibility estimation method, P.563, lacks intelligibility estimation performance in various noise environments. We propose a more accurate speech intelligibility estimation method based on long-short term memory (LSTM) neural network whose input and output are an autoencoder bottleneck features and a short-time objective intelligence (STOI) score, respectively, where STOI is a standard tool for measuring intrusive speech intelligibility with reference speech signals. We showed that the proposed method has a superior performance by comparing with the conventional standard P.563 and mel-frequency cepstral coefficient (MFCC) feature-based intelligibility estimation methods for speech signals in various noise environments.

Real-time pulse rate (PR) monitoring based on photoplethysmography (PPG) has been drawn much attention in recent years. However, PPG signal detected under movement is easily affected by random noises, especially motion artifacts (MA), affecting the accuracy of PR estimation. In this paper, a parallel method structure is proposed, which effectively combines wavelet threshold denoising with recursive least squares (RLS) adaptive filtering to remove interference signals, and uses spectral peak tracking algorithm to estimate real-time PR. Furthermore, we propose a parallel structure RLS adaptive filtering to increase the amplitude of spectral peak associated with PR for PR estimation. This method is evaluated by using the PPG datasets of the 2015 IEEE Signal Processing Cup. Experimental results on the 12 training datasets during subjects' walking or running show that the average absolute error (AAE) is 1.08 beats per minute (BPM) and standard deviation (SD) is 1.45 BPM. In addition, the AAE of PR on the 10 testing datasets during subjects' fast running accompanied with wrist movements can reach 2.90 BPM. Furthermore, the results indicate that the proposed approach keeps high estimation accuracy of PPG signal even with strong MA.

This paper presents new encoding and decoding methods for Berlekamp-Preparata convolutional codes (BPCCs) based on tail-biting technique. The proposed scheme can correct a single block of n bit errors relative to a guard space of m error-free blocks while no fractional rate loss is incurred. The proposed tail-biting BPCCs (TBBPCCs) can attain optimal complete burst error correction bound. Therefore, they have the optimal phased-burst-error-correcting capability for convolutional codes. Compared with the previous scheme, the proposed scheme can also improve error correcting capability.

To estimate angle, velocity, and range information of multiple targets jointly in FMCW MIMO radar, two-dimensional (2D) MUSIC with matched filtering and FFT algorithm is proposed. By reformulating the received FMCW signal of the colocated MIMO radar, we exploit 2D MUSIC to estimate the angle and Doppler frequency of multiple targets. Then by using a matched filter together with the estimated angle and Doppler frequency and FFT operation, the range of the target is estimated. To effectively estimate the parameters of multiple targets with large distance differences, we also propose a successive interference cancellation method that uses the orthogonal projection. That is, rather than estimating the multiple target parameters simultaneously using 2D MUSIC, we estimate the target parameters sequentially, in which the parameters of the target having strongest reflected power are estimated first and then, their effect on the received signal is canceled out by using the orthogonal projection. Simulations verify the performance of the proposed algorithm.

Addition is a key fundamental function for many error-tolerant applications. Approximate addition is considered to be an efficient technique for trading off energy against performance and accuracy. This paper proposes a carry-maskable adder whose accuracy can be configured at runtime. The proposed scheme can dynamically select the length of the carry propagation to satisfy the quality requirements flexibly. Compared with a conventional ripple carry adder and a conventional carry look-ahead adder, the proposed 16-bit adder reduced the power consumption by 54.1% and 57.5%, respectively, and the critical path delay by 72.5% and 54.2%, respectively. In addition, results from an image processing application indicate that the quality of processed images can be controlled by the proposed adder. Good scalability of the proposed adder is demonstrated from the evaluation results using a 32-bit length.