This paper investigates an adaptive notch digital filter that employs normal state-space realization of a single-frequency second-order IIR notch digital filter. An adaptive algorithm is developed to minimize the mean-squared output error of the filter iteratively. This algorithm is based on a simplified form of the gradient-decent method. Stability and frequency estimation bias are analyzed for the adaptive iterative algorithm. Finally, a numerical example is presented to demonstrate the validity and effectiveness of the proposed adaptive notch digital filter and the frequency-estimation bias analyzed for the adaptive iterative algorithm.

In [31], Shin et al. proposed a Leakage-Resilient and Proactive Authenticated Key Exchange (LRP-AKE) protocol for credential services which provides not only a higher level of security against leakage of stored secrets but also secrecy of private key with respect to the involving server. In this paper, we discuss a problem in the security proof of the LRP-AKE protocol, and then propose a modified LRP-AKE protocol that has a simple and effective measure to the problem. Also, we formally prove its AKE security and mutual authentication for the entire modified LRP-AKE protocol. In addition, we describe several extensions of the (modified) LRP-AKE protocol including 1) synchronization issue between the client and server's stored secrets; 2) randomized ID for the provision of client's privacy; and 3) a solution to preventing server compromise-impersonation attacks. Finally, we evaluate the performance overhead of the LRP-AKE protocol and show its test vectors. From the performance evaluation, we can confirm that the LRP-AKE protocol has almost the same efficiency as the (plain) Diffie-Hellman protocol that does not provide authentication at all.

The Smith method has been used to control physical plants with dead time components, where plant states after the dead time is elapsed are predicted and a control input is determined based on the predicted states. We extend the method to the symbolic control and design a symbolic Smith controller to deal with a nondeterministic embedded system. Due to the nondeterministic transitions, the proposed controller computes all reachable plant states after the dead time is elapsed and determines a control input that is suitable for all of them in terms of a given control specification. The essence of the Smith method is that the effects of the dead time are suppressed by the prediction, however, which is not always guaranteed for nondeterministic systems because there may exist no control input that is suitable for all predicted states. Thus, in this paper, we discuss the existence of a deadlock-free symbolic Smith controller. If it exists, it is guaranteed that the effects of the dead time can be suppressed and that the controller can always issue the control input for any reachable state of the plant. If it does not exist, it is proved that the deviation from the control specification is essentially inevitable.

To cope with complicated interference scenarios in realistic acoustic environment, supervised deep neural networks (DNNs) are investigated to estimate different user-defined targets. Such techniques can be broadly categorized into magnitude estimation and time-frequency mask estimation techniques. Further, the mask such as the Wiener gain can be estimated directly or derived by the estimated interference power spectral density (PSD) or the estimated signal-to-interference ratio (SIR). In this paper, we propose to incorporate the multi-task learning in DNN-based single-channel speech enhancement by using the speech presence probability (SPP) as a secondary target to assist the target estimation in the main task. The domain-specific information is shared between two tasks to learn a more generalizable representation. Since the performance of multi-task network is sensitive to the weight parameters of loss function, the homoscedastic uncertainty is introduced to adaptively learn the weights, which is proven to outperform the fixed weighting method. Simulation results show the proposed multi-task scheme improves the speech enhancement performance overall compared to the conventional single-task methods. And the joint direct mask and SPP estimation yields the best performance among all the considered techniques.

In conclusion, an initialization method has been introduced and studied to improve the SET speed in PCM. Before experiment verification, a two-dimensional finite analysis is used, and the results illustrate the proposed method is feasible to improve SET speed. Next, the R-I performances of the discrete PCM device and the resistance distributions of a 64 M bits PCM test chip with and without the initialization have been studied and analyzed, which confirms that the writing speed has been greatly improved. At the same time, the resistance distribution for the repeated initialization operations suggest that a large number of PCM cells have been successfully changed to be in an intermediate state, which is thought that only a shorter current pulse can make the cells SET successfully in this case. Compared the transmission electron microscope (TEM) images before and after initialization, it is found that there are some small grains appeared after initialization, which indicates that the nucleation process of GST has been carried out, and only needs to provide energy for grain growth later.

We propose an algorithm for the constrained design of FIR filters with sparse coefficients. In general filter design approaches, as the length of the filter increases, the number of multipliers used to construct the filter increases. This is a serious problem, especially in two-dimensional FIR filter designs. The FIR filter coefficients designed by the least-squares method with peak error constraint are optimal in the sense of least-squares within a given order, but not necessarily optimal in terms of constructing a filter that meets the design specification under the constraints on the number of coefficients. That is, a higher-order filter with several zero coefficients can construct a filter that meets the specification with a smaller number of multipliers. We propose a two-step approach to design constrained sparse FIR filters. Our method minimizes the number of non-zero coefficients while the frequency response of the filter that meets the design specification. It achieves better performance in terms of peak error than conventional constrained least-squares designs with the same or higher number of multipliers in both one-dimensional and two-dimensional filter designs.

Information exchange through a relay node is attracting attention for applying machine-to-machine communications. If the node demodulates the received signal in relay processing confidentially, the information leakage through the relay station is a problem. In wireless MIMO switching, the frequency spectrum usage efficiency can be improved owing to the completion of information exchange within a short time. This study proposes a novel wireless MIMO switching method for secure information exchange. An overloaded situation, in which the access nodes are one larger than the number of antennas in the relay node, makes the demodulation of the relay node difficult. The access schedule of nodes is required for maintaining the overload situation and the high information exchange efficiency. This study derives the equation model of the access schedule and constructs an access schedule with fewer time periods in the integer programming problem. From the computer simulation, we confirm that the secure capacity of the proposed MIMO switching is larger than that of the original one, and the constructed access schedule is as large as the ideal and minimum time period for information exchange completion.

In distributed processing for communication services, a proper server selection scheme is required to reduce delay by ensuring the event occurrence order. Although a conservative synchronization algorithm (CSA) has been used to achieve this goal, an optimistic synchronization algorithm (OSA) can be feasible for synchronizing distributed systems. In comparison with CSA, which reproduces events in occurrence order before processing applications, OSA can be feasible to realize low delay communication as the processing events arrive sequentially. This paper proposes an optimal server selection scheme that uses OSA for distributed processing systems to minimize end-to-end delay under the condition that maximum status holding time is limited. In other words, the end-to-end delay is minimized based on the allowed rollback time, which is given according to the application designing aspects and availability of computing resources. Numerical results indicate that the proposed scheme reduces the delay compared to the conventional scheme.

The phenomenon where a speaker mixes two or more languages within the same conversation is called code-switching (CS). Handling CS is challenging for automatic speech recognition (ASR) and text-to-speech (TTS) because it requires coping with multilingual input. Although CS text or speech may be found in social media, the datasets of CS speech and corresponding CS transcriptions are hard to obtain even though they are required for supervised training. This work adopts a deep learning-based machine speech chain to train CS ASR and CS TTS with each other with semisupervised learning. After supervised learning with monolingual data, the machine speech chain is then carried out with unsupervised learning of either the CS text or speech. The results show that the machine speech chain trains ASR and TTS together and improves performance without requiring the pair of CS speech and corresponding CS text. We also integrate language embedding and language identification into the CS machine speech chain in order to handle CS better by giving language information. We demonstrate that our proposed approach can improve the performance on both a single CS language pair and multiple CS language pairs, including the unknown CS excluded from training data.

In this study, we propose a method to estimate body composition-related health indicators (e.g., ratio of body fat, body water, and muscle, etc.) using video-based gait analysis. This method is more efficient than individual measurement using a conventional body composition meter. Specifically, we designed a deep-learning framework with a convolutional neural network (CNN), where the input is a gait energy image (GEI) and the output consists of the health indicators. Although a vast amount of training data is typically required to train network parameters, it is unfeasible to collect sufficient ground-truth data, i.e., pairs consisting of the gait video and the health indicators measured using a body composition meter for each subject. We therefore use a two-step approach to exploit an auxiliary gait dataset that contains a large number of subjects but lacks the ground-truth health indicators. At the first step, we pre-train a backbone network using the auxiliary dataset to output gait primitives such as arm swing, stride, the degree of stoop, and the body width — considered to be relevant to the health indicators. At the second step, we add some layers to the backbone network and fine-tune the entire network to output the health indicators even with a limited number of ground-truth data points of the health indicators. Experimental results show that the proposed method outperforms the other methods when training from scratch as well as when using an auto-encoder-based pre-training and fine-tuning approach; it achieves relatively high estimation accuracy for the body composition-related health indicators except for body fat-relevant ones.

In 2019, a new selection method, named fitness-distance balance (FDB), was proposed. FDB has been proved to have a significant effect on improving the search capability for evolutionary algorithms. But it still suffers from poor flexibility when encountering various optimization problems. To address this issue, we propose a functional weights-enhanced FDB (FW). These functional weights change the original weights in FDB from fixed values to randomly generated ones by a distribution function, thereby enabling the algorithm to select more suitable individuals during the search. As a case study, FW is incorporated into the spherical search algorithm. Experimental results based on various IEEE CEC2017 benchmark functions demonstrate the effectiveness of FW.

Sketch face recognition refers to matching photos with sketches, which has effectively been used in various applications ranging from law enforcement agencies to digital entertainment. However, due to the large modality gap between photos and sketches, sketch face recognition remains a challenging task at present. To reduce the domain gap between the sketches and photos, this paper proposes a cascaded transformation generation network for cross-modality image generation and sketch face recognition simultaneously. The proposed cascaded transformation generation network is composed of a generation module, a cascaded feature transformation module, and a classifier module. The generation module aims to generate a high quality cross-modality image, the cascaded feature transformation module extracts high-level semantic features for generation and recognition simultaneously, the classifier module is used to complete sketch face recognition. The proposed transformation generation network is trained in an end-to-end manner, it strengthens the recognition accuracy by the generated images. The recognition performance is verified on the UoM-SGFSv2, e-PRIP, and CUFSF datasets; experimental results show that the proposed method is better than other state-of-the-art methods.

This paper proposes an area- and energy-efficient DLL-based body bias generator (BBG) for minimum energy operation that controls p-well and n-well bias independently. The BBG can minimize total energy consumption of target circuits under a skewed process condition between nMOSFETs and pMOSFETs. The proposed BBG is composed of digital cells compatible with cell-based design, which enables energy- and area-efficient implementation without additional supply voltages. A test circuit is implemented in a 65-nm FDSOI process. Measurement results using a 32-bit RISC processor on the same chip show that the proposed BBG can reduce energy consumption close to a minimum within a 3% energy loss. In this condition, energy and area overheads of the BBG are 0.2% and 0.12%, respectively.

This paper demonstrates that a 360° radio-frequency phase detector consisting of a combination of symmetrical mixers and 45° phase shifters with tunable devices can achieve a low phase-detection error over a wide frequency range. It is shown that the phase detection error does not depend on the voltage gain of the 45° phase shifter. This allows the usage of tunable devices as 45° phase shifters for a wide frequency range with low phase-detection errors. The fabricated phase detector having tunable low-pass filters as the tunable device demonstrates phase detection errors lower than 2.0° rms in the frequency range from 3.0 GHz to 10.5 GHz.

Crowd flow prediction in high density urban scenes is involved in a wide range of intelligent transportation and smart city applications, and it has become a significant topic in urban computing. In this letter, a CNN-based framework called Pyramidal Spatio-Temporal Network (PSTNet) for crowd flow prediction is proposed. Spatial encoding is employed for spatial representation of external factors, while prior pyramid enhances feature dependence of spatial scale distances and temporal spans, after that, post pyramid is proposed to fuse the heterogeneous spatio-temporal features of multiple scales. Experimental results based on TaxiBJ and MobileBJ demonstrate that proposed PSTNet outperforms the state-of-the-art methods.

Quantum noise ultimately restricts the transmission distance in fiber communication systems using optical amplifiers. This paper investigates the quantum-noise-limited performance of optical binary phase-shift keying transmission using gain-saturated phase-sensitive amplifiers (PSAs) as optical repeaters. It is shown that coherent state transmission, where ultimately clean light in the classical sense is transmitted, and endless transmission, where the transmission distance is not restricted, are theoretically achievable under certain system conditions owing to the noise suppression effects of the gain-saturated PSA.

Cognitive radio provides a feasible solution for alleviating the lack of spectrum resources by enabling secondary users to access the unused spectrum dynamically. Spectrum sensing and learning, as the fundamental function for dynamic spectrum sharing in 5G evolution and 6G wireless systems, have been research hotspots worldwide. This paper reviews classic narrowband and wideband spectrum sensing and learning algorithms. The sub-sampling framework and recovery algorithms based on compressed sensing theory and their hardware implementation are discussed under the trend of high channel bandwidth and large capacity to be deployed in 5G evolution and 6G communication systems. This paper also investigates and summarizes the recent progress in machine learning for spectrum sensing technology.

In this paper, we propose a method to enhance the download efficiency of BitTorrent protocol with the notion of structures in the set of pieces generated from a shared file and the swarm of peers downloading the same shared file. More specifically, as for the set of pieces, we introduce the notion of super-pieces called clusters, which is aimed to enlarge the granularity of the management of request-and-reply of pieces, and as for the swarm of peers, we organize a clique consisting of several peers with similar upload capacity, to improve the smoothness of the flow of pieces associated with a cluster. As is shown in the simulation results, the proposed extensions significantly reduce the download time of the first 75% of the downloaders, and thereby improve the performance of P2P-assisted video streaming such as Akamai NetSession and BitTorrent DNA.

Recently, broadband wireless communication has been significantly enhanced; thus, frequency spectrum scarcity has become an extremely serious problem. Spatial frequency reuse based on spectrum databases has attracted significant attention. The spectrum database collects wireless environment information, such as the radio signal strength indicator (RSSI), estimates the propagation coefficient for the propagation loss and shadow effect, and finds a vacant area where the secondary system uses the frequency spectrum without harmful interference to the primary system. Wireless sensor networks are required to collect the RSSI from a radio environmental monitor. However, a large number of RSSI values should be gathered because numerous sensors are spread over the wireless environment. In this study, a data compression technique based on spatial features, such as buildings and houses, is proposed. Using computer simulation and experimental evaluation, we confirm that the proposed compression method successfully reduces the size of the RSSI and restores the original RSSI in the recovery process.

The Dynamic Spectrum Sharing (DSS) system, which uses the frequency band allocated to incumbent systems (i.e., primary users) has attracted attention to expand the available bandwidth of the fifth-generation mobile communication (5G) systems in the sub-6GHz band. In Japan, a DSS system in the 2.3GHz band, in which the ARIB STD-B57-based Field Pickup Unit (FPU) is assigned as an incumbent system, has been studied for the secondary use of 5G systems. In this case, the incumbent FPU is a mobile system, and thus, the DSS system needs to use not only a spectrum sharing database but also radio sensors to detect primary signals with high accuracy, protect the primary system from interference, and achieve more secure spectrum sharing. This paper proposes highly efficient sensing methods for detecting the ARIB STD-B57-based FPU signals in the 2.3GHz band. The proposed methods can be applied to two types of the FPU signal; those that apply the Continuous Pilot (CP) mode pilot and the Scattered Pilot (SP) mode pilot. Moreover, we apply a sample addition method and a symbol addition method for improving the detection performance. Even in the 3GPP EVA channel environment, the proposed method can, with a probability of more than 99%, detect the FPU signal with an SNR of -10dB. In addition, we propose a quantized reference signal for reducing the implementation complexity of the complex cross-correlation circuit. The proposed reference signal can reduce the number of quantization bits of the reference signal to 2 bits for in-phase and 3 bits for orthogonal components.