In a multi-agent system, it is important to consider a design method of cooperative actions in order to achieve a common goal. In this paper, we propose two novel multi-agent reinforcement learning methods, where the control specification is described by linear temporal logic formulas, which represent a common goal. First, we propose a simple solution method, which is directly extended from the single-agent case. In this method, there are some technical issues caused by the increase in the number of agents. Next, to overcome these technical issues, we propose a new method in which an aggregator is introduced. Finally, these two methods are compared by numerical simulations, with a surveillance problem as an example.

The Volterra filter is one of the digital filters that can describe nonlinearity. In this paper, we analyze the dynamic behaviors of an adaptive signal processing system with the Volterra filter for nonwhite input signals by a statistical-mechanical method. Assuming the self-averaging property with an infinitely long tapped-delay line, we derive simultaneous differential equations that describe the behaviors of macroscopic variables in a deterministic and closed form. We analytically solve the derived equations to reveal the effect of the nonwhiteness of the input signal on the adaptation process. The results for the second-order Volterra filter show that the nonwhiteness decreases the mean-square error (MSE) in the early stages of the adaptation process and increases the MSE in the later stages.

Statistical wafer-level characteristic variation modeling offers an attractive method for reducing the measurement cost in large-scale integrated (LSI) circuit testing while maintaining test quality. In this method, the performance of unmeasured LSI circuits fabricated on a wafer is statistically predicted based on a few measured LSI circuits. Conventional statistical methods model spatially smooth variations in the wafers. However, actual wafers can exhibit discontinuous variations that are systematically caused by the manufacturing environment, such as shot dependence. In this paper, we propose a modeling method that considers discontinuous variations in wafer characteristics by applying the knowledge of manufacturing engineers to a model estimated using Gaussian process regression. In the proposed method, the process variation is decomposed into systematic discontinuous and global components to improve estimation accuracy. An evaluation performed using an industrial production test dataset indicates that the proposed method effectively reduces the estimation error for an entire wafer by over 36% compared with conventional methods.

In a field of biomedical engineering, not only low-pass filters for high frequency elimination but also notch filters for suppressing powerline interference are necessary to process low-frequency biosignals. For integration of low-frequency filters, chip implementation of large capacitances is major difficulty. As methods to enhance capacitances with small chip area, use of capacitance multipliers is effective. This letter describes design consideration of integrated low-frequency low-pass notch filter employing capacitance multipliers. Two main points are presented. Firstly, a new floating capacitance multiplier is proposed. Secondly, a technique to reduce the number of capacitance multipliers is proposed. By this technique, power consumption is reduced. The proposed techniques are applied a 3rd order low-pass notch filter. Simulation results show the effectiveness of the proposed techniques.

Seen from the Internet Service Provider (ISP) side, network traffic monitoring is an indispensable part during network service provisioning, which facilitates maintaining the security and reliability of the communication networks. Among the numerous traffic conditions, we should pay extra attention to traffic anomaly, which significantly affects the network performance. With the advancement of Machine Learning (ML), data-driven traffic anomaly detection algorithms have established high reputation due to the high accuracy and generality. However, they are faced with challenges on inefficient traffic feature extraction and high computational complexity, especially when taking the evolving property of traffic process into consideration. In this paper, we proposed an online learning framework for traffic anomaly detection by embracing Gaussian Process (GP) and Sparse Representation (SR) in two steps: 1). To extract traffic features from past records, and better understand these features, we adopt GP with a special kernel, i.e., mixture of Gaussian in the spectral domain, which makes it possible to more accurately model the network traffic for improving the performance of traffic anomaly detection. 2). To combat noise and modeling error, observing the inherent self-similarity and periodicity properties of network traffic, we manually design a feature vector, based on which SR is adopted to perform robust binary classification. Finally, we demonstrate the superiority of the proposed framework in terms of detection accuracy through simulation.

Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable performance in detection and classification tasks. Nevertheless, their feature extraction cannot consider both local and global information, so the detection and classification performance can be further improved. In addition, more and more deep learning networks are designed as more and more complex, and the amount of computation and storage space required is also significantly increased. This paper proposes a combination of CNN and transformer, and designs a local feature enhancement module and global context modeling module to enhance the cascade network. While the local feature enhancement module increases the range of feature extraction, the global context modeling is used to capture the feature maps' global information. To decrease the model complexity, a shared sublayer is designed to realize the sharing of weight parameters between the adjacent convolutional layers or cross convolutional layers, thereby reducing the number of convolutional weight parameters. Moreover, to effectively improve the detection performance of neural networks without increasing network parameters, the optimal transport assignment approach is proposed to resolve the problem of label assignment. The classification loss and regression loss are the summations of the cost between the demander and supplier. The experiment results demonstrate that the proposed Combination of CNN and Transformer with Shared Sublayer (CCTSS) performs better than the state-of-the-art methods in various datasets and applications.

The fingerprint verification system is widely used in mobile devices because of fingerprint's distinctive features and ease of capture. Typically, mobile devices utilize small sensors, which have limited area, to capture fingerprint. Meanwhile, conventional fingerprint feature extraction methods need detailed fingerprint information, which is unsuitable for those small sensors. This paper proposes a novel fingerprint verification method for small area sensors based on deep learning. A systematic method combines deep convolutional neural network (DCNN) in a Siamese network for feature extraction and XGBoost for fingerprint similarity training. In addition, a padding technique also introduced to avoid wraparound error problem. Experimental results show that the method achieves an improved accuracy of 66.6% and 22.6% in the FingerPassDB7 and FVC2006DB1B dataset, respectively, compared to the existing methods.

This letter introduces a prime-factor Galois field Fourier transform (PF-GFFT) architecture to frequency domain decoding (FDD) of cyclic codes. Firstly, a fast FDD scheme is designed which converts the original single longer Fourier transform to a multi-dimensional smaller transform. Furthermore, a ladder-shift architecture for PF-GFFT is explored to solve the rearrangement problem of input and output data. In this regard, PF-GFFT is considered as a lower order spectral calculation scheme, which has sufficient preponderance in reducing the computational complexity. Simulation results show that PF-GFFT compares favorably with the current general GFFT, simplified-GFFT (S-GFFT), and circular shifts-GFFT (CS-GFFT) algorithms in time-consuming cost, and is nearly an order of magnitude or smaller than them. The superiority is a benefit to improving the decoding speed and has potential application value in decoding cyclic codes with longer code lengths.

As a potential member of next generation wireless communications, the reconfigurable intelligent surface (RIS) can control the reflected elements to adjust the phase of the transmitted signal with less energy consumption. A novel RIS-assisted index modulation scheme is proposed in this paper, which is named the generalized reflected phase modulation (GRPM). In the GRPM, the transmitted bits are mapped into the reflected phase combination which is conveyed through the reflected elements on the RIS, and detected by the maximum likelihood (ML) detector. The performance analysis of the GRPM with the ML detector is presented, in which the closed form expression of pairwise error probability is derived. The simulation results show the bit error rate (BER) performance of GRPM by comparing with various RIS-assisted index modulation schemes in the conditions of various spectral efficiency and number of antennas.

In this paper, we investigate the evolution of an optical network architecture and discuss the future direction of research on optical network design and control. We review existing research on optical network design and control and present some open challenges. One of the important open challenges lies in multilayer resource optimization including IT and optical network resources. We propose an adaptive joint optimization method of IT resources and optical spectrum under time-varying traffic demand in optical networks while avoiding an increase in operation cost. We formulate the problem as mixed integer linear programming and then quantitatively evaluate the trade-off relationship between the optimality of reconfiguration and operation cost. We demonstrate that we can achieve sufficient network performance through the adaptive joint optimization while suppressing an increase in operation cost.

The novel optical path routing architecture named flexible waveband routing networks is reviewed in this paper. The nodes adopt a two-stage path routing scheme where wavelength selective switches (WSSs) bundle optical paths and form a small number of path groups and then optical switches without wavelength selectivity route these groups to desired outputs. Substantial hardware scale reduction can be achieved as the scheme enables us to use small scale WSSs, and even more, share a WSS by multiple input cores/fibers through the use of spatially-joint-switching. Furthermore, path groups distributed over multiple bands can be switched by these optical switches and thus the adaptation to multi-band transmission is straightforward. Network-wide numerical simulations and transmission experiments that assume multi-band transmission demonstrate the validity of flexible waveband routing.

To cope with the drastic increase in traffic, space division multiplexing elastic optical networks (SDM-EONs) have been investigated. In multicore fiber environments that realize SDM-EONs, crosstalk (XT) occurs between optical paths transmitted in the same frequency slots of adjacent cores, and the quality of the optical paths is degraded by the mutual influence of XT. To solve this problem, we propose a core and spectrum assignment method that introduces the concept of prohibited frequency slots to protect the degraded optical paths. First-fit-based spectrum resource allocation algorithms, including our previous study, have the problem that only some frequency slots are used at low loads, and XT occurs even though sufficient frequency slots are available. In this study, we propose a core and spectrum assignment method that introduces the concepts of “adjacency criterion” and “XT budget” to suppress XT at low and middle loads without worsening the path blocking rate at high loads. We demonstrate the effectiveness of the proposed method in terms of the path blocking rate using computer simulations.

A city-level digital twin is a critical enabling technology to construct a smart city that helps improve citizens' living conditions and quality of life. Currently, research and development regarding the digital replica city are pursued worldwide. However, many research projects only focus on creating the 3D city model. A mechanism to involve key players, such as data providers, service providers, and application developers, is essential for constructing the digital replica city and producing various city applications. Based on this motivation, the authors of this paper are pursuing a research project, namely Decentralized Digital Twin EcoSystem (D2EcoSys), to create an ecosystem to advance (and self-grow) the digital replica city regarding time and space directions, city services, and values. This paper introduces an overview of the D2EcoSys project: vision, problem statement, and approach. In addition, the paper discusses the recent research results regarding networking technologies and demonstrates an early testbed built in the Kashiwa-no-ha smart city.

We evaluated the transmission performance, including received power and transmission throughput characteristics, in 4×4 single-user multiple-input multiple-output (SU-MIMO) transmission for synchronous time division duplex (TDD) and downlink data channels in comparison with single-input single-output (SISO) transmission in an environment where a local 5G wireless base station was installed on the roof of a research building at our university. Accordingly, for the received power characteristics, the difference between the simulation value, which was based on the ray tracing method, and the experimental value at 32 points in the area was within a maximum difference of approximately 10 dB, and sufficient compliance was obtained. Regarding the transmission throughput versus received power characteristics, after showing a simulation method for evaluating throughput characteristics in MIMO, we compared the results with experimental results. The cumulative distribution function (CDF) of the transmission throughput shows that, at a CDF of 50%, in SISO transmission, the simulated value is approximately 115Mbps, and the experimental value is 105Mbps, within a difference of approximately 10Mbps. By contrast, in MIMO transmission, the simulation value is 380Mbps, and the experimental value is approximately 420Mbps, which is a difference of approximately 40Mbps. It was shown that the received power and transmission throughput characteristics can be predicted with sufficient accuracy by obtaining the delay profile and the system model at each reception point using the both ray tracing and MIMO simulation methods in actual environments.

Information-Centric Networking (ICN) originally innovated for efficient data distribution, is currently discussed to be applied to edge computing environment. In this paper, we focus on a more flexible context, in-network computing, which is enabled by ICN architecture. In ICN-based in-network computing, a function chaining (routing) method for chaining multiple functions located at different routers widely distributed in the network is required. Our proposal is a twofold approach, On-demand Routing for Responsive Route (OR3) and Route Records (RR). OR3 efficiently chains data and multiple functions compared with an existing routing method. RR reactively stores routing information to reduce communication/computing overhead. In this paper, we conducted a mathematical analytics in order to verify the correctness of the proposed routing algorithm. Moreover, we investigate applicabilities of OR3/RR to an edge computing context in the future Beyond 5G/6G era, in which rich computing resources are provided by mobile nodes thanks to the cutting-edge mobile device technologies. In the mobile environments, the optimum from viewpoint of “routing” is largely different from the stable wired environment. We address this challenging issue and newly propose protocol enhancements for OR3 by considering node mobility. Evaluation results reveal that mobility-enhanced OR3 can discover stable paths for function chaining to enable more reliable ICN-based in-network computing under the highly-dynamic network environment.

Transcatheter renal denervation (RDN) is a novel treatment to reduce blood pressure in patients with resistant hypertension using an energy-based catheter, mostly radio frequency (RF) current, by eliminating renal sympathetic nerve. However, several inconsistent RDN treatments were reported, mainly due to RF current narrow heating area, and the inability to confirm a successful nerve ablation in a deep area. We proposed microwave energy as an alternative for creating a wider ablation area. However, confirming a successful ablation is still a problem. In this paper, we designed a prediction method for deep renal nerve ablation sites using hybrid numerical calculation-driven machine learning (ML) in combination with a microwave catheter. This work is a first-step investigation to check the hybrid ML prediction capability in a real-world situation. A catheter with a single-slot coaxial antenna at 2.45 GHz with a balloon catheter, combined with a thin thermometer probe on the balloon surface, is proposed. Lumen temperature measured by the probe is used as an ML input to predict the temperature rise at the ablation site. Heating experiments using 6 and 8 mm hole phantom with a 41.3 W excited power, and 8 mm with 36.4 W excited power, were done eight times each to check the feasibility and accuracy of the ML algorithm. In addition, the temperature on the ablation site is measured for reference. Prediction by ML algorithm agrees well with the reference, with a maximum difference of 6°C and 3°C in 6 and 8 mm (both power), respectively. Overall, the proposed ML algorithm is capable of predicting the ablation site temperature rise with high accuracy.

In this survey we summarize properties of pseudorandomness and non-randomness of some number-theoretic sequences and present results on their behaviour under the following measures of pseudorandomness: balance, linear complexity, correlation measure of order k, expansion complexity and 2-adic complexity. The number-theoretic sequences are the Legendre sequence and the two-prime generator, the Thue-Morse sequence and its sub-sequence along squares, and the prime omega sequences for integers and polynomials.

In this paper we obtain a new method to compute the correlation values of two arbitrary sequences defined by a mapping from F4n to F4. We apply this method to demonstrate that the usual nonbinary maximal length sequences have almost ideal correlation under the canonical complex correlation definition and investigate some decimations giving good cross correlation. The techniques we develop are of independent interest for future investigation of sequence design and related problems, including Boolean functions.

A set consisting of K subsets of Msequences of length L is called a complementary sequence set expressed by A(L, K, M), if the sum of the out-of-phase aperiodic autocorrelation functions of the sequences within a subset and the sum of the cross-correlation functions between the corresponding sequences in any two subsets are zero at any phase shift. Suehiro et al. first proposed complementary set A(Nn, N, N) where N and n are positive integers greater than or equal to 2. Recently, several complementary sets related to Suehiro's construction, such as N being a power of a prime number, have been proposed. However, there is no discussion about their inclusion relation and properties of sequences. This paper rigorously formulates and investigates the (generalized) logic functions of the complementary sets by Suehiro et al. in order to understand its construction method and the properties of sequences. As a result, it is shown that there exists a case where the logic function is bent when n is even. This means that each series can be guaranteed to have pseudo-random properties to some extent. In other words, it means that the complementary set can be successfully applied to communication on fluctuating channels. The logic functions also allow simplification of sequence generators and their matched filters.

This paper focuses on a pseudorandom number generator called an NTU sequence for use in cryptography. The generator is defined with an m-sequence and Legendre symbol over an odd characteristic field. Since the previous researches have shown that the generator has maximum complexity; however, its bit distribution property is not balanced. To address this drawback, the author introduces dynamic mapping for the generation process and evaluates the period and some distribution properties in this paper.