Recent embedded systems require both traditional machinery control and information processing, such as network and GUI handling. A dual-OS platform consolidates a real-time OS (RTOS) and general-purpose OS (GPOS) to realize efficient software development on one physical processor. Although the dual-OS platform attracts increasing attention, it often suffers from energy inefficiency in the GPOS for guaranteeing real-time responses of the RTOS. This paper proposes an energy minimization method called DVFS virtualization, which allows running multiple DVFS policies dedicated to the RTOS and GPOS, respectively. The experimental evaluation using a commercial microcontroller showed that the proposed hardware could change the supply voltage within 500 ns and reduce the energy consumption of typical applications by 60 % in the best case compared to conventional dual-OS platforms. Furthermore, evaluation using a commercial microprocessor achieved a 15 % energy reduction of practical open-source software at best.

The multi-agent surveillance problem is to find optimal trajectories of multiple agents that patrol a given area as evenly as possible. In this paper, we consider the multi-agent surveillance problem based on travel cost minimization. The surveillance area is given by an undirected graph. The penalty for each agent is introduced to evaluate the surveillance performance. Through a mixed logical dynamical system model, the multi-agent surveillance problem is reduced to a mixed integer linear programming (MILP) problem. In model predictive control, trajectories of agents are generated by solving the MILP problem at each discrete time. Furthermore, a condition that the MILP problem is always feasible is derived based on the Chinese postman problem. Finally, the proposed method is demonstrated by a numerical example.

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.

In recent years, with the wide spread of the Internet of Things (IoT) devices, security issues for hardware devices have been increasing, where detecting their anomalous behaviors becomes quite important. One of the effective methods for detecting anomalous behaviors of IoT devices is to utilize consumed energy and operation duration time extracted from their power waveforms. However, the existing methods do not consider the shape of time-series data and cannot distinguish between power waveforms with similar consumed energy and duration time but different shapes. In this paper, we propose a method for detecting anomalous behaviors based on the shape of time-series data by incorporating a shape-based distance (SBD) measure. The proposed method first obtains the entire power waveform of the target IoT device and extracts several application power waveforms. After that, we give the invariances to them, and we can effectively obtain the SBD between every two application power waveforms. Based on the SBD values, the local outlier factor (LOF) method can finally distinguish between normal application behaviors and anomalous application behaviors. Experimental results demonstrate that the proposed method successfully detects anomalous application behaviors, while the existing state-of-the-art method fails to detect them.

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.

This letter provides an efficient massive multiple-input multiple-output (MIMO) detector based on quasi-newton methods to speed up the convergence performance under realistic scenarios, such as high user load and spatially correlated channels. The proposed method leverages the information of the Hessian matrix by merging Barzilai-Borwein method and Limited Memory-BFGS method. In addition, an efficient initial solution based on constellation mapping is proposed. The simulation results demonstrate that the proposed method diminishes performance loss to 0.7dB at the bit-error-rate of 10-2 at 128×32 antenna configuration with low complexity, which surpasses the state-of-the-art (SOTA) algorithms.

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.

This paper introduces an inverter-based true random number generator (I-TRNG). It uses a single CMOS inverter to amplify thermal noise multiple times. An adaptive calibration mechanism based on clock tuning provides robust operation across a wide range of supply voltage 0.5∼1.1V and temperature -40∼140°C. An 8-bit Von-Neumann post-processing circuit (VN8W) is implemented for maximum raw entropy extraction. In a 130nm CMOS technology, the I-TRNG entropy source only occupies 635μm2 and consumes 0.016pJ/raw-bit at 0.6V. The I-TRNG occupies 13406μm2, including the entropy source, adaptive calibration circuit, and post-processing circuit. The minimum energy consumption of the I-TRNG is 1.38pJ/bit at 0.5V, while passing all NIST 800-22 and 800-90B tests. Moreover, an equivalent 15-year life at 0.7V, 25°C is confirmed by an accelerated NBTI aging test.

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.

In software defined radio systems, a channelizer plays an important role in extracting the desired signals from a wideband signal. Compared to the conventional methods, the proposed scheme provides a solution to design a digital channelizer extracting the multiple subband signals at different center frequencies with low complexity. To do this, this paper formulates the problem as an optimization problem, which minimizes the required multiplications number subject to the constraints of the ripple in the passbands and the stopbands for single channel and combined multiple channels. In addition, a solution to solve the optimization problem is also presented and the corresponding structure is demonstrated. Simulation results show that the proposed scheme requires smaller number of the multiplications than other conventional methods. Moreover, unlike other methods, this structure can process signals with different bandwidths at different center frequencies simultaneously only by changing the status of the corresponding multiplexers without hardware reimplementation.

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.

In previous machine learning based fast intra mode decision algorithms for screen content videos, feature design is a key task and it is always difficult to obtain distinguishable features. In this paper, the idea of interaction of features is introduced to fast video coding algorithm, and a fast intra mode decision algorithm based on feature cross is proposed for screen content videos. The numeric features and category features are designed based on the characteristics of screen content videos, and the adaptive factorization network (AFN) is improved and adopted to carry out feature interaction to designed features, and output distinguishable features. The experimental results show that for AI (All Intra) configuration, compared with standard VVC/H.266, the coding time is reduced by 29.64% and the BD rate is increased only by 1.65%.

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.

We propose a demodulation framework to extend the maximum distance of unrepeated transmission systems, where the simplest back propagation (BP), polarization and phase recovery, data arrangement for machine learning (ML), and symbol decision based on ML are rationally combined. The deterministic waveform distortion caused by fiber nonlinearity and chromatic dispersion is partially eliminated by BP whose calculation cost is minimized by adopting the single-step Fourier method in a pre-processing step. The non-deterministic waveform distortion, i.e., polarization and phase fluctuations, can be eliminated in a precise manner. Finally, the optimized ML model conducts the symbol decision under the influence of residual deterministic waveform distortion that cannot be cancelled by the simplest BP. Extensive numerical simulations confirm that a DP-16QAM signal can be transmitted over 240km of a standard single-mode fiber without optical repeaters. The maximum transmission distance is extended by 25km.

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.

The fifth-generation (5G) new radio (NR) standard employs ultra-reliable and low-latency communication (URLLC) to provide real-time wireless interactive capability for the internet of things (IoT) applications. To satisfy the stringent latency and reliability demands of URLLC services, grant-free (GF) transmissions with the K-repetition transmission (K-Rep) have been introduced. However, fading fluctuations can negatively impact signal quality at the base station (BS), leading to an increase in the number of repetitions and raising concerns about interference and energy consumption for IoT user equipment (UE). To overcome these challenges, this paper proposes novel adaptive K-Rep control schemes that employ site diversity reception to enhance signal quality and reduce energy consumption. The performance evaluation demonstrates that the proposed adaptive K-Rep control schemes significantly improve communication reliability and reduce transmission energy consumption compared with the conventional K-Rep scheme, and then satisfy the URLLC requirements while reducing energy consumption.

Fifth generation mobile communication system (5G) mobile operators need to explore new use cases and/or applications together with vertical industries, the industries that are potential users of 5G, in order to fully exploit the new 5G capabilities in terms of its application. Vehicle-to-Everything (V2X) communications for platooning are considered to be one of new 5G use cases whose ultra reliable and low latency communication (URLLC) aspects are required. The authors build a field experimental environment, towards application to truck platooning, with actual large-size trucks and a prototype system, for 5G New Radio (NR) technology based V2X communications. Its most distinctive feature is that the 5G NR-V2X prototype system is equipped with UE-to-UE radio interface (i.e., sidelink) for V2V Direct communication, in addition to the traditional radio interfaces between BS and UE for V2N/V2N2V communications. This paper presents performance evaluation and demonstration of real-time vehicle control information exchange using over the sidelink of 5G NR-V2X prototype system for automated follower truck platooning. This paper evaluates the V2V Direct communication latency and reliability performance of the sidelink, and clarify 5G NR sidelink achieves lower peak of latency and higher packet reception rate in V2V Direct communication performance than an optical wireless communication system product. Then, it also introduces a 5G URLLC use case demonstration of automated follower truck platooning trial employed with the prototype system in a public expressway environment.