In cyber-physical systems (CPSs) that interact between physical and information components, there are many sensors that are connected through a communication network. In such cases, the reduction of communication costs is important. Event-triggered control that the control input is updated only when the measured value is widely changed is well known as one of the control methods of CPSs. In this paper, we propose a design method of output feedback controllers with decentralized event-triggering mechanisms, where the notion of uniformly ultimate boundedness is utilized as a control specification. Using this notion, we can guarantee that the state stays within a certain set containing the origin after a certain time, which depends on the initial state. As a result, the number of times that the event occurs can be decreased. First, the design problem is formulated. Next, this problem is reduced to a BMI (bilinear matrix inequality) optimization problem, which can be solved by solving multiple LMI (linear matrix inequality) optimization problems. Finally, the effectiveness of the proposed method is presented by a numerical example.

In this paper, we consider a distributed stochastic nonconvex optimization problem for multiagent systems. We propose a distributed stochastic gradient-tracking method with event-triggered communication. A group of agents cooperatively finds a critical point of the sum of local cost functions, which are smooth but not necessarily convex. We show that the proposed algorithm achieves a sublinear convergence rate by appropriately tuning the step size and the trigger threshold. Moreover, we show that agents can effectively solve a nonconvex optimization problem by the proposed event-triggered algorithm with less communication than by the existing time-triggered gradient-tracking algorithm. We confirm the validity of the proposed method by numerical experiments.

We address a path planning problem for heterogeneous multi-robot systems under specifications consisting of temporal constraints and routing tasks such as package delivery services. The robots are partitioned into several groups based on their dynamics and specifications. We introduce a concise description of such tasks, called a work, and extend counting LTL to represent such specifications. We convert the problem into an ILP problem. We show that the number of variables in the ILP problem is fewer than that of the existing method using cLTL+. By simulation, we show that the computation time of the proposed method is faster than that of the existing method.

Self-dithered digital delta-sigma modulators (DDSMs) are commonly used in fractional-N frequency synthesizers due to their ability to eliminate unwanted spurs from the synthesizer’s spectra without requiring additional hardware. However, when operating with a low-bit input, self-dithered DDSMs can still suffer from spurious tones at certain inputs. In this paper, we propose a self-dithered MASH 1-1-1-1 structure to mitigate the spur issue in the self-dithered MASH DDSMs. The proposed self-dithered MASH 1-1-1-1 suppresses the spurs with shaped dithering and achieves 4th order noise shaping.

A 150 GHz fundamental oscillator employing an inter-stage matching network based on a transmission line is presented in this letter. The proposed oscillator consists of a two-stage common-emitter amplifier loop, whose inter-stage connections are optimized to meet the oscillation condition. The oscillator is designed in a 130-nm SiGe BiCMOS process that offers fT and fMAX of 350 GHz and 450 GHz. According to simulation results, an output power of 3.17 dBm is achieved at 147.6 GHz with phase noise of -115 dBc/Hz at 10 MHz offset and figure-of-merit (FoM) of -180 dBc/Hz.

A wireless sensor terminal module of 5cc size (2.5 cm × 2.5 cm × 0.8 cm) that does not require a battery is proposed by integrating three kinds of circuit technologies. (i) a low-power sensor interface: an FM modulation type CMOS sensor interface circuit that can operate with a typical power consumption of 24.5 μW was fabricated by the 0.7-μm CMOS process technology. (ii) power supply to the sensor interface circuit: a wireless power transmission characteristic to a small-sized PCB spiral coil antenna was clarified and applied to the module. (iii) wireless sensing from the module: backscatter communication technology that modulates the signal from the base terminal equipment with sensor information and reflects it, which is used for the low-power sensing operation. The module fabricated includes a rectifier circuit with the PCB spiral coil antenna that receives wireless power transmitted from base terminal equipment by electromagnetic resonance coupling and converts it into DC power and a sensor interface circuit that operates using the power. The interface circuit modulates the received signal with the sensor information and reflects it back to the base terminal. The module could achieve 100 mm communication distance when 0.4 mW power is feeding to the sensor terminal.

L-type LC/CL matching circuits are well known for their simple analytical solutions and have been applied to many radio-frequency (RF) circuits. When actually constructing a circuit, parasitic elements are added to inductors and capacitors. Therefore, each L and C element has a self-resonant frequency, which affects the characteristics of the matching circuit. In this paper, the parallel parasitic capacitance to the inductor and the series parasitic inductor to the capacitance are taken up as parasitic elements, and the details of the effects of the self-resonant frequency of each element on the S11, voltage standing wave ratio (VSWR) and S21 characteristics are reported. When a parasitic element is added, each characteristic basically tends to deteriorate as the self-resonant frequency decreases. However, as an interesting feature, we found that the combination of resonant frequencies determines the VSWR and passband characteristics, regardless of whether it is the inductor or the capacitor.

Although baud-rate clock and data recovery (CDR) such as Mueller-Müller (MM) CDR is adopted to ADC-based receivers (RXs), it suffers from false-lock points when the RXs handle PAM4 data pattern because of the absence of edge data. In this paper, a false-lock-aware locking scheme is proposed to address this issue. After the false-lock-aware locking scheme, a clock phase is adjusted to achieve maximum eye height by using a post-1-tap parameter for an FFE in the CDR loop. The proposed techniques are implemented in a 56-Gb/s PAM4 transceiver. A PLL uses an area-efficient “glasses-shaped” inductor. The RX comprises an AFE, a 28-GS/s 7-bit time-interleaved SAR ADC, and a DSP with a 31-tap FFE and a 1-tap DFE. A TX is based on a 7-bit DAC with a 4-tap FFE. The transceiver is fabricated in 16-nm CMOS FinFET technology, and achieves a BER of less than 1e-7 with a 30-dB loss channel. The measurement results show that the MM CDR escapes from false-lock points, and converges to near the optimum point for large eye height.

Wide deployment of artificial intelligence (AI) is inducing exponentially growing energy consumption. Traditional digital platforms are becoming difficult to fulfill such ever-growing demands on energy efficiency as well as computing latency, which necessitates the development of high efficiency analog hardware platforms for AI. Recently, optical and electrooptic hybrid computing is reactivated as a promising analog hardware alternative because it can accelerate the information processing in an energy-efficient way. Integrated photonic circuits offer such an analog hardware solution for implementing photonic AI and machine learning. For this purpose, we proposed a photonic analog of support vector machine and experimentally demonstrated low-latency and low-energy classification computing, which evidences the latency and energy advantages of optical analog computing over traditional digital computing. We also proposed an electrooptic Hopfield network for classifying and recognizing time-series data. This paper will review our work on implementing classification computing and Hopfield network by leveraging silicon photonic circuits.

This article reviews the author’s group research achievements in analog/mixed-signal circuit and system area with introduction of how they came up with the ideas. Analog/mixed-signal circuits and systems have to be designed as well-balanced in many aspects, and coming up ideas needs some experiences and discussions with researchers. It is also heavily dependent on researchers. Here, the author’s group own experiences are presented as well as their research motivations.

Compared to subword based Neural Machine Translation (NMT), character based NMT eschews linguistic-motivated segmentation which performs directly on the raw character sequence, following a more absolute end-to-end manner. This property is more fascinating for machine translation (MT) between Japanese and Chinese, both of which use consecutive logographic characters without explicit word boundaries. However, there is still one disadvantage which should be addressed, that is, character is a less meaning-bearing unit than the subword, which requires the character models to be capable of sense discrimination. Specifically, there are two types of sense ambiguities existing in the source and target language, separately. With the former, it has been partially solved by the deep encoder and several existing works. But with the later, interestingly, the ambiguity in the target side is rarely discussed. To address this problem, we propose two simple yet effective methods, including a non-parametric pre-clustering for sense induction and a joint model to perform sense discrimination and NMT training simultaneously. Extensive experiments on Japanese⟷Chinese MT show that our proposed methods consistently outperform the strong baselines, and verify the effectiveness of using sense-discriminated representation for character based NMT.

This Letter focuses on deep learning-based monkeys' head swing counting problem. Nowadays, there are very few papers on monkey detection, and even fewer papers on monkeys' head swing counting. This research tries to fill in the gap and try to calculate the head swing frequency of monkeys through deep learning, where we further extend the traditional target detection algorithm. After analyzing object detection results, we localize the monkey's actions over a period. This Letter analyzes the task of counting monkeys' head swings, and proposes the standard that accurately describes a monkey's head swing. Under the guidance of this standard, the monkeys' head swing counting accuracy in 50 test videos reaches 94.23%.

Video text detection (VTD) aims to localize text instances in videos, which has wide applications for downstream tasks. To deal with the variances of different scenes and text instances, multiple models and feature fusion strategies were typically integrated in existing VTD methods. A VTD method consisting of sophisticated components can efficiently improve detection accuracy, but may suffer from a limitation for real-time applications. This paper aims to achieve real-time VTD with an adaptive lightweight end-to-end framework. Different from previous methods that represent text in a spatial domain, we model text instances in the Fourier domain. Specifically, we propose a scale-aware Fourier Contour Embedding method, which not only models arbitrary shaped text contours of videos as compact signatures, but also adaptively select proper scales for features in a backbone in the training stage. Then, we construct VTD-FCENet to achieve real-time VTD, which encodes temporal correlations of adjacent frames with scale-aware FCE in a lightweight and adaptive manner. Quantitative evaluations were conducted on ICDAR2013 Video, Minetto and YVT benchmark datasets, and the results show that our VTD-FCENet not only obtains the state-of-the-arts or competitive detection accuracy, but also allows real-time text detection on HD videos simultaneously.

Infrared and visible image fusion can combine the thermal radiation information and the textures to provide a high-quality fused image. In this letter, we propose a hybrid variational fusion model to achieve this end. Specifically, an ℓ0 term is adopted to preserve the highlighted targets with salient gradient variation in the infrared image, an ℓ1 term is used to suppress the noise in the fused image and an ℓ2 term is employed to keep the textures of the visible image. Experimental results demonstrate the superiority of the proposed variational model and our results have more sharpen textures with less noise.

With the recent advances in IoT, there is a growing interest in multi-surface computing, where a mobile app can cooperatively utilize multiple devices' surfaces. We propose a novel framework that seamlessly augments mobile apps with multi-surface computing capabilities. It enables various apps to employ multiple surfaces with acceptable performance.

In this note, we consider the problem of finding a step-by-step transformation between two longest increasing subsequences in a sequence, namely LONGEST INCREASING SUBSEQUENCE RECONFIGURATION. We give a polynomial-time algorithm for deciding whether there is a reconfiguration sequence between two longest increasing subsequences in a sequence. This implies that INDEPENDENT SET RECONFIGURATION and TOKEN SLIDING are polynomial-time solvable on permutation graphs, provided that the input two independent sets are largest among all independent sets in the input graph. We also consider a special case, where the underlying permutation graph of an input sequence is bipartite. In this case, we give a polynomial-time algorithm for finding a shortest reconfiguration sequence (if it exists).

Air Traffic Control (ATC) communication suffers from issues such as high electromagnetic interference, fast speech rate, and low intelligibility, which pose challenges for downstream tasks like Automatic Speech Recognition (ASR). This article aims to research how to enhance the audio quality and intelligibility of civil aviation speech through speech enhancement methods, thereby improving the accuracy of speech recognition and providing support for the digitalization of civil aviation. We propose a speech enhancement model called DIUnet_V (DenseNet & Inception & U-Net & Volume) that combines both time-frequency and time-domain methods to effectively handle the specific characteristics of civil aviation speech, such as predominant electromagnetic interference and fast speech rate. For model evaluation, we assess the denoising and enhancement effects using three metrics: Signal-to-Noise Ratio (SNR), Mean Opinion Score (MOS), and speech recognition error rate. On a simulated ATC training recording dataset, DIUnet_Volume10 achieved an SNR value of 7.3861, showing a 4.5663 improvement compared to the original U-net model. To address the challenge of the absence of clean speech in the ATC working environment, which makes it difficult to accurately calculate SNR, we propose evaluating the denoising effects indirectly based on the recognition performance of an ATC speech recognition system. On a real ATC speech dataset, the average word error rate decreased by 1.79% absolute and the average sentence error rate decreased by 3% absolute for DIUnet_V processed speech compared to the unprocessed speech in the built speech recognition system.

Owing to the several cases wherein abnormal sounds, called adventitious sounds, are included in the lung sounds of a patient suffering from pulmonary disease, the objective of this study was to automatically detect abnormal sounds from auscultatory sounds. To this end, we expressed the acoustic features of the normal lung sounds of healthy people and abnormal lung sounds of patients using Gaussian mixture model (GMM)-hidden Markov models (HMMs), and distinguished between normal and abnormal lung sounds. In our previous study, we constructed left-to-right GMM-HMMs with a limited number of states. Because we expressed abnormal sounds that occur intermittently and repeatedly using limited states, the GMM-HMMs could not express the acoustic features of abnormal sounds. Furthermore, because the analysis frame length and intervals were long, the GMM-HMMs could not express the acoustic features of short time segments, such as heart sounds. Therefore, the classification rate of normal and abnormal respiration was low (86.60%). In this study, we propose the construction of ergodic GMM-HMMs with a repetitive structure for intermittent sounds. Furthermore, we considered a suitable frame length and frame interval to analyze acoustic features. Using the ergodic GMM-HMM, which can express the acoustic features of abnormal sounds and heart sounds that occur repeatedly in detail, the classification rate increased (89.34%). The results obtained in this study demonstrated the effectiveness of the proposed method.