Engine anomaly detection is crucial in the marine systems sector as it enables the timely identification of potential faults, prevents major accidents, and ensures navigational safety. In this paper, we propose an unsupervised anomaly detection method to detect marine engines using a graph network autoencoder with multi-scale feature fusion. This method efficiently identifies temporal relationships in typical time series data, thus removing the requirement for labeled failure data. We selected the data with obvious anomalies for testing, compared the original data with the reconstructed data, marked the abnormal data, and achieved good results.

In battery-powered devices, power-efficient power management systems, such as high-power-efficiency buck-boost converters, are critical. In this paper, we propose a novel digital-input average current mode high-power-efficiency buck-boost converter. The proposed buck-boost converter embodies a novel full-cycle continuous current sensing circuit, which eliminates the need for a current selection circuit by continuously sensing the inductor current throughout the entire switching cycle. Unlike conventional approaches which rely on non-continuous sensing, this method improves current sensing accuracy and eliminates false switching, leading to reduced output noise, increased power efficiency, and improved circuit robustness. The proposed buck-boost converter further embodies a PWM controller with novel adaptive mode control, which dynamically adjusts the pulse width of the power transistors in buck-boost operation - this is to avoid narrow pulses hence preventing false switching. This further enhances the power efficiency and the robustness of the buck-boost converter. Both the proposed full-cycle continuous current sensing circuit and the adaptive mode control circuit are simple, with little hardware or power dissipation overheads. The proposed buck-boost converter is designed and fabricated in a 55nm BCD process with an IC area of 2.4mm2. Based on measurements, the proposed buck-boost converter features a high peak power efficiency of 95% and maintains a high efficiency of >90% across a wide output range (0.3W to 4.3 W) with a supply voltage of 2.5 V to 5.5 V

We present an authoring tool, called CAST+ (Canis Studio Plus), that enables the interactive creation of chart animations through the direct manipulation of keyframes. It introduces the visual specification of chart animations consisting of keyframes that can be played sequentially or simultaneously, and animation parameters (e.g., duration, delay). Building on Canis (Ge et al. 2020), a declarative chart animation grammar that leverages data-enriched SVG charts, CAST+ supports auto-completion for constructing both keyframes and keyframe sequences. It also enables users to refine the animation specification (e.g., aligning keyframes across tracks to play them together, adjusting delay) with direct manipulation. We report a user study conducted to assess the visual specification and system usability with its initial version. We enhanced the system's expressiveness and usability: CAST+ now supports the animation of multiple types of visual marks in the same keyframe group with new auto-completion algorithms based on generalized selection. This enables the creation of more expressive animations, while reducing the number of interactions needed to create comparable animations. We present a gallery of examples and four usage scenarios to demonstrate the expressiveness of CAST+. Finally, we discuss the limitations, comparison, and potentials of CAST+ as well as directions for future research.

NJ) trees as a new way to visually explore cluster structures and outliers in multi-dimensional data. Neighbor-joining (NJ) trees are widely used in biology, and their visual representation is similar to that of dendrograms. The core difference to dendrograms, however, is that NJ trees correctly encode distances between data points, resulting in trees with varying edge lengths. We optimize NJ trees for their use in visual analysis in two ways. First, we propose to use a novel leaf sorting algorithm that helps users to better interpret adjacencies and proximities within such a tree. Second, we provide a new method to visually distill the cluster tree from an ordered NJ tree. Numerical evaluation and three case studies illustrate the benefits of this approach for exploring multi-dimensional data in areas such as biology or image analysis.

We present CAST, an authoring tool that enables the interactive creation of chart animations. It introduces the visual specification of chart animations consisting of keyframes that can be played sequentially or simultaneously, and animation parameters (e.g., duration, delay). Building on Canis [19], a declarative chart animation grammar that leverages data-enriched SVG charts, CAST supports auto-completion for constructing both keyframes and keyframe sequences. It also enables users to refine the animation specification (e.g., aligning keyframes across tracks to play them together, adjusting delay) with direct manipulation and other parameters for animation effects (e.g., animation type, easing function) using a control panel. In addition to describing how CAST infers recommendations for auto-completion, we present a gallery of examples to demonstrate the expressiveness of CAST and a user study to verify its learnability and usability. Finally, we discuss the limitations and potentials of CAST as well as directions for future research.

We present a non-uniform recursive sampling technique for multi-class scatterplots, with the specific goal of faithfully presenting relative data and class densities, while preserving major outliers in the plots. Our technique is based on a customized binary kd-tree, in which leaf nodes are created by recursively subdividing the underlying multi-class density map. By backtracking, we merge leaf nodes until they encompass points of all classes for our subsequently applied outlier-aware multi-class sampling strategy. A quantitative evaluation shows that our approach can better preserve outliers and at the same time relative densities in multi-class scatterplots compared to the previous approaches, several case studies demonstrate the effectiveness of our approach in exploring complex and real world data.

Sensing and its electronics based on printed/flexible electronics offer unique attributes of mechanical flexibility of its substrate, hence the unique applications. Nevertheless, one of the ensuing key challenges of flexible-electronics-based sensing is the issues associated with consistency and repeatability of its parameters of the flexible electronics elements/sensors due to their variations, which are sometimes intractable. These may be due to manufacturing variations, aging, when the substrate is bent, and so on. In this article, we describe our codesign between the different chains of the flexible electronics supply chain to derive practically flexible electronics and sensors for applications where the substrate is expected to bend, e.g., in an augmented sensing e-skin smart glove application. This effort includes our fully additive low-temperature all-air low-cost screen printing process, and how we obtain consistency and repeatability. To improve the matching of thin-film transistors-a critical consideration for conditioning sensor outputs-we describe layout techniques where relatively good matching can be achieved, but with area overheads. We describe how we accommodate the variations of printed elements and circuits embodying printed elements when the substrate is bent-a self-compensating means-and propose the application of the same for printed sensors. The cost of our self-compensation means is without power or area overheads, albeit more (uncomplicated) printing steps. We finally describe our process development kit (PDK) encompassing all of the aforesaid to predict the performance of the printed circuits and sensors, including the effects of bending and our proposed self-compensation thereto. We demonstrate the efficacy of our methods based on measurements on printed elements and circuits.

Appropriate choice of colors significantly aids viewers in understanding the structures in multiclass scatterplots and becomes more important with a growing number of data points and groups. An appropriate color mapping is also an important parameter for the creation of an aesthetically pleasing scatterplot. Currently, users of visualization software routinely rely on color mappings that have been pre-defined by the software. A default color mapping, however, cannot ensure an optimal perceptual separability between groups, and sometimes may even lead to a misinterpretation of the data. In this paper, we present an effective approach for color assignment based on a set of given colors that is designed to optimize the perception of scatterplots. Our approach takes into account the spatial relationships, density, degree of overlap between point clusters, and also the background color. For this purpose, we use a genetic algorithm that is able to efficiently find good color assignments. We implemented an interactive color assignment system with three extensions of the basic method that incorporates top K suggestions, user-defined color subsets, and classes of interest for the optimization. To demonstrate the effectiveness of our assignment technique, we conducted a numerical study and a controlled user study to compare our approach with default color assignments; our findings were verified by two expert studies. The results show that our approach is able to support users in distinguishing cluster numbers faster and more precisely than default assignment methods.

Flexible Hybrid Electronics (FHE), heterogeneous electronics embodying both conventional silicon electronics and printed electronics, is an emerging technology with huge market potential as it is advantageous compared to conventional silicon electronics and the emerging Printed Electronics - FHE features better mechanical flexibility/conformability and lower cost compared to conventional silicon electronics, and higher performance compared to Printed Electronics. In this paper, a comprehensive literature view on FHE is provided, including the state-of-the-art FHE development, FHE supply chains, and design challenges.

Almost all of the fish have the ability to recognize the underwater environment, among of which current forecasting is very critical for the movement of fish as well as an underwater vehicle. Perceiving water flow is inspired by the study on lateral line system of fish, and pressure sensor array is utilized to perceive changes in pressure of water. Due to the high dimension of sensor data, it is necessary to analyze the characteristics of the signal and obtain feature vectors to speed up the process of forecasting. Linear Discriminant Analysis (LDA) is proposed to preprocess the sensor data to obtain the compressed sampling of sensor data at first. Then, Support Vector Machine (SVM) is utilized to train a vigorous and robust classifier for recognizing the direction of flow. The forecast is evaluated according to the effectiveness and the prediction performance of the established pattern recognition model, which is able to recognize flow direction accurately and estimate the magnitude within a certain range like animals. Results indicate that the processed method not only can forecast flow direction, but also can combine with IMU sensor to estimate the attitude of the vehicle.

Despite the huge market potential of Printed Electronics on Flexible substrates, the printed circuits and systems are yet to be manufacturable due to high cost fabrication processes, large process variations between devices, large and somewhat intractable variations when the devices and their substrates are bent, and lack of a comprehensive Process Development Kit (PDK). In this review paper, we review our Low-Cost Fully-Additive printing process with these four shortcomings addressed. To the best of our knowledge, this process is arguably the Fully-Additive printing process that is closest to being manufacturable and for the realization of practical intelligent printed electronics.

Class D amplifiers are routinely employed as audio amplifiers due to their high power efficiency. Total Harmonic Distortion (THD) is one of the most important parameters to qualify and quantify their performance. In this paper, THD of a commonly used Bridge-Tied-Load (BTL) Class D amplifier is investigated, including the derivation of the analytical expression for the THD of the BTL Class D amplifier. We show that in some cases, the THD of the BTL Class D amplifier is independent of the integrator gain of the amplifier - this is unlike other Class D amplifiers and linear amplifiers whose THD is largely determined by their integrator gain. Instead, the THD of the BTL Class D amplifier is largely determined by the feedback amplifier. Further, unlike the single-ended Class D amplifier whose THD increases as the modulation index increases, the THD of the BTL Class D amplifier is maximum at the modulation index = 0.5. The analysis herein provide good insight to the design of BTL Class D amplifiers, including how various parameters may be varied/optimized to meet a given THD specification.

Printed/Organic Electronics (PE) is an emerging technology with gargantuan market potential, particularly if its realization is low cost and the supply chain associated with its design is manageable/established. For the former, a Fully-Additive All-Air Low-Temperature process (vis-à-vis a Subtractive process) is desirable, while for the latter, a comprehensive Process Development Kit (PDK) for Electronic Design Automation tools is necessary. In this paper, we delineate, arguably the first-ever PDK for PE - this PDK is developed for our Fully-Additive All-Air Low-Temperature printing process with very-low process variations, and capable of complete circuit realizations on a myriad of substrates, including low-cost low-temperature flexible plastic films. Our proposed PDK embodies accurate modeling of the printed transistor and printed passive elements. With these models, comprehensive schematic simulations can be performed, including Monte Carlo simulations. We demonstrate the efficacy of our PDK, in part based on our measured process variations. The layout design rules for our Fully-Additive printing process to facilitate layout design - imperative for estimating the printing area and the printing cost - are also delineated herein.

Class D amplifiers are routinely employed in power-critical hearing aids for their high power-efficiencies, typically ~90% at high modulation indexes. Nevertheless, at the nominal operation condition where the modulation index M=0.1, the power-efficiency is typically 32%. In this paper, we show that the comparator embodied in the over-current protection circuit of the Class D output stage is dominant at M=0.1. We propose the design of a novel comparator (with performance parameters comparable with conventional comparators) featuring ~40% lower power dissipation. This improved lower power dissipation translates to a worthwhile 12% improvement in the power-efficiency of the Class D amplifier output stage. The proposed design herein is verified by computer simulations.

In this paper, the effects of power supply noise, qualified by power supply rejection ratio (PSRR), on two types of bridge-tied load (BTL) pulse width modulation (PWM) class D amps (denoted as Type-I BTL and Type-II BTL respectively) are investigated and the analytical expressions for PSRR of the two designs derived. The derived analytical expressions are verified by means of HSPICE simulations. The relationships derived herein provide good insight to the design of BTL class D amps, including how various parameters may be varied/optimized to meet a given PSRR specification. Furthermore, the PSRR of the two BTL Class D amps are compared against the single-ended class D amp, and the former designs show superior PSRR compared to the latter.

In this paper, the effect of power supply noise, qualified by power supply rejection ratio (PSRR), on bang-bang control class D amplifier (BCCD) is investigated. By modeling the BCCD with power supply noise, the mechanisms are modeled. Further, by means of a novel analysis based on a double-Fourier series, the parameters related to power supply noise is derived and thereafter expressions for PSRR are derived. The analyses are verified by means of computer simulations and by measurements on practical circuits. The relationships derived herein provide good insight to the design of BCCDs, including how various parameters may be varied/optimized to meet a given PSRR specification.

In this paper, we propose and derive a linear model of a closed-loop PWM class D amplifier (CDA) to model its power supply rejection ratio (PSRR). On the basis of the proposed model, we derive a simple expression that depicts the effects of 3 critical parameters on PSRR: the gain of the integrator, the gain of the PWM stage and the feedback factor. We recommend, from a practical viewpoint, that the first and third parameters be increased if higher PSRR is desired. We validate our model and analysis on the basis of HSPICE simulations and on experimental measurements. Our model and analysis are useful as they provide insight to a CDA designer, in particular how various parameters may be varied/compromised to meet a given set of PSRR specifications

We derive Fourier series expressions for modeling analog closed-loop PWM class D amplifiers. Based on our derived expressions, we are able to analyze the mechanisms of several nonlinearities, specifically the power supply noise and fold-back distortions. We investigate the influence of the finite loop gain on these nonlinearities, and we show that those nonlinearities can be effectively suppressed by the higher loop gain. We verify our analysis by computer simulations and also on the basis of experimental measurements. The derived expressions are practically useful as they provide insights to a designer for PWM class D amplifier designs

USS is a novel underwater self-reconfigurable robot, which consists of modules of the same type. By the modules' connecting and disconnecting, USS can change its configuration in real-time. A feature of USS is that it can execute underwater spatial maneuver with only the planar eel-like configuration, which is due to the module's capability of buoyancy regulation and one degree metamorphosing. This article focuses on control of the type of maneuver. We built the nonlinear 6-DOF dynamics model firstly, and then perturb it around the nominal state of straight forward motion with constant speed, which gives two uncoupled linear models for the horizontal maneuver and the perpendicular motion. The perpendicular controller is designed based on the robust control technique of mix sensitivity, while the horizontal controller is designed based on Masashi Saito's method, in which maneuver is achieved by serpentine gait, and the speed and heading are stabilized by feeding back the metamorphosing frequency and the average joint angles respectively. The control strategy is then verified by digital simulation, which shows that the expected maneuver is achieved well by the suggested method.