A novel crystal configuration of sandwiched S-Mo-Se structure (Janus SMoSe) at the monolayer limit has been synthesized and carefully characterized in this work. By controlled sulfurization of monolayer MoSe2 the top layer of selenium atoms are substituted by sulfur atoms while the bottom selenium layer remains intact. The peculiar structure of this new material is systematically investigated by Raman, photoluminescence and X-ray photoelectron spectroscopy and confirmed by transmission-electron microscopy and time-of-flight secondary ion mass spectrometry. Density-functional theory calculations are performed to better understand the Raman vibration modes and electronic structures of the Janus SMoSe monolayer, which are found to correlate well with corresponding experimental results. Finally, high basal plane hydrogen evolution reaction (HER) activity is discovered for the Janus monolayer and DFT calculation implies that the activity originates from the synergistic effect of the intrinsic defects and structural strain inherent in the Janus structure.

Janus Monolayer Transition Metal Dichalcogenides

The last decade has witnessed significant advances in energy harvesting technologies as a possible alternative to provide a continuous power supply for small, low-power devices in applications, such as wireless sensing, data transmission, actuation, and medical implants. Piezoelectric energy harvesting (PEH) has been a salient topic in the literature and has attracted widespread attention from researchers due to its advantages of simple architecture, high power density, and good scalability. This paper presents a comprehensive review on the state-of-the-art of piezoelectric energy harvesting. Various key aspects to improve the overall performance of a PEH device are discussed, including basic fundamentals and configurations, materials and fabrication, performance enhancement mechanisms, applications, and future outlooks.

A comprehensive review on piezoelectric energy harvesting technology: Materials, mechanisms, and applications

Nowadays, great effort has been devoted to establishing wearable electronics with excellent stretchability, high sensitivity, good mechanical strength, and multifunctional characteristics. Herein, a soft conductive hydrogel is rationally designed by proportionally mixing silk fibroin, polyacrylamide, graphene oxide, and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate). The resultant hydrogel has considerable stretchability and compressibility, which enables it to be assembled into a strain/pressure sensor with a wide sensing range (strain, 2%-600%; pressure, 0.5-119.4 kPa) and reliable stability. Then, the corresponding sensor is capable of monitoring a series of physical signals of the human body (e.g., joint movement, facial gesture, pulse, breathing, etc.). In particular, the hydrogel-based sensor is biocompatible, with no anaphylactic reaction on human skin. More interestingly, this conductive hydrogel exhibits a positive response when it works in a triboelectric nanogenerator; consequently, it lights up 20 commericial green light-emitting diodes. Thus, this silk fibroin-based hydrogel is a kind of multifunctional material toward wearable electronics with versatile applications in health and exercise monitors, soft robots, and power sources.

Stretchable, Biocompatible, and Multifunctional Silk Fibroin-Based Hydrogels toward Wearable Strain/Pressure Sensors and Triboelectric Nanogenerators.

Sensors based on the detection of small resistance variations are universally recognized as piezoresistive. Being one of the simplest, most common and most investigated classes of sensors, continuous efforts are focused on creating improved devices with higher performance that can be used in many commercial and non–commercial applications (e.g. evaluation of strain, pressure, acceleration, force etc.). Consequently, despite the fact that more than 150 years have passed since the discovery of the piezoresistive effect in some classes of metals and semiconductors, the development of such sensors remains interesting and topical. Moreover, with the advent of second–generation robotics, research on piezoresistive sensors has undergone a massive increase. This paper aims to be a short, self–consistent vademecum which would be useful to researchers and engineers, since it focuses on the fundamentals of theory, materials, and readout–circuit design pertinent to the most recent developments in the field of piezoresistive sensors.

Theory, technology and applications of piezoresistive sensors: A review

Technology has become ubiquitous, it is all around us and is becoming part of us. Togetherwith the rise of the Internet of Things (IoT) paradigm and enabling technologies (e.g., Augmented Reality (AR), Cyber-Physical Systems, Artificial Intelligence (AI), blockchain or edge computing), smart wearables and IoT-based garments can potentially have a lot of influence by harmonizing functionality and the delight created by fashion. Thus, smart clothes look for a balance among fashion, engineering, interaction, user experience, cybersecurity, design and science to reinvent technologies that can anticipate needs and desires. Nowadays, the rapid convergence of textile and electronics is enabling the seamless and massive integration of sensors into textiles and the development of conductive yarn. The potential of smart fabrics, which can communicate with smartphones to process biometric information such as heart rate, temperature, breathing, stress, movement, acceleration, or even hormone levels, promises a new era for retail. This article reviews the main requirements for developing smart IoT-enabled garments and shows smart clothing potential impact on business models in the medium-term. Specifically, a global IoT architecture is proposed, the main types and components of smart IoT wearables and garments are presented, their main requirements are analyzed and some of the most recent smart clothing applications are studied. In this way, this article reviews the past and present of smart garments in order to provide guidelines for the future developers of a network where garments will be connected like other IoT objects: the Internet of Smart Clothing.

https://www.mdpi.com/2079-9292/7/12/405/pdf

Towards The Internet of Smart Clothing: A Review on IoT Wearables and Garments for Creating Intelligent Connected E-Textiles

Wearable health monitoring systems have gained considerable interest in recent years owing to their tremendous promise for personal portable health watching and remote medical practices. The sensors with excellent flexibility and stretchability are crucial components that can provide health monitoring systems with the capability of continuously tracking physiological signals of human body without conspicuous uncomfortableness and invasiveness. The signals acquired by these sensors, such as body motion, heart rate, breath, skin temperature and metabolism parameter, are closely associated with personal health conditions. This review attempts to summarize the recent progress in flexible and stretchable sensors, concerning the detected health indicators, sensing mechanisms, functional materials, fabrication strategies, basic and desired features. The potential challenges and future perspectives of wearable health monitoring system are also briefly discussed.

Flexible, Stretchable Sensors for Wearable Health Monitoring: Sensing Mechanisms, Materials, Fabrication Strategies and Features

Field programmable gate array (FPGA) is widely considered as a promising platform for convolutional neural network (CNN) acceleration. However, the large numbers of parameters of CNNs cause heavy computing and memory burdens for FPGA-based CNN implementation. To solve this problem, this paper proposes an optimized compression strategy, and realizes an accelerator based on FPGA for CNNs. Firstly, a reversed-pruning strategy is proposed which reduces the number of parameters of AlexNet by a factor of 13× without accuracy loss on the ImageNet dataset. Peak-pruning is further introduced to achieve better compressibility. Moreover, quantization gives another 4× with negligible loss of accuracy. Secondly, an efficient storage technique, which aims for the reduction of the whole overhead cache of the convolutional layer and the fully connected layer, is presented respectively. Finally, the effectiveness of the proposed strategy is verified by an accelerator implemented on a Xilinx ZCU104 evaluation board. By improving existing pruning techniques and the storage format of sparse data, we significantly reduce the size of AlexNet by 28×, from 243 MB to 8.7 MB. In addition, the overall performance of our accelerator achieves 9.73 fps for the compressed AlexNet. Compared with the central processing unit (CPU) and graphics processing unit (GPU) platforms, our implementation achieves 182.3× and 1.1× improvements in latency and throughput, respectively, on the convolutional (CONV) layers of AlexNet, with an 822.0× and 15.8× improvement for energy efficiency, separately. This novel compression strategy provides a reference for other neural network applications, including CNNs, long short-term memory (LSTM), and recurrent neural networks (RNNs).

Optimized Compression for Implementing Convolutional Neural Networks on FPGA

Wearable sensors are attracting great attentions due to their potential applications in human health monitoring and caring systems. The wide utilization of wearable electronics may cause great burden to the environment, due to the vast contaminants generated in their fabrication and after their usage. Consequently, great efforts are devoted to the eco-friendly strategies for the material and fabrication in wearable sensors. Herein, recent advantages in developing wearable sensors with eco-friendly materials and green manufacturing approaches are reviewed. The functional materials with accessibility, recoverability and degradability have participated in the sensors as substrates, sensing elements and conductors. The fabrication strategies, from facile manual schemes to low-emission automated techniques are also introduced with their merits and demerits. Finally, the existing challenges and future opportunities in this field are summarized.

Eco-friendly Strategies for the Material and Fabrication of Wearable Sensors

In this paper, a high-resolution time-to-digital converter (TDC) based on a field programmable gate array (FPGA) device is proposed and tested. During the implementation, a new architecture of TDC is proposed which consists of a measurement matrix with 1024 units. The utilization of routing resources as the delay elements distinguishes the proposed design from other existing designs, which contributes most to the device insensitivity to variations of temperature and voltage. Experimental results suggest that the measurement resolution is 7.4 ps, and the INL (integral nonlinearity) and DNL (differential nonlinearity) are 11.6 ps and 5.5 ps, which indicates that the proposed TDC offers high performance among the available TDCs. Benefitting from the FPGA platform, the proposed TDC has superiorities in easy implementation, low cost, and short development time.

A 7.4 ps FPGA-Based TDC with a 1024-Unit Measurement Matrix

Deep learning has attracted increasing attention across a number of disciplines in recent years. In the field of remote sensing, ship detection based on deep learning for synthetic aperture radar (SAR) imagery is replacing traditional methods as a mainstream research method. The multiple scales of ship objects make the detection of ship targets a challenging task in SAR images. This paper proposes a new methodology for better detection of multi-scale ship objects in SAR images, which is based on YOLOv5 with a small model size (YOLOv5s), namely the multi-scale ship detection network (MSSDNet). We construct two modules in MSSDNet: the CSPMRes2 (Cross Stage Partial network with Modified Res2Net) module for improving feature representation capability and the FC-FPN (Feature Pyramid Network with Fusion Coefficients) module for fusing feature maps adaptively. Firstly, the CSPMRes2 module introduces modified Res2Net (MRes2) with a coordinate attention module (CAM) for multi-scale features extraction in scale dimension, then the CSPMRes2 module will be used as a basic module in the depth dimension of the MSSDNet backbone. Thus, our backbone of MSSDNet has the capabilities of features extraction in both depth and scale dimensions. In the FC-FPN module, we set a learnable fusion coefficient for each feature map participating in fusion, which helps the FC-FPN module choose the best features to fuse for multi-scale objects detection tasks. After the feature fusion, we pass the output through the CSPMRes2 module for better feature representation. The performance evaluation for this study is conducted using an RTX2080Ti GPU, and two different datasets: SSDD and SARShip are used. These experiments on SSDD and SARShip datasets confirm that MSSDNet leads to superior multi-scale ship detection compared with the state-of-the-art methods. Moreover, in comparisons of network model size and inference time, our MSSDNet also has huge advantages with related methods.

/pdf/ship-detection-in-sar-images-based-on-multi-scale-feature-i32wsdba.pdf

Hai Wang

Papers

Flexible, Stretchable Sensors for Wearable Health Monitoring: Sensing Mechanisms, Materials, Fabrication Strategies and Features

Optimized Compression for Implementing Convolutional Neural Networks on FPGA

Eco-friendly Strategies for the Material and Fabrication of Wearable Sensors

A 7.4 ps FPGA-Based TDC with a 1024-Unit Measurement Matrix

Ship Detection in SAR Images Based on Multi-Scale Feature Extraction and Adaptive Feature Fusion