Miracle-dz/UniFAR-FTUs huggingface dataset:
The Application of Machine Learning and Low Frequency Sonar for Subsea Power Cable Integrity Evaluation

Subsea power cables are essential assets for the electrical transmission and distribution networks. They are crucial in ensuring the security of electricity supply and supporting the global expansion in offshore renewable energy generation. After reviewing historical data on subsea cable failure modes, we established that existing monitoring systems do not account for over 70% of subsea cable failure modes. The current technologies focus on electrical failure modes and subsea cable asset management strategies are typically reactive or time based, with inspection limited to diver and/or ROV supported video footage which has several limitations, such as requiring good visibility, access to the cable, challenges in locating the cable and inability to identify failure modes at the interface of the seabed. To overcome these limitations, we propose an innovative sensor technology that can provide the in-situ integrity analysis of the subsea cable. In this paper, we applied low frequency sonar technology to undertake detailed and in-situ assessment of subsea cable integrity. Specifically, in our work, a wideband low frequency (LF) sonar scanning system is manufactured to collect acoustic response from different subsea power cable samples with different inner structure and external failure modes. In addition, accelerated life cycle testing was conducted by manually introduce controlled stages of corrosion and abrasion to the cables to obtain integrity data at various cable degradation levels. Seminal results provide a detailed library of LF sonar responses to cable type and failure mode variations. The results of preliminary data analysis demonstrate the ability to distinguish subsea cables by differences in diameter and cable types and achieve an overall 95%+ accuracy rate to detect different cable degradation stages.
Using deep learning approaches to overcome limited dataset issues within semiconductor domain

Today, in semiconductor manufacturing, wafer failures are frequent problems with the production lines. To increase the production yield, images are the most important pieces of data used to detect defect-free wafers. However, there are many tools that can be installed specifically to monitor production lines, inspect mapped defects and detect the main causes of die failures by using wafers images during semiconductor manufacturing process. The underlying objective is to overcome the need for a physical check on the wafer which are in most cases too long. Thus, the need to have a design that will measure and detect these visual faults in an automated fashion is a big challenge for the industry. Recently, deep learning approaches have proven to be a suitable way to overcome this issue. However, they rely on the availability of sufficiently representative datasets which is not our case: data on anomalies is scarce. The goal of this paper is to evaluate state of the art deep learning methodology such as GAN and VAE to overcome this challenge. Implementation results show that the GAN architecture achieves a convincing image generation in a limited sample setting, while the VAE architecture fails to converge at training time.
Research on High-accuracy Clock Synchronization Based on IEEE 1588 Protocol

With the continuous development of mobile Internet3G/4G/5G, the requirement of time synchronization is becoming higher and higher. The IEEE1588 protocol has received extensive attention and research because it can provide sub-microsecond time synchronization to meet the actual needs. This research is based on Kalman Filter(KF) and Sliding Mode Controller(SMC) to achieve master-slave clock synchronization. Firstly, SMC is used to compensate the frequency drift caused by temperature, thereby reducing the influence of temperature on the frequency drift of crystal oscillator. Then, KF is used to suppress and optimize clock frequency jitter or random error. Through simulation, it is proved that this method can effectively improve the synchronization accuracy and meet the clock synchronization requirements of IEEE1588 protocol.
Simulation for Multi-point Haptic Feedback Based on Ultrasound Phased Array

As a new type of interaction way, haptic feedback has attracted a lot of attention. Among different kinds of haptic feedback methods, contactless haptic feedback based on ultrasound array has become one of the most promising methods because of its simple structure, low cost, diverse feedback forms and flexible adjustment. This paper firstly discusses the mechanism of human body's tactile sensation in detail, and then analyzes the mechanism of the realization of haptic feedback by ultrasound phased array. Then the mathematical model of ultrasound phased array’s acoustic field is established, and the methods for solving single point and multi-point haptic feedback are presented. The energy efficiency is further optimized for the solution of multi-point haptic feedback.
Joint Torque Estimation for Dynamic Jumping Control of Compliant Gear-Driven Legged Robot*

This paper is to explain compliant torque control for gear driven legged robot. In this paper, active torque compliance control is realized without torque sensor or any mechanically elastic component. The joint torque estimation is very important for torque control. However, Joint torque sensor for medium-size legged robot is so large and heavy that it increases the inertia of the leg, slows down the dynamic response, and leads to complicated electrical system. Therefore, we only use the encoder information and the motor current to estimate the joint torque without torque sensors. Compliant torque control have advantages of absorbing impacts and saving energy. But, torque control is difficult to guarantee the position precision, because the accurate model of the robot is hard to obtain. In order to ensure both compliance and position accuracy, hybrid torque and position control is applied to our robot. In the jumping motion experiments, it is verified that the joint torque can reach peck value in 4.75 ms when jumping up. While The landing peak torque in hybrid control is lower than 1/6 of that under position control and 1/2 of that in torque control. This indicates that the compliance torque control can absorb a large part of shock.
Deep Learning for DOA Estimation Using a Vector Hydrophone

Source azimuth can be estimated via the complex sound intensity method based on a single vector hydrophone, which exploits the physical properties of acoustic pressure and particle velocity components. Deep learning has been successfully used to estimate source depth and distance via training and prediction; herein it is adapted to estimate the source azimuth by analyzing the sound fields provided by a single vector hydrophone. This study compares the estimation results of the two abovementioned approaches that applied to the data due to a moving ship circling around a moored vector hydrophone. Preliminary results demonstrate that these two approaches generally agree; however their accuracies are unsatisfactory because of a low signal-to-noise ratio and strong interference. To increase the estimation accuracy, herein, cross-spectral processing of input data is used for deep learning. The proposed deep-learning method with appropriate input data can effectively suppress interference and accurately estimate source azimuth.
Design and Safety Control of a High-Payload Nursing Robotic Arm with Tactile Skin

Service robots are being increasingly used to replace human beings in carrying out various sophisticated, heavy and even dangerous tasks. In order to solve the problem of labor shortage in the medical and nursing industry, this paper proposes a novel high-payload robotic arm for nursing tasks, which can help caregivers to lift and transfer patients. Firstly, we simulate and analyze the specific nursing tasks and application scenarios, and determine its kinematics configuration and load capacity parameters of each joint. Then we design and fabricate the modular and low flexibility robotic joints for the prototype. To avoid the occurrence of safety accidents while lifting patients, we design a new kind of tactile skin based on the resistance characteristic of varistor materials, using which a safety control algorithm is proposed. Experiments show that it can meet the requirements of load capacity and position control accuracy, and the proposed safety control method can effectively avoid many dangerous situations.
Robust Interaural Time Difference Estimation Based on Convolutional Neural Network

This paper proposes a novel cross correlation function (CCF) extraction method based on convolutional neural network for time difference of arrival (TDOA) estimation or further direction of arrival (DOA) estimation. CNN is utilized to learn the relationship between the cross correlation localization features and the pre-processed waveform signal which may include not only the source signal but also the background noise and reverberation. In contrast to many previous sound source localization approaches, the proposed method focuses on the spatial feature extraction. Two kind of outputs, grouped or encoded CCF, are designed to capture the implicit tendency of location information. The experimental results demonstrate that the proposed method outperforms the conventional TDOA estimation methods under environments with different levels of noise and reverberation.
The first JANUS Interoperability Fest - a field report

Underwater acoustic communications is a global domain in which different manufacturers produce different devices and implement different (and usually incompatible) communication protocols. The myriad of vendor-specific protocols is not conducive to collaboration amongst different industry, academia and research partners. The need for interoperability drove the efforts of NATO Science and Technology Organization (STO) Centre for Maritime Research and Experimentation (CMRE) towards the establishment of a standard for digital underwater acoustic communications. The JANUS standard was recently published as a NATO Standardard (STANAG 4748) and has been implemented by an increasing number of vendors. To validate these implementations and ensure interoperability, CMRE organised the first JANUS Interoperability Fest in May 2018. This article details the tests performed and the main outcomes of the event. The success of this Fest led to the organisation of a second Fest in November 2019 with an increased number of participants.
DoA Estimation Using Reconfigurable Antennas in Millimiter-Wave Frequency 5G Systems

To achieve low latency and high throughputs, future 5G systems will have to utilize complex antenna systems able to provide beamforming and direction-of-arrival (DoA) estimation capabilities. Most of the concepts available in the literature rely on analog or digital beamforming, which is well developed and can be used both at a base station and in a user terminal. However, in applications, in which energy-efficiency or cost is one of the key concerns, the use of traditional beamforming systems is limited. To overcome this limitation, reconfigurable antennas can be used as they rely on a simple but effective concept, in which a transceiver is connected to a single input port and beamforming capabilities are enabled by a number of passive elements electronically switched via transceiver's digital input-output ports. In the article, we show how simple single-input mm-wave reconfigurable antenna can be used to provide not only beamforming but also DoA capabilities relying on received signal strength measurements. Therefore, the proposed approach can be applied in future 5G millimeter-wave nodes or gateways, in which low-cost and power-efficiency are important.
Accurate and Efficient Interdependent Timing Model for Flip-Flop in Wide Voltage Region

The interdependency of the setup and hold constraints and clock-to-q (c2q) delay for flip-flops (FF) has been studied in static timing analysis (STA) to facilitate timing closure and improve circuit performance. However circuits operating in near-threshold voltage (NTV) region pose severe challenge to the interdependent timing modelling due to its significant nonlinear relation and much wider constraint range compared with super-threshold voltage (STV) region. In this paper an accurate and efficient modelling approach is proposed by employing artificial neuron network (ANN) to characterize the interdependency among the setup time hold time and c2q delay of FF in wide voltage region. Experimental results show the prediction errors of c2q delay with varied setup and hold constraints are lower than 2.1% and 1.8% in NTV and STV regions with 3.51x and 1.09x accuracy improvement compared with the prior work. Besides the modelling efficiency is validated by 1.41 x/1.10x simulation cost reduction and 21.08x/4.14x library storage saving for NTV/STV domain with accuracy advantage.
A Grasping CNN with Image Segmentation for Mobile Manipulating Robot

This paper presents a grasping convolutional neural network with image segmentation for mobile manipulating robot. The proposed method is cascaded by a feature pyramid network FPN and a grasping network DrGNet. The FPN network combined with point cloud clustering is used to obtain the mask of the target object. Then, the grayscale map and the depth map corresponding to the target object are combined and sent to the DrGNet network for providing multi-scale images. On this basis, depthwise separable convolution is used for encoding. The results of encoders are refined according to the light-weight RefineNet as well as sSE, which can achieve a better grasp detection. The proposed method is verified by the experiments on mobile manipulating robot.
Exploring Schmitt Trigger Circuits for Process Variability Mitigation

Process variability has become one of the major factors concerning variations in IC metrics, such as performance and energy consumption, due to the aggressive technology and voltage scaling. This work analyzes the impact on the variability robustness of a Schmitt Trigger-based technique. Several works point out that the considered technique helps to improve the variability robustness at the electrical level. The technique was applied on four Full Adder considering two different Schmitt Triggers designs at the layout level using the 7nm FinFET technology node from ASAP7. Performance, energy, and area are taken into account. Results show, on average, up to 36% and 45% improvement in delay and energy variability robustness, respectively.
A Capacitor-Less CMOS Neuron Circuit for Neuromemristive Networks

CMOS neuron circuits used to implement neuromorphic chips require extensive circuitry to program the memristive cell, thus eliminating most of the density advantage gained by the adoption of memristive synapses. This paper presents a CMOS neuron circuit that provides a compact and cost-efficient programming interface in which semiconductor capacitors are replaced by a memristive device. The neuron circuit also features an adjustable firing threshold with a strict control of the power consumption during the learning process.
Evaluation of PoseNet for 6-DOF Underwater Pose Estimation

Autonomy in underwater intervention operations requires localization systems of high accuracy. State-of-the-art methods rely on computer vision to provide the necessary localization accuracy. However, traditional computer vision solutions rely on hand-crafted features, which often exhibit low robustness to variations in the lighting conditions. Furthermore, the most common image localization method, Perspective-n-Point (PnP), relies on specific knowledge of the distances in the scene, something which is not always available. Recent advances within deep learning, in particular, convolutional neural networks (CNNs), have resulted in promising methods for pose estimation based on imagery input. This article investigates the potential of applying a specific CNN architecture, named PoseNet, to estimate the 6-DoF pose between an underwater vehicle and a fixed object, without the need for artificial markers.
Compliance Control of the Hydraulic-Driven Single Legged Robot

This paper presents the force control strategies and a kind of compliance control strategies of the hydraulic- driven unit (HDU) in the legged robots. The model of HDU is established and the transfer function of the HDU force control system is given. The analysis of the force control strategies is introduced according to the characteristic of the HDU system. Based on the force control strategy called velocity feedback control strategy which performs the best, the virtual model control strategy which is a kind of compliance control strategy is presented to enhance the adaptability of the legged robot. Finally, the experiments are taken on the HDU experiment platform to test the performance of the force control strategies and the compliance control strategy. The landing buffer experiments are taken on the single leg robot to verify the effectiveness of the compliance control strategy. In the landing buffer experiments, the impact force of the single leg robot's knee HDU is about 0.33 time of what it is without the compliance control strategy while landing.
Localizability of Laser SLAM Robot Based on Deep Learning*

Positioning is the key technology in the field of mobile robot, and it is also the basis of various tasks such as autonomous movement. Localizability is a measure of the ability to achieve accurate positioning for a given location, and it is an important indicator to further avoid positioning failure in upper tasks. Taking the fusion positioning method based on map matching and trajectory estimation as the research object, this paper analyses its working mechanism, designs different neural networks for different positioning methods, and proposes a neural network model composed of convolutional neural network, recurrent neural network and multi-layer perceptron. The model can be used for mobile robot estimate localizability. The simulation and experimental results show that the model can accurately estimate the localizability of the robot in a given map.
A Single Driven Bionic Water Strider Sliding Robot Mimicking the Spatial Elliptical Trajectory *

When a water strider sliding on the water, the spatial elliptical trajectory of the driving leg plays a critical role in ensuring efficient sliding movement and stability. At present, the bionic water strider robot prototype generally adopts the dual motor drive mode to realize this complex spatial trajectory, increasing the weight of the robot and go against the maximization of the performance. Moreover, the research on bionic water strider robots mainly focuses on the tiny scales, the load capacity is weak, and difficult to achieve practical application. In response to these problems, this paper proposes a new driving principle, the driving leg realizes the spatial elliptical trajectory by the elliptical slot cam and the two rotating mechanisms driven by a single motor. Based on this, a small water surface sliding robot based on buoyancy support is designed. In order to verify the motion performance of the robot, the dynamic model was established. Then, ADAMS and MATLAB co-simulation method was adopted to analyze the sliding movement of the robot Finally, the forward and turning motion experiments of the robot prototype with different stroke speeds were conducted. The total weight of the robot is 137g, with an additional load capacity of 130g, the maximum straight stroke speed of the robot is 24.3cm/s, and the maximum turning speed is 35°/s.
A Comparison of Frequency Synthesizers Up to 25 GHz for 130 nm CMOS Implementation

This paper provides a qualitative comparison of three frequency synthesizer schemes implemented in 130 nm CMOS technology, with the aim to select the best architecture to use given the application specs. The presented architectures can generate carrier frequency bands from a maximum frequency down to any lower frequency. All systems are designed based on 130 nm CMOS technology and can reach frequencies up to 25 GHz. We show that the single switched capacitors voltage controlled oscillator (S-VCO) architecture is the best choice for frequencies below 6 GHz, and multiple core VCOs (Multi-VCO) synthesizers show the best performances for frequencies above 16 GHz. On the other hand, the synthesizer equipped with a single side band mixer (SSB-m) is the choice for applications requiring frequencies between 6 GHz and 16 GHz.
Fault Attacks on Cryptographic Circuits

Fault-injection attacks on cryptographic circuits are physical manipulations applied by an adversary during the circuit's operation with the purpose of extracting sensitive information, such as secret key bits. Fault attacks gain in relevance for cyberphysical applications and autonomous systems, where the system's hardware is naturally exposed to a potential attacker. This paper will provide an introduction into fault attacks, give an overview of fault-injection techniques and known countermeasures, and point out new research directions in this area.
The Gray Analysis and Machine Learning for Device-Free Multitarget Localization in Passive UHF RFID Environments

The device-free localization (DFL) has promising application prospects in intrusion detection, emergency rescue, and smart homes, because it does not require the target to carry any auxiliary positioning equipment. Radio tomographic imaging (RTI) is one of the most potential DFL techniques and has many advantages over other methods. However, in passive ultrahigh frequency radio frequency identification scenario, there are few researches and many problems to be solved. The difficult but urgent matter is how to identify the locations of multiple targets from many false targets and artifacts. This paper proposes a novel method based on cross-sectional scan (CSS), gray value distribution analysis (GVDA), and naive Bayes classifier to solve this problem. The CSS obtains the gray value distributions of the local maximum pixel in an RTI reconstructed image. Then, the GVDA extracts several characteristic parameters from gray value distributions, such as the size, height, and shape of the peak. Finally, the naive Bayes classifier utilizes these series of characteristics to judge whether local maximum pixels are false targets or real targets. The method can also recognize the number of targets that are very close to each other. Simulation and experimental results show that this method can accurately determine the locations and the number of targets.
The Effect of Online Reviews on E-book Pricing: A Text Analytics Approach

In this paper, we explore how online reviews affect e-book prices via analyzing the sheer volume online data from the e-book websites. Namely, we first employ a domain ontology-based method to select the most discriminative features that may affect the e-book prices. Then, the topic modeling method latent Dirichlet allocation and aspect-oriented sentiment analysis method are applied as a supplement. Using the multiple regression method, we identify the key attributes that may influence the prices of e-books and give the related regression equation. The managerial implication is that firms can obtain a reference price for an e-book and may dynamically adjust the price to increase e-book sales according to our data analysis results.