Accurate partial discharge (PD) detection and insulation diagnosis are essential for ensuring the operational safety of high-voltage (HV) power equipment, while wideband high sensitivity PD detection is extremely imperative since most of the PD events reflecting potential insulation defects have low strength (<1 pC) with wideband features. However, conventional wideband detection methods such as high-frequency (HF) antennas often suffer from the trade-off between low sensitivity, increased noise and the bandwidth requirements due to their low-Q features in the wideband sensing, and the vulnerability to in-band narrowband interference, especially in complex industrial environments. To address these challenges, this paper proposes a novel high sensitivity super-wideband PD detection system for Industrial Internet of Things (IIoT) applications integrating a reconfigurable high-frequency antenna sensor (RHAS), a bandwidth-reconfigurable low-noise amplifier (BRLNA), and a sub-band synthesis algorithm with built-in narrowband interference rejection (NIR) function. The RHAS captures weak PD signals across 48 high-Q sub-bands, which are individually amplified and digitally synthesized to reconstruct the wideband signal with enhanced sensitivity. The proposed system enables remote, clamp-free PD detection with strong NIR capability near grounded conductors, significantly outperforming the traditional sensors such as High-Frequency Current Transformers (HFCTs). Experimental results demonstrate a detection bandwidth from 1.4 MHz to 98.2 MHz (194.38% relative bandwidth) and a minimum detectable PD level of 0.3 pC. Compared with HFCT based systems, the proposed method achieves a 0.4–4.2 dB improvement in PD signal-to-noise ratio (SNR) and a 6.1–8.4 dB increase in total system gain. These results validate the effectiveness and robustness of the proposed approach for high-sensitivity wideband PD monitoring.

This paper presents a cryogenic qubit controller for scalable superconducting quantum computing. A phase-selection digital power amplifier (DPA) topology is utilized for the XY-driving pulses generation. With a compact digital extensive architecture, the amplitude modulation and qubit phase rotation can be directly implemented at the phase-selection DPA stage for power reduction. A multi-phase envelope-tracking supply unit is also employed to enhance the power efficiency. The controller was designed and simulated in 28nm bulk CMOS technology. The controller can cover a band of 4-6GHz with an output power of at least -8dBm. The simulated SNR/SFDR is better than 55dB/46dB with a phase rotation error of less than 0.7°. The envelope-tracking supply unit can reduce the DPA power consumption by at least 24% compared to a constant power supply. The total power consumption of the controller is 3.64mW.

The timely detection of partial discharge (PD) of high-voltage (HV) power equipment is crucial to mitigate serious consequences such as the degradation of insulation and equipment failure. The ultra-high frequency (UHF) detection method stands out for its efficacy in this regard. In this study, a novel UHF antenna sensor is proposed for PD detection. The antenna's offset structure enables it to detect PD events near a conducting ground wire without being clamped to the wire like the typical high-frequency current transformer (HFCT). Meanwhile, its equivalent circuit is modeled as a ladder-structure band-pass filter (within the consideration of mutual inductances) to realize its wideband properties. Fabricated on a substrate and integrated with a low-noise amplifier, the antenna sensor exhibits a broad impedance bandwidth between 1 MHz and 108 MHz, as demonstrated through measurements in an anechoic chamber. In-lab and on-site systematic experiments affirm the efficiency of the proposed antenna sensor in PD detection. Notably, the Phase-Resolved Partial Discharge (PRPD) pattern is distinctly observable at the backend through Internet connectivity, further confirming the overall monitoring capabilities.

Gentle suction flow at the fingertips of a compliant hand can enhance object acquisition and increase the robustness of pinch grasps under water. The approach adds a low-pressure pump and flexible tubes that terminate at the distal phalanges. The light flow rate does not create a powerful suction force, nor does it stir up significant sediment. The method works on porous and rough objects in addition to smooth objects as it does not require forming a seal. It changes contact conditions - normal force and coefficient of friction - and enlarges the acquisition region when grasping free objects under water. A simple hydrodynamic model matches empirical force measurements adequately for incorporation in a dynamic simulation to explore the effects of flow rate and object mass. Simulations and experiments show that effects of fingertip suction flow are most pronounced for acquiring objects on the order of 1 kg or less and when pinching large objects. Gentle suction flow is an effective, versatile, and convenient addition for robots that must grasp and manipulate objects under water.

This paper presents a method for efficiently testing the stability of an object under contact that accommodates empirical models of admissible forces at individual contact points. It handles a diverse range of possible geometries of the admissible force volume, including anisotropy, adhesion, and even nonconvexity. The method discretizes the contact region into patches, performs a convex decomposition of a polyhedral approximation to each admissible force volume, and then formulates the problem as a mixed integer linear program. The model can also accommodate articulated robot hands with torque limits and joint frictions. Predictions of our method are evaluated experimentally in object lifting tasks using a gripper that exploits microspines to exert strongly anisotropic forces. The method is applied to calculate gripper loading capabilities and equilibrium predictions for a quadruped climbing robot on steep and overhanging terrain.

We present a new palm design that features a dense array of micro-spines for the JPL Robosimian human-scale climbing robot. A linearly-constrained spine mechanism is introduced and analyzed using adhesion and stiffness models. This mechanism achieves a spine density of 19/cm2and a mean adhesion of 67N (207kPa) on coarse concrete surfaces. The models are validated on two different surfaces with two sets of experiments. A 120×100mm palm consisting of 12 spine tiles and a pulley differential system for load sharing are designed and tested on 9 different surfaces. The mean shear adhesion goes up to 710N (183kPa) on concrete blocks. Design considerations include scaling efficiency and maximal single-spine force. Desirable properties for load sharing in the palm design are discussed.

Pinching is an important capability for mobile robots handling small items or tools. Successful pinching requires force-closure and, in underwater applications, gentle suction flow at the fingertips can dramatically improve the handling of light objects by counteracting the negative effects of water lubrication and enhancing friction. In addition, monitoring the flow gives a measure of suction-engagement and can act as a binary tactile sensor. Although a suction system adds complexity, elastic tubes can double as passive spring elements for desired finger kinematics.

We present a light, actuated smart staff with 5DOF tip force sensing which can be used by a humanoid robot operating in challenging terrain. The staff has an extension mechanism that employs mechanical multiplexing to achieve a high extension ratio in a stiff and compact package. The tip force sensor uses two metal diaphragms to achieve decoupling of axial and radial forces and the ability to tune the maximum range of forces in each direction independently. With the force sensor, the robot can characterize the coefficient of friction and orientation of a surface with simple motion primitives. Two sets of experiments were conducted with the smart staff manipulated by a 7 DOF robot arm. Using the sensor, the robot was able to determine the orientations of sloped surfaces within two degrees in two orthogonal directions.

Stroke remains the third leading cause of death. The rising of stroke illness and the continuous aging of the global population requires a collaboration of stroke services systems based on relations and exchange of information to address needs of caregiver and patients in the healthcare community. The collaborative stroke services system involves interconnected changes and the development of integrated healthcare information systems and novel healthcare services. We present the SS4CSHC, a services system for the collaboration in stroke healthcare cycle, to contribute to information sharing and cooperation for the stroke surveillance, prevention, emergency therapy, diagnosis, treatment, and rehabilitation. Some of critical and novel stroke healthcare services are developed and deployed, and innovative models and services for delivery and the coordination of stroke healthcare are explored by the stroke healthcare center. The feasibility and efficiency of our services systems are validated in the local medical institutes.

Fingertip suction is investigated using a compliant, underactuated, tendon-driven hand designed for underwater mobile manipulation. Tendon routing and joint stiffnesses are designed to provide ease of closure while maintaining finger rigidity, allowing the hand to pinch small objects, as well as secure large objects, without diminishing strength. While the hand is designed to grasp a range of objects, the addition of light suction flow to the fingertips is especially effective for small, low-friction (slippery) objects. Numerical simulations confirm that changing suction parameters can increase the object acquisition region, providing guidelines for future versions of the hand.

We address the problem of action recognition. Our aim is to recognize single person activities in surveillance scenes. To meet the requirements of real scene action recognition, we present a compact motion representation for human activity recognition. With the employment of efficient features extracted from optical flow as the main part, together with global information, our motion representation is compact and discriminative. We also build a novel human action dataset(CASIA) in surveillance scene with three vertically different viewpoints and distant people. Experiments on CASIA dataset and WEIZMANN dataset show that our method can achieve satisfying recognition performance with low computational cost as well as robustness against both horizontal(panning) and vertical(tilting) viewpoint changes.

Human behavior analysis is an important area of research in computer vision and is also driven by a wide spectrum of applications, such as smart video surveillance and human-computer interface. In this paper, we present a novel approach for human behavior analysis. Two research challenges, motion representation and behavior recognition, are addressed. A novel motion descriptor, which is an improved feature based on optical flow, is proposed for motion representation. Optical flow is improved with a motion filter, and feature fusion with the shape and trajectory information. To recognize the behavior, the support vector machine is employed to train the classifier where the concatenation of histograms is formed as the input features. Experimental results on the Weizmann behavior database and the Institute of Automation, Chinese Academy of Science real-world multiview behavior database demonstrate the robustness and effectiveness of our method.