This paper develops a feedback-controlled neurmuscular stimulator focussed on hand rehabilitation, particularly grasping, which was tested in a multi-session study with paralyzed patients.  We also evaluate the accuracy of controlling neuromuscular stimulation devices using EEG-based brain-machine interfaces. “This work has been submitted to the IEEE for possible publication.  Copyright may be transferred without notice, after which this version  may no longer be accessible.”

Failure management is one of the key aspects in cellular networks. 5G technology is introducing several novel features that will cause the occurrence of new problems in the network. The objective of the present work is to identify which features may lead to potential network failures. In this way, a subset of problems has been identified and categorized. They were analyzed in order to know how they are in line with 3GPP specifications. Based on this, the present work proposes a novel failure management approach, applying it for failure simulation and impact analysis over the network performance.

X-ray computed tomography (CT) is an important non-destructive imaging technique, particularly in clinical diagnostics. Even with the latest innovations like dual-energy and photon-counting CT, the image contrast is solely generated from attenuation in the tissue. An extension – fully compatible with these novelties – is dark-field CT, which retrieves an additional, so-called dark-field contrast. Unlike the attenuation channel, the dark-field channel is sensitive to tissue microstructure and porosity below the resolution of the imaging system, which allows additional insights into the health of the lung tissue or the structure of calcifications. The potential clinical value has been demonstrated in several preclinical studies and recently also in radiography patient studies. Just recently the first dark-field CT for the human body was established at the Technical University of Munich and in this paper, we discuss the performance of this prototype. We evaluate the interferometer components and the imposed challenges that the integration into the CT gantry brings by comparing the results to simulations and measurements at a laboratory setup. The influence of the clinical X-ray source on the Talbot-Lau interferometer and the impact of vibrations, which are immanent on the clinical CT gantry, are analyzed in detail to reveal their characteristic frequencies and origin. A beam hardening correction is introduced as an important step to adapt to the poly-chromatic spectrum and make quantitative dark-field imaging possible. We close with an analysis of the image resolution and the applied patient dose, and conclude that the performance is sufficient to suggest initial patient studies using the presented dark-field CT system.

This study provides a novel mechanical design and a control law for tendon-driven flexible manipulator based on the anatomy of an ostrich neck. We carried out a dissection of ostrich neck which clarified muscle attachment site and cross-sectional views of the neck. We proposed a novel wire arrangement and control method, that performs the same movement as the ostrich's neck by integrating our findings from the dissection with previous studies on bird's necks. The implemented manipulator showed that morphology, such as joint angle limit and muscle-tendon arrangement, provides dexterity and structural stability to flexible manipulators. These results are suggestive for biology because they indicate that actual birds may also use the proposed mechanism and control laws.

For several decades, the offline handwriting recognition problem has escaped a satisfactory solution. In the field of online recognition, researchers have had more successful performance, but the ability to extract dynamic information from static images has not been well explored yet. In this paper, we introduce a novel multi-lingual word handwriting recovery framework based on a convolutional denoising autoencoder with an attention model for pen up / down, velocity and temporal order recovery. The proposed framework consists of extracting robust features from a handwriting image using a stacked denoising autoencoder and an encoder Bidirectional Gated Recurrent Unit (BGRU) model. Then, the obtained vectors are decoded to produce an online script with dynamic characteristics using a BGRU with temporal attention. Evaluation is done on a Latin and Arabic Online and offline handwriting character / word databases and the proposed framework achieves high competitive results. To the best of our knowledge, this is the first work of its kind.

The homogeneous description of a linear, uncoupled circuit is based on the assignment to each device of a triad $(p: q: s)$, where the parameters are defined up to a nonzero multiplicative constant and characterize a voltage-current relation of the form $pv-qi=s$. Given a one-port, the open-circuit and short-circuit network determinants, to be denoted as $p_e$ and $q_e$, are polynomial functions of the $p$- and $q$-parameters of the individual devices. With this formalism, we may state the Thévenin-Norton theorem in a uniform manner by saying that, for any given set of parameter values, if at least one of the functions $p_e$ and $q_e$ does not vanish then the voltage-current behavior at the port is characterized by a homogeneous triad $(p_e: q_e: s_e)$. In particular, the assumptions $p_e \neq 0$ and $q_e \neq 0$, respectively, characterize the existence of the Thévenin and the Norton equivalents, but the formulation proposed above avoids the need to make an a priori distinction between one form and another. We also show that the excitation parameter $s_e$ can be computed by inserting any admissible load at the port, but also analytically, in terms of the topology of the underlying digraph. The results hold without the need to specify whether each circuit element is a source or a passive device, much less to assume whether they are voltage- or current-controlled.

In the manuscript, as a result of comparing experimental and calculated kerrograms, it is shown that during electro-optical measurements in nitrobenzene using the Kerr effect, a strong electric field and absorption of radiation from a probing laser affect the properties of the medium under study. An estimate of such influence on the value of the Kerr constant is made. It is found that in the tip-plane system when using a ruby laser, the Kerr constant near the tip changes. The established dependence of the Kerr constant on the distance to the tip made it possible to obtain a calculated kerrogram that coincides with the experimental one. Thus, it is shown that the measuring instruments can influence the results obtained, that is, under certain conditions, the electro-optical research method is invasive. This fact should be taken into account when processing experimental kerrograms.

The development of an ambient radio frequency (RF) powered Internet of Things (IoT) wireless sensor system is reported. The system integrates all major components including multiple antennas, rectifiers, a power management unit (PMU), a microcontroller unit (MCU) with RF transceiver module and sensing unit. A novelty of this applied engineering innovation is the design and use of multi-band dual-polarized multiple antennas to handle the low ambient RF power density found in the everyday environment. Our prototype has demonstrated that it is able to sense temperature, pressure and humidity and wirelessly transmit these signals to a central server with a duty cycle of approximately 15 minutes, when the background ambient RF signals are as low as −28.6 dBm at 950 MHz. The proposed system can address the important issue of energy supply in the development of IoT providing an alternative energy source to batteries or solar cells. Ambient RF powered IoT devices do not require periodic replacement of their batteries or to be placed visible regions which makes them suitable for wall, ceiling and floor applications. Our ambient RF powered IoT wireless sensor system provides concrete proof, through demonstration, that harnessing ambient RF energy for powering IoT sensor systems is feasible.

The ever-emerging mobile market induced a blooming interest in stylus-based interactions. Most state-of-the-art styluses either provide no haptic feedback or only deliver one type of sensation, such as vibration or skin stretch. Improving these devices with display abilities of a palette of tactile feels can pave the way for rendering realistic surface sensations, resulting in more natural virtual experiences. However, integrating necessary actuators and sensors while keeping the compact form factor of a stylus for comfortable user interactions challenges their design. Consequently, it limits the scientific knowledge of relevant parameters for rendering compelling artificial textures for stylus-based interactions. Here, we developed FeelPen, a haptic stylus that can display multimodal texture properties (compliance, roughness, friction, and temperature) on touchscreens. We created a set of artificial textures and conducted a user study in which human participants rated these textures based on various perceptual adjectives describing their haptic properties. Afterward, we extracted the dominant perceptual dimensions of these textures using the semantic differential method. Our results revealed four major tactile dimensions, with the first two related to surface textural properties, and the third and fourth dimensions linked to material softness and coldness, respectively. Our unique design and experimental results have the potential to open new perspectives for stylus-based interactions on future touchscreens.

A novel power hardware-in-the-loop interface algorithm, the Virtual Shifting Impedance, is developed, validated and demonstrated in this paper. Building on existing interface algorithms, this method involves shifting a part of the software impedance to the hardware side to improve the stability and accuracy of PHIL setups. However, compared to existing approaches, this impedance shifting is realized by modifying the command signals of the power amplifier controller, thus avoiding the requirement for hardware passive components. The mathematical derivation of the Virtual Shifting Impedance interface algorithm is realized step-by-step, while its stability and accuracy properties are thoroughly examined. Finally, the applicability of the proposed method is verified through PHIL simulation results.

This reference data set describes a representative Norwegian radial, medium voltage (MV) electric power distribution system operated at 22 kV. The data set is developed in the Norwegian research centre CINELDI and will in brief be referred to as the CINELDI MV reference system. Data for a real Norwegian distribution system were provided by a distribution grid company. The data have been anonymized and processed to obtain a simplified but still realistic grid model with 124 nodes. The data set consists of the following three parts: 1. Grid data files: describe the base version of the reference system that represents the present-day state of the grid, including information about topology, electrical parameters, and existing load points. 2. Load data files: comprise load demand time series for a year with hourly resolution and scenarios for the possible long-term development of peak load. These data describe an extended version of the reference system with information about possible new load points being added to the system in the future. 3. Reliability data files: contain data necessary for carrying out reliability of supply analyses for the system. This work is funded by CINELDI - Centre for intelligent electricity distribution, an 8 year Research Centre under the FME-scheme (Centre for Environment-friendly Energy Research, 257626/E20). The authors gratefully acknowledge the financial support from the Research Council of Norway and the CINELDI partners. The work is also funded by ChiNoZen - a Chinese-Norwegian collaboration project (Grant No. 2019YFE0104900).

This article describes a reference data set for a representative Norwegian radial, medium voltage (MV) electric power distribution system operated at 22 kV. The data set is developed in the Norwegian research centre CINELDI and will in brief be referred to as the CINELDI MV reference system. Data for a real Norwegian distribution system were provided by a distribution grid company. The data have been anonymized and processed to obtain a simplified but still realistic grid model with 124 nodes. The data set consists of the following three parts: 1. Grid data files: describe the base version of the reference system that represents the present-day state of the grid, including information about topology, electrical parameters, and existing load points. 2. Load data files: comprise load demand time series for a year with hourly resolution and scenarios for the possible long-term development of peak load. These data describe an extended version of the reference system with information about possible new load points being added to the system in the future. 3. Reliability data files: contain data necessary for carrying out reliability of supply analyses for the system.

This paper, submitted to IEEE Transactions on Geoscience and Remote Sensing, discusses ways to quantify the light collection by a camera, which is critical for the signal to noise ratio of the imagery. Based on a generalized analysis of a camera from an outside “black box” perspective, a preferred metric for light collection is identified, denoted A*. It is argued that this quantity can serve as a comparative figure of merit for cameras, as well as an element of image metadata. The work addresses conventional cameras as well as hyperspectral cameras. The proposed metric is part of the draft standard for hyperspectral imaging being developed by IEEE SA P4001.

Future private and public transportation will be dominated by Autonomous Vehicles (AV), which are potentially safer than regular vehicles. However, ensuring good performance for the autonomous features requires fast processing of heavy computational tasks. Providing each AV with powerful enough computing resources is certainly a practical solution but may result in increased AV cost and decreased driving range. An alternative solution being explored in research is to install low-power computing hardware on each AV and offload the heavy tasks to powerful nearby edge servers. In this case, the AV’s reaction time depends on how quickly the navigation tasks are completed in the edge server. To reduce task completion latency, the edge servers must be equipped with enough network and computing resources to handle the vehicle demands. However, this demand shows large spatio-temporal variations. Thus, deploying the same amount of resources in different locations may lead to unnecessary resource over-provisioning. Taking these challenges into consideration, in this paper, we discuss the implications of deploying different amounts of resources in different city areas based on real traffic data to sustain peak versus average demand. Because deploying edge resources to handle the average demand leads to lower deployment costs and better system utilization, we then investigate how peak-hour demand affect the safe travel time of AVs and whether current turn-by-turn navigation apps would still provide the fastest travel route. The insights and findings of this paper will inspire new research that can considerably speed up the deployment of edge-assisted AVs in our society.

Magnetic induction tomography based artificial robotic skin. Showing the imaging results and also quantification of force of touch to skin.

Data-driven approaches for molecular diagnostics are emerging as an alternative to perform an accurate and inexpensive multi-pathogen detection. A novel technique called Amplification Curve Analysis (ACA) has been recently developed by coupling machine learning and real-time Polymerase Chain Reaction (qPCR) to enable the simultaneous detection of multiple targets in a single reaction well. However, target classification purely relying on the amplification curve shapes currently faces several challenges, such as distribution discrepancies between different data sources of synthetic DNA and clinical samples (i.e., training vs testing). Optimisation of computational models is required to achieve higher performance of ACA classification in multiplex qPCR through the reduction of those discrepancies. Here, we proposed a novel transformer-based conditional domain adversarial network (T-CDAN) to eliminate data distribution differences between the source domain (synthetic DNA data) and the target domain (clinical isolate data). The labelled training data from the source domain and unlabelled testing data from the target domain are fed into the T-CDAN, which learns both domains’ information simultaneously. After mapping the inputs into a domain-irrelevant space, T-CDAN removes the feature distribution differences and provides a clearer decision boundary for the classifier, resulting in a more accurate pathogen identifi- cation. Evaluation of 198 clinical isolates containing three types of carbapenem-resistant genes (blaNDM , blaIMP and blaOXA-48 ) illustrates a curve-level accuracy of 93.1% and a sample- level accuracy of 97.0% using T-CDAN, showing an accuracy improvement of 20.9% and 4.9% respectively, compared with previous methods. This research emphasises the importance of deep domain adaptation to enable high-level multiplexing in a single qPCR reaction, providing a solid approach to extend qPCR instruments’ capabilities without hardware modification in real- world clinical applications.

Temperature is an important factor for evaluating the seismic response of deep reservoirs. We develop an amplitude variation with offset (AVO) approximation based on the Lord-Shulman (LS) thermoelasticity theory. The model predicts two compressional (P and T) waves (the second is a thermal mode) and a shear (S) wave. The T mode is due to the coupling between the elastic and heat equations. In the thermoelastic case, the approximation is more accurate than in the elastic case. Its accuracy is veried by comparison with the exact equations calculated in terms of potential functions. We examine two reservoir models with high temperatures and compute synthetic seismograms that illustrate the reliability of the approximation. Moreover, we consider real data to build a model, and show that the approximate equation not only simplies the calculations, but is accurate enough and can be used to evaluate the temperature-dependent elastic properties, providing a basis for further application of the thermoelasticity theory, such as geothermal exploration, thermal enhanced oil recovery, and ultra-deep oil and gas resources subject to high temperatures. Temperature is an important factor for evaluating the seismic response of deep reservoirs. We develop an amplitude variation with offset (AVO) approximation based on the Lord-Shulman (LS) thermoelasticity theory. The model predicts two compressional (P and T) waves (the second is a thermal mode) and a shear (S) wave. The T mode is due to the coupling between the elastic and heat equations. In the thermoelastic case, the approximation is more accurate than in the elastic case. Its accuracy is veried by comparison with the exact equations calculated in terms of potential functions. We examine two reservoir models with high temperatures and compute synthetic seismograms that illustrate the reliability of the approximation. Moreover, we consider real data to build a model, and show that the approximate equation not only simplies the calculations, but is accurate enough and can be used to evaluate the temperature-dependent elastic properties, providing a basis for further application of the thermoelasticity theory, such as geothermal exploration, thermal enhanced oil recovery, and ultra-deep oil and gas resources subject to high temperatures.

The paper deals with ultrasound tomography as robotic skin.  Various sensor system with different  frequecny and varied number of sensor and size of skin was examined.