Prostheses are becoming more advanced and biomimetic with time, providing additional capabilities to their users. However, prosthetic sensation lags far behind its natural limb counterpart, limiting the use of sensory feedback in prosthetic motion planning and execution. Without actionable sensation, prostheses may never meet the functional requirements to match biological performance. We propose an approach for upper-limb prosthetic object slip prediction and notification, delivered to the wearer through direct nerve stimulation. The method is based on sensory synthesis, training a linear regression of the sensors embedded in a prosthetic hand to predict slip before it occurs. Four participants with transhumeral amputation performed block pulling tasks against increasing resistance, attempting to pull the block as far as possible without slip. These trials were performed with two different prediction notification paradigms. At lower grasp forces, spike notification stimulation reduced the incidence of object slip by 32%, and at higher grasp forces, the maximum achieved pull forces increased by 19% across participants when provided with stimulation proportional to the likelihood of a predicted slip. These results suggest that this approach may be effective in recreating a lost sense of grip stability in the missing limb and may reduce unanticipated slips.

Design and verification of two-and three-pole differential bridged T-coils applicable to wideband and high-speed integrated circuits, broadband I/O terminations, capacitive loads, and ESD protection circuits are described. T-coil prototypes with 50-Ω terminations, designed for maximally flat amplitude (MFA) and envelope delay (MFED) responses, are characterized. Measured transimpedance bandwidth and group delay of the two-pole MFA design are 43.2GHz and 7±2ps across 45GHz, respectively. The bandwidth extension ratio (BWER) is 2.43x compared to an unpeaked R-C circuit. The three-pole MFED T-coil realizes a 2.2x BWER, with 23-GHz bandwidth and 12±2-ps group delay across 30GHz. Implemented in 22-nm FD-SOI CMOS technology, the T-coils occupy 224 x 215µm^2 .

The growing field of superconducting technology is witnessing remarkable advancements driven by the global energy transition. These advancements bear significance, offering promising avenues for optimizing the design and performance of superconducting systems modeled by homogenized technique and/or thin strip approximation, including electrical machines, fusion energy generation, fault current limiters, superconducting cables, and superconducting maglev trains. This article introduces a thin strip homogenizing approximation and a versatile constraint methodology tailored for analyzing series, parallel, or series-parallel connections within superconducting devices. Leveraging the J-A formulation, comprehensive modeling of coils, stacks, thin strips, racetracks, and double crossed loops (DCLs) is undertaken to validate the proposed methodologies. Through meticulous investigations, encompassing analyses of magnetic flux density, current density distribution, current profiles, and losses, the efficacy and applicability of the proposed techniques are demonstrated.

Rodents and Felidae whiskers are highly sensitive, detecting extrinsic inputs like airflow or contact and intrinsic inputs such as base vibrations or self-induced motion. Building effective artificial whisker sensors faces a challenge due to the intricate coupling of responses at the whisker base. There’s a research gap in understanding whisker sensors’ responses to intrinsic and extrinsic inputs. To address this, we propose two methods, using base acceleration as a reference input, 1) employing frequency domain adaptive filtering (FDAF) and 2) introducing the base vibration response model (BVRM), mathematically representing the whisker sensor’s behavior as a linear time-invariant (LTI) system. Validation of FDAF and BVRM is conducted through simulation and experimentation. The BVRM excels in both simulation and experiment with an SNR value of 35.63, proving superior. BVRM also shows potential in filtering sensor responses for independent use in terrain identification, flow sensing, and surface profile identification. Separating responses to extrinsic and intrinsic inputs without discarding either makes whisker sensors more versatile and multipurpose.

This review article delves deeply into the various machine learning (ML) methods and algorithms employed in discerning protein functions. Each method discussed is assessed for its efficacy, limitations, potential improvements, and future prospects. We present an innovative hierarchical classification system that arranges algorithms into intricate categories and unique techniques. This taxonomy is based on a tri-level hierarchy, starting with the methodology category and narrowing down to specific techniques. Such a framework allows for a structured and comprehensive classification of algorithms, assisting researchers in understanding the interrelationships among diverse algorithms and techniques. The study incorporates both empirical and experimental evaluations to differentiate between the techniques. The empirical evaluation ranks the techniques based on four criteria. The experimental assessments rank: (1) individual techniques under the same methodology subcategory, (2) different sub-categories within a same category, and (3) the broad categories themselves. Integrating the innovative methodological classification, empirical findings, and experimental assessments, the article offers a well-rounded understanding of ML strategies in protein function identification. The paper also explores techniques for multi-task and multi-label detection of protein functions, in addition to focusing on single-task methods. Moreover, the paper sheds light on the future avenues of ML in protein function determination.

In deep learning of medical image data, skin lesion classification remains a challenging problem due to imbalanced distribution of training data. We propose a visual memory transfer (VMT) method by means of transferring visual knowledge from majority classes to minority classes. As a result, our method enriches feature of minority classes with pre-calculated memory features. In addition, VMT defines a refined feature map to perform fine-grained classification. Our classification results outperform SOTA methods on the largest public available dermoscopic image dataset on averaged F-score and Top-1 classification accuracy.

The futuristic sixth-generation (6G) networks will empower ultra-reliable and low latency communications (URLLC), enabling a wide array of mission-critical applications such as mobile edge computing (MEC) systems, which are largely unsupported by fixed communication infrastructure. To remedy this issue, unmanned aerial vehicle (UAV) has recently come to the limelight to facilitate MEC for internet of things (IoT) devices as they provide desirable line-of-sight (LoS) communications compared to fixed terrestrial networks, thanks to their added flexibility and three-dimensional (3D) positioning. In this paper, we consider UAV-enabled relaying for MEC systems for uplink transmissions in 6G networks, and we aim to optimize mission completion time subject to the constraints of resource allocation, including UAV transmit power, UAV CPU frequency, decoding error rate, blocklength, communication bandwidth, and task partitioning as well as 3D UAV positioning. Moreover, to solve the non-convex optimization problem, we propose three different algorithms, including successive convex approximations (SCA), altered genetic algorithm (AGA) and smart exhaustive search (SES). Thereafter, based on time-complexity, execution time, and convergence analysis, we select AGA to solve the given optimization problem. Simulation results demonstrate that the proposed algorithm can successfully minimize the mission completion time, perform power allocation at the UAV side to mitigate information leakage and eavesdropping as well as map a 3D UAV positioning, yielding better results compared to the fixed benchmark sub-methods. Lastly, subject to 3D UAV positioning, AGA can also effectively reduce the decoding error rate for supporting URLLC services.

Detecting and segmenting cracks in infrastructure, such as roads and buildings, is crucial for safety and cost-effective maintenance. In spite of the potential of deep learning, there are challenges in achieving precise results and handling diverse crack types. With the proposed dataset and model, we aim to enhance crack detection and infrastructure maintenance. This study proposes a novel approach termed Hybrid-Segmentor, which uses a convolutional neural network path that is well-suited for extracting fine-grained local features and a transformer path to extract global features that benefit from understanding the overall structure. This hybrid method makes the model more generalizable to various shapes, surfaces, and sizes of cracks. To achieve a balanced computational cost, the study incorporates efficient self-attention in the transformer path and introduces a comparatively simpler decoder compared to the complexity of the two encoder paths. This combination strategically optimizes the extraction of global and local features while maintaining computational efficiency. The model was trained using a combined binary cross entropy and Dice loss function on a large refined dataset of 12,000 crack images generated from 13 publicly available datasets. Our studies demonstrate that the model efficiently utilizes convolutional layers and transformers to extract local and global features. Hybrid-Segmentor outperforms existing benchmark models across 5 quantitative metrics (accuracy 0.971, precision 0.804, recall 0.744, F1-score 0.770, and IoU score 0.630), achieving state-of-the-art status. Finally, through careful qualitative analysis, we show that the model is capable of addressing discontinuities, detecting small non-crack regions, handling low-quality images, and detecting crack contours more accurately than existing models.

The formulation of the Complex Dissipating Energy Flow (CDEF), used for locating sources of forced oscillations, is expressed by the sum of terms that represent the contributions of power system components to the complex energy function of the system. These terms are calculated through integrals that are evaluated over the trajectory of the system. The integrands are the active and reactive power flows through system branches, and the integration variables are the voltage magnitude and the voltage angle. In this letter, we show that the terms whose integration variable is the voltage magnitude can be grouped separately from the terms whose integration variable is the voltage angle. Furthermore, the contribution to the complex energy function of the system of each of these grouped terms is zero. This allows for an alternative definition of CDEF using only the terms whose integration variable is voltage angle (discarding terms whose integration variable is voltage). It is shown that using the alternative definition for CDEF produces a higher success rate compared to the original CDEF. The tests are performed on the WECC 240 system model considering sources of FO located in the governor and the excitation system of conventional generators, as well as in renewable plants, alternatively.

Despite the emergence of numerous Deep Learning (DL) models for breast cancer detection via mammograms, there is a lack of evidence about their robustness to perform well on new unseen mammograms. To fill this gap, we introduce StethoNet, a DL-based framework that consists of multiple Convolutional Neural Network (CNN) trained models for classifying benign and malignant tumors. StethoNet was trained on the Chinese Mammography Database (CMMD), and tested on unseen images from CMMD, as well as on images from two independent datasets, i.e., the Vindr-Mammo and the INbreast datasets. To mitigate domain-shift effects, we applied an effective entropy-based domain adaptation technique at the preprocessing stage. Furthermore, a Bayesian hyperparameters optimization scheme was implemented for StethoNet optimization. To ensure interpretable results that corroborate with prior clinical knowledge, attention maps generated using Gradient-weighted Class Activation Mapping (GRADCAM) were compared with Regions of Interest (ROIs) identified by radiologists. StethoNet achieved impressive Area Under the receiver operating characteristics Curve (AUC) scores: 90.7% (88.6%-92.8%), 83.9% (76.0%-91.8%), and 85.7% (82.1%-89.4%) for the CMMD, INbreast, and Vindr-Mammo datasets, respectively. These results surpass the current state of the art and highlight the robustness and generalizability of StethoNet, scaffolding the integration of DL models into breast cancer mammography screening workflows.

Point cloud classification and segmentation are crucial tasks for point cloud processing and have wide range of applications, such as autonomous driving and robot grasping. Some pioneering methods, including PointNet, VoxNet, DGCNN, etc., have made substantial advancements. However, most of these methods don't consider the large-distance geometric relationships among points in different perspectives within the point cloud, which limits the features extraction and leads to the accuracy of classification and segmentation cannot be further improved. To address this issue, we propose an adaptive multiview graph convolutional network (AM-GCN), which comprehensively synthesizes both the global geometric features of the point cloud and the local features within the projection planes of multiple views through an adaptive graph construction method. First, an adaptive rotation module in AM-GCN is proposed to predict a more favorable angle of view for projection. Then, a multi-level feature extraction network can flexibly be constructed by spatial-based or spectral-based graph convolution layers. Finally, AM-GCN is evaluated on ModelNet40 for classification, ShapeNetPart for part segmentation, ScanNetv2 and S3DIS for scene segmentation, which demonstrates the robustness of the AM-GCN with competitive performance compared with existing methods. It's worth noting that it performs state-of-the-art performance in many categories.

Integration of grid-forming inverter-based resources (GFM-IBRs) is considered as a viable solution to address the challenges associated with the dynamics of power systems with high penetration levels of inverter-based resources. Yet, various aspects of dynamic behaviour of grid-forming inverters during disturbances have remained unknown. This paper for the first time introduces the concept of pole slipping in GFM-IBRs. The differences between stable and unstable pole slipping are highlighted. The determining factors for the occurrence of stable and unstable pole slipping in grid forming inverters and equipment-level implications of pole slipping are identified and studied. Afterwards, a solution is proposed to mitigate stable and unstable pole slipping in grid-forming inverters. The validity of the proposed concept of pole slipping in GFM-IBRs and the proposed solution is tested and verified through extensive timedomain simulations.

This paper introduces a novel nonparametric framework for data imputation, coined multilinear kernel regression and imputation via the manifold assumption (MultiL-KRIM). Motivated by manifold learning, MultiL-KRIM models data features as a point cloud located in or close to a user-unknown smooth manifold embedded in a reproducing kernel Hilbert space. Unlike typical manifold-learning routes, which seek low-dimensional patterns via regularizers based on graph-Laplacian matrices, MultiL-KRIM builds instead on the intuitive concept of tangent spaces to manifolds and incorporates collaboration among point-cloud neighbors (regressors) directly into the data modeling term of the loss function. Multiple kernel functions are allowed to offer robustness and rich approximation properties, while multiple matrix factors offer low-rank modeling, integrate dimensionality reduction, and streamline computations with no need of training data. Two important application domains showcase the functionality of MultiL-KRIM: time-varying graph-signal (TVGS) recovery, and reconstruction of highly accelerated dynamic-magnetic-resonance-imaging (dMRI) data. Extensive numerical tests on real and synthetic data demonstrate MultiL-KRIM's remarkable speedups over its predecessors, and outperformance over prevalent "shallow" data-imputation techniques, with a more intuitive and explainable pipeline than deep-image-prior methods.

Our current digital landscape relies heavily on existing data centers to store and process information for online applications accessed by millions of users. Any failure of these operations significantly affects productivity as well as significant operations of an organization. Additionally, data centers are high energy consumers. Climate change and the scarcity of energy resources due to political or local constraints emphasize the need for us to deeply analyze the strategic placement of these data centers to understand factors contributing to risks and opportunities in data center placement.

In this paper we will present the results of two DTN demonstration activities carried out in the ESA Ground Segment. The first demonstration has been prepared with the OPS-SAT spacecraft, to demonstrate a full DTN protocol stack with CFDP, Bundle Protocol, LTP, CCSDS Space Packet Protocol and show the ESA Ground Segment Bundle Protocol implementation capabilities. The second demonstration has been performed in collaboration with Morehead State University, NASA JPL and D3TN, with the aim to show interoperability of DTN implementations across space agencies and external partners.

Mixed Reality (MR) and Artificial Intelligence (AI) are increasingly becoming integral parts of our daily lives. Their applications range in fields from healthcare to education to entertainment. MR has opened a new frontier for such fields as well as new methods of enhancing user engagement. In this paper, We propose a new system one that combines the power of Large Language Models (LLMs) and mixed reality (MR) to provide a personalized companion for educational purposes. We present an overview of its structure and components as well tests to measure its performance. We found that our system is better in generating coherent information, however it's rather limited by the documents provided to it. This interdisciplinary approach aims to provide a better user experience and enhance user engagement. The user can interact with the system through a custom-design smart watch, smart glasses and a mobile app.

As naval power systems are forced to serve larger and more numerous pulsed loads, new techniques will need to be applied to maintain power quality. Extended Droop Control (EDC) provides robust, adaptable system ­level control capable of serving pulsed loads while maintaining tight bus voltage regulation. Pulsed loads are served from fast responding energy storage resources while slower generation assets are allowed to serve the baseload at their most efficient operating point. EDC achieves transient current sharing between sources through converter output impedance shaping; the limitations of the shaping and the droop impedance maximum frequency are developed. Inter ­converter interactions under EDC cause output current oscillations in multi­ converter systems, which can be corrected with a virtual damping series resistance for energy storage converters. Recommendations for the minimum damping resistance for virtual capacitors are made based on the output impedance shaping analysis. Hardware results validating nominal transient current sharing, the effect of damping resistance, and the ability to transition between EDC and Resistive Droop Control (RDC) are presented. EDC is found to be a powerful method to integrate diverse sources into a DC distribution system serving pulsed loads.

A document by ValentinaVisani. Click on the document to view its contents.