Wilkinsonkirkeby2526

Moreover, we also systematically summarized the methods of vehicle detection in adverse weather. Finally, the remaining challenges and future research trends were analyzed according to the development of intelligent vehicle sensors and algorithms.Ultrasound (US) is an attractive modality for wireless power transfer (WPT) to biomedical implants with millimeter (mm) dimensions. To compensate for misalignments in WPT to a mm-sized implant (or powering a network of mm-sized implants), a US transducer array should electronically be driven in a beamforming fashion (known as US phased array) to steer focused US beams at different locations. This paper presents the theory and design methodology of US WPT links with phased arrays and mm-sized receivers (Rx). For given constraints imposed by the application and fabrication, such as load (RL) and focal distance (F), the optimal geometries of a US phased array and Rx transducer, as well as the optimal operation frequency (fc) are found through an iterative design procedure to maximize the power transfer efficiency (PTE). An optimal figure of merit (FoM) related to PTE is proposed to simplify the US array design. A design example of a US link is presented and optimized for WPT to a mm-sized Rx with a linear array. In measurements, the fabricated 16-element array (10.9×9×1.7 mm3) driven by 100 V pulses at fc of 1.1 MHz with optimal delays for focusing at F = 20 mm generated a US beam with a pressure output of 0.8 MPa. The link could deliver up to 6 mW to a ∼ 1 mm3 Rx with a PTE of 0.14% (RL = 850 Ω). The beam steering capability of the array at -45o to 45o angles was also characterized.In gene-based therapies, local perturbations associated with one disease can lead to comorbidity as it influences the pathways involved with the other diseases. The key genes orchestrating the common biological mechanisms are need to be prioritized for addressing the challenges introduced by the cross talks between disease modules. Here, a local centrality measure named Sub graph based Average Path length Double Specific Betweenness centrality (SAPDSB) for prioritizing the comorbid genes via Protein-Protein Interaction Network (PPIN) analysis is presented. This approach can be used to identify putative biomarkers which can be repurposed for the management of comorbidity. Proposed network based topological measure is designed specifically to prioritize the comorbid genes that are most likely to be present in the overlap of disease modules. In order to attain this, the estimated average path length of the seed network which holds Protein-Protein Interactions (PPIs) of the disease genes is exploited. Prioritized comorbid genes are further pruned using centrality-based cut-off values and specificity scores. The biological significance of the resultant genes is corroborated with connectivity analysis using leave-one-out method, pathway enrichment analysis and a comparative analysis using single disease-based gene prioritization tools.Cognitive workload recognition is pivotal to maintain the operator's health and prevent accidents in the human-robot interaction condition. So far, the focus of workload research is mostly restricted to a single task, yet cross-task cognitive workload recognition has remained a challenge. Furthermore, when extending to a new workload condition, the discrepancy of electroencephalogram (EEG) signals across various cognitive tasks limits the generalization of the existed model. To tackle this problem, we propose to construct the EEG-based cross-task cognitive workload recognition models using domain adaptation methods in a leave-one-task-out cross-validation setting, where we view any task of each subject as a domain. Specifically, we first design a fine-grained workload paradigm including working memory and mathematic addition tasks. Then, we explore four domain adaptation methods to bridge the discrepancy between the two different tasks. Finally, based on the supporting vector machine classifier, we conduct experiments to classify the low and high workload levels on a private EEG dataset. Experimental results demonstrate that our proposed task transfer framework outperforms the non-transfer classifier with improvements of 3% to 8% in terms of mean accuracy, and the transfer joint matching (TJM) consistently achieves the best performance.Human life is populated with articulated objects. Current Category-level Articulation Pose Estimation (CAPE) methods are studied under the single-instance setting with a fixed kinematic structure for each category. Considering these limitations, we aim to study the problem of estimating part-level 6D pose for multiple articulated objects with unknown kinematic structures in a single RGB-D image, and reform this problem setting for real-world environments and suggest a CAPE-Real (CAPER) task setting. This setting allows varied kinematic structures within a semantic category, and multiple instances to co-exist in an observation of real world. To support this task, we build an articulated model repository ReArt-48 and present an efficient dataset generation pipeline, which contains Fast Articulated Object Modeling (FAOM) and Semi-Authentic MixEd Reality Technique (SAMERT). Accompanying the pipeline, we build a large-scale mixed reality dataset ReArtMix and a real world dataset ReArtVal. Accompanying the CAPER problem and the dataset, we propose an effective framework that exploits RGB-D input to estimate part-level pose for multiple instances in a single forward pass. In our method, we introduce object detection from RGB-D input to handle the multi-instance problem and segment each instance into several parts. To address the unknown kinematic structure issue, we propose an Articulation Parsing Network to analyze the structure of detected instance, and also build a Pair Articulation Pose Estimation module to estimate per-part 6D pose as well as joint property from connected part pairs. Extensive experiments demonstrate that the proposed method can achieve good performance on CAPER, CAPE and instance-level Robot Arm pose estimation problems. We believe it could serve as a strong baseline for future research on the CAPER task. The datasets and codes in our work will be made publicly available.Imaging tissue mechanical properties has shown promise in noninvasive assessment of numerous pathologies. Researchers have successfully measured many linear tissue mechanical properties in laboratory and clinical settings. Currently, multiple complex mechanical effects such as frequency-dependence, anisotropy, and nonlinearity are being investigated separately. However, a concurrent assessment of these complex effects may enable more complete characterization of tissue biomechanics and offer improved diagnostic sensitivity. In this work, we report for the first time a method to map the frequency-dependent nonlinear parameters of soft tissues on a local scale. We recently developed a nonlinear elastography model that combines strain measurements from arbitrary tissue compression with radiation-force-based broadband shear wave speed (WS) measurements. Here, we extended this model to incorporate local measurements of frequency-dependent shear modulus. This combined approach provides a local frequency-dependent nonlinear parameter that can be obtained with arbitrary, clinically realizable tissue compression. Initial assessments using simulations and phantoms validate the accuracy of this approach. We also observed improved contrast in nonlinearity parameter at higher frequencies. Results from ex-vivo liver experiments show 32, 25, 34, and 38 dB higher contrast in elastograms than traditional linear elasticity, elastic nonlinearity, viscosity, and strain imaging methods, respectively. A lesion, artificially created by injection of glutaraldehyde into a liver specimen, showed a 59% increase in the frequency-dependent nonlinear parameter and a 17% increase in contrast ratio.Color Doppler imaging (CDI) is the modality of choice for simultaneous visualization of myocardium and intracavitary flow over a wide scan area. This visualization modality is subject to several sources of error, the main ones being aliasing and clutter. Mitigation of these artifacts is a major concern for better analysis of intracardiac flow. One option to address these issues is through simulations. In this article, we present a numerical framework for generating clinical-like CDI. Synthetic blood vector fields were obtained from a patient-specific computational fluid dynamics CFD model. Realistic texture and clutter artifacts were simulated from real clinical ultrasound cineloops. We simulated several scenarios highlighting the effects of 1) flow acceleration; 2) wall clutter; and 3) transmit wavefronts, on Doppler velocities. As a comparison, an "ideal" color Doppler was also simulated, without these harmful effects. This synthetic dataset is made publicly available and can be used to evaluate the quality of Doppler estimation techniques. Besides, this approach can be seen as a first step toward the generation of comprehensive datasets for training neural networks to improve the quality of Doppler imaging.Low intensity focused ultrasound (FUS) therapies use low intensity focused ultrasound waves, typically in combination with microbubbles, to non-invasively induce a variety of therapeutic effects. FUS therapies require pre-therapy planning and real-time monitoring during treatment to ensure the FUS beam is correctly targeted to the desired tissue region. To facilitate more streamlined FUS treatments, we present a system for pre-therapy planning, real-time FUS beam visualization, and low intensity FUS treatment using a single diagnostic imaging array. Therapy planning was accomplished by manually segmenting a B-mode image captured by the imaging array and calculating a sonication pattern for the treatment based on the user-input region of interest. Canagliflozin cell line For real-time monitoring, the imaging array transmitted a visualization pulse which was focused to the same location as the FUS therapy beam and ultrasonic backscatter from this pulse was used to reconstruct the intensity field of the FUS beam. The therapy planning and beam monitoring techniques were demonstrated in a tissue-mimicking phantom and in a rat tumor in vivo while a mock FUS treatment was carried out. The FUS pulse from the imaging array was excited with an MI of 0.78, which suggests that the array could be used to administer select low intensity FUS treatments involving microbubble activation.

Persons with normal arm function can perform complex wrist and hand movements over a wide range of limb positions. However, for those with transradial amputation who use myoelectric prostheses, control across multiple limb positions can be challenging, frustrating, and can increase the likelihood of device abandonment. In response, the goal of this research was to investigate convolutional neural network (RCNN)-based position-aware myoelectric prosthesis control strategies.

Surface electromyographic (EMG) and inertial measurement unit (IMU) signals, obtained from 16 non-disabled participants wearing two Myo armbands, served as inputs to RCNN classification and regression models. Such models predicted movements (wrist flexion/extension and forearm pronation/supination), based on a multi-limb-position training routine. RCNN classifiers and RCNN regressors were compared to linear discriminant analysis (LDA) classifiers and support vector regression (SVR) regressors, respectively. Outcomes were examined to determine whether RCNN-based control strategies could yield accurate movement predictions, while using the fewest number of available Myo armband data streams.