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Experimental results indicate that the proposed method can shorten the calibration process and improve the calibration accuracy simultaneously.In this paper, we present an assistive mobility control for a robotic hip-knee exoskeleton intended for gait training. The robotic hip-knee exoskeleton is designed with an active flexion/extension and a passive abduction/adduction at each hip joint and an active flexion/extension at each knee joint to comply with the movement of lower limbs. While facilitating walking with the robotic exoskeleton, model-free linear extended state observer (LESO)-based controllers are proposed for gait control, in which the LESO is used to deal with each user's different lower limb parameters and unknown exerted torques. Walking and ascending experiments were conducted to evaluate the performance of the proposed methods, and the results are shown with respect to walking parameters. Moreover, a preliminary study for an extended application to the recovery of normal gaits that relieves the freezing of gait (FOG) in Parkinson's disease (PD) patients is also investigated in the paper.Indeterminate lung nodules detected on CT scans are common findings in clinical practice. Their correct assessment is critical, as early diagnosis of malignancy is crucial to maximise the treatment outcome. In this work, we evaluated the role of form factors as imaging biomarkers to differentiate benign vs. malignant lung lesions on CT scans. click here We tested a total of three conventional imaging features, six form factors, and two shape features for significant differences between benign and malignant lung lesions on CT scans. The study population consisted of 192 lung nodules from two independent datasets, containing 109 (38 benign, 71 malignant) and 83 (42 benign, 41 malignant) lung lesions, respectively. The standard of reference was either histological evaluation or stability on radiological followup. The statistical significance was determined via the Mann-Whitney U nonparametric test, and the ability of the form factors to discriminate a benign vs. a malignant lesion was assessed through multivariate prediction models based on Support Vector Machines. The univariate analysis returned four form factors (Angelidakis compactness and flatness, Kong flatness, and maximum projection sphericity) that were significantly different between the benign and malignant group in both datasets. In particular, we found that the benign lesions were on average flatter than the malignant ones; conversely, the malignant ones were on average more compact (isotropic) than the benign ones. The multivariate prediction models showed that adding form factors to conventional imaging features improved the prediction accuracy by up to 14.5 pp. We conclude that form factors evaluated on lung nodules on CT scans can improve the differential diagnosis between benign and malignant lesions.In this paper, we dive into sign language recognition, focusing on the recognition of isolated signs. The task is defined as a classification problem, where a sequence of frames (i.e., images) is recognized as one of the given sign language glosses. We analyze two appearance-based approaches, I3D and TimeSformer, and one pose-based approach, SPOTER. The appearance-based approaches are trained on a few different data modalities, whereas the performance of SPOTER is evaluated on different types of preprocessing. All the methods are tested on two publicly available datasets AUTSL and WLASL300. We experiment with ensemble techniques to achieve new state-of-the-art results of 73.84% accuracy on the WLASL300 dataset by using the CMA-ES optimization method to find the best ensemble weight parameters. Furthermore, we present an ensembling technique based on the Transformer model, which we call Neural Ensembler.High-accurate and real-time localization is the fundamental and challenging task for autonomous driving in a dynamic traffic environment. This paper presents a coordinated positioning strategy that is composed of semantic information and probabilistic data association, which improves the accuracy of SLAM in dynamic traffic settings. First, the improved semantic segmentation network, building on Fast-SCNN, uses the Res2net module instead of the Bottleneck in the global feature extraction to further explore the multi-scale granular features. It achieves the balance between segmentation accuracy and inference speed, leading to consistent performance gains on the coordinated localization task of this paper. Second, a novel scene descriptor combining geometric, semantic, and distributional information is proposed. These descriptors are made up of significant features and their surroundings, which may be unique to a traffic scene, and are used to improve data association quality. Finally, a probabilistic data association is created to find the best estimate using a maximum measurement expectation model. This approach assigns semantic labels to landmarks observed in the environment and is used to correct false negatives in data association. We have evaluated our system with ORB-SLAM2 and DynaSLAM, the most advanced algorithms, to demonstrate its advantages. On the KITTI dataset, the results reveal that our approach outperforms other methods in dynamic traffic situations, especially in highly dynamic scenes, with sub-meter average accuracy.This study determined if using alternative sleep onset (SO) definitions impacted accelerometer-derived sleep estimates compared with polysomnography (PSG). Nineteen participants (48%F) completed a 48 h visit in a home simulation laboratory. Sleep characteristics were calculated from the second night by PSG and a wrist-worn ActiGraph GT3X+ (AG). Criterion sleep measures included PSG-derived Total Sleep Time (TST), Sleep Onset Latency (SOL), Wake After Sleep Onset (WASO), Sleep Efficiency (SE), and Efficiency Once Asleep (SE_ASLEEP). Analogous variables were derived from temporally aligned AG data using the Cole-Kripke algorithm. For PSG, SO was defined as the first score of 'sleep'. For AG, SO was defined three ways 1-, 5-, and 10-consecutive minutes of 'sleep'. Agreement statistics and linear mixed effects regression models were used to analyze 'Device' and 'Sleep Onset Rule' main effects and interactions. Sleep-wake agreement and sensitivity for all AG methods were high (89.0-89.5% and 97.2%, respectively); specificity was low (23.6-25.1%). There were no significant interactions or main effects of 'Sleep Onset Rule' for any variable. The AG underestimated SOL (19.7 min) and WASO (6.5 min), and overestimated TST (26.2 min), SE (6.5%), and SE_ASLEEP (1.9%). Future research should focus on developing sleep-wake detection algorithms and incorporating biometric signals (e.g., heart rate).Hybrid nanomaterial film consisting of multi-walled carbon nanotubes (MWCNT) and graphene nanoplatelet (GNP) were deposited on a highly flexible polyimide (PI) substrate using spray gun. The hybridization between 2-D GNP and 1-D MWCNT reduces stacking among the nanomaterials and produces a thin film with a porous structure. Carbon-based nanomaterials of MWCNT and GNP with high electrical conductivity can be employed to detect the deformation and damage for structural health monitoring. The strain sensing capability of carbon-based hybrid nanomaterial film was evaluated by its piezoresistive behavior, which correlates the change of electrical resistance with the applied strain through a tensile test. The effects of weight ratio between MWCNT and GNP and the total amount of hybrid nanomaterials on the strain sensitivity of the nanomaterial thin film were investigated. Experimental results showed that both the electrical conductivity and strain sensitivity of the hybrid nanomaterial film increased with the increase of the GNP contents. The gauge factor used to characterize the strain sensitivity of the nanomaterial film increased from 7.75 to 24 as the GNP weight ratio increased from 0 wt.% to 100 wt.%. In this work, a simple, low cost, and easy to implement deposition process was proposed to prepare a highly flexible nanomaterial film. A high strain sensitivity with gauge factor of 24 was achieved for the nanomaterial thin film.Electrical impedance tomography (EIT) is a promising technique for large area tactile sensing for robotic skin. This study presents a novel EIT-based force and touch sensor that features a latex membrane acting as soft skin and an ionic liquid domain. The sensor works based on fringing field EIT where the touch or force leads to a deformation in the latex membrane causing detectable changes in EIT data. This article analyses the performance of this electronic skin in terms of its dynamical behaviour, position accuracy and quantitative force sensing. Investigation into the sensor's performance showed it to be hypersensitive, in that it can reliably detect forces as small as 64 mN. Furthermore, multi-touch discrimination and annular force sensing is displayed. The hysteresis in force sensing is investigated showing a very negligible hysteresis. This is a direct result of the latex membrane and the ionic liquid-based domain design compared to more traditional fabric-based touch sensors due to the reduction in electromechanical coupling. A novel test is devised that displayed the dynamic performance of the sensor by showing its ability to record a 1 Hz frequency, which was applied to the membrane in a tapping fashion. Overall, the results show a considerable progress in ionic liquid EIT-based sensors. These findings place the EIT-based sensors that comprise a liquid domain, at the forefront of research into tactile robotic skin.In this paper, we proposed two methods for reducing the pyro-shock of the MEMS Inertial Measurement Unit (IMU). First, we designed the vibration isolator for reducing the pyro-shock inside the IMU. However, it turned out that there is a limit to reducing the pyro-shock with only the vibration isolator. Therefore, we improved the pyro-shock reduction performance by changing the method of mounting on the flight vehicle. Four mounting options were tested and one of them was adopted. The results showed the best reduction performance when we designed the vibration isolator with an aluminum integrated structure. When mounting, two methods were applied. One was to insert a bracket with a different material between the mounting surface and IMU and the other was to insert a set of three washers that was stacked in a PEEK-metal-PEEK order at each part of the screw connections.The paper considers the problem of cooperative control synthesis for a complex of N underwater vehicle-manipulator systems (UVMS) to perform the work of moving a cargo along a given trajectory. Here, we used the approach based on the representation of nonlinear dynamics models in the form of state space with state-dependent coefficients (SDC-form). That allowed us to apply methods of suboptimal control with feedback based on the state-dependent differential Riccati equation (SDDRE) solution at a finite time interval, providing the change in control intensity with the transient effect of the system matrices in SDC form. The paper reveals two approaches to system implementation a general controller for the whole system and a set of N independent subcontrollers for UVMSs. The results of both approaches are similar; however, for the systems with a small number of manipulators, the common structure is recommended, and for the systems with a large number of manipulators, the approach with independent subcontrollers may be more acceptable.