Dominating the motion is mechanical coupling, which leads to a singular frequency experienced by the majority of the finger.

Augmented Reality (AR) in vision achieves the superposition of digital content onto real-world visual data, through the well-understood see-through principle. A hypothetical feel-through wearable device, operating within the haptic domain, should allow for the modulation of tactile sensations, while preserving the direct cutaneous perception of the tangible objects. To the best of our understanding, the effective implementation of a comparable technology remains elusive. We describe, in this study, a method, implemented through a feel-through wearable featuring a thin fabric interactive surface, for the first time enabling the manipulation of the perceived softness of real-world objects. Interaction with tangible objects allows the device to adjust the surface area of contact on the fingerpad, maintaining constant force for the user, and consequently altering the perceived level of softness. The system's lifting mechanism, in pursuit of this objective, distorts the fabric surrounding the fingerpad in a manner analogous to the pressure exerted on the subject of investigation. To maintain a relaxed connection with the fingerpad, the fabric's stretch is actively managed simultaneously. We demonstrated that distinct softness perceptions in relation to the same specimens can be obtained, dependent upon the precise control of the lifting mechanism.

Intelligent robotic manipulation's study is a demanding aspect of machine intelligence. Although countless nimble robotic hands have been engineered to aid or substitute human hands in performing numerous tasks, the manner of instructing them to perform dexterous manipulations like those of human hands remains an ongoing hurdle. Metabolism inhibitor This prompts an in-depth exploration of human object manipulation techniques and a corresponding proposal for an object-hand manipulation representation. An intuitive and clear semantic model, provided by this representation, outlines the proper interactions between the dexterous hand and an object, guided by the object's functional areas. This functional grasp synthesis framework, proposed concurrently, doesn't demand real grasp label supervision, but instead is guided by our object-hand manipulation representation. Furthermore, to achieve superior functional grasp synthesis outcomes, we suggest a network pre-training approach that effectively leverages readily accessible stable grasp data, coupled with a network training strategy that harmonizes the loss functions. Our real robot platform serves as the testing ground for object manipulation experiments, allowing us to evaluate the effectiveness and adaptability of our object-hand manipulation representation and grasp synthesis approach. The project's website is located at https://github.com/zhutq-github/Toward-Human-Like-Grasp-V2-.

The procedure of feature-based point cloud registration is fundamentally dependent on the successful removal of outliers. This paper re-examines the model generation and selection within the classical RANSAC framework for the swift and robust alignment of point clouds. For model generation, we propose the second-order spatial compatibility (SC 2) measure to assess the similarity of correspondences. Global compatibility is favored over local consistency, resulting in more pronounced separation of inliers and outliers in the initial clustering steps. The proposed measure promises to identify a specific quantity of consensus sets, devoid of outliers, through reduced sampling, thereby enhancing the efficiency of model generation. For model selection, a new evaluation metric, FS-TCD, is proposed, incorporating Feature and Spatial consistency constraints within the Truncated Chamfer Distance framework, to assess the quality of generated models. The model selection process, which simultaneously analyzes alignment quality, the validity of feature matches, and spatial consistency, enables the correct model to be chosen, even if the inlier rate in the putative correspondence set is remarkably low. A detailed exploration of our method's performance necessitates a large number of carefully conducted experiments. Beyond theoretical analysis, we empirically show that the SC 2 measure and the FS-TCD metric can be effortlessly implemented within deep learning environments. The GitHub repository https://github.com/ZhiChen902/SC2-PCR-plusplus contains the code.

This end-to-end solution addresses the challenge of object localization in scenes with incomplete 3D data. Our aim is to estimate the position of an object in an unknown space, provided solely with a partial 3D scan of the scene. Metabolism inhibitor We advocate for a novel scene representation, the Directed Spatial Commonsense Graph (D-SCG). It leverages a spatial scene graph, but incorporating concept nodes from a commonsense knowledge base to enable geometric reasoning. D-SCG's nodes signify scene objects, while their interconnections, the edges, depict relative positions. Each object node is linked to a number of concept nodes, using different commonsense relationships. Employing a graph-based scene representation, we leverage a Graph Neural Network, equipped with a sparse attentional message passing mechanism, to ascertain the target object's unknown location. The network employs a rich object representation, derived from the aggregation of object and concept nodes in the D-SCG model, to initially predict the relative positions of the target object in relation to each visible object. Ultimately, these relative positions are combined to yield the final position. Our method, when applied to Partial ScanNet, exhibits a 59% leap in localization accuracy and an 8x increase in training speed, thus exceeding the current state-of-the-art performance.

Few-shot learning's focus is on recognizing novel inquiries with limited support data points, using pre-existing knowledge as a cornerstone. This recent progress in this area necessitates the assumption that base knowledge and fresh query samples originate from equivalent domains, a precondition infrequently met in practical application. In relation to this concern, we propose an approach for tackling the cross-domain few-shot learning problem, featuring a significant scarcity of samples in the target domains. Considering this practical setting, we highlight the noteworthy adaptability of meta-learners, employing a dual adaptive representation alignment method. To recalibrate support instances into prototypes, we introduce a prototypical feature alignment in our approach. This is followed by the reprojection of these prototypes using a differentiable closed-form solution. The learned knowledge's feature spaces are adjusted to match query spaces through the dynamic interplay of cross-instance and cross-prototype relations. Alongside feature alignment, a normalized distribution alignment module is developed, which draws upon prior query sample statistics to resolve covariant shifts present in support and query samples. These two modules are integral to a progressive meta-learning framework, enabling fast adaptation with extremely limited sample data, ensuring its generalizability. Our approach, as demonstrated through experiments, establishes new state-of-the-art results across four CDFSL and four fine-grained cross-domain benchmarks.

Centralized and adaptable control within cloud data centers is enabled by software-defined networking (SDN). An adaptable collection of distributed SDN controllers is frequently essential to deliver adequate processing capacity at a cost-effective rate. Despite this, a new challenge is presented: the task of request dispatching among controllers handled by SDN switches. A well-defined dispatching policy for each switch is fundamental to regulating the distribution of requests. Existing policies are designed predicated on certain suppositions, such as a singular, centralized agent, full awareness of the global network, and a constant number of controllers; these assumptions are not typically found in practical settings. This paper introduces MADRina, Multiagent Deep Reinforcement Learning for request dispatching, demonstrating the creation of dispatching policies with both high performance and adaptability. Initially, a multi-agent system is conceived to counteract the constraints imposed by a globally-networked, centralized agent. A deep neural network-based adaptive policy is proposed for dynamically dispatching requests among a flexible cluster of controllers; this constitutes our second point. To train adaptive policies in a multi-agent environment, we develop a new and innovative algorithm in our third phase. Metabolism inhibitor To evaluate the performance of MADRina, a prototype was built and a simulation tool was developed, utilizing real-world network data and topology. The results suggest that MADRina offers a significant performance enhancement in response time, diminishing it by up to 30% compared to current approaches.

For consistent mobile health monitoring, body-worn sensors must demonstrate performance identical to clinical devices, while remaining lightweight and unobtrusive. The weDAQ system, a complete and versatile wireless electrophysiology data acquisition solution, is demonstrated for in-ear EEG and other on-body electrophysiological measurements, using user-defined dry-contact electrodes made from standard printed circuit boards (PCBs). The weDAQ devices incorporate 16 recording channels, a driven right leg (DRL) system, a 3-axis accelerometer, local data storage, and diversified data transmission protocols. A body area network (BAN), using the 802.11n WiFi protocol, is deployable via the weDAQ wireless interface, enabling the simultaneous aggregation of various biosignal streams from multiple wearable devices. Resolving biopotentials over five orders of magnitude, each channel has a 0.52 Vrms noise level in a 1000 Hz bandwidth, resulting in a remarkable peak SNDR of 119 dB and CMRR of 111 dB at 2 ksps. The device's dynamic electrode selection for reference and sensing channels relies on in-band impedance scanning and an input multiplexer to identify suitable skin-contacting electrodes. The modulation of alpha brain activity, eye movements (EOG), and jaw muscle activity (EMG) were detected through simultaneous in-ear and forehead EEG measurements taken from the study participants.