The motion is dictated by mechanical coupling, resulting in a single frequency that is felt throughout the bulk of the finger.

Augmented Reality (AR) overlays digital content onto real-world visuals in vision, leveraging the tried-and-true see-through method. A hypothetical feel-through wearable device within the haptic domain should facilitate modifications of the tactile experience, ensuring that the physical objects' cutaneous perception remains undistorted. Based on our current knowledge, a similar technology is far from a state of effective implementation. Through a novel feel-through wearable that utilizes a thin fabric as its interaction surface, we introduce in this study a method enabling, for the first time, the modulation of perceived softness in real-world objects. The device, during interaction with physical objects, can regulate the contact area over the fingerpad, leaving the user's force unchanged, and therefore influencing the perceived softness. The lifting mechanism of our system, dedicated to this intention, adjusts the fabric wrapped around the finger pad in a way that corresponds to the force applied to the explored specimen. The fabric's tension is regulated to ensure a relaxed touch with the fingertip at all times. We demonstrated that the same specimens, when handled with subtly adjusted lifting mechanisms, can lead to varied softness perceptions.

A challenging pursuit in machine intelligence is the study of intelligent robotic manipulation. 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. Box5 manufacturer We are driven to conduct a detailed analysis of how humans manipulate objects, and to formulate a representation for object-hand manipulation. The semantic implications of this representation are crystal clear: it dictates how the deft hand should touch and manipulate an object, referencing the object's functional zones. Concurrently, our functional grasp synthesis framework operates without real grasp label supervision, but rather utilizes our object-hand manipulation representation for its guidance. To bolster functional grasp synthesis results, we present a network pre-training method that takes full advantage of readily available stable grasp data, and a complementary training strategy that balances the loss functions. We utilize a real robot to conduct object manipulation experiments, assessing the performance and adaptability of our object-hand manipulation representation and grasp synthesis. The URL for the project's website is https://github.com/zhutq-github/Toward-Human-Like-Grasp-V2-.

Feature-based point cloud registration workflows often include a crucial stage of outlier removal. We reconsider the model creation and selection steps of the RANSAC algorithm, aiming for a faster and more resilient approach to point cloud registration. A second-order spatial compatibility (SC 2) metric is proposed for calculating the similarity between correspondences in the context of model generation. The system prioritizes global compatibility over local consistency, which allows for a more marked distinction between inliers and outliers early in the process. The proposed measure, by reducing sampling, pledges to locate a specific quantity of outlier-free consensus sets, thereby increasing the efficiency of model generation. We suggest a novel evaluation metric, FS-TCD, based on the Truncated Chamfer Distance, integrating Feature and Spatial consistency constraints for selecting the best generated models. The system's ability to select the correct model is enabled by its simultaneous evaluation of alignment quality, the accuracy of feature matching, and the spatial consistency constraint, even when the inlier ratio within the proposed correspondences is extremely low. Our method is evaluated through a comprehensive experimental program designed to probe its performance. In addition, our experimental results highlight the general nature of the SC 2 measure and the FS-TCD metric, which are easily implementable within existing deep learning frameworks. The GitHub repository https://github.com/ZhiChen902/SC2-PCR-plusplus contains the code.

We are introducing an end-to-end solution for precisely locating objects in partially observed scenes. Our objective is to estimate the position of an object in an uncharted section of space, relying solely on a partial 3D scan of the scene. Box5 manufacturer 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. In the D-SCG, scene objects are expressed through nodes, and their mutual locations are depicted by the connecting edges. Connections between object nodes and concept nodes are established through diverse commonsense relationships. The proposed graph-based scene representation allows us to estimate the target object's unknown position via a Graph Neural Network, which utilizes a sparse attentional message passing mechanism. The network, by means of aggregating object and concept nodes within D-SCG, first creates a rich representation of the objects to estimate the relative positions of the target object against every visible object. In order to calculate the final position, these relative positions are combined. Through testing on Partial ScanNet, our method yields a 59% enhancement in localization accuracy and an 8-fold speedup during training, thereby surpassing the current state-of-the-art.

By leveraging foundational knowledge, few-shot learning seeks to discern novel queries utilizing a restricted selection of supporting examples. Current advancements in this environment postulate a shared domain for underlying knowledge and fresh inquiry samples, a constraint typically untenable in practical implementations. In regard to this point, we present a solution for handling the cross-domain few-shot learning problem, which is characterized by the paucity of samples in target domains. This realistic setting motivates our investigation into the rapid adaptation capabilities of meta-learners, utilizing a dual adaptive representation alignment methodology. 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. By leveraging cross-instance and cross-prototype relationships, learned knowledge's feature spaces can be dynamically adapted to align with query spaces. We propose a normalized distribution alignment module, in addition to feature alignment, that capitalizes on statistics from previous query samples to resolve covariant shifts affecting 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. Observations from experiments show our technique surpassing existing best practices on four CDFSL benchmarks and four fine-grained cross-domain benchmarks.

Centralized and adaptable control within cloud data centers is enabled by software-defined networking (SDN). A distributed network of SDN controllers, that are elastic, is usually needed for the purpose of providing a suitable and cost-efficient processing capacity. Still, this introduces a fresh difficulty: the assignment of request dispatching among controllers by SDN network switches. To ensure optimal request distribution, a specific dispatching policy must be created for every switch. Current policies are constructed under the premise of a single, centralized decision-maker, full knowledge of the global network, and a fixed number of controllers, but this presumption is frequently incompatible with the demands of real-world implementation. This article introduces MADRina, a Multiagent Deep Reinforcement Learning approach to request dispatching, aiming to create policies that excel in adaptability and performance for dispatching tasks. Our initial strategy for overcoming the restrictions of a globally connected centralized agent is the implementation of a multi-agent system. Secondly, an adaptive policy based on a deep neural network is proposed to facilitate request distribution across a flexible collection of controllers. Finally, the development of a novel algorithm for training adaptive policies in a multi-agent context represents our third focus. Box5 manufacturer We developed a simulation tool to measure MADRina's performance, using real-world network data and topology as a foundation for the prototype's construction. MADRina's results signify a substantial reduction in response time, potentially reducing it by as much as 30% in contrast to prior solutions.

For seamless, on-the-go health tracking, wearable sensors must match the precision of clinical equipment while being lightweight and discreet. The versatile wireless electrophysiology data acquisition system weDAQ is presented here, demonstrating its applicability to in-ear electroencephalography (EEG) and other on-body electrophysiological measurements. It incorporates user-designed dry-contact electrodes constructed 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. The 802.11n WiFi protocol is employed by the weDAQ wireless interface to support a body area network (BAN) capable of collecting and aggregating biosignal streams from multiple devices worn simultaneously on the body. A 0.52 Vrms noise level, present within a 1000 Hz bandwidth, is characteristic of each channel that resolves biopotentials over five orders of magnitude. This superior performance is reinforced by an impressive 119 dB peak SNDR and a 111 dB CMRR achieved at a rate of 2 ksps. For the dynamic selection of suitable skin-contacting electrodes for reference and sensing channels, the device incorporates in-band impedance scanning and an input multiplexer. Subjects' in-ear and forehead EEG signals, coupled with their electrooculogram (EOG) and electromyogram (EMG), indicated the modulation of their alpha brain activity, eye movements, and jaw muscle activity.