Instead of other motions, the mechanical coupling of the motion results in a single frequency being felt by most of the finger.

The see-through technique is employed by Augmented Reality (AR) in vision to superimpose digital content onto the visual information of the real world. In the haptic realm, a prospective feel-through wearable device should enable alterations to tactile experiences, without hindering the physical objects' cutaneous perception. To the best of our understanding, the effective implementation of a comparable technology remains elusive. A new approach, presented in this work, allows for the modulation of the perceived softness of physical objects for the first time, using a feel-through wearable with a thin fabric surface as the interaction point. Real-object interaction allows the device to adjust the contact area on the fingertip without changing the force felt by the user, thereby modifying the perceived texture's softness. To accomplish this, the lifting mechanism of our system modifies the fabric encircling the finger pad in a manner commensurate with the pressure exerted on the specimen under study. Careful management of the fabric's stretching state is essential to retain a loose contact with the fingerpad at all moments. We observed distinct softness perceptions for the same samples, which were contingent upon adjustments to the system's lifting apparatus.

Intelligent robotic manipulation is a complex and demanding subject within the broader study of machine intelligence. Although numerous dexterous robotic appendages have been conceived to support or replace human hands in a spectrum of activities, the problem of enabling them to perform delicate manipulations similar to human hands remains unresolved. POMHEX in vivo Motivated by this, we undertake a meticulous investigation into human object manipulation and propose a new representation framework for object-hand manipulation. This representation offers a clear and intuitive semantic guide, detailing how the skillful hand should interact with an object, focusing on the object's functional zones for precise manipulation. We concurrently devise a functional grasp synthesis framework that avoids the need for real grasp label supervision, instead relying on the directive of our object-hand manipulation representation. 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 investigate object manipulation on a real robot, evaluating the efficiency and adaptability of our object-hand manipulation representation and grasp synthesis method. 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. We reconsider the model creation and selection steps of the RANSAC algorithm, aiming for a faster and more resilient approach to point cloud registration. For the purpose of model generation, we introduce a second-order spatial compatibility (SC 2) measure for determining the similarity between correspondences. By emphasizing global compatibility instead of local consistency, the model distinguishes inliers and outliers more prominently during the initial clustering phase. Fewer samplings are anticipated in the proposed measure, which seeks to isolate a predetermined number of outlier-free consensus sets, leading to enhanced efficiency in model generation. To select the best-performing models, we introduce FS-TCD, a novel metric based on the Truncated Chamfer Distance, taking into account the Feature and Spatial consistency of generated models. The selection of the correct model is facilitated by the system's simultaneous consideration of alignment quality, the appropriateness of feature matching, and the requirement for spatial consistency. This is maintained even when the inlier rate within the hypothesized correspondence set is exceptionally low. Our method is evaluated through a comprehensive experimental program designed to probe its performance. The SC 2 measure and FS-TCD metric are not confined to specific deep learning structures, as evidenced by their easy integration demonstrated experimentally. The source code is accessible on the GitHub repository: https://github.com/ZhiChen902/SC2-PCR-plusplus.

For object localization in partial 3D environments, we propose an end-to-end solution focused on determining the position of an object in an unmapped area. Our method utilizes only a partial 3D scan of the scene. POMHEX in vivo A new approach to scene representation, the Directed Spatial Commonsense Graph (D-SCG), facilitates geometric reasoning. This spatial graph is enriched by adding concept nodes sourced from a commonsense knowledge base. D-SCG's nodes signify scene objects, while their interconnections, the edges, depict relative positions. Object nodes are linked to concept nodes using a spectrum of commonsense relationships. Estimating the target object's unknown position, facilitated by a Graph Neural Network implementing a sparse attentional message passing mechanism, is achieved using the proposed graph-based scene representation. Initially, the network learns a detailed representation of objects, using the aggregation of object and concept nodes in D-SCG, to forecast the relative positioning of the target object compared to each visible object. The final position emerges from the amalgamation of these relative positions. 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.

Leveraging base knowledge, few-shot learning seeks to categorize novel queries presented with limited training instances. Recent achievements in this context are contingent upon the assumption that fundamental knowledge and novel query samples share the same domain, an assumption often inappropriate for realistic situations. Concerning this matter, we suggest tackling the cross-domain few-shot learning challenge, where only a minuscule number of examples are present in the target domains. Considering this pragmatic environment, we scrutinize the swift adaptability of meta-learners with a method for dual adaptive representation alignment. Our approach initially proposes a prototypical feature alignment to redefine support instances as prototypes. These prototypes are then reprojected using a differentiable closed-form solution. Feature spaces representing learned knowledge can be reshaped into query spaces through the adaptable application of cross-instance and cross-prototype relations. Furthermore, a normalized distribution alignment module, exploiting prior query sample statistics, is presented in addition to feature alignment, addressing covariant shifts between the support and query samples. These two modules are utilized to design a progressive meta-learning framework, facilitating fast adaptation from a very limited set of samples while preserving its generalizability. Empirical data validates our method's attainment of cutting-edge performance 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. Nevertheless, this presents a fresh predicament: request routing amongst controllers by Software-Defined Networking switches. The distribution of requests requires a bespoke dispatching policy for each individual switch. Existing regulations are structured based on assumptions, like a sole, centralized authority, complete understanding of the global network, and a stable controller count, which is a scenario seldom replicated in the real world. To achieve high adaptability and performance in request dispatching, this article presents MADRina, a Multiagent Deep Reinforcement Learning model. The first step in addressing the limitations of a globally-aware centralized agent involves constructing a multi-agent system. To enable the dispatching of requests across a flexible cluster of controllers, we present a deep neural network-based adaptive policy, second. In the third place, we devise a fresh algorithm for training adaptable strategies within a multi-agent framework. POMHEX in vivo 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. Analysis of the results indicates that MADRina can decrease response times by as much as 30% in comparison to existing solutions.

In order to provide continuous mobile health monitoring, body-worn sensors should exhibit performance comparable to clinical devices, within a compact, discreet package. A complete and adaptable wireless system for electrophysiological data acquisition, weDAQ, is presented and validated for in-ear electroencephalography (EEG) and other on-body applications. It employs user-configurable dry contact electrodes constructed from standard printed circuit boards (PCBs). A driven right leg (DRL), a 3-axis accelerometer, and 16 recording channels, along with local storage and versatile data transmission methods, are provided in each weDAQ device. By employing the 802.11n WiFi protocol, the weDAQ wireless interface supports a body area network (BAN) which is capable of simultaneously aggregating various biosignal streams from multiple worn devices. Each channel boasts the ability to resolve biopotentials across a range of five orders of magnitude, coupled with a 1000 Hz bandwidth noise level of 0.52 Vrms. This is complemented by a high peak SNDR of 119 dB and an equally impressive CMRR of 111 dB, all achieved 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. EEG measurements from in-ear and forehead sensors, alongside electrooculographic (EOG) recordings of eye movements and electromyographic (EMG) readings from jaw muscles, captured modulation of subjects' alpha brain activity.