The finger, primarily, experiences a singular frequency due to the motion being governed by mechanical coupling.
Augmented Reality (AR) overlays digital content onto real-world visuals in vision, leveraging the tried-and-true see-through method. A postulated feel-through wearable device, designed for the haptic domain, ought to permit the modification of tactile sensations, leaving the physical objects' cutaneous perception intact. Our assessment indicates a significant gap between current capabilities and the effective implementation of a comparable technology. This investigation details a novel approach that enables the manipulation of the perceived softness of real-world objects for the first time, facilitated by a feel-through wearable with a thin fabric interaction surface. When interacting with real objects, the device modulates the fingerpad's contact area without alteration of the applied force, resulting in a modulation of the perceived 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. Maintaining a loose grip with the fingerpad is achieved by concurrently controlling the fabric's state of elongation. 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 represents a demanding facet of machine intelligence research. While numerous adept robotic hands have been engineered to aid or supplant human hands in diverse tasks, the method of instructing them in nimble manipulations akin to human dexterity remains a significant hurdle. Human hepatic carcinoma cell This prompts an in-depth exploration of human object manipulation techniques and a corresponding proposal for an object-hand manipulation representation. The representation offers a clear semantic indication of the hand's touch and manipulation required for interacting with an object, guided by the object's own functional areas. In tandem, a functional grasp synthesis framework is proposed, eschewing the necessity of real grasp label supervision while relying on our object-hand manipulation representation for direction. To yield superior functional grasp synthesis, a network pre-training method, leveraging readily available stable grasp data, is proposed in conjunction with a coordinated network training strategy for loss functions. We experimentally assess the object manipulation capabilities of a real robot, examining the performance and generalizability of our object-hand manipulation representation and grasp synthesis framework. To visit the project's website, the address you need is https://github.com/zhutq-github/Toward-Human-Like-Grasp-V2-.
For accurate feature-based point cloud registration, outlier removal is essential. This paper provides a new perspective on the RANSAC algorithm's model generation and selection to ensure swift and robust registration of point clouds. Our proposed model generation method utilizes a second-order spatial compatibility (SC 2) measure to determine the similarity between correspondences. Early-stage clustering of inliers and outliers is enhanced by a focus on global compatibility over local consistency. By employing fewer samplings, the proposed measure pledges to discover a defined number of consensus sets, free from outliers, thereby improving the efficiency of model creation. Model selection is facilitated by our newly introduced FS-TCD metric, a variation of the Truncated Chamfer Distance, which considers the Feature and Spatial consistency of the 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 experimental procedures are extensive and meticulously designed to ascertain the performance of our method. 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. Within the GitHub repository, https://github.com/ZhiChen902/SC2-PCR-plusplus, the code is available.
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. psychopathological assessment For enhanced geometric reasoning, we present the Directed Spatial Commonsense Graph (D-SCG), a novel scene representation. This spatial scene graph is further developed by incorporating concept nodes from a commonsense knowledge source. The D-SCG structure uses nodes to denote scene objects, with edges showcasing their spatial relationships. A network of commonsense relationships connects each object node to a selection of concept nodes. By implementing a sparse attentional message passing mechanism within a Graph Neural Network, the proposed graph-based scene representation facilitates estimation of the target object's unknown position. 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. The final position is then derived by merging these relative positions. In evaluating our method on Partial ScanNet, we observe a 59% elevation in localization accuracy and an 8-fold acceleration in training time, surpassing the state-of-the-art.
Few-shot learning's strength lies in discerning novel queries using a constrained set of illustrative examples, derived from the foundation of existing knowledge. Recent progress in this context is predicated on the assumption that base knowledge and new query samples stem from comparable domains, a limitation typically encountered in real-world applications. To address this point, we propose a solution to the cross-domain few-shot learning problem, which is characterized by the availability of only a very limited number of samples in target domains. Under this realistic condition, our focus is on the meta-learner's prompt adaptability, using an effective dual adaptive representation alignment strategy. Our method begins by proposing a prototypical feature alignment to recalibrate support instances as prototypes. Subsequently, a differentiable closed-form solution is used to reproject these prototypes. The learned knowledge's feature spaces are adjusted to match query spaces through the dynamic interplay 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. A progressive meta-learning structure, built upon these two modules, allows for fast adaptation with minimal training examples, maintaining its generalizability. Our approach, proven through experimentation, attains superior performance on four CDFSL benchmarks and four fine-grained cross-domain benchmarks, marking a significant advancement in the field.
Centralized and adaptable control within cloud data centers is enabled by software-defined networking (SDN). Distributed SDN controllers, with their elasticity, are frequently required to provide both sufficient and economical processing capacity. Despite this, a new challenge is presented: the task of request dispatching among controllers handled by SDN switches. A dispatching policy, tailored to each switch, is crucial for directing request traffic. Current regulations are built upon underlying assumptions involving a single, centralized governing entity, thorough understanding of the global network, and a fixed number of controllers, conditions that are often not met in reality. The article proposes MADRina, employing Multiagent Deep Reinforcement Learning for request dispatching, to craft policies with significant dispatching adaptability and impressive performance. To overcome the limitations of a centralized agent relying on global network information, we first develop a multi-agent system. Our second proposal involves a deep neural network-based adaptive policy for the purpose of dynamically routing requests to a group of controllers. A novel algorithm is constructed in our third phase, for the purpose of training adaptive policies within a multi-agent context. Ras inhibitor Utilizing real-world network data and topology, we build a simulation tool to evaluate the performance of the MADRina prototype. MADRina's results demonstrate a substantial reduction in response time, achieving up to a 30% improvement over conventional methods.
For consistent mobile health monitoring, body-worn sensors must demonstrate performance identical to clinical devices, while remaining lightweight and unobtrusive. Demonstrating its adaptability, weDAQ, a complete wireless electrophysiology data acquisition system, is presented for in-ear electroencephalography (EEG) and other on-body applications. It utilizes user-specific dry contact electrodes constructed from standard printed circuit boards (PCBs). Each weDAQ unit features a driven right leg (DRL), a 3-axis accelerometer, and 16 recording channels, along with local data storage and customizable data transmission modes. The weDAQ wireless interface, using the 802.11n WiFi protocol, supports the deployment of a body area network (BAN) that collects and combines biosignal streams from numerous concurrently worn 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 dynamically selects suitable skin-contacting electrodes for reference and sensing channels, leveraging 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.