With the growing reliability of modern ad hoc networks, it is encouraging to analyze the potential involvement of autonomous ad hoc agents in critical situations where human involvement could be perilous. One such critical scenario is the Search and Rescue effort in the event of a disaster, in which timely discovery and help deployment is of utmost importance. This paper demonstrates the applicability of a bio-inspired technique, namely Ant Algorithms (AA), in optimizing the search time for a route or path to a trapped victim, followed by the application of Dijkstra’s algorithm in the rescue phase. The inherent exploratory nature of AA is put to use for faster mapping and coverage of the unknown search space. Four different AA are implemented, with different effects of the pheromone in play. An inverted AA, with repulsive pheromones, was found to be the best fit for this particular application. After considerable exploration, upon discovery of the victim, the autonomous agents further facilitate the rescue process by forming a relay network, using the already deployed resources. Hence, the paper discusses a detailed decision-making model of the swarm, segmented into two primary phases that are responsible for the search and rescue, respectively. Different aspects of the performance of the agent swarm are analyzed as a function of the spatial dimensions, the complexity of the search space, the deployed search group size, and the signal permeability of the obstacles in the area.

Electrical Impedance Tomography (EIT) tactile sensors have limited success in equipping robots with tactile sensing capabilities due to the low spatial resolution of the resulting tactile images and the presence of image artifacts. To address these limitations, we propose a modular framework for invariant recognition of objects, within the context of an EIT artificial skin sensor. Three interconnected problems, i.e., EIT image reconstruction, segmentation and object recognition, are tackled in this work with the aid of machine learning. A novel conductivity surface decomposition approach, based on low order bivariate polynomials and RBF networks is introduced for the efficient solution of the EIT inverse problem. Next, segmentation of the reconstructed images is performed using a convolutional neural network and transfer learning. Finally, a subspace KNN ensemble classifier is trained on the set of object descriptors extracted from the segmented inhomogeneities to classify the objects. The proposed framework provides an accuracy of 97.5% on unseen data.

Electrical Impedance Tomography (EIT) is a non-invasive imaging method with promising applications in skin-like tactile sensors. Image reconstruction in EIT is challenging due to its non-linear and ill-posed nature, resulting in fuzzy edges, ringing, and boundary artifacts. These distortions reduce the accuracy of localizing and discriminating between touch stimuli. This work focuses on EIT difference imaging and proposes a novel representation scheme for changes in the conductivity distribution using the Discrete Cosine Transform. The superior energy compaction and decorrelation properties of DCT are utilized to efficiently represent the conductivity distribution, which is decoded by a committee of local expert, machine learning (ML)-based, inverse solvers. The performance of the method is evaluated based on prediction accuracy and reconstruction quality using the mean squared error and structural similarity index (SSIM). The DCT framework outperforms state-of-the-art works and improves SSIM up to 0.67, which is a 21.6% improvement from benchmarked methods, and leads to better localization and discrimination of multiple objects.

Everyday experience is multi-sensory, and user experience (UX) design aims to extend this to interactions with products, services, and designed worlds. However, tools and pedagogies for UX are overwhelmingly visual, whereas human-rights-based accessibility legislation mandates the inclusion of diverse peoples, including blind and partially sighted individuals. Coupling auditory and haptic UX techniques from human-computer interaction with industrial design’s (ID) cross-modal tradition of prototyping physical products fostered our novel cross-modal UX course for second-year ID undergraduates. Affordance-based theories of perception-action and Gestalt principles of perceptual organization were used to inform design in auditory, tactile, and visual sensory modalities situated in a novel pedagogical framework. Each week theoretical models were presented alongside hands-on workshops using the BBC micro:bit, developing computational literacy through cross-modal physical prototyping. Student projects demonstrate an understanding of theory and practice and include auditory and tactile interfaces.

As the number of EVs on the road increases, new and innovative charging solutions are needed to support their energy demand, especially in areas lacking infrastructure, in order to alleviate range anxiety in EV owners. Mobile Charging Stations (MCSs) have emerged as a viable solution, offering on-demand charging by traveling to areas of need. Researchers have explored several allocation mechanisms for MCS dispatch, including energy auctions, which perform well under the assumption that all participants adhere to protocol. However, customer no-shows—a common challenge in retail services—can significantly disrupt MCS operations. A high rate of EV no-shows leads to stagnation and resource wastage, ultimately reducing the efficiency of charging service delivery. To address this, we propose a reputation management system that enables MCS fleets to prioritize well-behaved customers, ensuring better resource utilization. Additionally, EVs are incentivized to form coalitions with their neighbors, submitting aggregate requests backed by a deposit to promote responsible behavior. The proposed coalition- and reputation-based energy auction system is evaluated across various quality-of-service metrics to assess its effectiveness.