Current Issue : October-December Volume : 2026 Issue Number : 4 Articles : 5 Articles
Soft robots capable of self-driven information transmission hold great promise for enabling intelligent interactions that better emulate the behavior of living organisms; however, achieving such systems remains elusive. Here, we present an all-in-one optically interactive soft robot that seamlessly integrates holographic command encoding, encryption, and display with on-demand task execution. By leveraging the unique combination of liquid crystal and silk fibroin, this system achieves a synergistic integration of multi-degree-of-freedom actuation and information multiplexing within an all-soft-matter modular architecture. This “information-machine” coupling paradigm encodes task instructions into encrypted holographic feedback, ensuring the reliable execution of complex operations only upon accurate decoding. As demonstrations, we showcase an intelligent gripper capable of precise object grasping and classification in response to decoded holographic commands, as well as a walking robot that navigates a maze guided by multi-level, decrypted holographic pathways. The proposed strategy establishes a new framework for developing interactive soft robots that closely mimic living organisms by employing light as a central information carrier....
Prognostic health management of rolling element bearings requires feature representations that reliably track degradation while remaining tractable for real-time deployment. This paper investigates whether uniform time-delay embedding can serve as a near-optimal substitute for computationally expensive non-uniform embedding in recurrence-based vibration analysis. We show empirically that optimally chosen uniform delay vectors yield phase-space reconstructions of bearing vibration signals not significantly inferior to those produced by globally optimized non-uniform delay vectors, compressing the parameter search from a combinatorial optimization to a single scalar selection. Building on this near-optimality result, we construct color recurrence plots from uniformly embedded phase spaces and apply them to remaining useful life (RUL) prediction on the Intelligent Maintenance Systems (IMS) bearing dataset. We further demonstrate that standard binary recurrence plots are poorly suited for RUL estimation: their dense and erratically varying local patterns obscure the degradation trends required for reliable prognostics. Color recurrence plots, by contrast, suppress these local instabilities by averaging recurrence structures across multiple phase-space projections, exposing a globally evolving intensity that tracks bearing health throughout its degradation trajectory. This work establishes uniform delay embedding combined with color recurrence representation as an efficient, principled, and practically deployable approach to recurrence-based condition monitoring in industrial predictive maintenance....
The increasing availability of locally executed artificial intelligence systems enables individuals to deploy personalised, multi-agent AI environments outside institutional control. While such systems offer privacy and autonomy advantages, they also shift ethical, legal, and governance responsibilities from organisations to individual users. Current AI ethics frameworks largely address institutional deployments and provide limited guidance for personal AI systems. This paper presents the design and evaluation of Tibor, a lightweight governance agent embedded in a personalised multi-agent AI environment. Tibor introduces ethical oversight through structured metadata that influences anonymisation, information routing, retrieval-augmented generation, and output review, without restricting user autonomy. Using a design–science methodology supported by longitudinal reflective observation, the study examines how embedded governance affects privacy awareness, ethical decision-making, academic integrity, and safety-related behaviour during real-world use. The findings indicate that simple, user-facing governance mechanisms can meaningfully influence AI-supported workflows by prompting responsible data handling, flagging ethically sensitive content, and supporting informed decision-making. The governance functions operate transparently and with minimal operational overhead, illustrating the feasibility of ethicsby- design approaches at the individual level. The paper contributes a practical model for embedding ethical governance into personal AI systems and highlights the need to extend AI ethics discourse beyond organisational settings to address emerging forms of user-managed AI....
Hands-on engineering design can help bridge the gap between theoretical coursework and real-world engineering practice, particularly during the middle years of undergraduate programs. This paper presents the implementation and outcomes of an open-ended final project in ELEC 310: Embedded Systems Design at the University of San Diego. Student teams designed and prototyped smart environmental monitoring systems using STM32 microcontrollers, selecting sensors and defining system objectives under realistic constraints. Project outcomes were evaluated using prototype demonstrations, team technical reports, and post-project student reflections analyzed through inductive thematic analysis. Reflections and project artifacts suggest that debugging and hardware–software integration functioned as key learning mechanisms through which students developed system-level thinking while also supporting teamwork and confidence. Students also reported common challenges encountered in embedded systems development, including peripheral integration, timing behavior, and intermittent hardware instability. Overall, the findings suggest that tightly structured but open-ended embedded-systems projects can provide authentic design experiences that promote both technical and professional skill development....
Energy efficiency has become a central concern in the deployment of AI systems. While most research targets hardwarelevel acceleration to reduce energy consumption, software-based strategies remain underexplored or often sacrifice performance. In this work, we present a software-oriented approach for embedded code generation using a multi-agent pipeline composed of small, locally executed LLMs. Our architecture divides the generation process into reasoning stages, aiming to reduce resource demands without relying on cloud services. We evaluated our system using real-world measurements of energy and memory consumption, comparing multiple model configurations and execution scenarios. The results suggest that running small LLMs locally is a feasible path for software generation under constrained resources, contributing to the broader discussion on low-energy AI without specialized hardware....
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