In beam hopping-based low earth orbit (LEO) satellite systems, spatially imbalanced and temporally varying ground traffic requires dynamic wireless resource allocation. This study proposes a long short-term memory (LSTM)-based approach that leverages real-world mobile network data to predict percell traffic demand. Based on these predictions, two-types of deep Q-network (DQN) agents are assigned to each beam to jointly optimize cell selection and bandwidth allocation. The proposed dual-agent DQN architecture decouples the complex action space, thereby enhancing learning efficiency. In addition, a unified reward function is designed to simultaneously capture traffic service efficiency, average queueing delay, and the number of beam switchings. Simulation results show that the proposed algorithm significantly outperforms existing benchmarks in terms of overall resource utilization efficiency.

The methodology used to design and optimize the very first general-purpose hyperdimensional (HD) processing unit capable of executing a broad spectrum of HD workloads (called “HPU”) is presented. HD computing is a brain-inspired computational paradigm that uses the principles of high-dimensional mathematics to perform cognitive tasks. While considerable efforts have been spent toward realizing efficient HD processors, all of these targeted specific application domains, most often pattern classification. In contrast, the HPU design addresses the multiple layers of a cognitive process. A structured methodology identifies the kernel HD computations recurring at each of these layers, and maps them onto a unified and parameterized architectural model. The effectiveness in terms of runtime and energy consumption of the approach is evaluated. The results show that the resulting HPU efficiently processes the full range of HD algorithms, and far outperforms baseline implementations on a GPU.

The Message Queuing Telemetry Transport (MQTT) protocol is widely used for communication in machine-to-machine (M2M) and Internet of Things (IoT) systems due to its lightweight publish-subscribe model. Although MQTT can be secured with Transport Layer Security (TLS), this introduces significant computational overhead on the message broker. We demonstrate that this overhead becomes unnecessarily high in subscription-heavy networks, where a single message must be encrypted individually for each subscriber. To address this issue, we present WHISPERMQTT, a solution designed to optimize the encryption process in MQTT brokers. Our approach utilizes both TLS and non-TLS connections with subscribers: the TLS channel is used to securely distribute a per-topic decryption key, while the encrypted payload is transmitted over the non-TLS connection. This allows the broker to encrypt each topic message only once, irrespective of the number of subscribers, which reduces processing overhead and enhances both latency and throughput. Importantly, WHISPERMQTT integrates seamlessly with the MQTT protocol and applications without requiring any modifications.

Hyperdimensional computing (HDC) is a brain-inspired computing paradigm that operates on pseudo-random hypervectors, an information-rich, hardware-efficient representation that is robust to noise and facilitates learning with limited training data. This work explores how robot navigation tasks can leverage the high-capacity hypervector representation to enable behavioral prioritization through a weighted encoding of heterogeneous sensor information. Experiments over 100 trials in each of the 100 randomly generated obstacle maps demonstrate that the proposed weighted sensor encoding scheme boosts the success rate of the navigation task by over 30% compared to an unweighted sensor encoding. A hybrid scheme using the HDC weighted scheme at the input of a deep feed-forward neural network achieves the highest success rate. The hybrid scheme furthermore is more robust when reducing the HDC dimension by 50%. However, the simple HDC implementation remains the most hardware efficient, making it desirable for resource-constrained systems.

This paper introduces a novel technique for fabricating functional robots using 3D printers. Simultaneously depositing photopolymers and a non-curing liquid allows complex, pre-filled fluidic channels to be fabricated. This new printing capability enables complex hydraulically actuated robots and robotic components to be automatically built, with no assembly required. The technique is showcased by printing linear bellows actuators, gear pumps, soft grippers and a hexapod robot, using a commercially-available 3D printer. We detail the steps required to modify the printer and describe the design constraints imposed by this new fabrication approach.