This paper presents a secure 2.4 GHz frequency shift keying (FSK) transmitter front-end with minimal overhead on the data stream using analog obfuscation techniques applied to the modulated waveform. An off-chip true random number generator (TRNG) unit is used to generate the required key for the encryption. Moving away from traditional FSK schemes, which benefit from constant local oscillator (LO) frequency within the channel, the proposed secure FSK scheme shifts the LO frequency in very small steps using an innovative capacitor-bank structure with a calibrated digitally controlled oscillator (DCO). The proposed capacitor bank uses a combination of parallel switches and series capacitors to minimize the impact of the layout parasitics on the minimum capacitor in the bank, thereby reliably creating sub-fF unit capacitors. When combined with the proposed capacitor bank, the cross-coupled CMOS LC voltage-controlled oscillator (VCO) forms a digitally controlled oscillator (DCO). The post-layout simulation results of the DCO reveal that the proposed scheme can achieve a resolution of <20 kHz for the LO frequency shifting while maintaining the phase-noise performance. The reported phase shift allows an equivalent entropy > 6 bits in the implemented analog-inspired secure transmitter front-end.

In the post-Moore’s Law era, conventional Von Neumann architectures face critical limitations, such as the “memory wall” and excessive power consumption, particularly when processing unstructured data. Neuromorphic computing, inspired by the human brain, offers a promising solution through parallel processing and adaptive learning. Among the candidates for artificial synapses, memristors based on two-dimensional MXenes (specifically Ti3C2Tx) have attracted significant attention due to their unique layered structure, high metallic conductivity, and tunable physicochemical properties. This review provides a comprehensive analysis of MXene-based memristors, from material synthesis to system-level applications. We examine how different synthesis strategies, including etching methods, directly influence device performance and elucidate the underlying resistive switching mechanisms driven by ion migration, valence change, and interfacial processes. Furthermore, the review demonstrates the efficacy of MXenes in emulating biological synaptic functions—such as spike-timing-dependent plasticity (STDP) and long-term potentiation/depression (LTP/LTD)—and their application in tasks like handwritten digit recognition. Finally, we highlight emerging frontiers in flexible electronics and in-sensor computing, offering insights into the future trajectory of integrated sensing, memory, and computation.

This research addresses the increasing need for efficient energy management in residential settings in response to the increasing global energy demands, focusing on the integration of artificial intelligence to identify energy burdens. We employ and compare some machine learning models, like Decision Trees, K-nearest neighbors, and Feedforward Neural Networks, with a primary focus on electrical current as a key parameter. The Fine K-NN model shows notable efficiency, achieving an accuracy of 99.1% in the identification of active household appliances using a single sensor. Our methodology encompasses rigorous data acquisition and preprocessing under controlled experimental conditions, ensuring the integrity and reliability of our results. This study contributes to the field by illustrating the effectiveness of specific AI models in energy management under controlled conditions, paving the way for future advancements in AI-driven energy conservation strategies.

In this work, the authors introduce an entirely solar-powered LoRa-based WSN consisting of several nodes, two stoplights, and four cameras. The system has been used to monitor the semi-rural area of Panni (FG), Puglia, Italy. The WSN has a totally custom implementation in both the node-gateway side and the gateway-user interface side. In particular, the communication framework is entirely IoT-based, featuring both the MQTT protocol, for the direct control of apparatuses from the system user interface, and the more traditional TCP/IP protocol, implemented on NB-IoT. The proposed system is entirely solar-powered and features a 34.68 mWh/day consumption. Around a single communication session, the average power consumption inside the single node amounts to 1.4 mW. This paper gives an overview of the proposed system, with detailed explanations of each part, and measurements retrieved over a wide period to assess the functionality of the system.

Neuromorphic circuits emulate the brain’s massively parallel, energy-efficient, and robust information processing by reproducing the behavior of neurons and synapses in dense networks. Memristive technologies have emerged as key enablers of such systems, offering compact and low-power implementations. In particular, locally active memristors (LAMs), with their ability to amplify small perturbations within a locally active domain to generate action potential-like responses, provide powerful building blocks for neuromorphic circuits and offer new perspectives on the mechanisms underlying neuronal firing dynamics. This paper introduces a novel second-order locally active memristor (LAM) governed by two coupled state variables, enabling richer nonlinear dynamics compared to conventional first-order devices. Even when the capacitances controlling the states are equal, the device retains two independent memory states, which broaden the design space for hysteresis tuning and allow flexible modulation of the current–voltage response. The second-order LAM is then integrated into a FitzHugh–Nagumo neuron circuit. The proposed circuit exhibits oscillatory firing behavior under specific parameter regimes and is further investigated under both DC and AC external stimulation. A comprehensive analysis of its equilibrium points is provided, followed by bifurcation diagrams and Lyapunov exponent spectra for key system parameters, revealing distinct regions of periodic, chaotic, and quasi-periodic dynamics. Representative time-domain patterns corresponding to these regimes are also presented, highlighting the circuit’s ability to reproduce a rich variety of neuronal firing behaviors. Finally, two unknown system parameters are estimated using the Aquila Optimization algorithm, with a cost function based on the system’s return map. Simulation results confirm the algorithm’s efficiency in parameter estimation.

Microcontroller units (MCUs) serve as the core components of embedded systems. In the era of smart IoT, embedded devices are increasingly deployed on mobile platforms, leading to a growing demand for low-power consumption. As a result, low-power technology for MCUs has become increasingly critical. This paper systematically reviews the development history and current technical challenges of MCU low-power technology. It then focuses on analyzing system-level low-power optimization pathways for integrating MCUs with artificial intelligence (AI) technology, including lightweight AI algorithm design, model pruning, AI acceleration hardware (NPU, GPU), and heterogeneous computing architectures. It further elaborates on how AI technology empowers MCUs to achieve comprehensive low power consumption from four dimensions: task scheduling, power management, inference engine optimization, and communication and data processing. Through practical application cases in multiple fields such as smart home, healthcare, industrial automation, and smart agriculture, it verifies the significant advantages of MCUs combined with AI in performance improvement and power consumption optimization. Finally, this paper focuses on the key challenges that still need to be addressed in the intelligent upgrade of future MCU low power consumption and proposes in-depth research directions in areas such as the balance between lightweight model accuracy and robustness, the consistency and stability of edge-side collaborative computing, and the reliability and power consumption control of the sensor-storage-computing integrated architecture, providing clear guidance and prospects for future research.

The rapid growth of the Internet of Things (IoT) has driven the need for ultra-low-power wireless communication systems. Wake-up receivers (WuRXs) have emerged as a key technology to enable energy-efficient, near-always-on operation for IoT devices. This review explores the state of the art in WuRXs design, focusing on low-power architectures, key trade-offs, and recent advancements. We discuss the challenges in achieving low power consumption while maintaining sensitivity, power consumption, and interference resilience. The review highlights the evolution from radio frequency (RF) envelope detection architectures to more complex heterodyne and subthreshold designs and concludes with future directions for WuRXs research.

For most, if not all, AI-accelerated hardware, communication with the agent is expensive and heavily bottlenecks the hardware performance. This omnipresent hardware restriction is also found in neuromorphic computing: a novel style of computing that involves deploying spiking neural networks to specialized hardware to achieve low size, weight, and power (SWaP) compute. In neuromorphic computing, spike trains, times, and values are used to communicate information to, from, and within the spiking neural network. Input data, in order to be presented to a spiking neural network, must first be encoded as spikes. After processing the data, spikes are communicated by the network that represent some classification or decision that must be processed by decoder logic. In this paper, we first present principles for interconverting between spike trains, times, and values using custom-designed spiking subnetworks. Specifically, we present seven networks that encompass the 15 conversion scenarios between these encodings. We then perform three case studies where we either custom design a novel network or augment existing neural networks with these conversion subnetworks to vastly improve their communication performance with the outside world. We employ a classic space vs. time tradeoff by pushing spike data encoding and decoding techniques into the network mesh (increasing space) in order to minimize intra- and extranetwork communication time. This results in a classification inference speedup of 23× and a control inference speedup of 4.3× on field-programmable gate array hardware.

Split-shaft wind turbines decouple the turbine’s shaft from the generator’s shaft, enabling several modifications in the drivetrain. One of the significant achievements of a split-shaft drivetrain is the reduction in size of the excitation circuit. The grid-side converter is eliminated, and the rotor-side converter can safely reduce its size to a fraction of a full-size excitation. Therefore, this low-power-rated converter operates at low voltage and handles regular operations well. However, fault conditions may expose weaknesses in the converter and push it to its limits. This paper investigates the effects of the reduced-size rotor-side converter on the voltage ride-through capabilities required from all wind turbines. Four different protection circuits, including the active crowbar, active crowbar along a resistor–inductor circuit (C-RL), series dynamic resistor (SDR), and new-bridge fault current limiter (NBFCL), are employed, and their effects are investigated and compared. Wind turbine controllers are also utilized to reduce the impact of faults on the power electronic converters. One effective method is to store excess energy in the generator’s rotor. The proposed low-voltage ride-through strategies are simulated in MATLAB Simulink (2022b) to validate the results and demonstrate their effectiveness and functionality.

Coverage path planning solves the problem of moving an effector over all points within a specific region with effective routes. Most existing studies focus on geometric constraints, often overlooking robot-specific features, like the available energy, weight, maximum speed, sensor resolution, etc. This paper proposes a coverage path planning algorithm for Unmanned Aerial Vehicles (UAVs) that minimizes energy consumption while satisfying a set of other requirements, such as coverage and observation resolution. To deal with these issues, we propose a novel energy-optimal coverage path planning framework for monitoring tasks. Firstly, the 3D terrain’s spatial characteristics are digitized through a combination of parametric modeling and meshing techniques. To accurately estimate actual energy expenditure along a segmented trajectory, a power estimation module is introduced, which integrates dynamic feasibility constraints into the energy computation. Utilizing a Digital Surface Model (DSM), a global energy consumption map is generated by constructing a weighted directed graph over the terrain. Subsequently, an energy-optimal coverage path is derived by applying a Genetic Algorithm (GA) to traverse this map. Extensive simulation results validate the superiority of the proposed approach compared to existing methods.