Search Results (4,361)

Artificial intelligence (AI) has become ubiquitous in modern computing systems, from high-performance data centers to resource-constrained edge devices. As AI applications continue to expand into mobile and IoT domains, the need for energy-efficient neural network implementations has become increasingly critical. To meet this requirement of energy-efficient computing, this work presents a BWNN (binary-weighted neural network) architecture implemented using FeRAM (Ferroelectric RAM)-based synaptic arrays. By leveraging the non-volatile nature and low-power computing of FeRAM-based CIM (computing in memory), the proposed CIM architecture indicates significant reductions in both dynamic and standby power consumption. Simulation results in this paper demonstrate that scaling the ferroelectric capacitor size can reduce dynamic power by up to 6.5%, while eliminating DRAM-like refresh cycles allows standby power to drop by over 258× under typical conditions. Furthermore, the combination of binary weight quantization and in-memory computing enables energy-efficient inference without significant loss in recognition accuracy, as validated using MNIST datasets. Compared to prior CIM architectures of SRAM-CIM, DRAM-CIM, and STT-MRAM-CIM, the proposed FeRAM-CIM exhibits superior energy efficiency, achieving 230–580 TOPS/W in a 45 nm process. These results highlight the potential of FeRAM-based BWNNs as a compelling solution for edge-AI and IoT applications where energy constraints are critical. Full article

Hydrogen, as a renewable and clean energy with a high energy density, is of great significance to the realization of carbon neutrality. In recent years, extensive research has been conducted on the electrocatalytic hydrogen evolution reaction (HER) by splitting water, with a focus on developing efficient electrocatalysts that can perform the HER at an overpotential with minimal power consumption. Tungsten oxide (WO₃), a non-noble-metal-based material, has great potential in hydrogen evolution due to its excellent redox capability, low cost, and high stability. However, it cannot meet practical needs because of its poor electrical conductivity and the limited number of active sites; thus, it is necessary to further improve HER performance. In this review, recent advances related to WO3-based electrocatalysts for the HER are introduced. Most importantly, several tactics for optimizing the electrocatalytic HER activity of WO₃ are summarized, such as controlling its morphology, phase transition, defect engineering (anion vacancies, cation doping, and interstitial atoms), constructing a heterostructure, and the microenvironment effect. This review can provide insight into the development of novel catalysts with high activity for the HER and other renewable energy applications. Full article

This paper investigates the problem of computation offloading and resource allocation in an integrated space–air–sea network based on unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellites supporting Maritime Internet of Things (M-IoT) devices. Considering the complex, dynamic environment comprising M-IoT devices, UAVs and LEO satellites, traditional optimization methods encounter significant limitations due to non-convexity and the combinatorial explosion in possible solutions. A multi-agent deep deterministic policy gradient (MADDPG)-based optimization algorithm is proposed to address these challenges. This algorithm is designed to minimize the total system costs, balancing energy consumption and latency through partial task offloading within a cloud–edge-device collaborative mobile edge computing (MEC) system. A comprehensive system model is proposed, with the problem formulated as a partially observable Markov decision process (POMDP) that integrates association control, power control, computing resource allocation, and task distribution. Each M-IoT device and UAV acts as an intelligent agent, collaboratively learning the optimal offloading strategies through a centralized training and decentralized execution framework inherent in the MADDPG. The numerical simulations validate the effectiveness of the proposed MADDPG-based approach, which demonstrates rapid convergence and significantly outperforms baseline methods, and indicate that the proposed MADDPG-based algorithm reduces the total system cost by 15–60% specifically. Full article

As an efficient and low-carbon renewable energy source, hydrogen plays a strategic role in the global energy transition, particularly in the transportation sector. However, the flammable and explosive nature of hydrogen makes leakage risks in enclosed environments a core challenge for the safe promotion of hydrogen fuel cell vehicles. Catalytic combustion sensors are ideal choices due to their high sensitivity and long lifespan. Nevertheless, they face technical bottlenecks under vehicle operational conditions, such as high-power consumption caused by elevated working temperatures, slow response rates, weak anti-interference capabilities, and catalyst poisoning. This paper systematically reviews the research status of catalytic combustion hydrogen sensors for vehicle applications, summarizes technical difficulties and development strategies from the perspectives of hydrogen-sensitive material design and integration processes, and provides theoretical references and technical guidance for the development of catalytic combustion hydrogen sensors suitable for vehicle use. Full article

In order to reduce gas consumption and increase the renewable energy proportion, this paper proposes a poly-generation system that couples geothermal, solar, and liquid natural gas (LNG) cold energy to produce steam, gaseous natural gas, and low-temperature nitrogen. The high-temperature flue gas is used to heat LNG; low-temperature flue gas, mainly nitrogen, can be used for cold storage cooling, enabling the staged utilization of the energy. Solar shortwave is used for power generation, and longwave is used to heat the working medium, which realizes the full spectrum utilization of solar energy. The influence of different equipment and operating parameters on the performance of a steam generation system is studied, and the multi-objective model of the multi-generation system is established and optimized. The results show that for every 100 W/m² increase in solar radiation, the renewable energy ratio of the system increases by 1.5%. For every 10% increase in partial load rate of gas boiler, the proportion of renewable energy decreases by 1.27%. The system’s energy efficiency, cooling output, and the LNG vaporization flow rate are negatively correlated with the scale of solar energy utilization equipment. The decision variables determined by the TOPSIS (technique for order of preference by similarity to ideal solution) method have better economic performance. Its investment cost is 18.14 × 10 CNY, which is 7.83% lower than that of the LINMAP (linear programming technique for multidimensional analysis of preference). Meanwhile, the proportion of renewable energy is only 0.29% lower than that of LINMAP. Full article

Hydrogen (H₂) gas sensors are essential for detecting leaks and ensuring safety, thereby supporting the broader adoption of hydrogen energy. The performance of H₂ sensors has been shown to be improved by the incorporation of TiO₂ nanostructures. The key findings are summarized as follows: (1) Resistive random-access memory (ReRAM) technology was used to fabricate extremely compact H₂ sensors via various forming techniques, and substantial sensor performance enhancement was investigated. (2) A nanocontact composed of titanium oxide (TiO_x)/platinum (Pt) was subjected to various forming operations to establish a Schottky junction with a nanogap structure on a tantalum oxide (Ta₂O₅) layer, and its properties were assessed. (3) When the Pt electrode was on the positive side during the forming operation used for ReRAM technology, a Pt nanopillar structure was produced. By contrast, when the forming operation was conducted with a positive bias on the TiO_x side, a mixed oxide film of Ta and Ti was produced, which indicates local Ta doping into the TiO_x. A sensor response of over 1000 times was achieved at a minimal voltage of 1 mV at room temperature. (4) This sensor fabrication technology based on the forming operation is promising for the development of low-power consumption sensors. Full article

Microchannel heat sinks (MCHSs) have emerged as an alternative for dissipating high heat rates. However, manufacturing MCHSs can be expensive, so exploring low-cost additive manufacturing using 3D printing is warranted. Before fabrication, the entropy minimization method helps to optimize MCHSs, enhancing their cooling capacity while maintaining their power consumption. We employed this method through computational simulation of laminar water flow in rectangular microchannels ($\mathsf{\mu }$C) and minichannels (mC), considering two heat fluxes (10 and 50 kW/m²). The results showed that the frictional entropy is only appreciable in the smallest and largest channels. These computational results enabled the fabrication of the optimal $\mathsf{\mu }$C and mC, whose experimental implementation validated the computational findings. Moreover, we computationally studied the effect of using rGO-Ag water-based nanofluids as a coolant. In general, a reduction in total entropy generation was observed at a heat flux of 50 kW/m². Although at lower heat flux (10 kW/m²), mC was the best option. Channels with lower heights were more effective at higher heat fluxes (≥50 kW/m²). Our findings offer a cost-effective strategy for fabricating high-performance cooling systems while also highlighting the interplay among heat flux, entropy generation, and nanofluid-enhanced cooling. Full article

Deep learning (DL) continues to play a pivotal role in a wide range of intelligent systems, including autonomous machines, smart surveillance, industrial automation, and portable healthcare technologies. These applications often demand low-latency inference and efficient resource utilization, especially when deployed on embedded or edge devices with limited computational capacity. As DL models become increasingly complex, selecting the right inference framework is essential to meeting performance and deployment goals. In this work, we conduct a comprehensive comparison of five widely adopted inference frameworks: PyTorch, ONNX Runtime, TensorRT, Apache TVM, and JAX. All experiments are performed on the NVIDIA Jetson AGX Orin platform, a high-performance computing solution tailored for edge artificial intelligence workloads. The evaluation considers several key performance metrics, including inference accuracy, inference time, throughput, memory usage, and power consumption. Each framework is tested using a wide range of convolutional and transformer models and analyzed in terms of deployment complexity, runtime efficiency, and hardware utilization. Our results show that certain frameworks offer superior inference speed and throughput, while others provide advantages in flexibility, portability, or ease of integration. We also observe meaningful differences in how each framework manages system memory and power under various load conditions. This study offers practical insights into the trade-offs associated with deploying DL inference on resource-constrained hardware. Full article

To address the challenges posed by the instability of renewable energy output and load fluctuations on grid operations and to support the low-carbon sustainable development of the energy system, this paper integrates artificial intelligence technology to establish an economic stability dispatch framework for distributed resource aggregators. A phased multi-contract collaborative scheduling model oriented toward sustainable development is proposed. Through intelligent algorithms, the model dynamically optimises decisions across the day-ahead and intraday phases: During the day-ahead scheduling phase, intelligent algorithms predict load demand and energy output, and combine with elastic performance-based response contracts to construct a user-side electricity consumption behaviour intelligent control model. Under the premise of ensuring user comfort, the model generates a 24 h scheduling plan with the objectives of minimising operational costs and efficiently integrating renewable energy. In the intraday scheduling phase, a rolling optimisation mechanism is used to activate energy storage capacity contracts and dynamic frequency stability contracts in real time based on day-ahead prediction deviations. This efficiently coordinates the intelligent frequency regulation strategies of energy storage devices and electric vehicle aggregators to quickly mitigate power fluctuations and achieve coordinated control of primary and secondary frequency regulation. Case study results indicate that the intelligent optimisation-driven multi-contract scheduling model significantly improves system operational efficiency and stability, reduces system operational costs by 30.49%, and decreases power purchase fluctuations by 12.41%, providing a feasible path for constructing a low-carbon, resilient grid under high renewable energy penetration. Full article

This study explores the complex interaction between the Internet of Things (IoT) and the new energy sector and analyzes how their integration can catalyze a transition toward a sustainable low-carbon economy. Through the full-sample and rolling sub-sample methods, we empirically examine the dynamic interrelationship between China’s IoT index (IoT) and the New Energy Index (NEI). Quantitative analysis reveals significant time-varying characteristics and bidirectional causal complexity in the interaction between the IoT and new energy. The IoT has a dual-edged impact on the development of new sources of energy. In the long run, the IoT plays a dominant role in incentivizing new energy, helping to enhance its stability and economic value. However, during stages characterized by technological bottlenecks or resource competition, the high energy consumption of IoT infrastructure may suppress the investment returns of new energy. Simultaneously, new energy has both positive and negative impacts on the IoT. On the one hand, new energy provides low-cost, sustainable power to support the IoT, driving the construction of the IoT ecosystem. On the other hand, it may threaten the continuity of IoT power supply, and the complexity of standardization and regulation in the sector may constrain the development of the IoT. This study provides a fresh perspective on promoting the integration of digital technology and green energy, uncovering nonlinear trade-offs between innovation-driven growth and carbon reduction goals, and offering policy insights for cross-sectoral collaboration to achieve sustainability. Full article

As the resolution and conversion speed of time-to-digital conversion (TDC) chips continue to improve, the bit error rate also increases, leading to a decrease in the linearity of TDC and seriously affecting measurement accuracy. This paper presents a high-linearity, low-power-consumption, and wide dynamic range TDC that was achieved based on the SMIC 180 nm BCD process. Compared with previous research methods, the proposed phase arbiter structure can eliminate sampling errors and improve the linearity of TDC. The preprocessing circuit can eliminate fixed errors caused by START and STOP signal transmission delays. Post-simulation results show that the TDC has high linearity, with ranges of DNL and INL being −0.98 LSB < DNL < 0.93 LSB and −0.88 LSB < INL < 0.95 LSB, respectively. The highest resolution is 156 ps, the maximum measurement time range is 1.2 μs, and the power consumption is 1.625 mW. The overall system architecture of TDC is very simple, and it can be applied to dToF LIDAR to measure photon flight time, capable of measuring a range of up to hundreds of meters, with an accuracy of 2.25 cm, high linearity, and without any post-processing or time calibration. Full article

The stretch film production is highly energy intensive. The components of the technological line are powered by electrical energy, and the heat is used to change the physical state of the raw material (granules). The raw material is poured into FCR (the first calender roller). To solidify the liquid raw material, the calendar must be cooled. The low-temperature heat, treated as waste heat, has dissipated in the atmosphere. Technological innovations were proposed: (a) the raw material comprises raw material (primary) and up to 80% recyclate (waste originating mainly from agriculture), (b) the use of low-temperature waste heat (the cooling of FCR in the process of foil stretch production). A heat recovery line based on two compressor heat pumps (HP, hydraulically coupled) was designed. The waste heat (by low-temperature HP) was transformed into high-temperature heat (by high-temperature HP) and used to prepare the raw material. The proposed technological line enables the management of difficult-to-manage post-production waste (i.e., agriculture and other economic sectors). It reduces energy consumption and raw materials from non-renewable sources (CO₂ and other greenhouse gas emissions are reducing). It implements a closed-loop economy based on renewable energy sources (according to the European Green Deal). Full article

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article

This study presents transistor-level simulation results for a novel memristor emulator circuit. The design incorporates an inverter and a current-mode-controlled operational transconductance amplifier to stabilize the output voltage. Transient performance is evaluated across a 20 MHz to 100 MHz frequency range. Simulations using 0.18 μm TSMC technology confirm the circuit’s functionality, demonstrating a power consumption of 0.1 mW at a 1.2 V supply. The memristor model’s reliability is verified through corner simulations, along with Monte Carlo and temperature variation tests. Furthermore, the emulator is applied in a Memristive Integrate-and-Fire neuron circuit, a CMOS-based system that replicates biological neuron behavior for spike generation, enabling ultra-low-power computing and advanced processing in retinal prosthesis applications. Full article

With the increasing integration of renewable energy and smart technologies in residential energy systems, proactive household energy management (HEM) have become critical for reducing costs, enhancing grid stability, and achieving sustainability goals. This study proposes a ultra-short-term forecasting-driven proactive energy consumption optimization strategy that integrates advanced forecasting models with multi-objective scheduling algorithms. By leveraging deep learning techniques like Graph Attention Network (GAT) architectures, the system predicts ultra-short-term household load profiles with high accuracy, addressing the volatility of residential energy use. Then, based on the predicted data, a comprehensive consideration of electricity costs, user comfort, carbon emission pricing, and grid load balance indicators is undertaken. This study proposes an enhanced mixed-integer optimization algorithm to collaboratively optimize multiple objective functions, thereby refining appliance scheduling, energy storage utilization, and grid interaction. Case studies demonstrate that integrating photovoltaic (PV) power generation forecasting and load forecasting models into a home energy management system, and adjusting the original power usage schedule based on predicted PV output and water heater demand, can effectively reduce electricity costs and carbon emissions without compromising user engagement in optimization. This approach helps promote energy-saving and low-carbon electricity consumption habits among users. Full article