Search Results (49)

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

This study presents a data-driven methodology to optimize the operational efficiency of a tugboat equipped with a Voith-Schneider Propeller (VSP) based on full-scale fuel consumption and vessel performance data. The objective is to identify optimal combinations of engine RPM and propeller pitch to reduce fuel consumption during low-demand phases without compromising maneuverability. Sea trials were conducted under controlled conditions using a dual flowmeter system and onboard speed measurements. The data enabled the construction of performance curves, efficiency ratios, and interpolated maps of fuel consumption. Optimal configurations were identified across defined speed ranges, and continuous efficiency zones were visualized through iso-consumption and contour plots. The results reveal a nonlinear relationship between propeller pitch, speed, and fuel demand, with maximum efficiency occurring at medium-to-high pitch values and speeds between 3 and 6 knots. This methodology provides a replicable tool for energy management in port operations and supports informed decisions during accompanying operations and standby periods. Efficiency differences over 300% between RPM–pitch settings were found, highlighting the operational impact of informed configuration choices. Moreover, the structured dataset and visual analysis framework lay the groundwork for future digital twin models aimed at enhancing operational efficiency in VSP-powered tugboats. Full article

Based on the GSMC 180 nm SiGe BiCMOS process, a parallel-summation logarithmic amplifier is presented in this paper. The logarithmic amplifier adopts a cascaded structure of nine-stage fully-differential limiting amplifiers (LA) to achieve high dynamic range. The ten-stage rectifier completes the conversion of amplified voltage to a logarithmic current signal. A log slope adjuster is proposed. It can provide slopes of 17–30 mV/dB by configuring an off-chip resistor to meet the detection requirements of different input power. Meanwhile, a power-down control unit is designed to reduce the power consumption to only 162 μW in standby mode. The post-simulation results show that under 5 V power supply voltage, the dynamic range exceeds 80 dB and the 3 dB bandwidth is 20 MHz–4 GHz. It also has a fast response time of 42 ns with a power consumption of 109 mW in normal operation mode. Full article

This paper presents a highly efficient, low-power, fully integrated neural stimulation circuit implemented using solely low-voltage devices. The circuit primarily consists of a high-voltage-generation circuit, an output driver circuit, and a constant-current source, designed and simulated using a 180 nm low-voltage CMOS process. The high-voltage-generation circuit utilizes a negative-voltage-generation module together with a series–parallel capacitor charge pump circuit, which effectively reduces the number of charge pump stages by three, and saves 29% of the area compared to a conventional charge pump circuit. A bootstrap clock generation circuit was utilized to generate the control signal to ensure that all transistors work within their voltage limit. To realize the high-voltage output driver circuit using low-voltage devices, a stacked transistor structure with deep N-well (DNW) devices was utilized. The four different output voltage levels from the high-voltage-generation circuit were utilized to generate a different voltage domain of control signals and bias voltage for the stacked transistors, making sure that all transistors work within their voltage limit. Simulation results show that the high-voltage-generation circuit can generate an output of up to 12.69 V from a 1.65 V low input voltage, with a maximum output current of 1 mA, achieving 74.9% efficiency. The overall efficiency of the neural stimulation circuit, including the high-voltage-generation circuit, output driver circuit and constant-current source, reaches 74% under the voltage-controlled stimulation (VCS) mode and 59.5% under the current-controlled stimulation (CCS) mode, whereas the standby static power consumption is as low as 66 pW. Full article

As a critical component of computer systems, static random access memory (SRAM) plays a significant role in the fields of high-performance computing, artificial intelligence, and the Internet of Things. However, the conventional silicon-based SRAM is facing problems such as high power consumption, integration limitations, and the need for performance improvement. For the power consumption challenge of SRAM, a novel read/write-decoupled SRAM cell is proposed in this paper. When SRAM is in a standby state, utilization of this cell can effectively reduce the leakage current, thereby reducing the static power consumption. In addition, a novel carbon–silicon heterogeneous-integrated SRAM technology is proposed in this paper. The carbon–silicon heterogeneous-integrated SRAM technology combines the high mobility, low power consumption, and low temperature process compatibility of carbon nanotubes (CNTs) with mature silicon-based fabrication processes, enabling significant optimization of access time and power consumption. The SRAM was implemented by a silicon-based 55 nm process and a carbon-based 500 nm process. The simulation results showed that when the power supply was 1.2 V, the access time of SRAM with the silicon 55 nm process was 886 ps. The static and dynamic power consumption was 91.51 nW and 1.48 mW, respectively. The SRAM with carbon–silicon heterogeneous-integrated technology achieved an access time of 872 ps, a static power consumption of 62.54 nW, and a dynamic power consumption of 1.07 mW, representing reductions of 1.58%, 31.66%, and 27.70%, respectively. Full article

Decoders are among the most fundamental components in digital circuit design. They are widely used in combinational logic to convert and route binary data, as well as in memory array logic, for decoding binary addresses that point to the memory locations to be accessed. Due to their extensive utilization, optimizing decoder cells can potentially yield perceivable improvements in a digital system. This paper introduces 3-Transistor Logic (3TL), a new design approach for the optimization of CMOS decoder circuits, which combines static CMOS, Transmission-Gate Logic, and Dual-Value Logic. A complete transistor-level design methodology is demonstrated for decoder sizes from $2\times 4$ up to $8\times 256$, using 15 nm FinFET technology. Furthermore, an extensive comparative analysis is conducted with transistor-level simulations, evaluating the new circuits against conventional static CMOS and other previously proposed designs. The results show that 3TL circuits offer the best overall performance in terms of active power consumption, standby power consumption, and delay, owing largely to the fact that they are designed with logic efficiency and the minimum possible number of transistors. Full article

Standby power consumption in household electrical and electronic equipment remains a persistent source of energy waste worldwide. Despite regulatory measures and ongoing technological developments, a considerable amount of electricity is still consumed by devices in standby or “off-mode”, resulting in higher utility costs and carbon emissions. This review synthesizes the latest research to clarify the scale of standby energy consumption, discusses relevant policies and regulations, and explores intelligent technologies and behavioral strategies that minimize energy consumption. Starting from the theoretical analysis and modeling of equipment consumption in standby mode to the implementation of intelligent systems to reduce it, the paper highlights heuristic optimization methods, smart grid integration, and occupant-centered interventions, all of which demonstrate tangible energy savings. This research was carried out in close connection with current policies regarding energy consumption and sustainable development, respectively, with the implementation of new technologies. Thus, in accordance with the latest European directives, the intelligent systems used have reduced the energy consumption of some common household appliances by 26.68 kWh. Additionally, knowledge gaps, particularly regarding user behavior, data granularity, and the integration of advanced analytics that limit the efficacy of current solutions, are identified. Recommendations for future research, emphasizing the importance of harmonized policies, precise data measurement, and artificial-intelligence-driven approaches for further reducing standby loads, are finally presented. Full article

This paper introduces the development of an acoustic underwater glider integrated with an underwater acoustic modem designed to enable the real-time transmission of ocean observation data. The glider features three sequentially connected, independent compartments and is capable of operating at depths exceeding 1000 m. To ensure stable communication, two acoustic transducers are mounted at the rear of the glider and optimized to maintain a consistent energy radiation angle despite variations in the glider’s attitude. The acoustic modem, housed within one of the compartments, operates with a standby power consumption as low as 5 mW, significantly enhancing the overall energy efficiency of the system. To address the glider’s motion dynamics and the unique characteristics of the underwater acoustic channel, a multi-carrier frequency shift keying-based underwater acoustic communication scheme combined with a Stop-and-Wait Automatic Repeat Request protocol was designed and implemented. The system’s performance and reliability were validated through sea trials conducted in the South China Sea. The results demonstrated that the glider achieved reliable underwater acoustic communication over distances of up to 5 km. This research highlights the potential of the acoustic underwater glider for applications such as underwater acoustic measurements and distributed networking collaboration. The system holds significant promise for advancing underwater acoustic communication and ocean observation technologies. Full article

Understanding subnivean life is crucial, particularly due to the major role in food webs played by small animals inhabiting this poorly known habitat. However, challenges such as remoteness and prolonged, harsh winters in the Arctic have hampered our understanding of subnivean ecology in this region. To address this problem, we present an improved autonomous, low-power system for monitoring small mammals under the snow in the Arctic. It comprises a compact camera paired with a single-board computer for video acquisition, a low-power-microcontroller-based circuit to regulate video acquisition timing, and motion detection circuits. We also introduce a novel low-power method of gathering complementary information on animal activities using passive infrared sensors. Meticulously designed to withstand extreme cold, prolonged operation periods, and the limited energy provided by batteries, the system’s efficacy is demonstrated through laboratory tests and field trials in the Canadian Arctic. Notably, our system achieves a standby power consumption of approximately 60 µW, representing a seventy-fold reduction compared to previous equipment. The system recorded unique videos of animal life under the snow in the High Arctic. This system equips ecologists with enhanced capabilities to study subnivean life in the Arctic, potentially providing insights to address longstanding questions in ecology. Full article

In the current literature, there exists a lack of analysis regarding the coordination of the spinning reserve and time-shift characteristics of hydrogen storage systems (HSS) and flexible carbon capture systems (FCCS) in terms of low-carbon economic operation. They are presently used solely as a tool to capture carbon dioxide, without fully utilizing the advantages of their flexible operation. The coordination and complementarity of the FCCS and HSS can ensure stable power supply and improve renewable energy (RE) consumption. Combined with demand side response (DSR), these factors can maximize the RE consumption capacity, reduce carbon emissions, and improve revenue. In this paper, a source-storage-load flexible scheduling strategy is proposed by considering the complementary nature of FCCS and HSS in terms of rotating standby and time-shift characteristics. First, the operational mechanisms of FCCS, HSS, and demand side response (DSR) are analyzed, and their mathematical models are constructed to improve flexibility in grid operation and regulation. Next, deficiencies in FCCS and HSS operation under rotating reserve requirements are analyzed to design a coordinated operation framework for the FCCS and HSS. This operational framework aims to enable the complementarity of the rotating reserve and time-shift characteristics of FCCS and HSS. Finally, based on the carbon emission trading mechanism, a three-stage ladder carbon emission trading cost model is constructed, and a source-storage-load flexible scheduling strategy is established to achieve an effective balance between low carbon emissions and economic performance. The simulation results demonstrate that the strategy reduces the overall cost by 8.57%, reduces the carbon emissions by 35.33%, and improves the renewable energy consumption by 3.5% compared with the unoptimized scheme. Full article

Residential consumption represents one of the most important percentages of total electricity consumption. A considerable number of household appliances consume energy even when they are not in operation, i.e., they are in the so-called standby state, thus producing additional costs, which become significant over time. In this context, one method to solve this problem is to develop a smart system capable of severing the power connection to devices in standby mode, thereby conserving energy and reducing the energy costs. The first step in the design of this system consists of the identification and accurate measurement of the standby state, which was carried out for three of the most common household appliances. Then, by using an ESP32 microcontroller, a system was designed to manage the operation of a relay module based on the current consumption of the connected equipment. Control over the system was achieved through a web application that works across all devices equipped with a web browser, offering functionalities to adjust current value time delays and to manually switch the system on or off. Finally, the deployment of this system across the three appliances studied led to a reduction in the energy consumption in standby mode of 26.68 kWh per month. Full article

Electricity consumption in homes is on the rise due to the increasing prevalence of home appliances and longer hours spent indoors. Home energy management systems (HEMSs) are emerging as a solution to reduce electricity consumption and efficiently manage power usage at home. In the past, numerous studies have been conducted on the management of electricity production and consumption through solar power. However, there are limited human-centered studies focusing on the user’s lifestyle. In this study, we propose an Intelligent Home Energy Management System (i-HEMS) and evaluate its energy-saving effectiveness through a demonstration in a standard house in Korea. The system utilizes an IoT environment, PID sensing, and behavioral pattern algorithms. We developed algorithms based on power usage monitoring data of home appliances and human body detection. These algorithms are used as the primary scheduling algorithm and a secondary algorithm for backup purposes. We explored the deep connection between power usage, environmental sensor data, and input schedule data based on Long Short-Term Memory network (LSTM) and developed an occupancy prediction algorithm. We analyzed the use of common home appliances (TV, computer, water purifier, microwave, washing machine, etc.) in a standard house and the power consumption reduction by the i-HEMS system. Through a total of six days of empirical experiments, before implementing i-HEMS, home appliances consumed 13,062 Wh. With i-HEMS, the total consumption was reduced to 10,434 Wh (a 20% reduction), with 9060 Wh attributed to home appliances and 1374 Wh to i-HEMS operation. Full article

Appropriate daylight control could maximize occupants’ visual comfort, potentially saving energy. However, the deployment of daylight control systems (DLCSs) is not happening, mainly due to the complex system calibration and the frequent reluctance of occupants toward automatic control systems that exclude their participation. In this paper, a human-in-the-loop DLCS is presented. The system is designed to allow the users to have direct interaction via smartphone Bluetooth communication, enabling them to set the lighting values deemed most comfortable nimbly. Special attention has been paid to the power consumption of the DLCS, especially in standby mode. Accessibility of configuration has been taken into consideration, leading to the choice of a wireless configured device. The performance of the prototype DLCS was evaluated experimentally in a side-lit room and compared with that of a commercial controller. The illuminance on a reference work plane was measured during the operation of the systems to observe the controllers’ effect on the lamp’s luminous flux while simultaneously considering the variation of daylight conditions. Moreover, the energy performance of the systems was studied to obtain information about the energetic effectiveness and convenience of the studied DLCSs. The main results showed that the proposed system could maintain the required target illuminance values on the work plane as daylight conditions vary: the maximum deviation measured using the prototype never exceeded 11 lx. In comparison, the commercial controller reached peaks of 220 lx. Moreover, the energy consumption of the prototype (resulting equal to 370 mVA) was lower than the consumption of the commercial system (equal to 600 mVA), allowing for increased energy savings over the long period. The more straightforward configuration allows the user to better interact with the DLCS. Full article

The Industrial Revolution brought major technological progress and the growth of manufacturing, which resulted in significant changes in energy use. However, it also brought about new environmental issues such as increased energy needs, unstable electricity costs, and worsened greenhouse gas effects. Nowadays, it is crucial to analyze energy use to stay competitive. Manufacturers, highly dependent on electricity, can save energy and enhance efficiency by improving production methods. This article presents the findings of a research study conducted on a Lithuanian manufacturing company, aiming to investigate its electricity consumption over a 15-month period from 2022.01 to 2023.03—detailed data about the monthly consumption of the six most powerful machines and their active and standby hours are presented. The total electricity consumption of those matched 173.62 MWh. Employing the Decision Support Method for Dynamic Production Planning (DSM DPP), which was previously developed and refined, the study examines the potential for time savings and, subsequently, energy savings, through process reorganization. A detailed three-month production orders observation period demonstrates tangible time savings while using the proposed DSM DPP—time savings of approximately 5% can be achieved. Compared to that, production might achieve a 20% productivity increase with advanced technology implementation, so 5% is a great result for an easily adaptable method. Based on this, changes in energy consumption and CO₂ emissions due to electricity consumption are calculated and presented knowing that the company uses energy from the grid. Adaptation of the replanning method resulted in a reduction of electricity use by 175 kWh and a reduction of CO₂ consumption by 27 kgCO₂. With proper production planning, energy and CO₂ consumption can be decreased, which is a high priority in today’s world. Full article

Distributed smart home systems using wireless communication are increasingly installed and operated in households. Their popularity is due to the ease of installation and configuration. This paper presents a comprehensive technical, quality, and energy analysis of several popular smart home modules. Specifically, it focuses on verifying their power consumption levels, both in standby and active mode, to assess their impact on the energy efficiency of building installations. This is an important aspect in the context of their continuous operation, as well as in relation to the relatively lower power of loads popular in buildings, such as LED lighting. The author presents the results of measurements carried out for seven different smart home modules controlling seven different types of loads. The analysis of the results shows a significant share of home automation modules in the energy balance; in particular, the appearance of reactive power consumption due to the installation of smart home modules is noteworthy. Bearing in mind all the threads of the analysis and discussion of the results of measurement experiments, a short SWOT analysis is presented, with an indication of important issues in the context of further development of smart systems and the Internet of Things with wireless communication interfaces, dedicated to home and building applications. Full article