Minimum Power Consumption Research Articles

Advancing precision fermentation: Minimizing power demand of industrial scale bioreactors through mechanistic modelling

Minimizing power consumption in large-scale aerobic fermentation is essential for cost-effective operations. A mechanistic model of aerobic precision fermentation was developed integrating microbial growth parameters, thermodynamic data, and bioreactor properties. Results showed that agitation power dominated energy consumption at low oxygen transfer rates (OTR), shifting to aeration power (70 % of total) at high cell growth rates. In high OTRs, mixing time reduced to 60 s from an initial value of 211 s. Scale-up from 5 m³ to 100 m³ decreased total specific power by 88 %. Operating at elevated headspace pressure lowered agitation speed, reducing total power consumption at high OTR. Impeller to bioreactor diameter ratio impacted the required agitation speed without significantly altering total power demand. Experimental data in a 100 L case study indicated a 0.43 kW.m⁻³ average power requirement across a 96-hour fermentation period. Our model demonstrates effective strategies for minimization of power consumption in industrial-scale aerobic fermentations.

The system of automatic control of the substrate temperature in the installation for the production of film material spray pyrolysis method

A system of automatic control of the substrate temperature is proposed, which ensures the required accuracy of temperature maintenance and its minimum gradient over the surface when obtaining transparent conductive coatings by spray pyrolysis. During pyrolysis, aerosol droplets are transferred to a heated substrate; at the right temperature, they decompose, and then synthesize or melt on a hot substrate. The ideal temperature regime for evaporation of aerosol droplets will be the mode when a drop appears at the substrate immediately after evaporation of the solvent from it. Precise control of the substrate temperature and temperature gradient ensures this mode. The functional scheme of the proposed automatic control system consists of an automatic temperature controller, a power regulator, an infrared heater on halogen lamps, and a temperature sensor. The variational problem of minimizing power consumption when the infrared heater is turned on has been solved. This activation eliminates current emissions and thereby increases the service life of the heater. It is proposed to connect the heater to a current source in order to eliminate current emissions, thereby ensuring the optimal switching mode. The synthesis and analysis of the specified automatic control system has been performed, its time and frequency parameters have been calculated. Acceptable performance, zero static control error and zero overshoot, a significant margin of stability in phase and amplitude have been confirmed. The use of such an automatic temperature control system contributes to the creation of an optimal mode of evaporation of aerosol droplets, allows you to stabilize the substrate temperature and temperature gradient, thereby ensuring the stability of the characteristics of the resulting films. The investigated transparent conductive coatings are part of glasses with adjustable transparency, and are also used as layers of solar cells.

Optimization of handoff for joint uplink base station association and power control with max‐min fairness in NOMA small‐cell networks

SummaryThe handoff rate and load balancing are two important issues that have a great impact on the spectrum and energy efficiency in the small‐cell networks (SCN). This paper investigates the handoff optimization in SCN with power‐domain non‐orthogonal multiple access (NOMA) that uses successive interference cancelation (SIC), considering the fairness among base stations (BSs). We study the joint base station association and power control problem by considering the motion of mobile users (MUs) and load balancing in the SCNs. Under the maximum allowable transmit power and minimum average‐rate constraints, two optimization problems are formulated using the number of associated MUs, the number of handoffs, and the transmit power of all MUs. The total power consumption minimization and the system‐wide and handoff utility maximization problems are combined into a single‐stage optimization problem through the weighted‐sum method. We solve the formulated problem using a game theory‐based algorithm and primal decomposition theory. The simulation results show that our proposed algorithm can significantly reduce the frequent handoffs and bring a fair power‐controlled BS association in SCN.

Unleashing the potential of spiking neural networks for epileptic seizure detection: A comprehensive review

Epilepsy is a prevalent neurological condition characterized by repeated seizures resulting from structural or functional changes in the brain. Standard treatment approaches include anti-epileptic medications and surgical interventions. However, for patients with drug-resistant epilepsy, long-term EEG recordings are essential to monitor medication efficacy and make necessary adjustments to minimize seizure frequency. Continuous monitoring of such patients requires compact and comfortable wearable devices. This review explores the potential of Spiking Neural Networks (SNNs) for low-power signal processing in neuromorphic systems, specifically addressing the demand for compact and wearable devices. SNNs offer rapid computation, minimal power consumption, and superior data representation compared to traditional artificial neural networks (ANNs). With biological plausibility and event-driven hardware support, SNNs are well-suited for modeling neural activity in epilepsy. The review delves into existing literature, investigating the potential of SNNs in seizure detection. It covers various aspects, including the neuronal system, neuromorphic computing, SNN architecture, spiking neuron models, spike encoding schemes, and training methodologies. Representative studies utilizing spike-based machine learning for seizure detection are examined. Furthermore, the review addresses gaps, challenges, strengths, weaknesses, and trade-offs in SNN implementations. By spotlighting the capabilities of SNNs, this review aims to stimulate further research and innovative approaches, contributing to the advancement of epilepsy diagnosis and management.

DRL-Based URLLC-Constraint and Energy-Efficient Task Offloading for Internet of Health Things.

Internet of Health Things (IoHT) is a promising e-Health paradigm that involves offloading numerous computational-intensive and delay-sensitive tasks from locally limited IoHT points to edge servers (ESs) with abundant computational resources in close proximity. However, existing computation offloading techniques struggle to meet the burgeoning health demands in ultra-reliable and low-latency communication (URLLC), one of the 5G application scenarios. This article proposes a Multi-Agent Soft-Actor-Critic-discrete based URLLC-constrained task offloading and resource allocation (MASACDUA) scheme to maximize throughput while minimizing power consumption on the remote side, considering the long-term URLLC constraints. The URLLC constraint conditions are formulated using extreme value theory, and Lyapunov optimization is employed to divide the problem into task offloading and computation resource allocation. MASAC-discrete and a queue backlog-aware algorithm are utilized to approach task offloading and computation resource allocation, respectively. Extensive simulation results demonstrate that MASACDUA outperforms traditional DRL algorithms under different IoHT points and data arrival rate intervals and achieves superior performance in delay, bound violation probability, and other characteristics related to URLLC.

Design and optimization of a modular Electro-Thermal Ice Protection System for a Horizontal Axis Wind Turbine

In this work, an Electro-Thermal Ice Protection System (IPS) design was refined and optimized on the NREL 5 MW reference wind turbine. These improvements involved optimization of the distribution of heat flux in both the stream-wise and span-wise directions to minimize power consumption and avoid run-back ice formation. The design conditions focus on a 3-hour rime ice accretion event. The chosen optimization algorithm is a derivative-free method called Mesh Adaptive Direct Search. his optimization effort results in a 55% reduction in power consumption necessary to maintain a completely clean blade surface. The power and energy losses associated with this new optimized design are compared with those from various ice shapes and with the clean blade conditions established in the initial design phase from previous work. The new optimized design allows to keep the blade clean from ice with much larger energy saving. Indeed, only initially, during the first two hours of ice accretion, the energy losses due to this IPS design are higher than without IPS. Beyond this short initial period, substantial energy savings are achieved, especially considering that after the accretion event, the blade remains ice-free, and there are no more power losses.

Green grant-free power allocation for ultra-dense Internet of Things: A mean-field perspective

Grant-free access, in which each Internet-of-Things (IoT) device delivers its packets through a randomly selected resource without spending time on handshaking procedures, is a promising solution for supporting the massive connectivity required for IoT systems. In this paper, we explore grant-free access with multi-packet reception capabilities, with an emphasis on ultra-low-end IoT applications with small data sizes, sporadic activity, and energy usage constraints. We propose a power allocation scheme aimed at maximizing throughput while minimizing power consumption by considering the traffic and energy constraints of IoT devices. Our approach employs a stochastic geometry framework and mean-field game theory to model and analyze the mutual interference among active IoT devices. Additionally, we utilize a Markov chain model to capture and track the queue length of IoT devices, enabling the derivation of the transmission success probability at steady-state. The simulation results illustrate the optimal power allocation strategy and evaluate the proposed approach’s performance in terms of packet transmission success probability and average delay.

An Hybrid Framework OTFS-OFDM Based on Mobile Speed Estimation

The Future wireless communication systems face the challenging task of simultaneously providing highquality service (QoS) and broadband data transmission, while also minimizing power consumption, latency, and system complexity. Although Orthogonal Frequency Division Multiplexing (OFDM) has been widely adopted in 4G and 5G systems, it struggles to cope with a significant delay and Doppler spread in high mobility scenarios. To address these challenges, a novel waveform named Orthogonal Time Frequency Space (OTFS). Designers aim to outperform OFDM by closely aligning signals with the channel behaviour. In this paper, we propose a switching strategy that empowers operators to select the most appropriate waveform based on an estimated speed of the mobile user. This strategy enables the base station to dynamically choose the waveform that best suits the mobile user’s speed. Additionally, we suggest retaining an Integrated Sensing and Communication (ISAC) radar approach for accurate Doppler estimation. This provides precise information to facilitate the waveform selection procedure. By leveraging the switching strategy and harnessing the Doppler estimation capabilities of an ISAC radar.Our proposed approach aims to enhance the performance of wireless communication systems in high mobility cases. Considering the complexity of waveform processing, we introduce an optimized hybrid system that combines OTFS and OFDM, resulting in reduced complexity while still retaining performance benefits.This hybrid system presents a promising solution for improving the performance of wireless communication systems in higher mobility.The simulation results validate the effectiveness of our approach, demonstrating its potential advantages for future wireless communication systems. The effectiveness of the proposed approach is validated by simulation results as it will be illustrated.

Integrated photonic encoder for low power and high-speed image processing.

Modern lens designs are capable of resolving greater than 10 gigapixels, while advances in camera frame-rate and hyperspectral imaging have made data acquisition rates of Terapixel/second a real possibility. The main bottlenecks preventing such high data-rate systems are power consumption and data storage. In this work, we show that analog photonic encoders could address this challenge, enabling high-speed image compression using orders-of-magnitude lower power than digital electronics. Our approach relies on a silicon-photonics front-end to compress raw image data, foregoing energy-intensive image conditioning and reducing data storage requirements. The compression scheme uses a passive disordered photonic structure to perform kernel-type random projections of the raw image data with minimal power consumption and low latency. A back-end neural network can then reconstruct the original images with structural similarity exceeding 90%. This scheme has the potential to process data streams exceeding Terapixel/second using less than 100 fJ/pixel, providing a path to ultra-high-resolution data and image acquisition systems.

Design and Implementation of a Low-Cost Intelligent Unmanned Surface Vehicle

This article describes the design and construction journey of a self-developed unmanned surface vehicle (USV). In order to increase the accessibility and lower the barrier of entry we propose a low-cost (under EUR 1000) approach to the vessel construction with great adaptability and customizability. This design prioritizes minimal power consumption as a key objective. It focuses on elucidating the intricacies of both the design and assembly processes involved in creating an economical USV. Utilizing easily accessible components, the boat outlined in this study has been already participated in the 1st Aegean Ro-boat Race 2023 competition and is tailored for entry into similar robotic competitions. Its primary functionalities encompass autonomous sea navigation coupled with sophisticated collision avoidance capabilities. Finally, we studied reinforcement learning strategies for constructing a robust intelligent controller for the task of USV navigation under disturbances and we show some preliminary simulation results we have obtained.

Stereoscopic artificial compound eyes for spatiotemporal perception in three-dimensional space.

Arthropods' eyes are effective biological vision systems for object tracking and wide field of view because of their structural uniqueness; however, unlike mammalian eyes, they can hardly acquire the depth information of a static object because of their monocular cues. Therefore, most arthropods rely on motion parallax to track the object in three-dimensional (3D) space. Uniquely, the praying mantis (Mantodea) uses both compound structured eyes and a form of stereopsis and is capable of achieving object recognition in 3D space. Here, by mimicking the vision system of the praying mantis using stereoscopically coupled artificial compound eyes, we demonstrated spatiotemporal object sensing and tracking in 3D space with a wide field of view. Furthermore, to achieve a fast response with minimal latency, data storage/transportation, and power consumption, we processed the visual information at the edge of the system using a synaptic device and a federated split learning algorithm. The designed and fabricated stereoscopic artificial compound eye provides energy-efficient and accurate spatiotemporal object sensing and optical flow tracking. It exhibits a root mean square error of 0.3 centimeter, consuming only approximately 4 millijoules for sensing and tracking. These results are more than 400 times lower than conventional complementary metal-oxide semiconductor-based imaging systems. Our biomimetic imager shows the potential of integrating nature's unique design using hardware and software codesigned technology toward capabilities of edge computing and sensing.

A 22-mW 0.9-2-GHz mixer-first receiver 8-phase overlap-suppressed 1/4 duty cycle LO for harmonic rejection

This paper presents a mixer-first wideband receiver front-end designed to achieve high linearity and harmonic rejection while minimizing power consumption. The proposed RF front-end adopts LO overlap suppression technology and uses 25 % duty cycle LO for 8-phase operation. In addition, the passive mixer with implicit capacitive stacking provides passive gain to the useful signal and improves harmonic rejection. The total power consumption of this work is less than 22 mW, the majority of which is consumed by the baseband amplifier and buffer, making it superior to existing active harmonic suppression RF front-ends in terms of low power consumption. The receiver is designed in TSMC 65-nm technology with an active area of 0.44 mm2 and is suitable for various communication standards from 0.9 GHz to 2 GHz. Post-layout simulation results show that < −15 dB S11, 16.7-dBm OOB-IIP3@80 MHz, 3.7–6.7 dB DSB-NF, 46.4–49.5 dB total system voltage gain and HRR3 of 35.4–42 dB are achieved. No active components are required for harmonic recombination.

Day–Night architecture: Development of an ultra-low power RISC-V processor for wearable anomaly detection

In healthcare, anomaly detection has emerged as a central application. This study presents an ultra-low power processor tailored for wearable devices dedicated to anomaly detection. Introducing a unique Day–Night architecture, the processor is bifurcated into two distinct segments: The Day segment and the Night segment, both of which function autonomously. The Day segment, catering to generic wearable applications, is designed to remain largely inactive, awakening only for specific tasks. This approach leads to considerable power savings by incorporating the Main-CPU and system interconnect, both major power consumers. Conversely, the Night segment is dedicated to real-time anomaly detection using sensor data analytics. It comprises a Sub-CPU and a minimal set of IPs, operating continuously but with minimized power consumption. To further enhance this architecture, the paper presents an ultra-lightweight RISC-V core, All-Night core, specialized for anomaly detection applications, replacing the traditional Sub-CPU. To validate the Day–Night architecture, we developed a prototype processor and implemented it on an FPGA board. An anomaly detection application, optimized for this prototype, was also developed to showcase its functional prowess. Finally, when we synthesized the processor prototype using 45 nm process technology, it affirmed our assertion of achieving an energy reduction of up to 57%.

Development of a high-performance arithmetic and logic unit for efficient digital signal processing based on reversible logic and quantum dots

Digital Signal Processing (DSP) finds a wide range of applications in various fields, including telecommunications, audio and video processing, biomedical engineering, radar systems, and image processing. Previous DSP designs faced limitations in available processing power and computational resources. Insufficient processing power could result in slower execution times, an inability to handle complex algorithms, or limited capacity to process high-speed or large-scale signals. As the demand for minimal power consumption in DSP circuits continues to grow, reversible logic and quantum-dot cellular automata (QCA) have emerged as promising technologies due to their inherent ability to reduce energy loss. Within this landscape, the arithmetic and logic unit (ALU) plays a vital role in complex circuitry, serving as a key component in digital signal processing applications. However, challenges persist, including high quantum cost and the need to limit the number of cells in the ALU design. To address these challenges, our research aims to develop an efficient ALU by integrating reversible logic and QCA technology. Our focus will be on generating essential components, such as Feynman gates, Fredkin gates, and full adder circuits, which serve as foundational building blocks for reversible logic and QCA designs. These components will be combined to construct a comprehensive ALU capable of performing 20 different operations. Our implementation efforts will be centered around QCADesigner, with a specific emphasis on digital signal processing systems that prioritize energy efficiency and optimal utilization of occupied areas.

ASIC Design and Implementation of Automated Coffee and Tea Brewing System with FPGA Validation

Abstract: This paper presents a pioneering approach to enhance the efficiency of automated coffee and tea brewing systems through the integration of an ASIC-GDSII and FPGA validation. Leveraging the power of 45nm CMOS Technology, the design achieves substantial reductions in area, power consumption, and delay. The Verilog HDL code undergoes meticulous verification using Cadence tools like Genus and Innovus, ensuring optimal design performance. Innovus validates timing constraints, ensuring adherence to acceptable delay parameters. The resulting compact design not only minimizes power consumption but also effectively addresses leakage issues. These comprehensive optimizations meet stringent performance, area, and power requirements, elevating the operational efficiency of automated brewing systems. Furthermore, the control algorithm is synthesized and implemented using Xilinx's ISE Design Suite, followed by validation on SPARTAN 6 FPGA, providing invaluable insights for future synthesis and implementation endeavors.

Potential of solar and wind-based green hydrogen production frameworks in African countries

Africa possesses a profusion of renewable energy resources that can address the continent's need for electricity to foster economic development and achieve international objectives for carbon dioxide mitigation. Green hydrogen stands out as promising among the plethora of available energy production, transportation, and storage technologies. The current study offers a general outlook on green hydrogen production utilizing solar and wind energies powering the water electrolyzers. This analysis estimates the electricity production and its production cost, hydrogen production and production cost, carbon dioxide mitigation, and estimation of the levelized cost of hydrogen (LCOH) by 2030 in each African country. The results reveal that hydrogen production from solar energy outweighs that from wind. The minimum and maximum power consumption are 64.26–65.46 kWh/kgH2 for the PV/H2 system and 53.62–64.8 kWh/kgH2 for the WT/H2 system. Moreover, the LCOH ranges from 4.6 to 7.31 $/kg for the PV/H2 system and 5.6 to more than 20 $/kg for the WT/H2 system. Namibia and Egypt have significant LCOH of the PV/H2 system with 4.6 and 4.64 $/kg, while Cape Verde and Djibouti have remarkable LCOH of 5.6 and 6.37 $/kg of the WT/H2 system. The percentage reduction in LCOH is 25.23% for the WT/H2 system and 37% for the PV/H2 system due to adopting the 2030 cost scenario. Moreover, adsorption-based atmospheric water is considered a suitable solution for water supply in arid regions. Finally, the CO2 mitigation ranges from 109.8 to 177.7 kg/m2 for the PV/H2 and 0.1–50.4 kg/m2 for the WT/H2.

The GeSe/SnSe heterojunction photodetector with self-powered characteristics and high infrared response performance

We present a GeSe/SnSe van der Waals heterojunction fabricated using the wet transfer technique. GeSe and SnSe were synthesized via a low-temperature and atmospheric-pressure chemical vapor deposition method. The GeSe/SnSe heterostructure photodetector demonstrates remarkable rectification characteristics, boasting a rectification ratio of 102, along with an exceptionally low dark current, indicating minimal power consumption. Furthermore, it exhibits a broad optical response, spanning from the visible spectrum (450 nm) to the near-infrared (1064 nm). Under 808 nm laser illumination and reverse bias, the device achieves a responsivity of 19.82 A/W, a detectivity of 4.74 × 109 Jones, and an external quantum efficiency of 3048.32%. Notably, the GeSe/SnSe heterojunction photodetector also exhibits self-powered characteristics, with a responsivity of 0.11 mA/W and a detectivity of 5.44 × 106 Jones at zero bias voltage, accompanied by a fast response time of 23/61 ms (rise/fall). These findings underscore the effectiveness of the GeSe/SnSe heterojunction as a strategy for near-infrared photodetectors to simultaneously achieve low power consumption, high photoresponsivity, and self-powered photodetection, which is promising for optoelectronic device applications.

Mathematical modeling of minimization of electricity consumption by industrial enterprises with continuous production

Relevance. Determined by the importance of minimizing electrical power consumption in industrial enterprises with continuous production, considering the specific characteristics of their technological processes and the requirements to maintain the output volume of their products. Aim. To solve the task of minimizing electrical power consumption based on a mathematical model and gradient method under optimal planning of the production volume of the industrial enterprises with continuous production; to develop a mathematical model for optimal distribution of production over a time cycle (month, quarter, year) across departments, taking into account both simple and functional constraints, derived from the condition of ensuring minimal electrical power consumption in industrial enterprises with continuous production. Methods. When developing the mathematical model for ensuring minimal electrical power consumption while preserving the production volume, classic Lagrange optimization methods were used. To ensure sufficient calculation accuracy, iterative methods were also applied. For the task under consideration, a calculation error margin of ε=0,1 was assumed and established. It is known that the choice of calculation error margin depends on the specifics of the problem at hand and the decision-maker. To verify the adequacy of the developed model, the method of finding the relative extremum of a function of several variables was used. Results. The use of the mathematical model, which takes into account the nature of the technological process and boundary conditions in both simple and integral forms, demonstrated the feasibility of optimal planning of electrical power consumption by the enterprise. The effectiveness of the developed approaches was verified using a metallurgical enterprise as an example of an industrial enterprise with continuous production, in solving the task of minimizing electrical power consumption for products produced during the reporting period. The use of the proposed model allowed for a reduction in annual electrical power consumption by 2.5% while maintaining the same production volume. One of the classic optimization methods – the method of finding the relative extremum of functions of several variables – showed almost identical results upon verification. This serves as further evidence of the adequacy of the proposed model.

Power hardware-in-loop emulation of a battery for charging systems and grid applications

Relevance. Batteries are playing an increasingly vital role in power systems due to their utilization in various applications including microgrids, electric vehicles, sustaining geographically isolated communities, and energization of automated devices. Since they are considered as the enabling technology for renewable energy integration, the absence of battery systems from islanded microgrids can result in decreased system reliability and compromised performance due to the intermittency of local sources. Nevertheless, the hazardousness associated with their charging mechanism has led to the urgent continuous development of charging technologies and battery management systems. Aim. To develop a safe testbed to examine the functionality of newly produced battery charging stations and battery managers without employing actual physical batteries to avoid the hazardous manipulation of batteries and increase flexibility during the design and validation stage. This is accomplished by modeling the electrochemical dynamics of the battery system and integrating the device-under-test to a DC converter, which reacts based on these modeled dynamics. Novelty. This work adapts one of the most successful Li-ion battery models available in the literature and utilizes it to interact with power electronic devices that exchange power signals. Unlike other work in this field, the design is based on power hardware-in-loop principles and has minimized power consumption characteristics due to its unique configuration. The constructed computer model can be easily reparametrized to describe the dynamics of various battery capacities. Methods. MATLAB-based simulations of the proposed testbed were conducted for high and low power capacity. A LabView-based program was interfaced with the testbed hardware using a NI-DAQ board to validate the proposed design practically. The testbed hardware components were entirely developed from scratch for experimentation purposes. Results. The proposed testbed successfully imitated the dynamics of the battery, while the practical results concurred the simulated ones.

Ultra-Low Power 9T FinFET Based SRAM Cell for IoT Applications

The rapid proliferation of Internet of Things (IoT) devices has escalated the demand for energy-efficient memory solutions. The development of SRAM (Static Random-Access Memory) has gathered significant attention, particularly for low-power applications and portable devices. This research introduces a novel approach to ensure robust performance and operational integrity, all while adhering to stringent power constraints amidst process and temperature fluctuations. This quest for alternative nano-devices has been ignited by the scaling and channel control challenges intrinsic to CMOS technology. Among the array of alternatives, including FinFETs, TFETs, and CNTFETs, FinFETs have emerged as strong contenders. FinFET SRAMs contribute to the ongoing drive for electronic devices to become more compact and energy-efficient. They offer a distinct advantage as a promising CMOS substitute, attributed to their high performance, stability, with low power consumption characteristics at the nanoscale. The study proposes an ultra-low power 9T FinFET based SRAM cell meticulously designed for IoT applications. Leveraging the unique attributes of FinFET based 9T cell, the proposed SRAM cell is tailored to achieving minimal power consumption while maintaining inherent stability. The study assesses the performance of the proposed SRAM cells for power dissipation, stability, speed, and energy efficiency, and compares them with existing SRAM structures. The results are obtained through the Monte Carlo simulation technique, which evaluates circuit performance under specified conditions. The study achieves remarkable efficiency gains with 9T SRAM cells compared to other configurations. Additionally, the reduced read and write access times facilitate expedited data retrieval and storage operations.