Power Consumption Of System Research Articles

A Single-Board Integrated Millimeter-Wave Asymmetric Full-Digital Beamforming Array for B5G/6G Applications

In this article, a single-board integrated millimeter-wave (mm-Wave) asymmetric full-digital beamforming (AFDBF) array is developed for beyond-fifth-generation (B5G) and sixth-generation (6G) communications. The proposed integrated array effectively addresses the challenge of arranging a large number of ports in a full-digital array by designing vertical connections in a three-dimensional space and successfully integrating full-digital transmitting (Tx) and receiving (Rx) arrays independently in a single board. Unlike the traditional symmetric array, the proposed asymmetric array is composed of an 8 × 8 Tx array arranged in a square shape and an 8 + 8 Rx array arranged in an L shape. The center-to-center distance between two adjacent elements is 0.54λ0 for both the Tx and Rx arrays, where λ0 is the free-space wavelength at 27 GHz. The proposed AFDBF array possesses a more compact structure and lower system hardware cost and power consumption compared with conventional brick-type full-digital arrays. In addition, the energy efficiency of the proposed AFDBF array outperforms that of a hybrid beamforming array. The measurement results indicate that the operating frequency band of the proposed array is 24.25–29.50 GHz. An eight-element linear array within the Tx array can achieve a scanning angle ranging from −47° to +47° in both the azimuth and the elevation planes, and the measured scanning range of each eight-element Rx array is –45° to +45°. The measured maximum effective isotropic radiated power (EIRP) of the eight-element Tx array is 43.2 dBm at 28.0 GHz (considering the saturation point). Furthermore, the measured error vector magnitude (EVM) is less than 3% when 64-quadrature amplitude modulation (QAM) waveforms are used.

Turnkey locking of quantum-dot lasers directly grown on Si

Ultralow-noise laser sources are crucial for a variety of applications, including microwave synthesizers, optical gyroscopes and the manipulation of quantum systems. Silicon photonics has emerged as a promising solution for high-coherence applications due to its ability to reduce the system size, weight, power consumption and cost. Semiconductor lasers based on self-injection locking have achieved fibre laser coherence, but typically require a high-quality-factor external cavity to suppress coherence collapse through frequency-selective feedback. Lasers based on external-cavity locking are a low-cost and turnkey operation option, but their coherence is generally inferior to self-injection locking lasers. In this work, we demonstrate quantum-dot lasers grown directly on Si that achieve self-injection-locking laser coherence under turnkey external-cavity locking. The high-performance quantum-dot laser offers a scalable and low-cost heteroepitaxial integration platform. Moreover, the chaos-free nature of the quantum-dot laser enables a 16 Hz Lorentzian linewidth under external-cavity locking using a low-quality-factor external cavity, and improves the frequency noise by an additional order of magnitude compared with conventional quantum-well lasers.

Performance analysis and comparison of a novel Steiner-Quadrature Space Shift Keying (S-QSSK) scheme in massive MIMO systems

This paper introduces Steiner-quadrature space shift keying (QSSK) (S-QSSK), an innovative modulation scheme tailored for multiple–input multiple–output (MIMO) systems. The study compares S-QSSK with existing modulation schemes (S-space shift keying (SSK)(S-SSK), QSSK, generalized quadrature space shift keying (GQSSK)) across various performance metrics, considering diverse channel conditions, transmit antenna numbers, and spectral efficiencies. Theoretical average bit error rate (ABER) analysis, validated by Monte Carlo simulations, establishes a close agreement between analytical and practical results, especially at realistic signal-to-noise-ratio (SNR) values. Additionally, capacity and mutual information formulas shed light on the system’s information transmission capabilities. The research delves into the outage probability, power consumption, and computational complexity of S-QSSK MIMO systems. It unveils a promising trade-off, demonstrating that S-QSSK achieves competitive performance in ABER, capacity, and mutual information, positioning it as a practical modulation technique. Importantly, S-QSSK exhibits about a 1 dB ABER performance decrease compared to S-SSK and QSSK, while offering a noteworthy 2 dB improvement over GQSSK. Despite requiring slightly more transmit antennas than GQSSK, S-QSSK demands significantly fewer than both S-SSK and traditional QSSK, particularly when targeting high spectral efficiencies. Moreover, the analysis underscores the advantageous power consumption and favorable computational complexity of S-QSSK relative to other modulation schemes. The proposed S-QSSK MIMO system thus presents itself as an efficient solution, enabling high data rates with fewer transmit antennas, resulting in substantial reductions in power consumption and computational complexity without a significant sacrifice in ABER and mutual information. These attributes make it a compelling choice for various applications, including those requiring efficient massive multiple–input multiple–output (mMIMO) operations.

Data Validation Algorithm Based on Vector Rank Analysis

In the process of solving the problem of managing large technical systems, the data obtained from various measuring devices are processed by known methods. On the basis of their analysis, acceptable solutions are formed, and as a result of the choice, the best is made. Some of the data are parametrized and are stochastic, i.e. they are random variables. However, the information for making management decisions must be strictly deterministic. Therefore, the main task of stochastic data processing is to obtain deterministic invariants suitable for use as information in the decision-making process. The article presents an algorithm for verifying data that allows you to determine which type they belong to: Gaussian or non-Gaussian. The results of this test will make it possible to make the right choice of mathematical apparatus for obtaining deterministic invariants. The scientific novelty of the algorithm lies in the fact that the mathematical apparatus of the algorithm is developed within the framework of vector rank analysis. Its essence lies in the fact that a sample is made from the "general population" of available data, on which the average and standard are determined. Then a part of the data taken from the "general population" is added to this sample, and the average and standard are determined again. Such a procedure for increasing the sample continues until the "general population" is completely exhausted. Next, the normalized dependence of the mean and standard values on the sample size is constructed. At the same time, if the dependence has a pronounced tendency to stabilize, then the data belong to the Gaussian type. In another case, they are considered non-Gaussian. The efficiency of the algorithm has been confirmed in the framework of studies of a significant number of samples of data on the power consumption of various large technical systems.

LEACH Protocol to Improve Energy Efficiency of Wireless Sensor Networks in Smart Agriculture

Smart agriculture is the application of technology to improve efficiency, productivity, and sustainability in agricultural practices. However, smart agriculture systems face major challenges related to connectivity and energy management. To address connectivity issues, the Wireless Sensor Network (WSN) architecture is utilized, consisting of sensor nodes to collect and transmit sensor data wirelessly. Despite the implementation of WSN, there are still issues related to high power consumption in smart agriculture systems. This can lead to reduced battery life for each sensor node in the WSN architecture. Therefore, increasing energy efficiency is crucial to optimizing the performance of smart agriculture systems. This study proposes the use of the LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol in smart agriculture to manage clusters within the WSN and reduce energy consumption in each sensor node. Experimental methods were conducted by building the WSN using the nRF24L01 as the sensor data transmitter and Arduino / Node MCU as the microcontroller. The use of the LEACH protocol aims to address energy issues. Additionally, data from each sensor is collected using the Message Queuing Telemetry Transport (MQTT) protocol to facilitate monitoring of sensor data transmission and battery power information. Test results show that the integration of the LEACH protocol into the WSN can be carried out at each stage, from Discovery-State to Steady-State, to Setup-State. These steps are aimed at significantly reducing energy consumption in sensor nodes by 13% over a 12-hour testing period. Furthermore, it can extend battery life and improve the overall system efficiency.

LoRa Based Emergency Communication System

The location and human activity are usually used as one of the important parameters to monitor the health status in healthcare devices. However, nearly all existing location and monitoring systems have the limitation of short-range communication and high power consumption. In this project, we propose a new mechanism to collect and transmit monitoring information based on LoRa technology. The monitoring device with sensors can collect the real-time activity and location information and transmit them to the cloud server through IOT. The user can check all his history and current information through the specific designed mobile applications. Experiment was carried out to verify the communication, power consumption and monitoring performance of the entire system. This system can collect monitoring and activity information accurately and provide the long rang coverage with low power consumption. Index Terms—Lora, Forest, temperature, humidity, soil moisture

Compact polarization transceiver module for fiber optic gyroscopes

This paper reports a transceiver module with a high polarization extinction ratio (PER) for the polarization-maintaining interferometric fiber optic gyroscope (PM-IFOG). This transceiver utilizes a graded-index lens featuring a slanted face coated with a multilayer film, enabling simultaneous collimation and beam splitting. Configurations of the graded-index splitter lens and the corresponding transceiver module are simulated and designed using the ray matrix method and physical optical propagation. The optical loss of the transceiver module is about 3.77 dB, and the PER exceeds 25 dB. The IFOG prototype is implemented by integrating the transceiver module with a PM sensing coil. The bias instability of the IFOG prototype is evaluated to be 0.51°/h by Allan variance. This research contributes to the reduction in size, weight, power consumption, and cost of PM-IFOG systems.

Optical to terahertz frequency conversion using near zero-index nonlinear hybrid waveguides

Terahertz communications has emerged as an excellent candidate for the next generation of wireless communication networks. Despite of significant advances, one still looks for a high-efficiency terahertz source using nonlinear optics, such as optical refraction and difference-frequency generation. Concurrently, recent years have witnessed significant progress in metamaterials-assisted nonlinear optical processes. In this study, we propose a theoretical approach using near zero-index nonlinear hybrid waveguides to realize a high generation efficiency of terahertz waves via on-chip difference-frequency generation. Remarkably, thanks to the simultaneous realization of near zero-index both at terahertz and telecom frequencies, such a nonlinear hybrid waveguide can overcome two major bottlenecks in the conversion from optical wave to terahertz waves: phase mismatch and weak nonlinear optical-terahertz waves interactions, resulting in a high conversion as 2.2 × 10−3W−1 just within the propagation length of several terahertz wavelengths. Our results demonstrate the feasibility of an on-chip terahertz source, offering unique properties such as high integration and ultra-low power consumption for terahertz communication systems.

Toward Intelligent Display with Neuromorphic Technology.

In the era of the Internet and the Internet of Things, display technology has evolved significantly toward full-scene display and realistic display. Incorporating "intelligence" into displays is a crucial technical approach to meet the demands of this development. Traditional display technology relies on distributed hardware systems to achieve intelligent displays but encounters challenges stemming from the physical separation of sensing, processing, and light-emitting modules. The high energy consumption and data transformation delays limited the development of intelligence display, breaking the physical separation is crucial to overcoming the bottlenecks of intelligence display technology. Inspired by the biological neural system, neuromorphic technology with all-in-one features is widely employed across various fields. It proves effective in reducing system power consumption, facilitating frequent data transformation, and enabling cross-scene integration. Neuromorphic technology shows great potential to overcome display technology bottlenecks, realizing the full-scene display and realistic display with high efficiency and low power consumption. This review offers a comprehensive summary of recent advancements in the application of neuromorphic technology in displays, with a focus on interoperability. This work delves into its state-of-the-art designs and potential future developments aimed at revolutionizing display technology.

Optimized Dual-Layer Distributed Energy Storage Configuration for Voltage Over-Limit Zoning Governance in Distribution Networks

In this study, an optimized dual-layer configuration model is proposed to address voltages that exceed their limits following substantial integration of photovoltaic systems into distribution networks. Initially, the model involved segmenting the distribution network’s voltage zones based on distributed photovoltaic governance resources, thereby elucidating the characteristics and governance requisites for voltages across distinct regions. Subsequently, a governance model for voltage limit exceedances, grounded in optimizing energy storage configurations, was formulated to mitigate photovoltaic power fluctuations by deploying energy storage systems. This model coordinates the reactive power output of photovoltaic installations with the active power consumption of energy storage systems, thereby augmenting voltage autonomy in the power grid. This study leveraged Karush–Kuhn–Tucker (KKT) conditions and the Big-M method to transform the dual-layer model into a single-layer linear model, thereby enhancing solution efficiency and precision. Finally, a simulation was carried out to demonstrate that the strategy proposed from this research not only achieves commendable economic efficiency, but also significantly improves the regional voltage effect by 28.7% compared to the optical storage capacity optimization model.

Wind Aggregation Enhanced Triboelectric-Electromagnetic Hybrid Generator with Slit Effect.

It is of great significance to establish a low-cost, high-efficiency, self-powered micrometeorological monitoring system for agriculture, animal husbandry, and transportation. However, each additional detection element in the meteorological monitoring system increases the power consumption of the whole system by about 0.7 W. As a renewable energy technology, a triboelectric nanogenerator has the advantages of low price and self-powered sensing. To reduce the power consumption of the micrometeorological monitoring system, this work introduces an innovative solution: the wind-gathering enhanced triboelectric-electromagnetic hybrid generator (WGE-TEHG). Coupling the thin-film vibrating triboelectric nanogenerator (TENG) and electromagnetic generator (EMG), the TENG is used to monitor wind direction and the EMG is used to monitor wind speed and provide energy needed by the system. In particular, the TENG can be used as a self-powered sensor to reduce the power consumption of the sensing system. Besides, the TENG is used to produce slit effect to enhance the output performance of EMG. The experimental results show that the WGE-TEHG can build a self-powered natural environment micrometeorological sensing system. It can monitor the wind direction, wind speed, temperature, and relative humidity. This research has great application value for the self-powered sensing implementation of a hybrid TENG and EMG.

Optimized register level transfer mechanism for ASIC-based memscomputing

Micro-electromechanical system-based semiconductor sensor systems typically need a reliable on-chip analog readout circuit to identify and process changes in physical properties. To improve the mobility, robustness, reliability and performance (in terms of power consumption and speed) of the sensor system, the remaining parts of the post-processing logic, such as the digital data processor (or part of it), can also integrated into the chip. Cost and integration are always important considerations in both analog and digital designs, but they become even more important; the subsystem of data processing must be squeezed into a very small space near the structure of the micro-electromechanical system (MEMS) sensor. The paper introduces a new Buffalo-based register-transfer level (BRTL) method that aims to improve the efficiency and reliability of the system of digital data processing for MEMS sensor post-processing algorithm. Initially, the memcomputing system was designed with the needed functional processing elements. Then, a novel Buffalo-based register-transfer level design is modelled in the MEMS architecture. The digital data transmission process is performed to estimate the reliability of the proposed BRTL model. Finally, the performance of the proposed model can be validated.

Liquid Crystal Materials for Electronically Reconfigurable Antennas: Overview Toward 5G Applications

This paper discusses the use of Liquid Crystal (LC) technology for Electronically Reconfigurable Antennas (ERAs) that are essential for future-based 5G communication systems and satellite platforms. This comprehensive study covers the development of liquid crystal performance metrics and the deployment of LC technology for RF front-end antenna systems, ranging from GHz to THz frequency bands. Here, low profile LC varactor with phase shifter-based antenna systems is investigated for beam steering, enabling to scan a wide range with minimum scan time of operation. The LC phase shifter-based antenna has shown an insertion loss of ≪3[Formula: see text]dB for 5G frequency bands and a high figure-of-merit over a bandwidth of 2.5[Formula: see text]GHz with minimum time response of 30[Formula: see text]ms. These LC-based ERAs can feature wide beam scanning angles with high power-handling capability, better efficiency, high linearity with low power consumption for satellite ground terminals and 5G communication systems.

Power Consumption Prediction in Real-Time Multitasking Systems

The paper presents a practical methodology for the prediction of power consumption of a multitask real-time system. The authors briefly recall the structure of the original multitasking time-predictable system. The system has a regular and scalable architecture. It consists of many cores based on pipeline processing with threads’ interleaving mechanism. In previous works, the authors have presented a number of issues related to the methodology of designing a time-predictable system and the scheduling of hardware threads with different design goals and constraints. The current article presents an accurate method for measuring the dynamic power dissipated in a system for various scenarios and the system configuration. The proposed approach is based on continuous monitoring of the power consumed by the FPGA, transmitting the measurements to the computer from the measuring device, and online analysis of the obtained results. The authors based their power estimation method on the original task frequency factor (TF). A theoretical analysis was made, and a series of practical experiments were carried out. The results obtained enable accurate estimation of the power requirements for the tasks performed in the real-time system. The presented technique can be useful during the design stage of multitasking real-time systems.

Research on the design and balancing strategy with multi-section lithium battery protection IC

This study uses 0.5um BCD technology to design a multi section lithium battery protection chip suitable for small mobile electronic devices such as Bluetooth earphones and smart bracelets. By providing a complete system structure diagram, the principles and design methods of several key circuits in the chip were introduced. This chip can effectively monitor the voltage of lithium batteries, take protective measures against abnormal situations such as overcharging, discharging, and overcurrent, and has a balancing module with dynamic power consumption management function. The balancing module adopts a passive balancing architecture, and the balancing algorithm used is an average based voltage detection algorithm. Using this algorithm can make the balancing system structure simple and reduce system power consumption, reduce the differences between batteries within the group, and thus improve the uniformity of the battery pack. The overcharging detection accuracy of the designed chip is ±25mV, the overcharging detection accuracy is 50mV, and the static working current of the chip is 7uA.

Improving efficiency of vapour phase soldering ovens with pressure and temperature-based process monitoring

During vapour phase reflow soldering used in electronics manufacturing, the process control of ovens is usually based on temperature measurements, which can be insufficient due to the saturation differences in the temperature and the vapour concentration. Therefore, the ovens use extended cycle times for process identification to ensure the proper formation of solder joints. This study offers a solution to eliminate the idle time by investigating the hydrostatic pressure conditions in the process zone. A pressure measurement system was developed for vapour phase soldering ovens which can monitor the vapour pressure changes by a gauge-type pressure sensor with fused temperature measurement. It works as an IoT-enabled node for data logging and subsequent control. The fusion of the hydrostatic pressure values with the temperature values allowed us to identify and reduce the idle time by approximately 60 s, which is 15–20% of the total soldering cycle time. This increases the oven's yield by 15–20%, reduces the system's power consumption by 15–20%, and reduces time above liquidus of the solder by ∼35%. The presented method could enhance the more sustainable electronics production according to the Sustainability Development and could improve the quality and reliability of the lead-free solder joints.

Real-time optimization of the liquid-cooled data center based on cold plates under different ambient temperatures and thermal loads

With the advent of the information age, the scale of data centers has developed unprecedentedly, in which the cooling system consumes a lot of energy. Therefore, it is crucial to adjust the operating parameters in real-time to maximize energy savings. In this article, the mathematical models of each component are established, and the functions of system power consumption and chip temperatures are obtained for the liquid-cooled data center based on cold plates. The method proposed in this article has good accuracy through experimental verification and can be used for the optimization of operating parameters. The server chips in data centers need to be maintained within a safe range, so we take the minimum system power consumption as the optimization goal and the chip temperature as the constraint condition to calculate the optimal cooling tower wind volume, primary side flow rate, and secondary side flow rate under different environmental temperatures and heat loads, and fit all three for real-time optimization and regulation. The results show that the intelligent control proposed in this article can save 42.7% energy and reduce PUE to 1.16 under variable heat load, and save 30.6% energy and improve PUE by 4.3% under variable environmental temperature. The intelligent control method described in this article provides guidance for real-time optimization in data centers.

Plasmonic electro-optic modulators on lead zirconate titanate platform

Abstract The advancement in material platforms exhibiting strong and robust electro-optic effects is crucial for further progress in developing highly efficient and miniaturized optoelectronic components with low power consumption for modern optical communication systems. In this work, we investigate thin-film lead zirconate titanate (PZT) substrates grown by a chemical solution deposition technique as a potential platform for on-chip plasmonic electro-optic modulators. A high modulation depth (>40 %) is achieved with 15 μm-long electro-optic directional coupler modulators. An unusual cutoff in the modulation frequency response at ∼200 kHz is observed and further studied with respect to possible reorientation effects. Second-harmonic generation signals are found influenced by the externally applied electric field, indicating that the domain reorientation effect can be responsible for the unusual frequency response observed.

Fundamental limits of dynamic phase change materials

To accommodate societal electrification and decarbonization, renewable energy resources continue to expand their share of the global energy market. The intermittency of renewable energy technologies as well as the high power density of modern electrified platforms necessitates the need for both efficient thermal management and high-density thermal storage. Phase change materials are a promising passive thermal energy storage solution. However, difficulties with efficient system implementation stemming from the inherent melt pool formation hinder their potential. We develop an innovative strategy, termed dynamic phase change material “dynPCM,” to address this thermal transport issue by ensuring a constant, thin, melt layer. We analyze the fundamental limits of dynPCMs, characterize the peak achievable heat flux and energy/power densities, estimate the power consumption of dynPCM systems, and investigate the fundamental physics which govern dynPCM behavior. We show that dynPCM can eliminate the classical trade-off seen between energy density and power density and achieve ultrahigh heat fluxes, ∼105 W/cm2, with heat flux-to-required power ratios as high as ∼107. We also demonstrate achievable power densities as high as ∼100 W/cm3 at energy densities as high as ∼10 kJ/cm3. Throughout this work, we develop a methodology to evaluate the operating limits, enabling adaptation of the dynPCM system concept to a variety of applications and industries.

In-sensor human gait analysis with machine learning in a wearable microfabricated accelerometer

In-sensor computing could become a fundamentally new approach to the deployment of machine learning in small devices that must operate securely with limited energy resources, such as wearable medical devices and devices for the Internet of Things. Progress in this field has been slowed by the difficulty to find appropriate computing devices that operate using physical degrees of freedom that can be coupled directly to degrees of freedom that perform sensing. Here we leverage reservoir computing as a natural framework to do machine learning with the degrees of freedom of a physical system, to show that a micro-electromechanical system can implement computing and the sensing of accelerations by coupling the displacement of suspended microstructures. We present a complete wearable system that can be attached to the foot to identify the gait patterns of human subjects in real-time. The computing efficiency and the power consumption of this in-sensor computing system is then compared to a conventional system with a separate sensor and digital computer. For similar computing capabilities, a much better power efficiency can be expected for the highly-integrated in-sensor computing devices, thus providing a path for the ubiquitous deployment of machine learning in edge computing devices.