Power Requirements Research Articles

Enhancing autonomous vehicle navigation using SVM-based multi-target detection with photonic radar in complex traffic scenarios

Efficient transportation systems are essential for the development of smart cities. Autonomous vehicles and Intelligent Transportation Systems (ITS) are crucial components of such systems, contributing to safe, reliable, and sustainable transportation. They can reduce traffic congestion, improve traffic flow, and enhance road safety, thereby making urban transportation more efficient and environmentally friendly. We present an innovative combination of photonic radar technology and Support Vector Machine classification, aimed at improving multi-target detection in complex traffic scenarios. Central to our approach is the Frequency-Modulated Continuous-Wave photonic radar, augmented with spatial multiplexing, enabling the identification of multiple targets in various environmental conditions, including challenging weather. Notably, our system achieves an impressive range resolution of 7 cm, even under adverse weather conditions, utilizing an operating bandwidth of 4 GHz. This feature is particularly crucial for precise detection and classification in dynamic traffic environments. The radar system's low power requirement and compact design enhance its suitability for deployment in autonomous vehicles. Through comprehensive numerical simulations, our system demonstrated its capability to accurately detect targets at varying distances and movement states, achieving classification accuracies of 75% for stationary and 33% for moving targets. This research substantially contributes to ITS by offering a sophisticated solution for obstacle detection and classification, thereby improving the safety and efficiency of autonomous vehicles navigating through urban environments.

Electrohydrodynamic-Jet-Printed SnO2-TiO2-Composite-Based Microelectromechanical Systems Sensor with Enhanced Ethanol Detection.

Ethanol sensors have found extensive applications across various industries, including the chemical, environmental, transportation, and healthcare sectors. With increasing demands for enhanced performance and reduced energy consumption, there is a growing need for developing new ethanol sensors. Micro-electromechanical system (MEMS) devices offer promising prospects in gas sensor applications due to their compact size, low power requirements, and seamless integration capabilities. In this study, SnO2-TiO2 nanocomposites with varying molar ratios of SnO2 and TiO2 were synthesized via ball milling and then printed on MEMS chips for ethanol sensing using electrohydrodynamic (EHD) printing. The study indicates that the two metal oxides dispersed evenly, resulting in a well-formed gas-sensitive film. The SnO2-TiO2 composite exhibits the best performance at a molar ratio of 1:1, with a response value of 25.6 to 50 ppm ethanol at 288 °C. This value is 7.2 times and 1.8 times higher than that of single SnO2 and TiO2 gas sensors, respectively. The enhanced gas sensitivity can be attributed to the increased surface reactive oxygen species and optimized material resistance resulting from the chemical and electronic effects of the composite.

Design method of off-axis reflective freeform zoom optical systems.

This paper presents a design method for off-axis reflective zoom optical systems. The method can be used to design off-axis zoom optical systems that include multiple mirrors. First, off-axis spherical systems for different zoom positions are solved to approximately meet the requirements for the optical power at different zoom positions, ensuring the convergence of subsequent calculations. Then, the system's optical power and aberrations are corrected by iterating the freeform surfaces point-by-point, thereby obtaining good starting points for further optimization. To illustrate the effectiveness of the method, three design examples are provided, including two off-axis three-mirror zoom optical systems and one off-axis four-mirror zoom optical system. Using the proposed method, good starting points for these systems are obtained. After optimization, the imaging quality of these three systems is close to diffraction limited.

Control of D-Statcom in Appropriation Framework Involving ANFIS for Further Developing Power Quality

This paper introduces an intelligent controller designed for the optimal operation of a D-STATCOM (Distribution Static Compensator) utilizing the ANFIS. In primary focus is improve the power quality within a 3 phase, four wire system. And distribution of fortifying and overall consistency. Then the ANFIS controller in-corporates fuzzy logic’s, devices are applied in “adaptive network framework”. To use the ANFIS method are established and using the MATLAB Simulink software. D-STATCOM, featuring a VSI and a DC side capacitance, excels in reactive power compensation and harmonic mitigation, even when subjected to dynamic load conditions and advice of the DC link capacitor V, the proposed ANFIS controller generates a compensation current reference. This reference adequately addresses the reactive power requirements imposed by nonlinear loads, ensuring the maintenance of DC voltage regulation. Through comprehensive simulation results, it is evident that the ANFIS controller surpasses in providing superior harmonic compensation and robust DC link voltage regulation under varying operating conditions. This research contributes to the advancement of intelligent control strategies for enhancing power quality and reliability in distribution systems.

Fundamental Barriers to Green Energy Production in Selected EU Countries

Most EU countries are trying to develop new sources of energy to meet local power requirements due to energy shortages. The most popular renewable energy developments include biogas stations, wind turbines, water turbines, and solar systems. This article focuses on reviewing studies concerning the utilization of solar energy systems, especially photovoltaic (PV) ones, in European countries such as Germany, Italy, Spain, and Poland, which are leaders in PV installations. The review identifies factors influencing the development of PV investments and the energy situation in these countries. Economic, market, environmental, and infrastructural barriers, as well as driving factors, are presented. In all countries, the majority of installations were in the prosumer sector, with only a very small percentage in the state-owned sector. The methodology of the study covered the mentioned barriers, which were identified using scientific databases such as Scopus, Web of Science, and branch organizations websites like the International Renewable Energy Agency (IRENA). The novelty of the article lies in its examination of special barriers concerning green energy production in chosen EU countries. Normally, when reading articles on PV installations, as presented in the References section, one primarily observes a description of the construction process without deep involvement in the presented ideas.

Intermittent Electrification with Battery Locomotives and the Post-Diesel Future of North American Freight Railroads

Changing attitudes, regulations, public policy, and international treaties concerning fossil fuels are likely to lead freight railroads toward carbon-neutral technologies, yet only electric traction matches the performance of diesel-electric locomotives. The tremendous power requirements of freight trains make efforts to reduce greenhouse gas (GHG) emissions challenging, but the prospect of 7.2 megawatt-hour battery-electric locomotives (BELs) offers promise, and intermittent electrification can facilitate battery charging. Train performance calculations under simulated real-world conditions show four 7.2 megawatt-hour BELs can power 8,000-ton trains up to 230 mainline miles unassisted, with average energy consumption of 12.5 watt-hours/ton-mile. This permits discontinuous electrification of major freight lines, leaving “gaps” of up to 200 mi to reduce capital costs, especially in rugged terrain or where the power grid is sparse. Massive onboard battery arrays and intermittent access to the electrical grid for traction power and recharging provide great energy savings by recycling energy now lost when traveling downhill or braking. A case study of a hypothetical Class I railroad found intermittent electrification with BELs more than 60% more cost-effective than contiguous electric districts, and dramatically reduced engine changes. To reduce railroad GHG emissions, governments must support technical development, show that electrification works in various North American settings, and develop institutional-financial frameworks to incentivize intermittent electrification with BELs, all in the context of massive environmental and capacity upgrades to electrical networks. Given proper assistance and incentives, early 21st century railroads may find discontinuous overhead-wire electrification offers great promise in operating terms.

A novel microencapsulated medium-temperature phase change material employing dicarboxylic acid for thermal energy storage

Microencapsulation enhances the performance of phase change materials (PCMs). However, the existing research on microencapsulation of dicarboxylic acids (DAs) is inadequate, with only one study dedicated to the development of microcapsules containing a low DA content and a shell that decomposes at low temperatures. In this study, glutaric acid (GA), as a representation of DA, was successfully microencapsulated with an enhanced encapsulation ratio. The results obtained by scanning electron microscopy (SEM) revealed a uniform spherical core–shell structure of the microencapsulated GA (MEGA). Fourier transform infrared spectroscopy (FT-IR) an X-ray diffractometer (XRD) analysis confirmed the absence of any chemical reaction between the core and shell materials. Additionally, differential scanning calorimetry (DSC) results demonstrated excellent heat storage. Notably, MEGA exhibited a thermal reliability index of 94.4 % after 200 accelerated thermal cycles. Thermogravimetry analyzer (TGA) results showed good thermal stability of MEGA. The dispersion stability and thermal conductivity of MEGA suspensions displayed significant enhancements compared to those of GA suspensions. Furthermore, compared with the base fluid, the MEGA suspensions with 1.0 wt%, 5.0 wt% and 10.0 wt% demonstrated reduced pumping power requirements by 16.9%, 54.6% and 70.0%, respectively. These findings suggest that MEGA holds promising prospects for applications in the medium temperature energy storage field.

Development of a novel hybrid hydrogen generator incorporated into a combined system for buildings in sustainable communities

The development of a new hybrid hydrogen generator, using both photoelectrochemical process and conventional electrolysis, integrated into a combination system for buildings is a major step forward in the quest for sustainable energy solutions in cities where it is specifically designed, implemented and evaluated for incorporation into sustainable community systems. A significant advantage of this hydrogen generator is the ability to produce hydrogen constantly, regardless of the absence of solar irradiation. Furthermore, both electrolyzers contribute to a rise in the amount of hydrogen produced. Furthermore, the utilization of PEC electrolysis for hydrogen generation results in a significant reduction in the overall power requirements of the system. This new study investigates an integrated power system that produces electricity from solar energy and tackles issues related to freshwater supply, hydrogen production, and heating/cooling demands. The purpose of this research is to integrate advanced system components, including a solar power tower system, a combined Brayton-Rankine cycle, a multi-effect desalination (MED) unit, a new hybridized hydrogen generator system, an absorption cooling cycle (ACS), and a hydrogen storage and refuelling station. The overall energy efficiency is determined to be 49%, while the exergy efficiency becomes 46.2%.

An acoustofluidic picoinjector

Droplet microfluidics has emerged as a valuable technology for a multitude of chemical and biomedical applications, offering the capability to create independent microenvironments for high-throughput assays. Central to numerous droplet microfluidic applications is the picoinjection of materials into individual droplets, yet existing picoinjection methods often exhibit high power requirements, lack biocompatibility, and/or suffer from limited controllability. Here, we present an acoustofluidic picoinjector that generates acoustic pressure at the droplet interface to enable on-demand, energy-efficient, and biocompatible injection at high precision. We validate our platform by performing acid-base titrations by iteratively injecting picoliter volume reagents into droplets to induce pH transitions detectable by color change in solution. Additionally, we demonstrate the versatility of the acoustofluidic picoinjector in the synthesis of metallic nanoparticles, yielding highly monodisperse and reproducible particle morphologies compared to conventional bulk-phase techniques. By facilitating controlled delivery of reagents or biological samples with unparalleled accuracy, acoustofluidic picoinjection broadens the utility of droplet microfluidics for a myriad of applications in chemical and biological research.

Optimizing Power Consumption in Machining Nickel-Based Superalloys: Strategies for Energy Efficiency

<div>In the face of the world’s population growth and ensuing demands, the industrial sector assumes a crucial role in the management of limited energy supplies. Superalloys based on nickel, which are well-known for their remarkable mechanical qualities and resilience to corrosion, are now essential in vital applications like rocket engines, gas turbines, and aviation. However, these metals’ toughness presents a number of difficulties during machining operations, especially with regard to power consumption. This abstract explores the variables that affect power consumption during the machining of superalloys based on nickel in great detail and suggests ways to improve energy efficiency in this area. The effects of important variables on power consumption are extensively investigated, including cutting speed, feed rate, depth of cut, tool geometry, and cooling/lubrication techniques. A careful balance between these factors is necessary to maximize machining efficiency and reduce power usage. Furthermore, this study reviews the effect of different heat source applications on power consumption and the resultant quality of machined nickel-based superalloys. Additionally, the critical role of cooling and lubrication in mitigating the adverse effects of high temperatures generated during machining is thoroughly examined. Innovative cooling strategies, including cryogenic or high-pressure coolant systems, are explored as potential avenues to enhance heat dissipation and minimize power requirements. In essence, this abstract not only sheds light on the challenges inherent in machining nickel-based superalloys but also offers actionable insights into how energy efficiency can be maximized through strategic parameter optimization and the adoption of innovative cooling techniques. By addressing these aspects, manufacturers can effectively navigate the complexities of machining superalloys while minimizing their environmental footprint and operational costs.</div>

Fed4UL: A Cloud–Edge–End Collaborative Federated Learning Framework for Addressing the Non-IID Data Issue in UAV Logistics

Artificial intelligence and the Internet of Things (IoT) have brought great convenience to people’s everyday lives. With the emergence of edge computing, IoT devices such as unmanned aerial vehicles (UAVs) can process data instantly at the point of generation, which significantly decreases the requirement for on-board processing power and minimises the data transfer time to enable real-time applications. Meanwhile, with federated learning (FL), UAVs can enhance their intelligent decision-making capabilities by learning from other UAVs without directly accessing their data. This facilitates rapid model iteration and improvement while safeguarding data privacy. However, in many UAV applications such as UAV logistics, different UAVs may perform different tasks and cover different areas, which can result in heterogeneous data and add to the problem of non-independent and identically distributed (Non-IID) data for model training. To address such a problem, we introduce a novel cloud–edge–end collaborative FL framework, which organises and combines local clients through clustering and aggregation. By employing the cosine similarity, we identified and integrated the most appropriate local model into the global model, which can effectively address the issue of Non-IID data in UAV logistics. The experimental results showed that our approach outperformed traditional FL algorithms on two real-world datasets, CIFAR-10 and MNIST.

Hybrid Beam-Forming Techniques for 6G Terahertz Communication: Challenges

The terahertz band is the main pillar of 6G wireless communication system. It is difficult to meet the high data rate of 1Tbps by millimeter frequency support systems. The terahertz band suffers huge propagation loss limiting wireless distance. Terahertz band imposes ultra massive multiple input multiple output antenna (UM-MIMO) systems which produce high array gain with narrow beamforming. The conventional methods for MIMO beamforming are Analog and Digital beamforming. The fully digital beamforming methods utilize dedicated structure of DAC/ADC and RF chains. These structures increase hardware complexity and are power hungry. The analog beamforming structures utilize ADC/DAC with phase shifters with less hardware complexity but support less data rates. As a result, a hybrid beamforming method can be adapted for UM-MIMO systems. This paper investigates challenges in hybrid beamforming architecture which will address the low spatial degrees of freedom (SDoF) limitation in Terahertz (THz) Communication. The flexible hardware connections are proposed, in order to switch the system in an adaptive manner so as to minimize the power requirements.

A Comprehensive Survey on Deep Learning-Based LoRa Radio Frequency Fingerprinting Identification.

LoRa enables long-range communication for Internet of Things (IoT) devices, especially those with limited resources and low power requirements. Consequently, LoRa has emerged as a popular choice for numerous IoT applications. However, the security of LoRa devices is one of the major concerns that requires attention. Existing device identification mechanisms use cryptography which has two major issues: (1) cryptography is hard on the device resources and (2) physical attacks might prevent them from being effective. Deep learning-based radio frequency fingerprinting identification (RFFI) is emerging as a key candidate for device identification using hardware-intrinsic features. In this paper, we present a comprehensive survey of the state of the art in the area of deep learning-based radio frequency fingerprinting identification for LoRa devices. We discuss various categories of radio frequency fingerprinting techniques along with hardware imperfections that can be exploited to identify an emitter. Furthermore, we describe different deep learning algorithms implemented for the task of LoRa device classification and summarize the main approaches and results. We discuss several representations of the LoRa signal used as input to deep learning models. Additionally, we provide a thorough review of all the LoRa RF signal datasets used in the literature and summarize details about the hardware used, the type of signals collected, the features provided, availability, and size. Finally, we conclude this paper by discussing the existing challenges in deep learning-based LoRa device identification and also envisage future research directions and opportunities.

High-performance anti-series diode ring amplifier for switched capacitor circuits

Ring amplifiers enable efficient amplification with less power consumption. These are characterized by fairly power requirements, and innate rail-to-rail output swing and are robust against PVT variations. In this paper, we are presenting an improved self-biased anti-series diode-based ring amplifier (ASD-RAMP) design, implemented on 45-nm CMOS technology. The design uses two diode-connected PMOS transistors that are connected in an anti-series manner to generate a large resistance because of which a high dead-zone voltage is generated. The ASD-RAMP has a settling time of only 4.05 ns, which is nearly half of the conventional self-biased ring amplifier (CSB-RAMP). In comparison to CSB-RAMP, the proposed ASD-RAMP improves the dead-zone voltage by 1.1× while requiring 6.76% less power. The circuit is durable and suitable for high-performance applications since it exhibits great resilience to PVT variations in addition to the improved dead zone voltage and reduced settling time.

Observation of interband Kelly sidebands in coupled-ring soliton microcombs

Kelly sidebands are a special type of dispersive wave that appear in mode-locked systems and they have recently been observed by pulsed excitation in integrated microcombs. Here, Kelly sidebands are generated by continuous-wave excitation in a partially coupled racetrack-resonator microcomb. The coupled-racetrack system supports two optical bands so that, in contrast to earlier studies, the soliton and Kelly sideband reside in distinct bands. The resulting interband excitation of the Kelly sidebands relaxes power requirements and continuous-wave sideband excitation is demonstrated. Tuning of sideband spectral position under pulsed excitation is also studied. Numerical simulation and the experiment show that the sidebands rely upon symmetry breaking caused by partial coupling of the two-ring system. More generally, multiband systems provide a new way to engineer Kelly sidebands for spectral broadening of microcombs.

Empirical Modelling of Power Requirements in Olive Pruning Residue Shredding: Effects of Varying Moisture Content and Rotary Speeds

Empirical Modelling of Power Requirements in Olive Pruning Residue Shredding: Effects of Varying Moisture Content and Rotary Speeds

Optimizing energy harvesting with diversity in cooperative simultaneous wireless information and power transfer systems

SummaryRadio frequency (RF) energy harvesting (EH) for wireless networks provides a green and sustainable solution and offers an energy‐efficient system. It is most crucial for fulfilling the power requirement of the rising number of low‐powered wireless devices and achieving sustainable development goals (SDG). It ensures the longevity of devices in the network even at inaccessible remote locations. Such RF‐EH‐based mechanisms are utilized in simultaneous wireless information and power transfer (SWIPT) systems. Most of the prevailing studies have analyzed the SWIPT system over conventional fading channels. However, due to the higher rate requirements, the current paradigm shift in frequency usage shifts towards the GHz band, which also offers better EH efficiency. Hence, SWIPT system analysis over the channel that is suitable for mm‐wave signal propagation is dealt with in this study. This study evaluates SWIPT performance over fluctuating two‐ray (FTR) fading channel that is crucial since it provides the best fit for characterizing GHz signal and also provides a generalized framework for most of the conventional fading channels. This study presents new analytical results assuming time switch relaying (TSR) protocol with (i) amplify and forward (AF) and (ii) decode and forward (DF) relaying. The results are also analyzed for H branches maximal ratio combining (MRC) diversity technique and evaluated the diversity gain for both AF and DF relays. The effectiveness of the system is measured in terms of system outage probability (SOP) to envisage the reliability of SWIPT based system. This work presents the simulation and modeling of such system and its obtained results are validated by Monte Carlo simulations for their accuracy.

Two-color 3D printing for reduction in femtosecond laser printing power.

Two-photon polymerization (TPP) has emerged as a favored advanced manufacturing tool for creating complex 3D structures in the sub-micron regime. However, the widescale implementation of this technique is limited partly due to the cost of a high-power femtosecond laser. In this work, a method is proposed to reduce the femtosecond laser 3D printing power by as much as 50% using a combination of two-photon absorption from an 800 nm femtosecond laser and single photon absorption from a 532 nm nanosecond laser. The underlying photochemical process is explained with modeling of the photopolymerization reaction. The results show that incorporating single-photon absorption from a visible wavelength laser efficiently reduces inhibitor concentration, resulting in a decreased requirement for femtosecond laser power. The radical to macroradical conversion is dominated by the reduction in oxygen concentration, while the reduction in photoinitiator concentration limits the threshold power reduction of the femtosecond laser.

Deep learning for economic transformation: a parametric review

Deep learning (DL) is increasingly recognized for its effectiveness in analyzing and forecasting complex economic systems, particularly in the context of Pakistan's evolving economy. This paper investigates DL's transformative role in managing and interpreting increasing volumes of intricate economic data, leading to more nuanced insights. DL models show a marked improvement in predictive accuracy and depth over traditional methods across various economic domains and policymaking scenarios. Applications include demand forecasting, risk evaluation, market trend analysis, and resource allocation optimization. These processes utilize extensive datasets and advanced algorithms to identify patterns that traditional methods cannot detect. Nonetheless, DL's broader application in economic research faces challenges like limited data availability, complexity of economic interactions, interpretability of model outputs, and significant computational power requirements. The paper outlines strategies to overcome these barriers, such as enhancing model interpretability, employing federated learning for better data privacy, and integrating behavioral and social economic theories. It concludes by stressing the importance of targeted research and ethical considerations in maximizing DL's impact on economic insights and innovation, particularly in Pakistan and globally.

The effect of temperature, pressure, and grind size on Total Dissolved Solids (TDS) and extraction yield of semi-automatic espresso machines

The number of coffee shops in Indonesia has grown from 1,083 stalls in 2016 to over 2,937 booths in 2019, representing a threefold growth. Coffee shop establishments must provide two essential kinds of equipment: espresso machines, which use pressure, and filter machines, which employ infusion. The issue lies in the exorbitant cost and immense power requirements of semi-automatic and automated espresso machines, which necessitate the use of a manual espresso machine for commercial operations. The equipment can generate satisfactory espresso but encounters several challenges; specifically, the espresso generated is characterized by inconsistency, constantly varying in quality. This might arise due to the erratic nature of the manufacturing process. The two objectives of this study are: (1) to design an economically efficient semi-automatic espresso machine capable of producing products that meet the international quality standards set by the Specialty Coffee Association (SCA standard) and (2) to investigate the impact of critical factors such as pressure, temperature, and grind size, on the consistent quality of the resulting coffee (measured by Total Dissolved Solids (TDS) and extraction yield (EXT)). The research employs the Research and Development (RD) methodology. The research findings indicate that the optimal levels of Total Dissolved Solids (TDS) and extraction yield (EXT) may be attained by using a pressure setting of 8 bars, a temperature of 90ºC, and a grind size of 3.2 on the scale. The technique for optimizing the response resulted in Total Dissolved Solids (TDS) levels of 10.03% and extraction yield (EXT) values of 19.56%. The results have been deemed acceptable based on the criteria set by the global standards of Specialty Coffee Association (SCA).