Search

This paper presents an overview for low-power successive approximation register (SAR) analog-to-digital converters (ADCs). It covers the operation principle, error analysis, and practical design issues. Furthermore, this paper provides a comprehensive survey of state-of-the-art low-power design techniques for every circuit block in the SAR ADC, including comparator, capacitive digital-to-analog converter (DAC), and SAR logic. The goal of this paper is to provide a useful overview to SAR ADC designers who want to improve the energy efficiency targeting low-to-medium speed applications. [ABSTRACT FROM AUTHOR]

Over the last decade, experience with raw and refined sugar quality and quantity has greatly impacted the design and concept of sugar refineries. In all those cases client expectations regarding final product quality, energy efficiency, process flexibility and degree of automation were regarded in tailor-made sugar factory designs. This paper will focus on implemented projects that demonstrate how differences in customer demand have influenced refinery designs. Moreover, the paper will reflect BMA's experiences on how to develop a successful project for a new or modernized sugar refinery within necessary project phases, influencing factors on costs and overall time schedule for implementation. [ABSTRACT FROM AUTHOR]

Accurate estimation of energy savings is crucial for the effective implementation of energy conservation measures (ECMs). Simultaneously, the integration of Artificial Intelligence (AI) has revolutionized software engineering by imbuing software with intelligent capabilities and autonomy. In this paper, we propose an ensemble model for precisely estimating baseline energy consumption within the realm of AI-empowered autonomous software. The ensemble model combines predictions from three tree-based Machine Learning models, namely Random Forest, XGBoost, and LightGBM. Notably, our model emphasizes the provision of explainability, granting transparency and insights into the key factors influencing baseline energy consumption. To validate its effectiveness, we conduct experimental evaluations on a diverse cluster of real-world buildings in Latvia. The results demonstrate the superiority of our proposed ensemble model over individual models and even a deep learning network tailored for energy consumption estimation. These findings underscore the efficacy of AI-empowered models in the energy sector, offering a robust and interpretable solution for estimating energy savings. [ABSTRACT FROM AUTHOR]

Paper presents computer simulations conducted to study the influence of the configuration of an energy harvesting system with tunnel mechanical characteristics in terms of its energy generation efficiency. In this context, numerical simulation experiments were carried out to assess the impact of physical properties and initial conditions on the occurrence of coexisting solutions and its energy efficiency. Chaotic motion zones were identified on the basis of Lyapunov exponent. To be able to choose the best solution in terms of energy efficiency, studies on the use of impulse excitation to change the orbit of the solution were also presented. Results show that modifying the energy harvesting system design with an impulse excitation subsystem can significantly improve (up to tenfold) the amount of energy harvested from vibrating mechanical devices. • A new energy harvesting system has been designed (TEEH). • The parameters' domains have been identified where the optimal energy harvesting is achieved. • The method of impulse excitation to change the unfavorable orbits is developed. • Basins of attraction of coexisting solutions are identified. [ABSTRACT FROM AUTHOR]

In this paper, we investigate one-bit massive multiple-input multiple-output (mMIMO) systems with defective radio frequency (RF) chains over Ricean fading channels in beyond 5G networks. The approximate achievable rate expressions are derived in closed-form for the scenarios with ideal and non-ideal channel state information by utilizing the Bussgang decomposition. Furthermore, the scaling laws for transmit powers and RF hardware impairments are established when the number of antennas, M , at the base station (BS) grows indefinitely. Specially, the power scaling laws under the case of a large number of Internet of Things (IoT) devices (users) are also investigated. It is revealed that, as M increases, the systems can yield a non-vanishing rate while cutting down the transmit powers of the IoT devices, and/or scaling up the RF impairments of the BS. Moreover, it is also found that the relationship between the number of IoT devices N and M can influence the results of the power scaling laws. Numerical results are conducted to demonstrate our analysis. It is also revealed that the appropriate M can contribute to better energy efficiency. [ABSTRACT FROM AUTHOR]