Energy-efficient System Design Research Articles

VLSI implementation of ultra power optimized adiabatic logic based full adder cell

Design of high performance and energy efficient digital systems are one of the most important research areas in VLSI system design which is suitable for real-time applications. One of the functional elements used in complex arithmetic circuits is an adder. To design an energy efficient adder one-bit full adder cell is designed based on adiabatic logic. The proposed ALFA cell is designed using adiabatic logic which results with the negligible amount of exchange of energy with the surrounding environment. Therefore, the application circuits based on this logic will have negligible energy loss due to heat dissipation. It requires 24 transistors to get the true and complimentary arithmetic sum and carry output. The proposed adiabatic logic based full adder (ALFA) cell processes the three single bit inputs and provides the output as sum, carry, sum bar and carry bar in a single architecture. The proposed ALFA cell reduces the power consumption by 98.49%, 90.93%,and 89.37%, respectively, when compared to CMOS full adder, 14T pass-transistor logic (PTL) with transmission gate (TG) full adder and 16T PTL with TG full adder.

Throughput maximisation in cognitive radio networks with residual bandwidth

Recent progress in cognitive radio networks (CRNs) promises to meet device-to-device communication requirements for spectrum utilisation and power control to support billions of machines/devices to be connected worldwide. The architecture of the CRN must maintain a high data rate (throughput) at low power consumption, which requires both spectrum efficient and energy efficient system design. To this aim, the proposed work adopts a CRN model, which operates in an interweave mode that allows spectrum sensing followed by opportunistic secondary user (SU) data transmission over the unused bandwidth of the primary user (PU) in a non-overlapping frame structure. Closed form expressions of the optimal spectrum sensing duration, bandwidth, and power allocation of each secondary node are obtained to maximise the sum throughput of the overall CRN while maintaining the constraints of sensing reliability of PU, individual SU transmission outage probability, permissible interference, and residual bandwidth. Numerical results highlight that the proposed scheme maximises the sum network throughput by $\sim 50.54$~50.54 and $\sim 63.76\%$~63.76% over the other existing techniques.

Process intensification in multicomponent distillation: A review of recent advancements

Process intensification (PI) is an emerging concept in chemical engineering that describes the design innovations that lead to significant shrinkage in size and boost in efficiency of a process plant. Distillation, the most commonly used separation technique in the chemical industry, is a crucial component of PI. Here, we systematically discuss the following aspects of PI in non-azeotropic multicomponent distillation: (1) Introducing thermal couplings to eliminate intermediate reboilers and condensers to save energy and capital cost; (2) Improving operability of thermally coupled columns by means of eliminating vapor streams in thermal couplings with only liquid transfers or column section rearrangement; (3) Enabling double and multi-effect distillation of thermally coupled configurations to further reduce heat duty; (4) Performing simultaneous heat and mass integration among thermally coupled columns to reduce the number of columns and heat duty; and (5) Conducting any thermally coupled distillation in n-product streams using 1 to n−2 column shells with operable novel dividing wall columns. We demonstrate these aspects of PI through examples to illustrate how they lead to compact, easy-to-operate, energy efficient and cost effective multicomponent distillation system designs.

Link Quality Optimization for Hybrid LEO-GSO Systems

In this paper, an optimised link design for LEO-GSO satellite systems is proposed so that a variable number of LEO satellites can maintain a required link quality when traversing GSO coverage. We identify the orbit, system and sata traffic parameters that are relevant, with which we obtain a parameterised optimal transmission energy function. Our results are useful for the design of energy-efficient hybrid satellite systems.

High Brightness and High Voltage Dimmable LED Driver for Advanced Lighting System

In energy efficient system designs, LED lighting leads to reduction in global energy demand. This paper proposes a high brightness, high efficiency, dimmable LED driver based on linear current regulator technology for DC grid distribution systems. The proposed driver has excellent characteristics like highest lumen per watt, long lifetime, high reliability, compact, low cost, both environmental and user friendly which makes it suitable for lighting applications. Steady state and small signal model of the proposed driver are performed which helps in minimizing ground current and accurate compensator design, respectively. These two modelling approaches result in the optimization of both footprint and cost of the driver. The performance of the proposed 20W driver is modeled using real-time simulation in spice software. Experimental prototype is developed to validate the performance of the proposed driver for different dimming levels and achieves a maximum efficiency of 97%.

Формирование общего подхода к оптимальному проектированию высокотехнологических энергоэффективных электротехнических комплексов на основе тихоходных синхронных магнитоэлектрических машин

Формирование общего подхода к оптимальному проектированию высокотехнологических энергоэффективных электротехнических комплексов на основе тихоходных синхронных магнитоэлектрических машин

A framework to predict energy related key performance indicators of manufacturing systems at early design phase

Increasing energy prices, growing market competition, strict environmental legislations, concerns over global climate change and customer interaction incentivise manufacturing firms to improve their production efficiency and minimise bad impacts to environment. As a result, production processes are required to be investigated from energy efficiency perspective at early design phase where most benefits can be attained at low cost, time and risk. This article proposes a framework to predict energy-related key performance indicators (e-KPIs) of manufacturing systems at early design and prior to physical build. The proposed framework is based on the utilisation and incorporation of virtual models within VueOne virtual engineering (VE) tool and WITNESS discrete event simulation (DES) to predict e-KPIs at three distinct levels: production line, individual workstations and the components as individual energy consumption units (ECU). In this framework, alternative designs and configurations can be investigated and benchmarked in order to implement and build the best energy-efficient system. This ensures realising energy-efficient production system design while maintaining predefined production system targets such as cycle-time and throughput rate. The proposed framework is exemplified by a use case of a battery module assembly system. The results reveal that the proposed framework results meaningful e-KPIs capable of supporting manufacturing system designers in decision making in terms of component selection and process design towards an improved sustainability and productivity.

A Deep Learning-Based Approach to Power Minimization in Multi-Carrier NOMA With SWIPT

Simultaneous wireless information and power transfer (SWIPT) and multi-carrier non-orthogonal multiple access (MC-NOMA) are promising technologies for future fifth generation and beyond wireless networks due to their potential capabilities in energy-efficient and spectrum-efficient system designs, respectively. In this paper, the joint downlink resource allocation problem for a SWIPT-enabled MC-NOMA system with time switching-based receivers is investigated, where pattern division multiple access (PDMA) technique is employed. We focus on minimizing the total transmit power of the system while satisfying the quality-of-service requirements of each user in terms of data rate and harvested power. The corresponding optimization problem is a non-convex and a mixed integer programming problem which is difficult to solve. Different from the conventional iterative searching-based algorithms, we propose an efficient deep learning-based approach to determine an approximated optimal solution. Specifically, we employ a typical class of deep learning model, namely, deep belief network (DBN), where the detailed procedure of the developed approach consists of three parts, i.e., data preparation, training, and running. The simulation results demonstrate that the proposed DBN-based approach can achieve similar performance of power consumption to the exhaustive search method. Furthermore, the results also confirm that MC-NOMA with PDMA outperforms MC-NOMA with sparse code multiple access, single-carrier non-orthogonal multiple access, and orthogonal frequency division multiple access in terms of power consumption in SWIPT-enabled systems.

On Maximum of Energy-Efficiency in Amplify-and-Forward Relay Networks Under Channel Estimation Errors

In this paper, we investigate the energy-efficiency for amplify-and-forward relay networks in the presence of channel estimation errors. Two kinds of transmission strategies, i.e., direct transmission (DT) and relay transmission (RT), are dealt with for the energy-efficient communication system design. The energy cost function in DT or RT is defined as the total energy consumption for transmitting one bit data successfully, in which the channel estimation error, the channel estimation cost (i.e., energy consumed for pilot transmission) and the outage probability are included. As the power allocation plays a crucial role in implementing relay networks in an energy-efficient manner, we propose a theoretical framework for energy-efficient power allocation in DT and RT strategies to minimize the energy cost. By simulation, we compare the two transmission strategies according to the energy cost under various network scenarios. Our results provide several insights into the choice between DT and RT strategies if some transmission parameters are known.

Algorithmic Design and Resilience Assessment of Energy Efficient High-Rise Water Supply Systems

High-rise water supply systems provide water flow and suitable pressure in all levels of tall buildings. To design such state-of-the-art systems, the consideration of energy efficiency and the anticipation of component failures are mandatory. In this paper, we use Mixed-Integer Nonlinear Programming to compute an optimal placement of pipes and pumps, as well as an optimal control strategy.Moreover, we consider the resilience of the system to pump failures. A resilient system is able to fulfill a predefined minimum functionality even though components fail or are restricted in their normal usage. We present models to measure and optimize the resilience. To demonstrate our approach, we design and analyze an optimal resilient decentralized water supply system inspired by a real-life hotel building.

Process Intensification in Multicomponent Distillation

Process Intensification (PI) is an emerging concept in chemical engineering which describes the design innovations that lead to significant shrinkage in size and dramatic boost in efficiency in a process plant. Distillation, which is one of the most important separation technologies in the chemical industry, is therefore a crucial component in PI. Here, we discuss two aspects of PI in multicomponent distillation: 1) Performing simultaneous heat and mass integration among thermally coupled distillation columns to reduce the number of columns and heat duty requirement; and 2) Conducting any thermally coupled distillation in only a single column shell using a dividing wall column that is fully operable. Through examples, we show that synergistic use of both strategies leads to the design of compact, easy-to-operate, energy efficient and cost effective multicomponent distillation systems.

Energy efficiency evaluation for machining systems through virtual part

Energy prices, environmental concerns, carbon dioxide emissions, and economic matters are driving factors for research on reducing machining system energy consumption. Energy efficiency evaluation for machining systems is an effective management strategy for reducing the energy consumption and improving the energy efficiency of machining systems. This paper proposes a methodology called virtual part method to evaluate the energy efficiency of machining systems, and the proposed method overcomes the deficiencies or limitations of major existing methods (such as the entitative part method) through equivalence and virtualization of all possible machining system parts that may be manufactured in future. Based on an analysis of the energy compositions and characteristics of the proposed virtual part, an energy efficiency evaluation for machining systems through virtual part is conducted in three steps: 1) selection of evaluation indexes; 2) corresponding data collection for calculating indexes; and 3) development of energy efficiency evaluation system. Its application in a machine tool suggests that the proposed method is more accurate than the existing ones and contributes to energy-saving activities including the development of energy efficiency standards, design of energy-efficient machining systems, and reform of old machining systems.

IEEE Transactions on Sustainable Computing: Guest Editorial on Special Issue on Sustainable Cyber-Physical Systems

The papers from this special section addresses the topic of sustainable cyber-physical systems (CPSs). The research on CPSs addresses the close interactions between the cyber computational components and the physical components spanning from mechanical components, energy systems, human activities, to surrounding environment. CPS is expected to play a major role in the development of next-generation smart energy systems and data centers. Innovative computational methodologies such as green and energy efficient cyber-physical system design have become critical to enable the sustainable development of such systems. These technologies can be used to tackle various sustainability challenges, such as the reduction of energy induced from the large scale data center computing infrastructures, the improvement of computational efficiency in smart energy systems and connected vehicle systems, and the exploration of the renewable energy resources to mitigate classical energy usages.

RF Energy Harvesting in Cognitive Radio: Towards Green Communication

There has been a continuous emphasis on an energy efficient communication system design. With the advent of 5G communication technologies, along with a faster and reliable data transfer mechanisms, energy management and conservation is gaining more attention and is becoming a major and indispensable part of communication research. This papers highlights the contemporary technological developments in the field of RF energy harvesting in a cognitive and high data rate network. It has been observed that an efficient RF energy harvesting technology in a cognitive platform definitely leads towards a greener communication paradigm.

Approximate Memristive In-memory Computing

The bottleneck between the processing elements and memory is the biggest issue contributing to the scalability problem in computing. In-memory computation is an alternative approach that combines memory and processor in the same location, and eliminates the potential memory bottlenecks. Associative processors are a promising candidate for in-memory computation, however the existing implementations have been deemed too costly and power hungry. Approximate computing is another promising approach for energy-efficient digital system designs where it sacrifices the accuracy for the sake of energy reduction and speedup in error-resilient applications. In this study, approximate in-memory computing is introduced in memristive associative processors. Two approximate computing methodologies are proposed; bit trimming and memristance scaling. Results show that the proposed methods not only reduce energy consumption of in-memory parallel computing but also improve their performance. As compared to other existing approximate computing methodologies on different architectures (e.g., CPU, GPU, and ASIC), approximate memristive in-memory computing exhibits better results in terms of energy reduction (up to 80x) and speedup (up to 20x) on a variety of benchmarks from different domains when quality degradation is limited to 10% and it confirms that memristive associative processors provide a highly-promising platform for approximate computing.

Energy-efficient Legionella control that mimics nature and an open-source computational model to aid system design

Although there is no direct connection, the incidence of Legionnaire’s disease has increased concurrently with increased usage of energy efficient domestic hot water (DHW) systems, which serve as ideal growth environments for Legionella pneumophila, the bacteria responsible for Legionnaire’s disease. The Duck Foot Heat Exchange Model (DFHXM) was developed to aid design of energy efficient thermal pasteurization systems with Legionella control specifically in mind. The model simulates a system design imitating the countercurrent heat exchange in the feet of ducks, an evolutionary adaption reducing environmental heat losses in cold climates. Such systems use a heat exchanger to preheat fluids prior to pasteurization and cool the same fluid after pasteurization. Thus, the design requires minimal addition of heat to achieve pasteurization temperatures and to cover environmental heat losses. This article describes the underlying principles and use of the freely available Microsoft Excel model, as well as compares results from the DFHXM to measurements of an experimental pilot system. Simulation outputs agreed well with experimental results for transient and steady-state temperatures, the largest discrepancy in steady-state temperatures being 4.6%. Lastly, we discuss the flexibility of the DFHXM to simulate a wide variety of designs with special emphasis on Legionella control and solar-thermal water disinfection.

Energy and electromagnetic pollution considerations in ARoF-based multi-operator multi-service systems

Energy-efficient system design with controlled levels of electromagnetic pollution, without compromising on the user quality of experience and operator revenue, are essential considerations in the design of next-generation wireless communication networks. Integration of multiple services and multiple operator transmissions onto a common shared infrastructure is the solution forward to satisfy the contradictory subscriber and operator requirements and also to derive a future-proof fronthaul to support new techniques like opportunistic, co-operative and cognitive communications. This paper reports the link budget analysis, energy efficiency estimation and electromagnetic radiation calculation for a Multi-Operator Multi-Service Analog Radio over Fiber fronthaul for a small cell configuration. The BER analysis and the link budget analysis are presented for the central base station to remote antenna unit (RAU) link as well as the RAU to user equipment link. The estimated minimum transmission power sufficient to establish and maintain a quality connection suggests the possibility of eliminating the energy consuming power amplifiers in the transceivers and also achieve a reduced electromagnetic pollution in the environment.

CS reconstruction in MIMO channel using square complex orthogonal STB codes

Compressed sensing (CS) is the process of signal reconstruction at a rate far below the Nyquist sampling rate. Sometimes, CS measurements need transmission over radio mobile channel for bandwidth preserving and energy efficient system design and/or application specific system design such as wireless body area network (WBAN), remote surveillance system etc. To this aim, this work proposes an integrated system design for quality improvement in CS reconstructed images over radio mobile channel modelled as Rayleigh flat fading. The design of a non-zero-entry Single Symbol Decodable Square Complex Orthogonal, Space Time Block Codes (STBC) for 16 antennas is proposed first. This multi-channel facility is then exploited for transmission of CS measurements, which at the receiver side, undergo Robbins–Monro (RM) method, a non-parametric stochastic approach for predicting the unobserved spaces from the observed ones through recursive filtering. Similar study is also done using a relaxed convex optimization of the basis pursuit algorithm, a parametric based approach. Extensive simulation results highlight the efficacy of the proposed CS and STBC (CS–STBC) scheme for the quality improvement on the reconstructed images as the variation in the number of antennas, change in the number of measurements and variation on signal-to-noise ratio. It also highlights significant performance gain in CS reconstruction over radio mobile channel using STBC framework.

Pilot-based joint ML estimation of timing offset, CFO and channel for OFDMA uplink using minimum residue decomposition technique

This paper addresses the challenging issue of joint maximum-likelihood estimation of carrier frequency offset, timing offset and channel response for orthogonal frequency division multiple access (OFDMA) uplink transmissions. A pilot preamble based approach is proposed that makes use of the concept of signal decomposition. Earlier works have demonstrated that it is possible to decompose the multidimensional optimization involved in this joint estimation problem into a series of one-dimensional searches. However, the computational complexity involved with such methods is also very high. In this work, we propose a new signal decomposition method based on a minimum residue criterion. The method with appropriate signal structures provides a computationally-efficient solution for the joint estimation of all required parameters of all users at the base station in an OFDMA link, leading to energy-efficient system design. Another advantage of the proposed technique is that it offers the flexibility of application to OFDMA systems with any subcarrier assignment scheme. Performance advantages of the proposed estimation method are substantiated through extensive computer simulation studies and are compared with that of some important recently-developed algorithms for the problem. Proof of convergence of the new estimation algorithm is also illustrated.

A Survey and Evaluation of FPGA High-Level Synthesis Tools

High-level synthesis (HLS) is increasingly popular for the design of high-performance and energy-efficient heterogeneous systems, shortening time-to-market and addressing today’s system complexity. HLS allows designers to work at a higher-level of abstraction by using a software program to specify the hardware functionality. Additionally, HLS is particularly interesting for designing field-programmable gate array circuits, where hardware implementations can be easily refined and replaced in the target device. Recent years have seen much activity in the HLS research community, with a plethora of HLS tool offerings, from both industry and academia. All these tools may have different input languages, perform different internal optimizations, and produce results of different quality, even for the very same input description. Hence, it is challenging to compare their performance and understand which is the best for the hardware to be implemented. We present a comprehensive analysis of recent HLS tools, as well as overview the areas of active interest in the HLS research community. We also present a first-published methodology to evaluate different HLS tools. We use our methodology to compare one commercial and three academic tools on a common set of C benchmarks, aiming at performing an in-depth evaluation in terms of performance and the use of resources.