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A novel ensemble machine learning approach for predicting energy consumption in smart appliances is presented in this paper. The main objective is minimizing the number of necessary sensors and improving the prediction accuracy at the same time. The proposed method combines the Fuzzy C-Means method with a multi-stream deep neural network to achieve this goal. The method focuses on prediction accuracy and aims to extract the minimum number of essential features from the dataset corresponding to the required sensors. These selected features are then scatter-reduced and homogenized into subsets. Each subset is used to train a cluster-specific deep neural network designed exclusively for that subset. The final prediction is obtained by computing the fuzzy-weighted sum of these cluster-specific network outputs. Numerical results show that the proposed prediction method outperforms conventional methods in terms of root mean square error and mean absolute percentage error criteria, despite using fewer sensors. This improvement can be attributed to the reduced dataset scatter, which improves the learning speed and model performance. Furthermore, the fuzzy combination of the outputs improves the final prediction accuracy. Overall, the proposed approach provides a more cost-effective and accurate solution for predicting the energy consumed by smart appliances. [ABSTRACT FROM AUTHOR]

In this paper, we propose an approach solving the holes problem in Wireless Sensor Networks (WSNs) based on Khalimsky k-Clustering and data routing protocol (MDKC). The aim of this solution is to establish optimized data routing paths between isolated nodes/clusters and the Sink in noisy environment with the presence of obstacles. This approach is an improvement of a previous work deploying stationary WSN not dealing with the problem of "holes". At first, the MDKC algorithm divides the WSN into k-hop (k ≥ 2) compact dynamic clusters. For each cluster, a node is elected cluster-head in its k-neighborhood according to some criteria such as the remaining energy, the k-degree and the communication probability average. Then, some nodes are selected as Khalimsky anchors to optimize the intra-cluster data routing process. The Khalimsky anchors at the border layers ensure the inter-cluster data routing between adjacent clusters. In the next phase of MDKC, Mobile Collectors (MCs) are used for data collecting and relaying from isolated nodes/clusters to the connected Khalimsky's nodes. The simulation results prove that MDKC minimizes the energy consumption and improves the connectivity rate between sensors and the delivery rate compared to some existing approaches. [ABSTRACT FROM AUTHOR]

Active suspension technique normally provides the best vibration mitigation performance at the cost of high-energy consumption. In contrast, the energy consumption of a semi-active suspension is normally much smaller than that of an active suspension, whereas the control performance is compromised as well. Observing the core issue that caused such a phenomenon is that the existing semi-active control force, unlike the active control force, shall always oppose to the relative motion of the actuator (i.e. clipping phenomenon). This paper subsequently proposes a novel semi-active vehicle suspension system that incorporates a passive negative stiffness (NS) spring and a semi-active damper (SD) to realize uncompromised active control force while consuming energy of a typical semi-active suspension system. In specific, the proposed system allows for decomposition of the target active control force and tracked via the collaboration of the NS and SD components. Herein, the NS element is capable of releasing the store potential energy that subsequently eases the aforementioned clipping phenomenon of a traditional semi-active suspension. In this paper, besides the relevant clarifications on the system topology and working mechanism, its feasibility and performance enhancement are also validated via systematic numerical simulations of a vehicle suspension; and the results indicate, for the first time, that the proposed semi-active suspension can fully track the active control force and subsequently achieve unprecedented control performance comparable to an active controller. [ABSTRACT FROM AUTHOR]

This paper proposes the hybrid approach for increasing the profit of prosumers and reducing the energy cost of the residents. The hybrid method is the combination of chaotic maps and the Henry gas solubility optimization algorithm (HGSO), hence it is called chaotic Henry gas solubility optimization (CHGSO). The proposed approach is the community-based microgrid that incorporates energy storage systems (ESS), electric vehicles (EVs) and photovoltaics (PV). Both ESS and EV are conserved as alternative sources of energy. The stored EV energy is used as a power source for homes and the grid by utilizing the Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) modes. The power flow of EV/ESS is controlled depending on the rate of charging and discharging. Here, the battery degradation cost is considered. The proposed approach solves the optimization problems, like battery degradation of EV/ESS, regulation of charge/discharge and provides optimal economic benefits. The proposed approach increases the prosumer profit through encouraging EV energy transactions through incentive-based pricing. Finally, the CHGSO method is performed in MATLAB and is compared with existing methods. From the simulation analysis, it can be concluded that the cost of CHGSO method is less and the prosumer participation has increased. [ABSTRACT FROM AUTHOR]

In this paper, the effect of combining (FTO/TiO2/Cs3Fe2Cl9/CuO/Al) in the energy conversion efficiency is studied. It creates a unique series with perovskite (Cs3Fe2Cl 9) serving as its primary source of visible light absorption. Using X-ray diffraction (XRD), a UV–Vis spectrophotometer, and scanning electron microscopy, the structural, optical and morphological features of the (CuO, Cs3Fe2Cl 9) nanoparticles were characterized (SEM). To create perovskite solar cells, these materials were effectively synthesized and deposited as thin films, and the efficiency of solar cells in photovoltaic (PV) devices has been verified. When the light intensity is 100 W/m2, the PV device's output demonstrates its energy conversion efficiency (PCE = 15.46%). [ABSTRACT FROM AUTHOR]

In the analogy where achieving the "carbon peaking and carbon neutrality" goals (the "dual carbon" goals) is compared to a battle, energy is the main battlefield, and electric power the main force. The low-carbon transformation path of the electric power industry exerts fundamental impact on progress towards carbon peaking and carbon neutrality of the whole society. Therefore, this paper first analyzes challenges and opportunities faced by the electric power industry to achieve the "dual carbon" goals; secondly, on the basis of deep low-carbon, zero-carbon and negative-carbon scenarios of the electric power industry, this paper quantifies and delves into the consumption structure of primary energy and final energy, as well as the future development positioning and trend of different categories of energy. Based on the research results, this paper proposes low-carbon transformation path of the electric power system at the stages of carbon peaking, deep low-carbon and carbon neutrality, and analyzes the trend of power supply cost changes in different scenarios. On this basis, a series of implementation key points and measures are proposed to adapt to green and low-carbon transformation of the electric power industry, covering areas such as coal-fired electric power development positioning, new energy development and utilization, diversified supply and demand, new energy industry chain, and power supply costs, and so forth. [ABSTRACT FROM AUTHOR]

After the carbon peaking and carbon neutrality goals were proposed, different institutions and scholars carried out research on China's medium- and long-term mitigation pathways. Through the literature review of China's research on carbon neutrality, this paper finds that the zero-carbon energy transition is the key to achieving carbon neutrality. The main driving factors of reducing energy-related carbon emissions include the cleanliness of primary and secondary energy supply systems, the electrification of energy consumption and the development of hydrogen energy, energy efficiency improvement, carbon capture and storage (CCS), and negative emissions. Furthermore, based on the literature, this paper conducts a quantitative comparative analysis, and selects key indicators for comparative analysis and summary from seven dimensions, including economic and social development, carbon emission pathways, primary energy consumption, final energy consumption, final hydrogen energy consumption, electricity demand and supply, and installed capacity and structure of power generation. Main conclusions are as follows: (i) In terms of carbon emission pathways, the institutions generally believed that China will peak carbon emissions around 2028 and achieve carbon neutrality between 2050 and 2060. Achieving net-zero or near-zero emissions first in the power sector is the key to carbon neutrality across the society; (ii) In terms of energy supply, it is a consensus to increase the proportion of clean energy and reduce carbon emissions from the source. The proportion of clean energy in primary energy will increase to more than 85%, and the proportion of clean energy power generation and installed capacity will reach more than 90%; (iii) In terms of energy use, electricity will become the core of final energy consumption in the future. The predicted electricity consumption across the society will range 14.3–18.4 PWh, and the predicted electrification rate will exceed 65%. [ABSTRACT FROM AUTHOR]

Given the difficulties in measuring the effective wind speed of wind turbine during the maximum power point tracking (MPPT) process and the unknown nature of the system, this paper proposes a neural network super-twisted sliding mode control (NNST-SMC) method with echo state network (ESN) wind speed estimation. The rotor speed and electromagnetic power are taken as ESN inputs, and the effective wind speed is estimated through the inverse model of wind turbine dynamics. The super-twisted algorithm (STA) can effectively improve the chattering problem of the traditional sliding mode control (SMC) system. The RBF neural network is introduced to compensate for disturbance and uncertainty characteristics of the wind turbine. Results show that compared with the neural network first-order sliding mode control (NNSMC) and super-twisted sliding mode control (ST-SMC), the proposed method can improve the efficiency of wind energy utilization. [ABSTRACT FROM AUTHOR]

In recent times, the virtual power plant (VPP) is gaining more attention in power system engineering due to its tremendous potential in enhancing sustainable urbanism, in which, it supplies clean energy from distributed generators. Electricity is deemed a basic requirement for future automotive and ultra-modern technologies. The deficiency of traditional energy resources and their complex generation process make the production cost of electricity increase dramatically. Moreover, traditional power distribution systems are encountering issues in distributing electrical energy to fulfill customer demands. Therefore, this paper proposes a novel power management system named 'the hybrid genetic local binary knowledge (HGLBK) algorithm' to manage power distribution in the transmission lines and to optimize the total operation cost of the network. The hybrid optimization algorithm effectively controls the load by supplying the surplus power load to the adjacent feeders thereby optimally selecting and placing the distributed energy resource (DER). The proposed concept is implemented at Kayathar, Tamil Nadu in India, and their real-time data are utilized for modeling the VPP. The proposed VPP concept is implemented in the IEEE-9 bus system and the performance of VPP is simulated using the MATLAB software. The performance of the proposed HGLBK algorithm is assessed by comparing its effectiveness with the existing approaches. [ABSTRACT FROM AUTHOR]

The congestion caused by ramp overflow has an essential impact on the energy consumption of autonomous vehicles. It is urgent to solve the energy waste of ramp overflow on connected and autonomous vehicles and improve the energy utilization rate. Aiming at the traffic congestion caused by ramp overflow, this paper obtains the data of autonomous vehicles and traffic flow based on vehicle road cooperative perception, analyzes the impact mechanism of vehicle energy consumption, and summarizes the energy consumption modes of autonomous vehicles into three categories. Second, an energy consumption evaluation framework is proposed based on the CSANS strategy. This strategy can make up for the deficiency of constructing neighborhoods within European distance, find important influencing variables on connected and autonomous vehicles' energy consumption, and accurately capture the manifold characteristics of energy consumption data. Finally, flow control is carried out from the macro perspective of traffic engineering to optimize ramp overflow's impact on autonomous vehicles' energy consumption. Through multiple groups of experiments, it has been found that it can effectively reduce the energy consumption of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Strategies to combat climate change may be based on mitigation of the phenomenon or on adaptation to its consequences. This paper aims to identify the driving factors of the choice between these two categories of strategies in the context of urban climate plans. The fight against climate change being characterized by a logic of free riding, the situation tilts the balance towards adaptation strategies in an urban context, to the detriment of mitigation. This hypothesis is tested here through a review of the existing literature on urban climate strategies. This study shows that, counterintuitively, mitigation prevails over adaptation in urban climate strategies up to now. This paper explores the explanations for this seemingly paradoxical situation. We argue that a big part of the explanation has to do with the institutional context of urban climate strategies, specifically the decision-making capacities of municipalities, or the fact that they take part in international networks promoting mitigation over adaptation. Other explanations rely on the cost/benefit impact of adopting mitigation or adaptation, like the collateral local/private benefits of urban climate strategies that are often bigger with mitigation than adaptation. Another finding is that there is no systematic planning making it compulsory to choose between mitigation and adaptation strategies, as they are in some instances complementary, providing co-benefits. [ABSTRACT FROM AUTHOR]

Application of big data analytics (BDA) is seen in various disciplines within an organization to predict trends, explore opportunities and monitor performance. Among all the industries, BDA presents immense value in sustainable manufacturing (SM) given that it is an industry that consumes a high amount of energy, emits high amounts of waste and carbon emissions and requires a large amount of manpower. This paper aims at illustrating the effects of BDA in supporting SM by studying the Indian manufacturing firms which have unfavorable labor laws compared to other developing countries. With an extensive literature review, this paper discusses the relationship between BDA and sustainability, the capabilities of BDA, the concept of SM, the BDA framework for SM, the relationship between Industry 4.0 and SM and the challenges of implementing BDA. Using qualitative meta-analysis research methodology, the paper examines the nine common critical success factors that enable SM through BDA implementation by comparing 15 primary studies. Finally, the paper concludes the research findings and outlines future research directions. The study provides theoretical and practical contributions to BDA implementation in achieving effective SM practices in emerging economies. [ABSTRACT FROM AUTHOR]

Addition and multiplication are some of the most broadly adopted arithmetic operations in a wide range of applications. This paper proposes new structures of approximate multipliers to optimize the area, delay, and power without affecting the accuracy metrics. Multipliers and adders play a significant role in the functioning of any digital circuit or system. The overall performance of a processor highly depends on the speed of adders and the energy consumption. In this paper, two types of compact error-tolerant approximate adders are designed and used along with approximate 4:2 compressors to improvise the efficiency of the approximate multipliers. The proposed approximate multipliers show good results when compared to the existing structures in terms of area, delay, power, and accuracy. The approximate multipliers are applied to image sharpening and image multiplication applications. The error-tolerant adder's performance is evaluated in the practical domain using the image blending application. Peak signal-to-noise ratio (PSNR) performance and the structural similarity index metric (SSIM) are used to assess the modeled designs. The proposed approximate multipliers and adders exhibit better performance in terms of PSNR and SSIM and are found to be an optimized design to apply effectively in various error-tolerant image processing applications. [ABSTRACT FROM AUTHOR]

Reliability and energy efficiency are two hostile objectives considered in designing task scheduling in most real-time multiprocessor systems on chip (MPSoC). Addressing and improving one of them may affect and degrade the efficiency of the other one and vice versa. In this paper, we intend to examine these challenges and ultimately achieve an optimal energy consumption and reliability state. This paper presents a novel scheduling technique that can adapt to the limitations of real-time systems and have optimal energy consumption and reliability. This is done by minimizing the overlap of tasks, adjusting the speed of processors and the number of backups of each task. The proposed scheme reduces energy consumption on average by 11% to 42% compared to the previous state-of-the-art techniques and keeps the reliability at a high level. [ABSTRACT FROM AUTHOR]

In the current research on data query for two-tiered WSN, the privacy-preservation range query is one of the hotspots. However, there are some problems in the existing researches in two-tiered wireless sensor networks such as high computational and communication costs for security comparison items and high energy consumption of sensing nodes. In this paper, a privacy-preservation range query protocol based on the integration reversal 0-1 encoding with Bloom filter is researched and designed. In the sensing data submission stage, the optimized reversal 0-1 encoding, HMAC algorithm, AES encryption algorithm and variable-length Bloom filter are used for generating the maximum–minimum comparison encoding and constructing a shorter verification index chain to reduce computational and communication costs of sensing nodes; in the private data range query stage, the base station uses the HMAC algorithm to convert the plaintext query range into the ciphertext query range and sends it to the storage node. In the storage node, the bitmap encoding information of the verification index chain is calculated with the comparison rule of the reversal 0-1 encoding and it is returned to the base station together with the verification index chain and the data ciphertext that compliance with the query rule; in the data integrity verification stage, the integrity of the query results using the verification index chain and bitmap encoding is verified at the base station. In the experimental section, the Cortex-M4 development board equipped with the Alios-Things operating system as sensing node and the Cortex-A9 development board equipped with the Linux operating system as storage node are implemented in this protocol, which is compared with the existing protocols in three aspects: the number of data collected in each cycle, the length of data and the number of data dimensions. The experimental results show that the energy consumption of this protocol is lower under the same experimental environment. [ABSTRACT FROM AUTHOR]

This paper presents a modified energy-efficient Dual Mode Transmission Gate Diffusion Input (DMTGDI) design and is termed as M-DMTGDI. A contention issue in dynamic mode operation of existing DMTGDI design and DMPL design is identified and illustrated through mathematical formulation and simulations. To resolve this concern, the pre-charge/pre-discharge transistor in existing DMTGDI design is replaced by dual mode inverter in the proposal. The functional verification and performance comparison of NAND, NOR, XOR gates and 1-bit full adder based on proposed M-DMTGDI is carried out using 90 nm BSIM4 model card for bulk CMOS using Symica DE tool. The performance of the circuits is evaluated in terms of power, delay and Power Delay Product (PDP) in both static and dynamic modes. The variation of PDP with the ratio of the time the circuit is designed to run in dynamic mode against static mode is also investigated to analyze the energy efficiency of the M-DMTGDI design. The proposed approach offers maximum PDP reduction of 33.52%, 99.39% and 96.61% for 2-input gates as compared to their footed DML, DMPL and DMTGDI counterparts, respectively. The reduction in PDP is quite significant in 1-bit full adder circuit where the corresponding values are 94.18%, 99.41% and 99.79%, respectively. [ABSTRACT FROM AUTHOR]

Problem: In next-generation networks, users can optimize or tune their preferences with a seamless transfer of diverse access methodologies for maximizing the Quality of Service (QoS) and cost savings. In these heterogeneous wireless environments, users are prepared with several multimode wireless devices for maximizing media services through several access networks. Such networks may vary regarding energy usage, available bandwidth, technology, coverage range, monetary cost, etc. In recent days, vertical handover has attained higher performance owing to the improvements in mobility models through adopting the Fourth Generation (4G) technologies. On the other hand, these improvements are restricted to some cases, so, it does not offer support for generic mobility. Consequently, diverse strategies were implemented by considering these mobility models. However, it suffers from improper network selection, late and too-early handovers, repeated handovers, high packet loss, etc. Aim: This paper tackles the problem of vertical handover problem in the heterogeneous network using deep learning with game theory. Methods: The proposed model develops a non-cooperative game approach, in which all base stations compete selfishly to transmit at higher power. The overall performance in terms of throughput, handover, energy consumption, and load balancing is attained by optimizing the transmission power by the game theory. For performing this model, the required data like path loss, SINR, data rate, load, etc are generated by the deep learning called Recurrent Neural Network (RNN). Results: From the simulation findings, the handoff probability of the recommended RNN+Game Theory is correspondingly secured at 6.9%, 22.6%, and 8.2% superior to TOPSIS, ABC-PSO, and game theory when taking the time like 5 secs for user velocity as 30 km/h. Conclusion: Results show that the proposed game theoretical approach with deep learning provides a throughput enhancement while reducing the power consumption, in addition, to minimizing the unnecessary handover and balancing the load between base stations. [ABSTRACT FROM AUTHOR]

There are cases in energy supply applications with low-power batteries, where the power required by the load is larger than the supply maximum deliverable power. Using a capacitor to create a high instantaneous power for the load was investigated as a useful solution. To increase the charging efficiency, a stepwise method for charging the capacitor was employed. This paper presents a new pulsed power supply approach in which the charging process is resumed after high instantaneous power delivery. Through this, the loss of residual energy in the capacitor was decreased and load turn-on speed and energy efficiency were significantly improved. The proposed structure consists of a buck converter that charges the capacitor using inductor current. A pulse frequency modulation (PFM) technique was used to generate the necessary signals for the buck converter switches, which resulted in controlling the inductor current. The proposed circuit was designed and simulated in a 0.18 μ m CMOS technology. It was shown that the designed CMOS circuit could reach a peak energy efficiency of 93.2% with a sleep period of 1.05 ms when the load consumes 7.06 μ J of energy. [ABSTRACT FROM AUTHOR]

With the virtual synchronous generator (VSG) technology gradually becoming an emerging technology for new energy consumption, its introduction has solved the problems of weak support and weak anti-interference of traditional new energy stations. However, the practice shows that the grid-connected characteristics of VSG limit its large-scale utilization, and there is little research on the interaction between VSG and a multi-machine system. In this paper, small signal models and time domain simulation models of each link of a photovoltaic(PV) power station with the PV virtual synchronous generator (PV-VSG) are first conducted, and then the influence of the grid-connected PV power station with the PV-VSG on the low-frequency oscillation of power system and its interaction mechanism with the multi-machine system are qualitatively obtained based on the small signal analysis method and prony analysis method from two dimensions of frequency domain and time domain, respectively. The analysis and simulation show that the PV power station based on VSG technology affects the dynamic characteristics of the system by changing the electromagnetic torque of each synchronous generator. Unreasonable operating conditions and parameter settings will aggravate the phenomenon of low-frequency oscillation of the system. Before the grid connection of PV power station, the operating parameters shall be reasonably set to ensure their safe and stable grid connection. [ABSTRACT FROM AUTHOR]

In this paper, it has been demonstrated that the tri-stable energy harvesters are more efficient than the bi-stable and mono-stable harvesters in energy harvesting. The oscillators of energy harvesters can be designed as a vibration absorber to attenuate the vibration of the primary system. This paper presents a combined vibration absorber and energy harvester (VAEH) system for simultaneous vibration control and energy harvesting, and the dynamic performances of the VAEH with two magnet arrays of the same geometric parameters, but opposite polarity external magnets, are studied. The mathematical model of the tri-stable VAEH is established, by which the dynamic and energetic characteristics of the VAEH system are obtained. It is found that the potential barrier and potential well depth of the repulsive magnet array are greater than the attractive one, reducing the voltage harvesting efficiency under harmonic and impulsive excitation. Both the amplitude and frequency of excitation are crucial to the dynamic response of the VAEH system. The attractive VAEH performs better than the repulsive VAEH in vibration suppression, especially for high impulse input. From the frequency response of the system, the vibration of the primary system is shown to be magnified at certain frequencies. The magnified primary system vibration and the harvested voltage can be attenuated by increasing the VAEH damping, which is a critical parameter to balance the weight of vibration suppression and energy harvesting. The attractive VAEH designed in this paper performs better than the repulsive one in vibration suppression and energy harvesting. [ABSTRACT FROM AUTHOR]

With the rapid development of digital technologies based on information and communication technology (ICT), some studies suggest that the development of the digital economy is bound to have a significant impact on energy consumption. There are also studies demonstrating from different perspectives that ICT plays an important role in improving energy efficiency. Then, will the rapid development of the digital economy lead to a sharp increase in energy demand (electricity demand), thus affecting China's socio-economic transition to green and low-carbon development? To this end, it is of great practical significance to explore the relationship between the digital economy and energy consumption from theoretical and practical perspectives. Focusing on the issue of ICT and its impacts on energy consumption, this paper clarifies the internal influencing mechanism between ICT and energy consumption. On this basis, this paper compares and analyzes the models, methods, and data in existing studies from the aspects of measurement of ICT's direct energy consumption, empirical research on ICT and energy consumption, and discussions on rebound effects. After a brief summary, this paper identifies their shortcomings and explores future research directions. [ABSTRACT FROM AUTHOR]

An efficient method primarily depending on nonisolated quasi Z-source (QZS) novel multilevel inverter (NIQZS-NMLI) topology to interconnect photovoltaic (PV) system is proposed in this paper. The proposed hybrid system is the combination of improved chameleon swarm algorithm (ICSA) and recurrent perceptron neural network (RPNN), hence it is named as ICSA–RPNN. Usually, the interface among the PV DC supply, the load is achieved through an NIQZS-NMLI topology. Here, the NIQZS-NMLI topology modeling design is increased with the new storage device to supply the largest amount of power from the PV power production system. The number of switches and full harmonic distortion of the machine are reduced through the NMLI topology, and it is used to achieve better upgrading capability, and reduce the voltage pressure on the entire active switching device has a better modulation index. At first, the objective function is described primarily depending on its controller parameters and control voltages, current, power, and modulation code. These parameters are useful to the inputs of the proposed ICSA-RPNN approach. The ICSA-RPNN methodology enhanced the voltage profile, reducing power distribution, power oscillations simultaneously distributing power to the load. The modulation load is controlled by proposed artificial intelligence (AI) mainly depends on the NIQZS-NMLI topography. In addition, the ICSA-RPNN methodology reduces the injected power, controls the voltage, current and frequency conditions of the DC hyperlink. The ICSA-RPNN approach is performed at MATLAB/Simulink site and the general performance of the output is compared to the existing systems for different load conditions. [ABSTRACT FROM AUTHOR]

China's automotive industry has been dedicated to a series of carbon-reduction efforts and has strived for comprehensive green and low-carbon transformation in order to achieve carbon peaking and carbon neutrality goals. On the basis of automobiles' whole life-cycle (WLC) carbon emission accounting, this paper calculates life-cycle carbon emissions per vehicle, proposes green and low-carbon development path for China's automotive industry, quantifies and analyzes implicated carbon-reduction potential, and puts forth suggestions for high-quality green and low-carbon development of China's automotive industry. The first is to establish a sound standard and data management system; the second is to promote research, development and application of low-carbon materials and low-carbon technologies; the third is to accelerate the pace of fostering a new consumption model in the automotive industry. The research results can further support national policy-making regarding carbon emissions, promote corporations' research, development and application of low-carbon technologies, encourage green and low-carbon consumption, and lead the automotive industry to achieve WLC neutrality. [ABSTRACT FROM AUTHOR]

In the present era, Underwater Wireless Sensor Network (UWSN) is an emerging technology that involves a huge amount of sensor nodes to collect and monitor information from the underwater environment. However, the data transmission process is constrained due to the collision and energy consumption which can adversely affect the performance. Hence, there is an essential need to develop a suitable mechanism that addresses these challenges using a data aggregation-based routing mechanism in UWSN. In this paper, a Multi-Slot Scheduling with a Two-Layer Hexagonal based Integrated Aggregation model (MSS-TLHIA) is proposed that offers a prolonged lifetime with less energy consumption and collision avoidance. In this model, data aggregation is performed using the aggregator node selection process. Initially, the entire network is partitioned into several hexagonal grids using the golden ratio. This partitioning offers an improved coverage area for every node which are participating in the network. Once the network is partitioned into coverage areas called clusters, a Cluster Head (CH) is selected using the ranking-based fuzzy mechanism. Then, an aggregator node is selected in common for both the layers of the hexagonal grids. In order to prevent the energy drain of the aggregator node completely and to prolong their lifetime, the aggregator node is re-selected for every time slot. Furthermore, the occurrence of collision is avoided by the multi-slot scheduling process. Experimental results demonstrate that the performance of the proposed model is compared with other existing protocols and achieves better results in terms of network lifetime, energy consumption, collision rate, packet dropped rate, packet delivery, and data forwarding measures. [ABSTRACT FROM AUTHOR]

The increasingly complicated and versatile convolutional neural networks (CNNs) models bring challenges to hardware acceleration in terms of performance, energy efficiency and flexibility. This paper proposes a reconfigurable neural accelerator (RNA) for flexible and efficient CNN acceleration. To provide hardware flexibility, RNA employs dynamically reconfigurable computing framework to rapidly configure data path between processing elements (PE) at run-time, as well as an interlaced data access mechanism for multi-bank RAM. To achieve high energy efficiency, three optimization mechanisms, including image row broadcasting dataflow (IRBD), tile-by-tile computing (TTC), and zero detection technology (ZDT), are dedicatedly designed for RNA to exploit data reuse and decrease memory bandwidth requirement, which is the key to improving performance and saving power consumption. To save hardware overhead, an online dynamic adaptive data truncation (DADT) mechanism is designed to compensate accuracy loss of multiplication results so that the computational precision in RNA can be reduced from 16-bit to 8-bit, which contributes to reducing the area of data path. The RNA architecture is implemented on Xilinx XC7Z100 FPGA and works at 250 MHz. Experimental results show that the performance of running LeNet, AlexNet and VGG are 500 GOPS, 598 GOPS and 660 GOPS, respectively. Compared to previous FPGA-based designs, RNA achieves 1. 5 × − 4. 3 × performance speedup and 7. 6 × − 8. 4 × improvements on energy efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a deduplication-based mixed page strategy, called DM-Pages, to improve the energy efficiency of disk storage systems and the cache performance. DM-Pages strives to improve the cache hit rate, simultaneously reduce the number of power-state transitions of a disk storage system and keep it in a low-power state as long as possible. DM-Pages deploys three innovative techniques. It adjusts the size of huge pages dynamically to maximize the cache hit rate. Meantime, content-aware deduplication is applied to eliminate redundant cache pages, targeting to enhance cache utilization. In addition, DM-Pages incorporates a mixed page mechanism into disk energy management to save energy. Our experimental results show that DM-Pages significantly improves the energy efficiency of disk storage systems and achieves a high cache hit rate. In particular, compared with traditional disk storage systems, DM-Pages conserves 6.59 times energy and improves the cache hit rate up to 3.36 times. [ABSTRACT FROM AUTHOR]

The Underwater Wireless Sensor Network (UWSN) is capable of supporting a wide range of low-data-rate acoustic sensor networks, as well as scalability and energy-efficient routing algorithms. Furthermore, because of the lower bandwidth and longer propagation delays, energy consumption is a major concern with underwater networks. Because radio transmissions are unstable in deep water, a UWSN often communicates via acoustic channels. UWSN features create constraints on data packet transmission and energy-efficient routing. The hardest challenge is creating an effective routing protocol for UWSNs that uses sensor node localization. There have been several routing algorithms reported for identifying nodes via a localization process. This paper proposes a novel vector-based forwarding (NVBF) and efficient depth-based routing (DBR) protocol to provide robust, scalable, and energy-efficient routing. Efficient DBR is a stochastic model that can survive even in acoustic channels with substantial transmission loss. An adjacent node table is created to decrease the energy consumption and end-to-end delay by limiting the adjacent node request generated every single time. The redundant packet transmission is reduced by creating a packet queue which also minimizes the energy. The NVBF algorithm uses a random waiting time to overcome the collision that occurs while sending the acknowledgment packets. Based on the previous sender's depth and the depth of the own node, the DBR makes packet transmission decisions. The experiments are conducted using the NS3 simulator and the efficiency of the proposed approach is evaluated in terms of propagation delay, network lifetime, energy consumption, and delivery ratio. When compared to the existing techniques such as DBR, VBF, and energy-efficient DBR (EDBR), the proposed methodology offers improvement up to 40% and 30% in terms of propagation delay and delivery ratio. [ABSTRACT FROM AUTHOR]

The crucial role played by digital multipliers in VLSI system design makes it indispensable to study the PPA cards of digital multiplier architectures before tailoring multiplier architecture into such systems. Therefore, in this work, we present a detailed investigation and analysis of PPA cards of popular digital multiplier architectures, for various wordlengths. Each multiplier has been implemented on Artix-7 FPGA xc7a200tfbg676-2 and analyzed using Xilinx Vivado Design Suite 2019.2. A major contribution in this paper is the study of energy profiles of the architectures, not focused on in the earlier literature. This study compares their device utilization, timing parameters, power consumption, and energy profiles. Results indicate that Dadda Tree Multiplier and Wallace Tree Multiplier (460 slices and 557 slices, respectively, for N = 3 2 bits) are undoubtedly the least slices consuming fast multipliers, but their power consumption and energy density are high as well. For 32-bit implementation, Vedic Multiplier consumes 48.9% lower power than Dadda Tree Multiplier, while the latter occupies 30.42% lesser slices. The speed of the Vedic Multiplier is in close proximity with the tree multipliers. The energy density of Vedic Multiplier (0.3 nJ/slice) is much lower than that of Dadda Tree Multiplier (0.77 nJ/slice). [ABSTRACT FROM AUTHOR]

Wireless sensor networks' energy consumption is the major challenge to be handled. Clustering is one of the techniques majorly used for reducing energy consumption. During the course of time, many methodologies are being proposed and the existing ones are hybrid. Still, the energy can be reduced more. The scope of optimization is always there. Existing approaches either reduce energy consumption or work on routing or only on data gathering capabilities. But the technique proposed increases the lifetime of the wireless sensor network (WSN) by reducing energy consumption and improves routing efficiency. This paper proposes an approach based upon Fractal Clustering to improve the lifetime of the sensor nodes. The proposed approach named Enhanced Energy Efficient Fuzzy-based Fractal Clustering (EEFFC) algorithm optimizes the performance of WSN. First, fractal clustering is used on sensor nodes to find the location of the sensors. Then, a fuzzy inference system (FIS) is applied to results produced by fractal clustering. Applying FIS on cluster heads generated will optimize the results. As a result, the cost of data transmission will reduce, and hence, the lifetime of the network will improve. FIS generates multi-level clustering, which will result in a better routing path for sensor nodes. Hence, routing will also be improved. MATLAB 2020 is the simulation tool. The results of the simulation depict that EEFFC shows optimized results and it works better than LEACH, LEACH-SF, TEEN and DEEC. The energy consumption is being reduced by reducing the listening time of a node and by reducing the communication distance, for which clustering is optimized. The energy consumption has been reduced by 2% as compared to the algorithms it is compared with. Also, the node's time of death has been delayed by 3% in total. [ABSTRACT FROM AUTHOR]

To explore the partition optimization and stability of telecommunication network under nonlinear control, a routing optimization algorithm for power control of distribution communication network is proposed, which is based on the Ad-hoc On-demand Distance Vector (AODV) routing optimization algorithm, based on the energy consumption model of wireless sensor network, the optimal routing mechanism for power control is constructed, and the optimal routing mechanism is used to select the route with low energy consumption to solve the problem of energy limitation of wireless sensor nodes. This paper aims at the network routing selection problem in the terminal information collection of the distribution network and designs the routing optimization algorithm of the wireless sensor network. Experimental results show that the model accuracy of the optimized model is 99.8%, the average residual error of the model is about 5.5 nm, the model accuracy meets the requirements, and the method is effective. According to the confidence interval prediction results of wireless communication link reliability, a node power control system structure of wireless sensor network is proposed, and an optimal control algorithm of communication link quality and reliability of microgrid wireless sensor network based on fuzzy control is studied. [ABSTRACT FROM AUTHOR]

This paper evaluates the low-carbon development of 176 Chinese cities in 2018 using the Low-carbon City Evaluation Indicator System developed by Research Institute for Eco-civilization, Chinese Academy of Social Sciences. The evaluation shows an overall improvement of low-carbon development of cities in China, with 10 cities scoring 90 points and above, and 106 cities scoring 80–89 points. From the perspective of city category, the low-carbon development scores present an order as follows: service-oriented cities > comprehensive cities > ecology -prioritized cities > industrial cities. Geologically speaking, eastern cites perform best, western cities come second and central cities come last. Of the three batches of low-carbon city pilots, the scores follow the pattern of Batch 1 > Batch   2 > Batch 3. Most cities are characterized by high carbon consumption and reduced efforts in low-carbon management and capital investment. Finally, this paper proposes to attach more importance to low-carbon development and strengthen capacity building for low-carbon governance; intensify efforts for constructing low-carbon pilots in order to help achieve the goal of carbon emission peak during the "14th Five-Year Plan" period; and to improve the Low-carbon City Evaluation Indicator System. [ABSTRACT FROM AUTHOR]

In this paper, we propose a low-jitter reference-less clock and data recovery (CDR) circuit with a speed range of 9.8–12.5 Gb/s. The proposed CDR uses a multi-stage half-rate bang-bang phase detector (ML-HR-BBPD) to maximize the quantization of the phase difference. In addition, a unit interval adjuster (UIA) is added to the CDR circuit. So that the circuit can minimize the phase detector's phase error before the output clock frequency is locked. Finally, the loop filter (LF) is improved to realize the coarse and fine adjustment of the phase error over a wide range of phase differences. The CDR circuit's total power consumption is reduced by using a half-rate phase detector. The CDR circuit was fabricated in TSMC 40 nm CMOS process. The measured results are obtained in the proposed CDR circuit at a data rate of 12.5 Gb/s. With a pseudo-random bit sequence (PRBS) of 2 3 1 − 1 , the measured result shows that the bit error rate (BER) is < 1 0 − 1 2 , and the root mean square jitter recovered in the output is 0.302 p s rms . The circuit's jitter tolerance (JTOL) is 0.46 UIpp, and its total power consumption is 74.8 mW with a 5.98 pJ/bit energy efficiency. [ABSTRACT FROM AUTHOR]

In this research on range query technology for two-tiered wireless sensor networks, the existing researches still need improvement in privacy security and communication cost of the sensor node. In this paper, a security multi-dimensional range query protocol is proposed. The left 0-1 encoding mechanism (a new and optimized encoding mechanism), HMAC algorithm, and BASE64 encoding are employed to optimize the calculation of comparison factor. The encryption index chain is constructed by using AES algorithm to replace the long encryption constraint chain, so as to lower the energy consumption of computation and communication. The base station can verify the authenticity and integrity of query results on the basis of continuity of storage time and encryption index chain. The experiment has been conducted in the real systems. The protocol is compared and analyzed with the latest proposed Communication-Efficient Secure Range Queries (CSRQ). The results have shown that the proposed protocol can gain a better performance in terms of four aspects, i.e., the length of sensing data, the number of data collected in each cycle, the number of data dimensions and the number of sensor nodes in each unit. Meanwhile, compared with CSRQ, the communication cost of the proposed protocol is reduced by at least 10%. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) allude to gathering of spatially fragmented and committed sensors for observing and documenting various physical and climatic variables like temperature, moistness and, so on. WSN is quickly growing its work in different fields like clinical, enterprises, climate following and so on. However, the sensor nodes have restricted battery life and substitution or re-energizing of these batteries in the sensor nodes is exceptionally troublesome for the most parts. Energy effectiveness is the significant worry in the remote sensor networks as it is significant for keeping up its activity. In this paper, clustering algorithms based on sensor module energy states to strengthen the network longevity of wireless sensor networks is proposed (i.e. modified MPCT algorithm) in which cluster head determination depends on the every cluster power centroid as well as power of the sensor nodes. Correspondence between cluster leader and sink module employ a parameter distance edge for lessening energy utilization. The outcome got shows a normal increment of 60% in network lifetime compared to Low energy adaptive protocol, Energy efficient midpoint initialization algorithm (EECPK-means), Park K-means algorithm and Mobility path selection protocol. [ABSTRACT FROM AUTHOR]

Neuromorphic computing plays an important role in brain-inspired computers, which can solve complex problems with higher efficiency and lower energy. As a promising candidate of neuromorphic devices, a locally active memristor has the ability to emulate complex neuromorphic functions, which has the potential to implement large-scale hardware spiking neural networks to unleash the efficiency and intelligence of the brain. In this paper, we analyze the characteristics of the locally active 4-lobe Chua corsage memristor (CCM) using small-signal equivalent circuits. In addition, we design a CCM-based third-order neuron circuit and illustrate its rich neuromorphic behaviors near the edge of chaos, such as self-sustained oscillation, periodic spiking, chaos, transient chaos, resting states, burst-number adaption, spike latency, and refractory period. Furthermore, collective behaviors such as exotic chimera states (e.g. coherent and incoherent patterns) have been observed in the ring neural network made of third-order memristive neurons with linear and nonlocal coupling. [ABSTRACT FROM AUTHOR]

Memristors can be used to build nonvolatile memory systems with in-memory computing (IMC) capabilities. A number of prior works demonstrate the design of an IMC-capable memory macro using a memristor crossbar. However, read disturbance limits the use of such memory systems built using a 0-transistor, 1-RRAM (0T1R) structure that suffers from the sneak path problem. In this paper, we introduce a scheme for both memory and logic operations using the 1-transistor, 1-RRAM (1T1R) memristor crossbar, which effectively mitigates the read disturbance problem. The memory array is designed using nMOS transistors and the VTEAM memristor model. The peripheral circuitry like decoders, voltage multiplexers, and sense amplifiers is designed using a 45 nm CMOS technology node. We introduce a mapping technique to realize arbitrary logic functions using Majority (MAJ) gate operations in the 1T1R crossbar. Through extensive experimentation on benchmark functions, it has been found that the proposed mapping method gives an improvement of 65% or more in terms of the number of time steps required, and 59% or more in terms of energy consumption as compared to some of the recent methods. [ABSTRACT FROM AUTHOR]

In the modern Block-chain and Artificial Intelligence era, energy efficiency has become one of the most important design concerns. Approximate computing is a new and an evolving field promising to provide energy-accuracy trade-off. Several applications are tolerant to small degradation in results, and hence tasks like image and video processing are candidates to benefit from Approximate Computing. In this paper, we propose a new design approach for designing approximate adders and further optimize the accuracy and cost metrics. Our approach is based on minimizing the errors while cascading more than one 1-bit adder. We insert | error | = 1 on specific locations to achieve a reasonable circuit minimization and reduce the error × count gate cost. We compare our design with exact adder and relevant state-of-the-art approximate adders. Through analysis and simulations, we show that our approach provides higher accuracy and far better performance compared with other designs. The proposed double bit approximate adder provides more than 25% savings in gate count compared with the exact adder, has a mean absolute error of 0.25 which is lowest among all the reference approximate adders and reduces the power-delay product by more than 60% compared to the exact adder. When employed for image filtering, the proposed design provides a PSNR avg of 96%, a SNR avg of 95% and a SSIM avg of 91% relative to the actual results, while the second best approximate adder only achieves 64%, 54% and 71% of these image quality metrics, respectively. [ABSTRACT FROM AUTHOR]

A novel multiband impulse radio ultra-wideband (MB-IR-UWB) transmitter with an energy-efficient and low-complexity pulse generator design technique compliant with the IEEE 802.15.4a standard is investigated in this paper. This transmitter is made up of a new differential narrow triangular pulse generator, a new multiband voltage-controlled oscillator (VCO), an active mixer and a variable-gain power amplifier (PA). It operates at 14 UWB bands and generates an output signal with the duration of 3 ns, more than 500 MHz of channel bandwidth and more than 20 dB of sidelobe suppression, while achieving an average energy efficiency of 51.36 pJ/pulse from 1.4-V supply. Improving spectral flexibility and worldwide compliance is the major contribution brought by this paper to make this circuit as a multifunction wireless device well suited for low-cost, low-power multiband applications. [ABSTRACT FROM AUTHOR]

This paper presents the adiabatic logic called 2 N – N –2 P , which can operate with less number of transistors and high energy efficiency than the existing circuit styles. It is a dual rail logic operated by four-phase power clock (PC). The 2 N – N –2 P adiabatic logic is capable of operating through a wide range of frequency from 100 MHz to 1 GHz. Relentless scaling of MOSFETs towards lower technology nodes results in short channel effects in addition to increasing higher leakage current issues. In this scenario, FinFET advantageously replaces MOSFET with its unique features of the elimination of the short channel effects encountered by the MOSFETs with its gate structure that wraps around the channel completely. It incurs that the lower energy consumption and the feasibility of designing energy recovery circuits using FinFETs are analyzed in this paper. Comparatively, the energy efficiency of FinFET-based 2 N – N –2 P against the 2 N 2 N 2 P and Positive Feedback Adiabatic Logic (PFAL) are analyzed. Simulation results also validate the robustness and efficiency of 2 N – N –2 P adiabatic logic circuit under process parameter variations of FinFET technology. Complex adiabatic adders and multipliers taken as bench mark circuits have been designed using 32-nm FinFET technology node and the results validate the enhanced energy efficiency characteristics of 2 N – N 2 P over 2 N 2 N 2 P and PFAL designs. [ABSTRACT FROM AUTHOR]

A mobile community can be composed of multiple mobile devices through D2D (Device-to-Device) network. In many cases, these mobile devices cannot conveniently connect to the Internet, for various reasons. To overcome this obstacle, one solution is to let the mobile devices cooperate with each other through a D2D-enabled network, forming a mobile community that, as a whole, may be able to autonomously execute the tasks requested by its members. To maximize the overall benefits of mobile communities, this paper proposes a novel task allocation approach, EDTG (Energy-aware and Deadline-constrained Task allocation using Game theory). In mobile communities, energy consumption is responsible for the largest part of the cost. Energy management can lead to performance degradation and even be perceived as a bottleneck, while load balancing between devices can improve service performance and resource utilization to the largest extent. EDTG has considered both the inevitable performance constraints at each device and a method based on the connectivity of graph theory, in order to narrow down the search scope of optimal target mobile devices where requested tasks can be executed. The "Bargaining Game" method is designed and exploited to obtain the final task allocation solution. Final experimental results demonstrate that compared to existing approaches, EDTG ensures high-performance task execution and reaches the goal of maximizing the overall benefits to some extent, by achieving better energy savings and exploiting load balancing between devices. [ABSTRACT FROM AUTHOR]

In this paper, a low-cost rectenna based on an antipodal Vivaldi antenna (AVA) with elliptical slots (AVA-ES) is developed and analyzed. The design of AVA-ES, when compared to a reference AVA, presents significant improvements in directivity and gain (58% at 2.45 GHz for both), evidencing its advantages for using in rectennas. The proposed rectenna prototype presents satisfactory results at 2.45 GHz, such as conversion efficiency for low-power input levels (27% at − 10 dBm) and maximum output voltage (550 mV), that supports its use for radio-frequency energy harvesting (RFEH). [ABSTRACT FROM AUTHOR]

Energy harvesting devices have emerged as a promising technology to not only meet global energy demands but also power biomedical electronics. The dramatic advancement in self-powered biomedical electronics used for monitoring and treatment of severe diseases is part of a paradigm shift that is on the horizon. The review paper highlights recent progress on energy harvesters for scavenging energy to realize self-powered systems. The emphasis is mainly on piezoelectric and triboelectric nanogenerators addressing the basic operating principle, electrical model, design techniques, newly developed materials and their performance as well as associated typical applications. Herein, piezoelectric devices have been compared on basis of their materials, energy conversion efficiency, piezoelectric coefficient and power harvesting circuit. In addition, the recent advances of hybrid nanogenerators in terms of its biomedical applications are also highlighted. Finally, the conclusions and future prospects towards self-powered systems for implantable and wearable medical electronic devices are discussed for effective health monitoring, bio-sensing and clinical therapy. [ABSTRACT FROM AUTHOR]

Reversible computations have attracted a lot of attention in recent years due to the ability to reduce energy dissipation that is needed in nanocircuits. The main purpose of designing reversible circuits is to reduce the energy consumption that occurs due to the loss of input bits in irreversible circuits. In this paper, we initially proposed a new 5 × 5 reversible block and then its quantum realization is given using the Miller et al. method. In the following, an effective reversible design of D flip-flop is introduced using the proposed reversible block. Finally, four types of reversible shift register including serial-in to parallel-out (SIPO), parallel-in to parallel-out (PIPO), parallel-in to serial-out (PISO) and shift counter are proposed using the proposed reversible flip-flop. The evaluation results show that the proposed circuits are superior compared to the existing designs in terms of quantum cost (QC). [ABSTRACT FROM AUTHOR]

China accounts for more than 22% of the total energy consumption worldwide. Building energy consumption, among which consumption in public buildings was about 40% took the second place. With the problems of high energy waste, error rate and complexity of the control systems available, an indoor intelligent lighting system based on occupants' location is proposed in this paper to improve the energy efficiency of the current lighting systems indoors. The transmission model of electromagnetic wave in free space is optimized in both aspects of reference signal strength and attenuation coefficient radiation in complex environment dynamically based on which occupants' positions are obtained. The smart lighting system will turn on or off corresponding lights adaptively to provide a more energy efficient platform. Experimental results show that the proposed system is able to improve the energy efficiency of indoor lighting by at least 15%, with a lower error rate below 2% compared with the existing lighting systems based on voice control. [ABSTRACT FROM AUTHOR]

Prolonging the lifetime of wireless sensor networks (WSN) is the biggest challenging issue. In wireless multimedia sensor networks (WMSNs), sensor nodes have limited energy resource and to increase the lifetime of the network, it is necessary to design an effective fast algorithm that aims at reducing the consumed power. This paper proposes an energy-efficient discrete cosine transform (DCT) approximation requiring only 12 additions. Associated with a JPEG compression chain, this DCT approximation ensures a very good rate-distortion compromise, but above all, a very low computational complexity and significant compatibility with the exact DCT. Simulation results clearly show that the proposed fast transform algorithm achieves a better trade-off between image quality, computational complexity and energy consumption compared to any existing pruned DCT approximations. Furthermore, it is very suitable for the resource constrained wireless visual sensor networks (WVSNs) requiring low bitrates. [ABSTRACT FROM AUTHOR]

In fifth generation (5G) systems, green heterogeneous network (HetNet) is capable of achieving energy efficiency by densely deploying renewable-powered small cells. However, the small cells may suffer performance degradation due to the limited backhaul from macro base station (BS) and renewable intermittency. In this paper, we introduce a distributed HetNet architecture in which the renewable-powered small cell BSs collaboratively exchange information and allocate the spectrum and power resources by themselves. Considering the uncertainty of the available spectrum, renewable energy supply and traffic loads, a stochastic optimization problem is formulated to maximize the energy efficiency for distributed small cell BSs. A distributed resource allocation algorithm is proposed to obtain the optimal spectrum and power allocating strategies for each small cell. Finally, the numerical results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Capacitor mismatch plays an important part in the spurious-free dynamic range (SFDR) performance of high-resolution successive approximation register (SAR) analog-to-digital converters (ADCs). This paper presents a symmetric group control dynamic element matching (SGCDEM) method aiming at enhancing SFDR of SAR ADCs applied in biomedical applications. The proposed scheme symmetrically divides the capacitor array into four sub-DACs, using quantization results corresponding to each sub-DAC as rotary control codes to symmetrically control the rotation of the capacitors. In order to improve the energy efficiency and save chip area, the proposed method replaces the pseudo-random number generator (PRNG) with quantization results of ADC as the rotary control code. Moreover, split rotator technology is applied in the proposed scheme to reduce the number of multiplexers (MUX) used in the rotator, which introduces a low logic depth. The proposed architecture has been implemented on a 12-bit SAR ADC model in MATLAB in order to verify its performances. After taking a standard deviation of 1% for the unit capacitance into consideration, this method achieves above 77.3 dB SFDR, enabling to correct the SFDR by more than 23.6 dB compared with the structure without DEM. [ABSTRACT FROM AUTHOR]

This paper presents two novel current-mode Full Adders based on Carbon Nanotube FET (CNFET). They comprise of three main parts: current-to-voltage converter, threshold detector and voltage-to-current converter. While other designs in the literature use either inverter or current source for detecting threshold, the new designs are based on a heuristic usage of an Ultra Low-Power Diode (ULPD) as voltage regulator. The use of diode does not lengthen the critical path and thereby ensuring high-speed operation. It does not increase power consumption significantly either. Simulation results by HSPICE and 32nm CNFET technology file demonstrate at least 19% higher performance in terms of energy consumption for the second proposed Full Adder compared to other traditional and state-of-the-art current-mode and mixed-mode designs. [ABSTRACT FROM AUTHOR]

With the rapid development of embedded systems, users and services have been greatly facilitated while also experiencing security threats as a result of cyber-attacks and system vulnerabilities. Currently, the real-time embedded system (RTES) focus is to deal with these security issues. In this paper, we introduce a short review of security-aware techniques for RTES. We mainly discuss two common approaches to improve the security of RTESs. The first approach is achieved by exploring specific attacks. The second approach is realized by deploying security-guaranteed services. However, improving the security of embedded systems may cause excessive energy consumption at the same time. Therefore, we investigate the secure and energy-aware RTESs on a wide range of research. In addition, we study a number of common applications used in secure RETSs. This paper stands for providing awareness and better understanding of the current RTES research status as well as technical theory behind it. Hence, the RTES security issues are resolved. [ABSTRACT FROM AUTHOR]

In this paper, we propose a system-level optimization approach for mixed-criticality distributed real-time systems with safety and energy considerations. We firstly depict a mixed-criticality distributed task model for real-time applications, in which the safety of the system is influenced by dynamic voltage and frequency scaling (DVFS). Due to the huge complexity of solving the problem optimally, a heuristic algorithm is proposed to approach the system-level optimization through a quasi-static scheduling strategy. The experiments demonstrate the efficiency of the proposed approach, which can obtain energy consumption while guaranteeing the system safety requirements. [ABSTRACT FROM AUTHOR]