Strict Energy Research Articles

μBrain: An Event-Driven and Fully Synthesizable Architecture for Spiking Neural Networks.

The development of brain-inspired neuromorphic computing architectures as a paradigm for Artificial Intelligence (AI) at the edge is a candidate solution that can meet strict energy and cost reduction constraints in the Internet of Things (IoT) application areas. Toward this goal, we present μBrain: the first digital yet fully event-driven without clock architecture, with co-located memory and processing capability that exploits event-based processing to reduce an always-on system's overall energy consumption (μW dynamic operation). The chip area in a 40 nm Complementary Metal Oxide Semiconductor (CMOS) digital technology is 2.82 mm2 including pads (without pads 1.42 mm2). This small area footprint enables μBrain integration in re-trainable sensor ICs to perform various signal processing tasks, such as data preprocessing, dimensionality reduction, feature selection, and application-specific inference. We present an instantiation of the μBrain architecture in a 40 nm CMOS digital chip and demonstrate its efficiency in a radar-based gesture classification with a power consumption of 70 μW and energy consumption of 340 nJ per classification. As a digital architecture, μBrain is fully synthesizable and lends to a fast development-to-deployment cycle in Application-Specific Integrated Circuits (ASIC). To the best of our knowledge, μBrain is the first tiny-scale digital, spike-based, fully parallel, non-Von-Neumann architecture (without schedules, clocks, nor state machines). For these reasons, μBrain is ultra-low-power and offers software-to-hardware fidelity. μBrain enables always-on neuromorphic computing in IoT sensor nodes that require running on battery power for years.

An Algorithm to Minimize Energy Consumption and Elapsed Time for IoT Workloads in a Hybrid Architecture.

Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to cloud computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in mobile edge computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among cloud or mobile edge computing (MEC) server or local IoT devices to achieve better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.

Open system dynamics from thermodynamic compatibility

Thermodynamics entails a set of mathematical conditions on quantum Markovian dynamics. In particular, strict energy conservation between the system and environment implies that the dissipative dynamical map commutes with the unitary system propagator. Employing spectral analysis we prove the general form of the ensuing master equation. We compare this result to master equations obtained from standard microscopic derivations. The obtained formal structure can be employed to test the compatibility of approximate derivations with thermodynamics. For example, it designates that global master equations are the compatible choice. The axiomatic approach sheds light on the validity of the secular approximation in microscopic derivations, the form of the steady state in heat transport phenomena, and indicates the lack of exceptional points in the dynamics of open quantum systems.

The potential for the Passive House standard in Longyearbyen – the High Arctic

Passive building design reduces a building’s energy consumption through mainly non-mechanical design strategies. The Passive House (or Passivhaus) Standard certifies such buildings that comply with its strict energy performance criteria. Achieving the Standard is very challenging for dwellings in extreme climates. There is limited knowledge of the Standard’s potential in Arctic regions, particularly the High Arctic. Through a review of the literature and energy modelling of a hypothetical dwelling, the challenges in achieving the Standard in Longyearbyen (78°N), Norway are investigated. Very low temperatures and 112 days without daylight create a high heating demand. Whereas previous studies measured actual building performances or used simple calculations, the findings in this investigation show the limitations of individual design parameters and technical limits of the building envelope. In theory the Standard can be achieved in Longyearbyen; however, the potential in practice is low due to the very tight margins in the heating criteria. The results show the significant impact of applying contextual (climatic) adjustments to the boundary conditions of the Standard. The investigation could contribute to a discussion on modifying the Passive House Standard for dwellings in the High Arctic and improving building design for the region. Practical application: Current knowledge regarding energy efficient building performance in Arctic climates is limited, while the urgency for improved efficiencies is extremely high. The modelling in this work shows the valuable impact of contextual adjustments to the Passive House boundary conditions; the impact of individual design parameters; and the potential for significant energy savings through adopting passive house principles for dwelling design in Longyearbyen or similar climates. This investigation could encourage new policy making, additional research and the development of an optimized Passive House Standard that considers High Arctic climate conditions, thus encouraging new energy efficient building construction in cold climates.

Scalable Output Linear Actuators, a Novel Design Concept Using Shape Memory Alloy Wires Driven by Fluid Temperature

In this paper, the concept of scalability for actuators is introduced and explored, which is the capability to freely change the output characteristics on demand: displacement and force for a linear actuator, angular position and torque for a rotational actuator. This change can either be used to obtain power improvement (with a constant scale factor), or to improve the usability of a robotic system according to variable conditions (with a variable scale factor). Some advantages of a scalable design include the ability to adapt to changing environments, variable resolution of step size, ability to produce designs that are adequate for restricted spaces or that require strict energy efficiency, and intrinsically safe systems. Current approaches for scalability in actuators have shortcomings: the method to achieve scalability is complex, so obtaining a variable scaling factor is challenging, or they cannot scale both output characteristics simultaneously. Shape Memory Alloy (SMA) wire-based actuators can overcome these limitations, because its two output characteristics, displacement and force, are physically independent from each other. In this paper we present a novel design concept for linear scalable actuators that overcome SMA design and scalability limitations by using a variable number of SMA wires mechanically in parallel, immersed in a liquid that transmits heat from a separate heat source (wet activation). In this concept, more wires increase the maximum attainable force, and longer wires increase the maximum displacement. Prototypes with different number of SMA wires were constructed and tested in isometric experiments to determine force vs. temperature behavior and time response. The heat-transmitting liquid was either static or flowing using pumps. Scalability was achieved with a simple method in all tested prototypes with a linear correlation of maximum force to number of SMA wires. Flowing heat transmission achieved higher actuation bandwidth.

Methods to improve the efficiency of the business process of repairing power-generating equipment

The paper considers the methods to improve the efficiency of the business process of repairing power-generating equipment, as one of the main directions for increasing the efficiency and safety of the company activity as a whole. The efficiency of power generating companies is currently determined by the quality of the field-oriented and supporting business processes arrangement. Companies in the power-generating sector have strict environmental and energy security requirements. Accidents at production and transport facilities can cause irreparable environmental deprivations. Also, the failure of the mains sections can completely stymie the work of large settlements, and endanger the life and health of people in the cold season. The performance of the equipment can be impaired as a result of natural causes (corrosive wear, metal fatigue, and other deformations) and due to violation of the operating mode. The good condition of the processing equipment is ensured by the maintenance and repair works performed in time. Company's experience has proven that, for the safe operation of processing equipment, it is necessary to annually appropriate funds in the amount of at least 2% of the initial cost of the equipment. The main focus should be on planning and arranging the processes involved. Safe operation can also mean availability to purchase equipment necessary for production tasks. Many large power generating companies are trying to act under the principle of technological self-sufficiency. It implies the presence of technological developments in intellectual property, as well as production facilities. The implementation of this principle is possible both through the development of own scientific resources, and through the absorption of industrial enterprises. All this gives a competitive advantage to power generating companies, as well as expands the range of services provided.

Innovation and environmental total factor productivity in China: the moderating roles of economic policy uncertainty and marketization process.

China's economic development practices are facing strict energy constraints and severe environmental pollution. Improvement of China's environmental total factor productivity (ETFP) through innovation is a major scientific focus of both policy-makers and academia. However, the boundary conditions where innovation affects ETFP are yet to be clarified. On this basis, the present study starts from two dimensions-policy context and market context, introduces economic policy uncertainty (EPU) and marketization process as moderating variables, and examines the impact mechanism of innovation on ETFP. The results show the following: (1) Innovation promotes ETFP. (2) Although EPU inhibits ETFP, it positively moderates the impact of innovation on ETFP. (3) The marketization process has an insignificant inhibitory effect on ETFP. Specifically, the development of product markets, the development of market intermediary organizations, and the legal system environment have a significant inhibitory effect on ETFP. In contrast, the marketization process positively moderates the impact of innovation on ETFP. (4) The impact of innovation on ETFP exhibits obvious regional heterogeneity. In regions with low pollution intensity, innovation and EPU promotes ETFP. The marketization process inhibits ETFP and shows a positive moderating effect. In regions with high pollution intensity, both innovation and EPU inhibits ETFP. Based on these conclusions, policy-makers should guide companies to increase investment in research and development, improve innovation capabilities, and focus on strengthening energy-saving and environmental protection technologies and process innovations. When formulating and adjusting economic policies, policy-makers should aim promote enterprise innovation and reduce the negative impact of economic policy uncertainty. Policies should fully utilize the decisive role of the market in the allocation of innovative factors and rely on market mechanisms to enhance the positive effect of innovation on regional ETFP.

Hydrodynamics and gas-liquid mass transfer in a cross-flow T-junction microchannel: Comparison of two operation modes

Hydrodynamics and gas-liquid mass transfer of CO2-monoethanolamine (MEA) aqueous solution were investigated experimentally in a cross-flow T-junction microchannel. Two operation modes were compared, namely OM-1 (gas entering the branch channel, liquid flowing into the main channel) and the opposite feed mode OM-2 (gas entering the main channel, liquid flowing into the branch channel). The effects of gas and liquid rates and MEA concentrations in the two operation modes were studied in terms of bubble and liquid slug lengths, specific interfacial area, liquid-side mass transfer coefficient (kL), liquid-side volumetric mass transfer coefficient (kLa) and pressure drop (Δp). The results show that the variations of kLa and Δp with the total rate of two phases present a turning point in both two operation modes. Before the turning point, the gas rate is the key factor increasing the kLa and Δp, after that, the liquid rate would predominate. Notably, kL is more likely to be affected by the liquid rate. Comparing with OM-2, more dispersed gas-liquid distribution in OM-1 can be observed, which enhances the mass transfer but simultaneously increases the pressure drop. However, OM-2 is beneficial to a narrower residence time distribution in the liquid phase and better mass transfer performance under a strict energy requirement. This work could provide guidance for the operation of a cross-flow T-junction microreactor based on specific product requirements and energy consumption restrictions.

Authority–enterprise equilibrium based mixed subsidy mechanism for carbon reduction and energy utilization in the coalbed methane industry

As coalbed methane, the natural gas adsorbed in coal seams, is a greenhouse gas that is 21 times stronger than CO2, coalbed methane extraction significantly influences carbon reduction and energy conservation efforts. While the injection of CO2 into coal seams is known to effectively enhance coalbed methane recovery, this process has been limited because of high investment and production costs. Therefore, to promote the application of CO2 injection techniques in coalbed methane industry, this paper proposes an authority–enterprise equilibrium based mixed subsidy mechanism which combines direct subsidies with indirect tax-incentives. Specifically, a multi-objective bi-level programming model is established under uncertainty to assign a practical constraint and achieve a trade-off between the local authority and the coalbed methane plants. A practical case validates the feasibility and efficiency of the proposed method, proving that an authority–enterprise equilibrium based mixed subsidy mechanism is able to achieve carbon emissions reductions and conserve energy. Further analysis indicated that the marginal energy efficiency gains were greater than the authority costs under a strict energy utilization policy and that the environmental protection target had larger impacts on the coalbed methane plant performances.

Evidence of speculative bubbles and regime switch in real estate market and crude oil price: Insight from Saudi Arabia

AbstractThe sector performances of many economies especially the oil‐producing ones, are consistently linked with the volatility of the international crude oil prices. Considering the significance of the international crude oil price to the economy of the Saudi Arabia, the current study investigates the dynamics of the crude oil price and the country's real estate market. This is achieved by investigating the evidence of both speculative bubbles and regime switching in the crude oil and real estate market from October 3, 2005, to March 29, 2018. As such, the Supremum Augmented Dickey–Fuller (SADF) and Markov switching regression model were employed. Although there is no significant evidence of speculative bubbles in the Saudi Arabia's real estate market, the result reveals statistical significant evidence of price bubbles in the Brent crude oil price especially between the period of 2007 to 2008, which coincides with the Global Financial Crisis period. However, the Markov switching investigation reveal that there is evidence of significant and positive impact of crude oil price return on the real estate market in the regimes. Importantly, empirical evidence shows that the impact is higher in Regime 2 even as the global factor (proxy volatility index) is evidently significant. Although the regimes are persistent, the expected regime duration of the stable regime is of the higher quarter. This investigation is statistically significant and robust, especially when Organization of the Petroleum Exporting Countries (OPEC) price of crude oil is used in lieu of the European Brent price. The estimation result encourages more strict energy policies that tackle the Saudi Arabia's energy and the real estate challenges.

The dynamics of crude oil price and the real estate market in Saudi Arabia: A Markov‐switching approach

The volatility evidence of the International crude oil prices has overtime been linked to the dynamics and sector performances of states' economies. By employing the Markov switching regression model over daily time series data from October 3, 2005 to March 29, 2018, this study examines the response of the Saudi Arabia real estate market to the crude oil price dynamics. An empirical observation revealed that there is statistical evidence of significant and positive impact of crude oil price return on the real estate market in the regimes. Am empirical evidence shows that the impact is higher in Regime 2 even as the global factor (proxied by volatility index) is evidently significant. Although the regimes are persistent, the expected regime duration of the stable regime is of the higher quarter. This investigation is statistically significant and robust, especially when OPEC price of crude oil is used in lieu of the Europe Brent price. By a further estimation techniques, it affirms that the model exhibits nonlinearity rather than a linear relationship. The estimation result encourages more strict energy policies such as the implementetion of energy diversification policy among others, thus rendering a panacea to Saudi Arabia's energy and the real estate challenges.

Low carbon roadmap of residential building sector in China: Historical mitigation and prospective peak

Fast-growing carbon emissions from the residential building sector are a hindrance for China to achieve its 2030 emission peak goal. To identify future low carbon roadmaps of residential buildings, this study is the first to assess the historical carbon mitigation and simulate the energy and emission peaks of China’s residential building sector using a dynamic emission scenario. It shows that the emission mitigation of the residential building sector during 2000–2015 is 1.817 (±0.643) billion tons of carbon dioxide (BtCO2), and the normal distribution-based scenario simulation demonstrates that the residential building sector will achieve its carbon emission peak in 2037 (±4) with a peak value of 1.419 (±0.081) BtCO2. The sensitivity analysis reveals that the impacts of floor space per capita and energy intensity of urban residential buildings are the most significant for the uncertainty of emission peaks. A strict energy demand benchmark of the residential building sector suggests a control at 0.661 billion tons of standard coal equivalent (Btce), and its peaking time is estimated for 2035, which is 5 years ahead of the business-as-usual scenario, with energy savings of 0.042 Btce. For the earliest peaking time, if the residential building sector aims to achieve its emission peak before 2030, the emission peak should be controlled at 1.258 BtCO2. Overall, this paper can assist the government in more accurate and feasible building emission mitigation strategies. Moreover, the results provide a more powerful decision-making reference in issuing targeted and feasible strategies for future residential building emission mitigation.

Near-Threshold Voltage Design Techniques for Heterogenous Manycore System-on-Chips

Aggressive power supply scaling into the near-threshold voltage (NTV) region holds great potential for applications with strict energy budgets, since the energy efficiency peaks as the supply voltage approaches the threshold voltage (VT) of the CMOS transistors. The improved silicon energy efficiency promises to fit more cores in a given power envelope. As a result, many-core Near-threshold computing (NTC) has emerged as an attractive paradigm. Realizing energy-efficient heterogenous system on chips (SoCs) necessitates key NTV-optimized ingredients, recipes and IP blocks; including CPUs, graphic vector engines, interconnect fabrics and mm-scale microcontroller (MCU) designs. We discuss application of NTV design techniques, necessary for reliable operation over a wide supply voltage range—from nominal down to the NTV regime, and for a variety of IPs. Evaluation results spanning Intel’s 32-, 22- and 14-nm CMOS technologies across four test chips are presented, confirming substantial energy benefits that scale well with Moore’s law.

Modulation instability in waveguides with an arbitrary frequency-dependent nonlinear coefficient.

In this Letter, we present, for the first time, to the best of our knowledge, the modulation instability (MI) gain spectrum of waveguides with an arbitrary frequency-dependent nonlinear coefficient ensuring strict energy and photon-number conservation of the parametric process. This is achieved by starting from a linear stability analysis of the recently introduced photon-conserving nonlinear Schrödinger equation. The derived MI gain is shown to predict some unique features, such as a nonzero gain extending beyond a zero-nonlinearity wavelength and a complex structure of the MI gain spectrum. Analytical results are shown to be in excellent agreement with numerical simulations.

SecRET

Node localization is a fundamental requirement in underwater sensor networks (UWSNs) due to the ineptness of GPS and other terrestrial localization techniques in the underwater environment. In any UWSN monitoring application, the sensed information produces a better result when it is tagged with location information. However, the deployed nodes in UWSNs are vulnerable to many attacks, and hence, can be compromised by interested parties to generate incorrect location information. Consequently, using the existing localization schemes, the deployed nodes are unable to autonomously estimate the precise location information. In this regard, similar existing schemes for terrestrial wireless sensor networks are not applicable to UWSNs due to its inherent mobility, limited bandwidth availability, strict energy constraints, and high bit-error rates. In this article, we propose SecRET , a <underline>Sec</underline>ure <underline>R</underline>ange-based localization scheme empowered by <underline>E</underline>vidence <underline>T</underline>heory for UWSNs. With trust-based computations, the proposed scheme, SecRET , enables the unlocalized nodes to select the most reliable set of anchors with low resource consumption. Thus, the proposed scheme is adaptive to many attacks in UWSN environment. NS-3 based performance evaluation indicates that SecRET maintains energy-efficiency of the deployed nodes while ensuring efficient and secure localization, despite the presence of compromised nodes under various attacks.

Optimization of secure wireless communications for IoT networks in the presence of eavesdroppers

The problem motivates this paper is that securing the critical data of 5G based wireless IoT network is of significant importance. Wireless 5G IoT systems consist of a large number of devices (low-cost legitimate users), which are of low complexity and under strict energy constraints. Physical layer security (PLS) schemes, along with energy harvesting, have emerged as a potential candidate that provides an effective solution to address this issue. During the data collection process of IoT, PHY security techniques can exploit the characteristics of the wireless channel to ensure secure communication. This paper focuses on optimizing the secrecy rate for simultaneous wireless information and power transfer (SWIPT) IoT system, considering that the malicious eavesdroppers can intercept the data. In particular, the main aim is to optimize the secrecy rate of the system under signal to interference noise ratio (SINR), energy harvesting (EH), and total transmits power constraints. We model our design as an optimization problem that advocates the use of additional noise to ensure secure communication and guarantees efficient wireless energy transfer. The primary problem is non-convex due to complex objective functions in terms of transmit beamforming matrix and power splitting ratios. We have considered both the perfect channel state information (CSI) and the imperfect CSI scenarios. To circumvent the non-convexity of the primary problem in perfect CSI case, we proposed a solution based on the concave-convex procedure (CCCP) iterative algorithm, which results in a maximum local solution for the secrecy rate. In the imperfect CSI scenario, we facilitate the use of S-procedure and present a solution based on the iterative successive convex approximation (SCA) approach. Simulation results present the validations of the proposed algorithms. The results provide an insightful view that the proposed iterative method based on the CCCP algorithm achieves higher secrecy rates and lower computational complexity in comparison to the other algorithms.

3PXNet

As the adoption of Neural Networks continues to proliferate different classes of applications and systems, edge devices have been left behind. Their strict energy and storage limitations make them unable to cope with the sizes of common network models. While many compression methods such as precision reduction and sparsity have been proposed to alleviate this, they don’t go quite far enough. To push size reduction to its absolute limits, we combine binarization with sparsity in Pruned-Permuted-Packed XNOR Networks (3PXNet), which can be efficiently implemented on even the smallest of embedded microcontrollers. 3PXNets can reduce model sizes by up to 38X and reduce runtime by up to 3X compared with already compact conventional binarized implementations with less than 3% accuracy reduction. We have created the first software implementation of sparse-binarized Neural Networks, released as open source library targeting edge devices. Our library is complete with training methodology and model generating scripts, making it easy and fast to deploy.

IMDfence: Architecting a Secure Protocol for Implantable Medical Devices

Over the past decade, focus on the security and privacy aspects of implantable medical devices (IMDs) has intensified, driven by the multitude of cybersecurity vulnerabilities found in various existing devices. However, due to their strict computational, energy and physical constraints, conventional security protocols are not directly applicable to IMDs. Custom-tailored schemes have been proposed instead which, however, fail to cover the full spectrum of security features that modern IMDs and their ecosystems so critically require. In this paper we propose IMDfence, a security protocol for IMD ecosystems that provides a comprehensive yet practical security portfolio, which includes availability, non-repudiation, access control, entity authentication, remote monitoring and system scalability. The protocol also allows emergency access that results in the graceful degradation of offered services without compromising security and patient safety. The performance of the security protocol as well as its feasibility and impact on modern IMDs are extensively analyzed and evaluated. We find that IMDfence achieves the above security requirements at a mere less than 7% increase in total IMD energy consumption, and less than 14 ms and 9 kB increase in system delay and memory footprint, respectively.

Reaction-field electrostatics in molecular dynamics simulations: development of a conservative scheme compatible with an atomic cutoff.

In molecular dynamics (MD) simulations of condensed-phase systems, straight-cutoff truncation of the non-bonded interactions is well known to cause cutoff noise and serious artifacts in many simulated properties. These effects can be drastically reduced by applying the truncation based on distances between neutral charge groups (CG) rather than between individual atoms (AT). In addition, the mean effect of the omitted electrostatic interactions beyond the cutoff distance can be reintroduced using the reaction-field (RF) method, where the medium outside the cutoff sphere is approximated as a dielectric continuum of permittivity equal to that of the solvent. The RF scheme is generally applied with CG truncation. This is justified for low solvent permittivities, where the RF correction is small and an AT truncation would lead to severe issues, just as in the straight-cutoff case. However, it is less appropriate for solvents with high permittivities, where the RF correction acts as a physically motivated shifting function, and a CG truncation may in turn lead to artifacts and poorer energy conservation. In this study, we assess the impact of truncation artifacts considering the 57 organic liquids which were used in the calibration of the GROMOS-compatible 2016H66 force field. Combinations of shifting or switching schemes with RF-based electrostatic interactions as well as van der Waals (Lennard-Jones) interactions are then introduced to resolve the issues with AT truncation. These shifting and switching schemes have the following properties: (i) they bring the force but not the potential energy to zero at the cutoff; (ii) as a result, they lead to a modification of the interaction that is comparatively small; (iii) they permit to conduct rigorously conservative simulations; (iv) the energies can easily be corrected back to the unmodified form, either on the fly or in a post-processing step. The mathematical formalism of these schemes is presented in detail, and their validation is performed using the 57 organic liquids.

Energy efficient path planning for Unmanned Surface Vehicle in spatially-temporally variant environment

Unmanned Surface Vehicles (USVs) are increasingly used for ocean missions, which typically require long duration of operations under strict energy constraints. Consequently, there is an increased interest in energy efficient path planning for USVs. This work proposes a novel energy efficient path planning algorithm to address the challenges with the presence of spatially-temporally variant sea current and complex geographic map data, by integrating the following algorithms, namely Voronoi roadmap, Dijkstra's searching, coastline expanding and genetic algorithm (GA). The selection, crossover and mutation operators are employed as part of the GA algorithm. The dividing, smoothing and exchanging operators are proposed to improve the quality of the path and adapt to the Voronoi-Visibility roadmap. The Global Self-Consistent Hierarchical High-Resolution Shorelines dataset and historical sea current dataset are applied to demonstrate the flexibility and practicability of the proposed algorithm. To evaluate the performance, the Voronoi-GA energy efficient algorithm and Voronoi-Visibility energy efficient path re-planning algorithm are also implemented to provide the baseline for comparison. The proposed algorithm generates the most energy efficient paths in ten USV missions, while keeping a configurable clearance from the coastlines. The practicability and scalability of this algorithm is also demonstrated by analysing the computational time in these ten missions.