Fast design space exploration of vibration-based energy harvesting wireless sensors

An energy-harvester-powered wireless sensor node is a complicated system with many design parameters. To investigate the various trade-offs among these parameters, it is desirable to explore the multi-dimensional design space quickly. However, due to the large number of parameters and costly simulation CPU times, it is often difficult or even impossible to explore the design space via simulation. This paper presents a response surface model (RSM) based technique for fast design space exploration of a complete wireless sensor node powered by a tunable energy harvester. As a proof of concept, a software toolkit has been developed which implements the proposed design flow and incorporates either real data or parametrized models of the vibration source, the energy harvester, tuning controller and wireless sensor node. Several test scenarios are considered, which illustrate how the proposed approach permits the designer to adjust a wide range of system parameters and evaluate the effect almost instantly but still with high accuracy. In the developed toolkit, the estimated CPU time of one RSM estimation is 25s and the average RSM estimation error is less than 16.5

Energy Efficient Sensor Nodes Powered by Kinetic Energy Harvesters – Design for Optimum Performance

In an energy harvester powered wireless sensor node system, as the energy harvester is the only energy source, it is crucial to configure the microcontroller and the sensor node so that the harvested energy is used efficiently. This paper outlines modelling, performance optimisation and design exploration of the complete, complex system which includes the analogue mechanical model of a tunable kinetic microgenerator, its magnetic coupling with the electrical blocks, electrical power storage and processing parts, the digital control of the microgenerator tuning system, as well as the power consumption models of sensor node. Therefore not only the energy harvester design parameters but also the sensor node operation parameters can be optimised in order to achieve the best system performance. The power consumption models of the microcontroller and the sensor node are built based on their operation scenarios so that the parameters of the digital algorithms can be optimised to achieve the best energy efficiency. In the proposed approach, two Hardware Description Languages, VHDL-AMS and SystemC-A is used to model the system's analogue components as well as the digital control algorithms which are implemented in the microcontroller and the sensor node. Simulation and performance optimisation results are verified experimentally. In the development of the fast design exploration tool based on the response surface technique, the response surface model (RSM) is constructed by carrying out a series of simulations. The RSM is then optimised using MATLAB's optimisation toolbox and the optimisation results are presented

Response surface modelling and performance optimisation of energy harvester-powered sensor nodes

The emerging technologies of harvesting the energy from the environment surrounding the application have recently attracted intensive attention of design automation researchers. Due to the universal presence of vibrations on machines, among the different mechanisms available to obtain electrical power from ambient energy, the vibration based harvesters have been the subject of particularly extensive development. A vibration-based (kinetic) harvester simply converts vibrations in the environment surrounding a wireless sensor node into electrical energy. This enables the wireless sensor node to be placed anywhere in the environment with no need for access to facilitate battery replacement. The basic structure of vibration-based energy harvester is composed of multi-domain components, it contains electrical, mechanical, and magnetic in the case of electromagnetic harvester. In addition, many design parameters from different domains need to be optimised in a holistic manner (i.e. treating all the system components as a connected unit); all these requirements besides the traditional approaches of optimisation, complicate the hardware description language for analog and mixed system (HDL-AMS) simulation and makes central processor unit (CPU) takes prohibitive time, even with today's multi-physics simulation tools. This research develops, a novel optimisation technique, which enables efficient optimisation and design exploration for such a complex system and reduce CPU computation time for optimisation purposes. The proposed methodology accelerates the optimisation by approximately two orders of magnitude due to the utilisation of the design of experiment (DoE) approach and response surface modelling (RSM). The contributions of this research can be summarised as follows: Firstly, a novel, response surface based design space exploration approach to energy harvester powered systems has been developed. The proposed technique enables designers to gain insight into the details of design parameters trade-offs and quantifies each design parameter effect on performance indicators via the response surface mathematical model. The method has been applied to a linear micro-electromagnetic cantilevered harvester. Secondly, a novel, fast performance optimisation technique for a wireless sensor node powered by a tunable kinetic energy harvester has been developed. The result of applying this technique reduces the total CPU optimisation time by two orders of magnitude compared with the classical approach, i.e. through multiple full simulations. Thirdly, a software tool set has been created, based on MATLAB and VHDL-AMS, for fast, multi-dimensional design space exploration and optimisation of a kinetic harvester

Structural Performance Assessment of Geothermal Asphalt Pavements: A Comparative Experimental Study

This paper introduces shallow geothermal systems as a potential solution for improving the thermo-mechanical performance of asphalt under extreme climate events. With the recent changes experienced in the climate, earlier infrastructure failure can be expected, predominantly for temperature-sensitive flexible pavements. With that in mind, the efficiency of geothermal systems in terms of heating and cooling was comprehensively argued in many studies. However, very limited studies discussed the structural performance of geothermal pavements. This study conducted a comparative experimental study to assess the change in the compressive and flexural strengths of asphalt under extreme heating and cooling conditions and to evaluate the change in asphalt structural performance due to integrating different types of geothermal pipes into the asphalt structure. This comparative analysis employed thirty-three asphalt specimens with and without copper and polyvinyl chloride (PVC) geothermal pipes. The results of this study show that the geothermal pipes negatively affected the compressive strength of the asphalt at a normal average temperature. However, their effect was relatively minimal on the asphalt (AC) compressive strength under extreme heating and cooling conditions. In contrast, under three thermal conditions—normal, heating, and cooling temperatures—the flexure strength of the AC was significantly improved by 14.3%, 85%, and 70%, respectively, due to the copper pipe integration into the AC. The study concluded that copper pipes were superior to PVC ones in terms of enhancing the AC structural performance

Role of CT Scan in Diagnosis of COVID-19 Infection-A Review

Since it was declared a worldwide pandemic, COVID-19 has ravaged almost all over the world and has overloaded several health-care systems. The pandemic also resulted in job losses as a result of lengthy shutdowns, which burdened the global economy. Even though significant clinical research progress has led to a better perceiving of the virus ( SARS-CoV-2) nature and the disease (COVID-19) management, preventing the virus's spread has become a major concern as SARSCoV-2 continues to wreak havoc around the world. Several countries suffered from the second or third wave of viral disease outbreaks, primarily caused by the mutation of SARS-CoV-2. Imaging is critical in the diagnosis and follow-up of patients with new coronavirus-infected pneumonia (NCIP). The primary imaging modality in clinically suspected cases is CT scan and it is useful for monitoring imaging changes following therapy. Therefore, CT is regarded as a useful diagnostic technique for clinically suspected cases of COVID-19. CT has the ability to detect patients who have a negative reverse transcription-polymerase chain reaction (RT-PCR) but are highly suspicious of NCIP in terms of clinical problems. In addition, the results of a CT scan may also reveal information concerning the severity of the condition. In this review article, the diagnosis of COVID-19 is discussed and CT characteristics are defined based on the newest research for the diagnosis and management of COVID-19