Bahareh Zaghari

An experimentally validated parametrically excited vibration energy harvester with time-varying stiffness

Vibration energy harvesting is the transformation of vibration energy to electrical energy. The motivation of this work is to use vibration energy harvesting to power wireless sensors that could be used in inaccessible or hostile environments to transmit information for condition health monitoring. Although considerable work has been done in the area of energy harvesting, there is still a demand for making a robust and small vibration energy harvesters from random excitations in a real environment that can produce a reliable amount of energy. Parametrically excited harvesters can have time-varying stiffness. Parametric amplification is used to tune vibration energy harvesters to maximize energy gains at system superharmonics, often at twice the first natural frequency. In this paper the parametrically excited harvester with cubic and cubic parametric nonlinearity is introduced as a novel work. The advantages of having cubic and cubic nonlinearity are explained theoretically and experimentally.

Wearable and autonomous computing for future smart cities: Open challenges

The promise of smart cities offers the potential to change the way we live, and refers to the integration of IoT systems for people-centred applications, together with the collection and processing of data, and associated decision making. Central to the realization of this are wearable and autonomous computing systems. There are considerable challenges that exist in this space that require research across different areas of electronics and computer science; it is this multidisciplinary consideration that is novel to this paper. We consider these challenges from different perspectives, involving research in devices, infrastructure and software. Specifically, the challenges considered are related to IoT systems and networking, autonomous computing, wearable sensors and electronics, and the coordination of collectives comprising human and software agents.

Phase dependent nonlinear parametrically excited systems

Nonlinear parametrically excited (NPE) systems govern the dynamics of many engineering applications, from cable-stayed bridges where vibrations need to be suppressed, to energy harvesters, transducers and acoustic amplifiers where vibrations need to be amplified. This work investigates the effect of different system parameters on the dynamics of a prototype NPE system. The NPE system in this work is a cantilever beam with an electromagnetic subsystem excited at its base. This system allows cubic stiffness, parametric stiffness, cubic parametric stiffness, and the phase difference between different sources of excitation to be varied independently to achieve different dynamic behaviors. A mathematical model is also derived, which provides theoretical understanding of the effects of these parameters and allows the analysis to be extended to other applications.

Opportunities and challenges for energy harvesting sensor systems for harsh environments

Wireless sensing systems for harsh environments, especially at high temperatures, are of great interest to many industries. Wireless sensing systems consist of sensors, electronic interfaces and processors, energy harvesters, and wireless transmission modules. Real-time data collection from sensors, combined with data analytics, can improve safety and performance, and reduce operational and maintenance cost in harsh environments. Even though some sensors are available for harsh environments, it is still impossible to measure the real-time data wirelessly due to the lack of high temperature electronics and energy storage for the selected sensors. Typically, data is transferred with cables to cooler regions, where an external electronic box is set up. Due to complex wiring connections, reliability is poor, the sensor locations are restricted and the cost and weight of the sensing system is increased. In this paper, the limits of wireless sensing systems for high temperature applications are discussed and the opportunities for future research are outlined.

Dynamic response of a nonlinear parametrically excited system subject to harmonic base excitation

A Nonlinear Parametrically Excited (NPE) system subjected to a harmonic base excitation is presented. Parametric amplification, which is the process of amplifying the system’s response with a parametric excitation, has been observed in mechanical and electrical systems. This paper includes an introduction to the equation of motion of interest, a brief analysis of the equations nonlinear response, and numerical results. The present work describes the effect of cubic stiffness nonlinearity, cubic parametric nonlinearity, and the relative phase between the base excitation and parametric excitation under parametric amplification. The nonlinearities investigated in this paper are generated by an electromagnetic system. These nonlinearities were found both experimentally and analytically in previous work [1]; however, their effect on a base excited NPE is demonstrated in the scope of this paper. This work has application in parametric amplification for systems, which are affected by strong stiffness nonlinearities and excited by harmonic motion. A careful selection of system parameters, such as relative phase and cubic parametric nonlinearity can result in significant parametric amplification, and prevent the jump from upper stable solutions to the lower stable solutions.

Fault detection in small diameter pipes using ultrasonic guided wave technology

Ultrasonic guided wave technology is one of the more recent developments in the field of non-destructive evaluation. In contrast to conventional ultrasonic, this technology requires exposing only the areas where the transducers will be placed, hence requiring minimal insulation removal and excavation for buried pipes. This paper discusses how this technology can be used to detect defects in pipes under different conditions. Here the experiments were performed on small diameter pipes (<5 cm diameter); which were bare pipe, buried pipe and bitumen coated pipe. The results were gathered to see the effectiveness of this technology in detecting defects. Experiments were conducted using two dry coupled piezoelectric transducers, where one of them transmitted guided waves along the pipe and the other received them. The transducers produced tangential displacement, thereby generating the fundamental torsional mode T(0,1). In order to assess whether having multiple transducers has any effect on the resultant waveform, the receiving transducer was rotated around the circumference of the pipe.

QUANTIFICATION OF DEPTH OF ANESTHESIA BY MEANS OF ADAPTIVE CALCULATION OF CORRELATION DIMENSION PARAMETERS

This paper proposes an approach for quantifying Depth of Anesthesia (DOA) based on correlation dimension (D2) of electroencephalogram (EEG). The single-channel EEG data was captured in both ICU and operating room while different anesthetic drugs, including propofol and isoflurane, were used. Correlation dimension was computed using various optimized parameters in order to achieve the maximum sensitivity to anesthetic drug effects and to enable real time computation. For better analysis, application of adaptive segmentation on EEG signal for estimating DOA was evaluated and compared to fixed segmentation, too. Prediction probability (PK) was used as a measure of correlation between the predictors and BIS index to evaluate the proposed methods. Appropriate correlation between DOA and correlation dimension is achieved while choosing (D2) parameters adaptively in comparison to fixed parameters due to the nonstationary nature of EEG signal.

Extraction of BIS™ index sub-parameters in different anesthetic and sedative levels

Monitoring the depth of anesthesia is important to prevent undesirable events during surgery. According to direct effect of anesthetic drugs on synaptic activity of neurons and after presentation of anesthesia depth monitor (BIS) in 1996, there was a great interest on electroencephalogram analysis to investigate depth of anesthesia. Now there are large numbers of methods and algorithms in this field and every new method is compared with BIS index. BIS algorithm is based on three sub-parameters including time, frequency and higher order statistics domain parameters but the detailed algorithm is not in the public domain. In this paper, proper methods are presented for calculating three sub-parameters. Results of applying these methods to collected clinical data are presented. Efficiency of these methods was evaluated based on appropriate statistical analysis.

Electroencephalogram Fractral Dimension as a Measure of Depth of Anesthesia

This paper proposes a combined method including adaptive segmentation and Higuchi fractal dimension (HFD) of electroencephalograms (EEG) to monitor depth of anesthesia (DOA). The EEG data was captured in both ICU and operating room and different anesthetic drugs, including propofol and isoflurane were used. Due to the non- stationary nature of EEG signal, adaptive segmentation methods seem to have better results. The HFD of a single channel EEG was computed through adaptive windowing methods consist of adaptive variance and auto correlation function (ACF) based methods. We have compared the results of fixed and adaptive windowing in different methods of calculating HFD in order to estimate DOA. Prediction probability (P^) was used as a measure of correlation between the predictors and BIS index to evaluate our proposed methods. The results show that HFD increases with increasing BIS index. In ICU, all of the methods reveal better performance than in other groups. In both ICU and operating room, the results indicate no obvious superiority in calculating HFD through adaptive segmentation.