Energy harvesting is one possibility to power up small sensor devices using ultra low power technologies. The work of this paper is based on electrostatic energy harvesters using a variable capacitor as a charge pump to convert mechanical into electrical energy [1-3, 6]. These capacitors can be implemented as discrete devices or as micro electromechanical systems (MEMS) integrated on a chip. The aim of this construction is to show a method to characterize the harvested energy of the intrinsic harvester. Due to low currents in the range of microamperes it is difficult to measure exactly without influence on the harvesting process. A new improved topology is used to ensure the autonomous operation of the harvester circuit. This topology allows for measuring makes it possible to measure the converted energy more accurately, even if there are resistive losses in the variable capacitor. Thus we can obtain comparable results on the efficiency of the harvester itself. The amount of harvested energy can be determined easily by processing the measured values. Finally this new measurement setup was implemented as a stand alone measurement device . To demonstrate the new method different types of harvesters were characterized. The electrical efficiency of the harvester output was discussed and a strategy for its optimization was developed.
Cost consideration of the development of electronic devices is one of prime importance. One simple approach to lower the cost of production of photovoltaic and detectors is by using low cost materials such as amorphous silicon and germanium. These two semiconductors have different optoelectronic properties, such as energy gap, photoconductivity and absorption coefficient. The use of an alloy from the mixing of silicon with certain percentages of germanium would produce photodetectors with improved electronic characteristics and photoconductivity. A number of a-SiGe alloy thin films with different quantities of germanium have been fabricated using thermal vacuum evaporation technique. Conduction mechanism and activation energy of the prepared samples had been calculated and analyzed. The I-V characteristics, the photogenerated current and detectivity of these samples are subjected to measurement and discussion. Hall measurements are also conducted so to calculate the Hall I-V characteristics, Hall mobility, carrier concentration and type identification of the samples.
This paper presents a new configuration for a linear operational transconductance amplifier (OTA) using a signal attenuation technique. The OTA is designed to operate with a ±0.8V supply voltage and consumes 0.45 mW power. All simulations are performed by ELDO model BSIM3v3 technology CMOS TSMC 0.18 μm. The simulation results of this circuit showed a high DC gain of 73.6 dB with a unity frequency of 50.19 MHz and a total harmonic distortion of −60.81 dB at 100 kHz for an input voltage of 1Vpp. In order to realize this circuit, we have implemented in this first step a universal filter, where the frequency can reach the 51.34 MHz. In the second step, we have implemented a floating inductor simulator. Finally, we have used the last inductor to implement a RLC Band-Pass filter whose simulation results are in good agreement with the theoretical calculations.
EMG signals in their raw form are very rich in information, but in order to be valuable in the process of myopathy’s diagnostic, they have first to be amplified then analog to digital converted and filtered. In this paper we investigate the possibilities to realize an EMG measurement system with low cost hardware components and improve the performance of the implementation by signal processing. The measurement system has a minimal design consisting of an amplifier interface conform to typical myopathy data bases and an Arduino-based acquisition system. When electrodes are placed on the skin in the right position, the developed device generates signal form and values, which are similar to those downloaded from EMG database. For data acquisition, signal analysis and classification, an intelligent LabView based data processing software was implemented, which uses also a clinical standard database for normal and myopathic EMG signals. For classification, significant signal features were selected, which are: the Shannon entropy, variance, mean absolute value and signal positive peak amplitude. The investigation shows, that these features are sufficient to discriminate between normal subjects and patients with myopathy. A PCA guided K-means clustering classifier was established and a classification accuracy of 91.7 %.