Digitally-tuned resolver converter

Sinusoidal encoders provide electrical signals related to the sine and cosine of the mechanical shaft angle θ. An analog converter is described for the linearization of these signals and hence for linear computation of θ. The converter was based upon the difference between the absolute values of the transducer signals, together with a simple signal diode-based shaping network. The optimal break points positions of the network, that minimize the absolute error of the converter, are determined experimentally and automatically using a LabVIEW-controlled setup. Despite its simplicity, the converter has an absolute error of only 0.12 °

A component map tuning method for performance prediction and diagnostics of gas turbine compressors

In this paper, a novel compressor map tuning method is developed with the primary objective of improving the accuracy and fidelity of gas turbine engine models for performance prediction and diagnostics. A new compressor map fitting and modeling method is introduced to simultaneously determine the best elliptical curves to a set of compressor map data. The coefficients that determine the shape of the compressor map curves are analyzed and tuned through a multi-objective optimization scheme in order to simultaneously match multiple sets of engine performance measurements. The component map tuning method, that is developed in the object oriented Matlab Simulink environment, is implemented in a dynamic gas turbine engine model and tested in off-design steady state and transient as well as degraded operating conditions. The results provided demonstrate and illustrate the capabilities of our proposed method in refining existing engine performance models to different modes of the gas turbine operation. In addition, the excellent agreement between the injected and the predicted degradation of the engine model demonstrates the potential of the proposed methodology for gas turbine diagnostics. The proposed method can be integrated with the performance-based tools for improved condition monitoring and diagnostics of gas turbine power plants. © 2014 Elsevier Ltd

Determining the Outdoor Air Ventilation with Carbon Dioxide (CO2) as a Tracer Gas

Insufficient ventilation can lead to occupant complaints of discomfort and reduced productivity as human and building generated pollutants build up. Some combinations of these elevated pollutants may have short or long-term detrimental health effects. Carbon Dioxide (CO2) is very rarely a pollutant of direct health concern itself. Rather a tracer gas, because building occupants exhale CO2 and is used as a tracer gas that is an excellent indicator of adequate (or inadequate) ventilation. Keeping in view this fact we measured CO2 as a marker, or tracer gas, to determine the outdoor air ventilation (dilution air) rate in an occupied space. Low CO2 concentration, when measured during periods of average and higher occupancy, implies that human generated pollutants are being properly diluted. And in the absence of a specific pollutant source, it is a rough estimator that the thousands of potential building generated pollutants are being dispersed. This makes it a key indoor air quality indicator.qscienc

Calibration & Temperature Controlled Setup for Air Quality Sensors

Indoor air pollution is a major issue affecting public health. Due to hot climate, humidity and lack of natural green spaces, life is mostly confined indoors in many countries of the MENA region. Vulnerable population, including young children and senior citizens who spend most of their time indoors, are at risk because of the effects of indoor air quality (IAQ) on their health. An indoor air quality monitoring system is a need of the hour to detect and improve Indoor Air Quality (IAQ). The monitoring systems presently available are bulky, expensive and need periodic calibration to maintain high degree of accuracy. Frequent recalibration of a number of densely deployed individual sensors in the network is a time-consuming and laborious task therefore self-calibration is indispensable. Gas sensors, even if factory-calibrated, tend to drift with time/usage. Therefore these should be regularly calibrated under controlled environments. Calibration may be carried out using test gas mixtures with known composition. Pre-mixed gas cylinders with known composition may be used for the purpose; however this solution is not flexible as the number of calibration points and testing conditions (e.g. effect of temperature and humidity on CO2 sensor) are limited. In the current project, a computer-controlled test and calibration test bed system is being designed and assembled along with temperature controller. Calibration set-up would help in self calibration of the air quality sensors. Calibration curves obtained from proposed calibration test bed are updated automatically and fed into the sensor node through wireless communication without going in the field or replacing the sensor. A computer-controlled test and calibration test bed system is designed and assembled containing the sensor(s) under test and in which gas composition; temperature can be precisely and dynamically controlled. ATMEGA328 micro controller is used to receive the temperature set point from the computer running the test rig.qscienc

An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification

Abstract: Increasing the number of sensors in a gas identification system generally improves its performance as this will add extra features for analysis. However, this affects the computational complexity, especially if the identification algorithm is to be implemented on a hardware platform. Therefore, feature reduction is required to extract the most important information from the sensors for processing. In this paper, linear discriminant analysis (LDA) and principal component analysis (PCA)-based feature reduction algorithms have been analyzed using the data obtained from two different types of gas sensors, i.e., seven commercial Figaro sensors and in-house fabricated 4×4 tin-oxide gas array sensor. A decision tree-based classifier is used to examine the performance of both the PCA and LDA approaches. The software implementation is carried out in MATLAB and the hardware implementation is performed using the Zynq system-on-chip (SoC) platform. It has been found that with the 4×4 array sensor, two discriminant functions (DF) of LDA provide 3.3% better classification than five PCA components, while for the seven Figaro sensors, two principal components and one DF show the same performances. The hardware implementation results on the programmable logic of the Zynq SoC shows that LDA outperforms PCA by using 50% less resources as well as by being 11% faster with a maximum running frequency of 122 MHz

Continuous-Time ΣΔ ADC with Implicit Variable Gain Amplifier for CMOS Image Sensor

This paper presents a column-parallel continuous-time sigma delta (CTSD) ADC for mega-pixel resolution CMOS image sensor (CIS). The sigma delta modulator is implemented with a 2nd order resistor/capacitor-based loop filter. The first integrator uses a conventional operational transconductance amplifier (OTA), for the concern of a high power noise rejection. The second integrator is realized with a single-ended inverter-based amplifier, instead of a standard OTA. As a result, the power consumption is reduced, without sacrificing the noise performance. Moreover, the variable gain amplifier in the traditional column-parallel read-out circuit is merged into the front-end of the CTSD modulator. By programming the input resistance, the amplitude range of the input current can be tuned with 8 scales, which is equivalent to a traditional 2-bit preamplification function without consuming extra power and chip area. The test chip prototype is fabricated using 0.18 m CMOS process and the measurement result shows an ADC power consumption lower than 63.5 W under 1.4 V power supply and 50 MHz clock frequency

Cunws/rgo Based Transparent Conducting Electrodes As A Replacement Of Ito In Opto-electric Devices

Transparent electrodes that conduct electrical current and allow light to pass through are widely used as the essential component in various opto-electric devices such as light emitting diodes, solar cells, photodectectors and touch screens. Currently, Indium Tin oxide (ITO) is the best, commercially available transparent conducting electrode (TCE). However, ITO is too expensive owing high cost on indium. Furthermore ITO thin films are too brittle to be used in flexible devices. To fulfill the demand of TCEs for wide range of applications, high performance ITO alternatives are required. Herein we demonstrate an approach for the successful, solution based synthesis of high aspect ratio copper nanowires, which were later combined with reduced graphene oxide (rGO), in order to produce smooth thin film TCEs on both glass and flexible substrate. Structure and component characterization for these electrodes was carried out through Four Probe, Spectrophotometer, Scanning electron Microscope (SEM), Transmission Electron Microscope (TEM) and Atomic Field Microscopy (AFM). In addition to the morphological and electrical characterization, these samples were also tested for their durability by carrying out experiments that involved exposure to various environmental conditions and electrode bending. Our fabricated transparent electrodes exhibited high performance with a transmittance of 91.6% and a sheet resistance of 9 O/sq. Furthermore, the electrodes showed no notable loss in performance during the durability testing experiments. Such results make them as replacement for indium tin oxide as a transparent electrode and presents a great opportunity to accelerate the mass development of devices like high efficiency hybrid silicon photovoltaics via simple and rapid soluble processes.qscienc

Fast Prototyping of KNN Based Gas Discrimination System on the Zynq SoC

Electronic noses (EN) or machine olfaction are systems used for the detection and identification of odorous compounds and gas mixtures. The accuracy of such systems is as important as the processing time. Therefore, the choice of the algorithm and the implementation platform are both crucial. In this abstract, a design and implementation of a gas identification system on the Zynq platform which shows promising results is presented. The Zynq-7000 based platforms are increasingly being used in different applications including image and signal processing. The Zynq system on chip (SoC) architecture combines a processing system based on a dual core ARM Cortex processor with a programmable logic (PL) based on a Xilinx 7 series field programmable gate arrays (FPGAs). Using the Zynq platform, real-time hardware acceleration of classification algorithms can be performed on the PL and controlled by a software running on the ARM-based processing system (PS). The gas identification system is based on a 16-Array SnO2 in-house fabricated gas sensor and k-Nearest Neighbors (KNN) for classification. The KNN algorithm is executed on the PL for hardware acceleration. The implementation takes the form of an IP developed in C and synthesized using Vivado High Level Synthesis (HLS), the synthesis includes the conversion from C to register transfer level (RTL). The implementation requires the creation of a hardware design for the entire system that allows the execution of the IP on the PL and the remaining parts of the identification system on the PS. The hardware design is developed in Vivado using IP Integrator. The communication between the PS and PL is performed using advanced extensible interface protocol (AXI). A software application is written and executed on the ARM processor to control the hardware acceleration on the PL of the previously designed IP core and the board is programmed using Software Development Kit (SDK). An overview of the system architecture can be seen in Figure 1. The system is designed to discriminate five types of gases including C6H6, CH2O, CO, NO2 and SO2 at various concentrations, from 0.25 to 5 parts per million (ppm) for C6H6 and CH2O, from 5 to 200 ppm for CO, from 1 to 10 ppm for NO2 and finally from 1 to 25 ppm for SO2. The experimental setup used in the laboratory to collect the data is shown in Figure 2. It consists of a gas chamber where the sensor array is placed. The gas chamber has two orifices, one to serve as an input for the in-flow of gases and the other one as an exhaust to evacuate the gases. Multiple gases are stored in various cylinders and connected to the gas chamber individually through several Mass Flow Controllers (MFCs). A control unit is connected to the MFCs to control the in-flow of gases and to the sensor array via a Data Acquisition (DAQ) system to collect and sample the response of the sensor array. In total, 192 samples are collected, 50% is used for training and the other 50% is used for testing. Simulations were performed in MATLAB environment prior to the implementation on the hardware where different k values have been used. The Euclidean distance has been used as a metric for the computation of distances between various points. The best results were obtained for k = 1 and k = 2 with a classification accuracy of 97.91% and 98.95% respectively. The system implemented on hardware is based on k = 1 since the accuracies are almost similar while the hardware resources required for k = 2 are much higher than for k = 1. This can be explained by the fact that in the case of k = 2 we need to sort the vector of distances to be able to find the nearest two neighbours while in k = 1 we only need to find the smallest distance. The target hardware implementation platform of the proposed KNN is the heterogeneous Zynq SoC. The implementation is based on the use of Vivado HLS. A summary of the design flow is presented in Figure 3. The starting point is Vivado HLS where the KNN block is converted from C/C++ implementation to a RTL based IP core. This allows a considerable gain in development time without scarifying on high parallelism characteristics because Vivado HLS provides a large number of powerful optimization directives. The generated IP-core is then exported and stored in the Xilinx IP Catalog before being used in Vivado IP Integrator to create the hardware block design with all needed components and interconnections. The next step is to export the generated hardware along with IP drivers to the SDK tool. The SDK tool is used to program the Xilinx ZC702 prototyping board via joint test action group (JTAG) interface and the terminal in SDK is used to communicate with the board via universal asynchronous receiver/transmitter (UART) interface. The KNN IP is implemented on the PL of the Zynq SoC and communicates with the PS part via the Xilinx AXI-Interconnect IP. A software is written in C/C++ and executed on the PS to manage the IP present in the PL in terms of sending the input data, waiting for the interrupt and then reading the output data. The block design and the resulting chip layout are shown in Figure 4. It is worth mentioning that the running frequency for the ARM processor is set to the maximum 667MHz while the PL frequency is set to 100 MHz which is the maximum for the KNN IP generated in HLS. The real execution of KNN on the PL side of the ZC702 board shows that one sample can be processed for gas identification in 0.0078 ms while the same sample requires 0.9228 ms if executes on the PS side in the ARM processor in a pure software manner. This means that a speed up of 118 times has been achieved. The main directive in Vivado HLS that helped to reach these performances is the "Loop pipelining" which allows the operations in a loop to be implemented in a concurrent manner. The hardware resources usage can be seen in Figure 5, it shows that 24% of lookup tables (LUT), 12% of flip-flops (FF), 6% of BRAM and 58% DSP have been used. As shown in Figure 6, the total power consumption is 1.895 W, 1.565 W is consumed by the PS and the remaining 0.33W is consumed by the PL.qscienc

A Low Power Reconfigurable Multi-sensing Platform For Gas Application

Presence of toxic gases and accidental explosions in gas industries have turned the researcher to innovate an electronic nose system which can indicate the nature and the parameters of the gas passing through different vessels. Therefore, in this research we propose a low power Radio Frequency Identification (RFID) based gas sensor tag which can monitor the parameters and indicate the type of gas. The research work is divided in to three main parts. The first two parts cover the design and analysis of low power multi-sensors and processing unit, while the last part focuses on a passive RFID module which can provide communication between the sensor and the processing unit, as shown in Fig. 1. In passive RFID applications, power consumption is one of the most prominent parameter because most of the power is harvested from the coming RF signal. Therefore a ring-oscillator based low power temperature sensor is designed to measure the gas thermodynamic conditions. The oscillator is designed using the Thyristor based delay element [7], in which the current source present for temperature compensation has been displaced to make the delay element as temperature dependent. The proposed temperature sensor consumes 47nW power at 27 °C, which increases linearly with temperature. Moreover, a 4x4 array of tin-oxide gas sensor based on convex Micro hotplates (MHP), is also utilized to identify the type of gas. The array is designed such that each sensor of an array provide different pattern for the same gas. The power consumption caused by the temperature and gas sensor is in the order of few µW's. The prime advantage of MHP can be visualized by the 950 °C annealed MHP, which exhibit the thermal efficiency of 13 °C /mW. Moreover it requires a driving voltage of only 2.8V to reach 300 °C in less than 5ms, which make it compatible with power supplies required by CMOS ICs. The gas sensor will provide 16 feature points at a time, which can results in hardware complexity and throughput degradation of the processing unit. Therefore, a principle component analysis (PCA) algorithm is implemented to reduce the number of feature points. Thereafter, a binary decision tree algorithm is adopted to classify the gases. We implemented both algorithms on heterogeneous Zynq platform. It is observed that the execution of PCA on Zynq programmable SoC is 1.41 times faster than the corresponding software execution, with a resource utilization of only 23% . Finally, a passive ultrahigh-frequency (UHF) RFID transponder is developed for communicating between the sensing block and processing unit. The designed module is responsible to harvest the power from the coming RF signal and accomplish the power requirement of both sensors. The designed transponder IC achieved minimum sensitivity of -17dBm with a minimum operational power of 2.6µW.qscienc