KEYWORDS: Sensors, Data acquisition, Signal to noise ratio, Signal processing, Digital signal processing, Electronic filtering, Filtering (signal processing), Interference (communication), Transducers, Computing systems
Smartphones are widely used in daily life. Can they also be used for wireless data acquisition from voltageoutput or current-output sensors in the lab or outside of a lab, for example, for taking part of the lab to the sample types of applications? Could they be used for digital signal processing (e.g., filtering) of noisy signals (e.g., using a Savitzky-Golay filter)? Could they also find applicability in IoT, in Industry 4.0 or in Society 5.0 applications? In this paper, these questions are addressed in some detail.
KEYWORDS: Fourier transforms, Signal to noise ratio, Electronic filtering, Filtering (signal processing), Chemical analysis, Signal generators, Resolution enhancement technologies
There are many applications requiring an instrument to be brought to the sample for chemical analysis onsite (rather than bringing a sample to a lab for analysis, as is done traditionally). Ideally, for such applications, a portable chemical analysis instrument must be capable of acquiring data using a smartphone, have wireless capability and it must be able to become part of the Internet-of-Things (IoT). But do smartphones have the required processing power to execute computationally-intensive algorithms, such as a Fast Fourier Transform (FFT)? Among others, FFTs are used for filtering (e.g., de-noising) of periodic signals, thus improving Signal-to-Noise Ratio (SNR). Using non-periodic signals and Fourier-domain interpolation for resolution enhancement, it will be shown that smartphones do have the necessary power.
Ideally, a chemical analysis instrument should to be brought to the sample for (near) real-time analysis onsite (rather than bringing a sample to a lab for analysis, as is usually done). In this paper, this paradigm shift is addressed using battery-operated microplasmas. But, how does one introduce an initially ambient temperature sample into a low-power (~10 W) gas-phase microplasma? One way is by using an eletrothermal vaporization sample introduction and a vaporization chamber for introduction of micro- (and nano-size) samples into a microplasma. But then, how does one develop an “optimized” vaporization chamber? To reduce cost and time-delays, rapid prototyping (via 3D printing) and smoke experiments were used, as detailed in this paper. In the future, candidate designs will be evaluated using CFD simulations.
There are many applications requiring measurements on-site for example when accidental spills occur either outdoors or on the floor of IoT-enabled smart-factories (e.g., Industry 4.0). In other cases, a portable, fiber-optic spectrometer may be required for “bringing part of the lab to the sample” types of chemical analysis applications. Conceptually, there two approaches that can be used to put an optical spectrometer on IoT. One, is to design it from the ground up. The other, is to purchase a portable, battery-operated fiberoptic spectrometer; to use the manufacturer’s software (without any modification), and to employ a wireless connection, so that user interface and data-display will take place on the screen of a smartphone. In this paper, a dual-processor approach was taken to accomplish these tasks, as will be described in some detail.
There are many applications requiring chemical analysis in the field and analytical results in (near) real-time. For example, when accidental spills occur. In others, collecting samples in the field followed by analysis in a lab increases costs and introduces time-delays. In such cases, “bring part of the lab to the sample” would be ideal. Toward this ideal (and to further reduce size and weight), we developed a relatively inexpensive, battery-operated, wireless data acquisition hardware system around an Arduino nano micro-controller and a 16-bit ADC (Analog-to- Digital Converter) with a max sampling rate of 860 samples/s. The hardware communicates the acquired data using low-power Bluetooth. Software for data acquisition and data display was written in Python. Potential ways of making the hardware-software approach described here a part of the Internet-of-Things (IoT) are presented.
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