A Capacitive-to-Digital Converter With Automatic Range Adaptation for Readout Instrumentation

Georgakopoulou, K.; Spathis, C.; Petrellis, Nikos; Birbas, Alexios

doi:10.1109/tim.2015.2498538

Cited by 18 publications

(3 citation statements)

References 30 publications

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“…[K. Georgakopoulou, et al,2016] The readout system consists of three separate subsystems, namely, certain blocks from the prototype chip of a digital part implemented in a field programmable gate array (FPGA), and an external bank of capacitors. One of the outputs of a 6 × 64 NOR address decoder (six address bits select one of 64 lines) is activated by relating one of the 64 biosensors to the reference signal (Vref).…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of Plant Disease Using Smart Phone Application

Bhagat¹

2019

IJRASET

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A steady plant monitoring is necessary to control the spread of disease but its cost may be high and as a result, the producers often miss critical preventive procedures to keep the production cost low. Though in the professional farming engineers are responsible for the recognition of plant diseases, intelligent systems can be used for their diagnosis in early stages. The recognition of infection can often be based on symptoms like lesions or spots in various parts of a plant. The color, area and the number of these spots can verify to a great extent the disease that has mortified a plant. A smartphone image processing application is described here which is capable of detecting the diseases through the pictures of leaves. The recognition of a disease can often be based on symptoms like lesions or spots in various parts of a plant. The color Area and the number of lesions or spots can often be used to determine the disease that has mortified plant. Hence image processing is used for the detection of plant leaf disease, an intelligent system can be used for their diagnosis in early stages. It includes several steps Image acquisition, the image resizes, RGB to GRAY, feature extraction on a convolution neural network. This paper also discussed the feature extraction algorithm used in plant disease.

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Section: Literature Reviewmentioning

confidence: 99%

Identification of Plant Disease Using Smart Phone Application

Bhagat¹

2019

IJRASET

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“…The clinical condition of a patient is assessed by an expert that observes various types of lesions, analyzes information given by the patient, detects unusual masses through his hands, etc. Plenty of indications are given by ordinary blood, urinary tests as well as molecular analysis that can be based on biosensors [1]. Several types of sensors can also be used to monitor the condition of a person like his temperature, respiratory, blood pressure, glucose, and skin perspiration, while more advanced tests like electrocardiogram, electromyogram, etc., can also be performed [2,3].…”

Section: Introductionmentioning

confidence: 99%

A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

Petrellis

2018

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Image processing has been extensively used in various (human, animal, plant) disease diagnosis approaches, assisting experts to select the right treatment. It has been applied to both images captured from cameras of visible light and from equipment that captures information in invisible wavelengths (magnetic/ultrasonic sensors, microscopes, etc.). In most of the referenced diagnosis applications, the image is enhanced by various filtering methods and segmentation follows isolating the regions of interest. Classification of the input image is performed at the final stage. The disease diagnosis approaches based on these steps and the common methods are described. The features extracted from a plant/skin disease diagnosis framework developed by the author are used here to demonstrate various techniques adopted in the literature. The various metrics along with the available experimental conditions and results presented in the referenced approaches are also discussed. The accuracy achieved in the diagnosis methods that are based on image processing is often higher than 90%. The motivation for this review is to highlight the most common and efficient methods that have been employed in various disease diagnosis approaches and suggest how they can be used in similar or different applications.

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“…Similar sensor systems were presented in Kajita et al (2001), Amini and Ayazi (2004), Georgakopoulou et al (2016) and Jiang et al (2012), which focus on the direct digitization of the measurement signal. The major drawback of such solutions, together with higher power consumption, is the extra amount of electrical and quantization noise from the ADC, which adds to the analog frontend's noise and thus reduces the system's DR.…”

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confidence: 99%

An ultra-low noise capacitance to voltage converter for sensor applications in 0.35  µm CMOS

Utz

Walk

Haas

et al. 2017

J. Sens. Sens. Syst.

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Abstract. In this paper we present a readout circuit for capacitive micro-electro-mechanical system (MEMS) sensors such as accelerometers, gyroscopes or pressure sensors. A flexible interface allows connection of a wide range of types of sensing elements. The ASIC (application-specific integrated circuit) was designed with a focus on ultra-low noise operation and high analog measurement performance. Theoretical considerations on system noise are presented which lead to design requirements affecting the reachable overall measurement performance. Special emphasis is put on the design of the fully differential operational amplifiers, as these have the dominant influence on the achievable overall performance. The measured input referred noise is below 50 zF/ √Hz within a bandwidth of 10 Hz to 10 kHz. Four adjustable gain settings allow the adaption to measurement ranges from ±750 fF to ±3 pF. This ensures compatibility with a wide range of sensor applications. The full input signal bandwidth ranges from 0 Hz to more than 50 kHz. A high-precision accelerometer system was built from the described ASIC and a high-sensitivity, low-noise sensor MEMS. The design of the MEMS is outlined and the overall system performance, which yields a combined noise floor of 200 ng/ √ Hz, is demonstrated. Finally, we show an application using the ASIC together with a CMOS integrated capacitive pressure sensor, which yields a measurement signal-to-noise ratio (SNR) of more than 100 dB.

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A Capacitive-to-Digital Converter With Automatic Range Adaptation for Readout Instrumentation

Cited by 18 publications

References 30 publications

Identification of Plant Disease Using Smart Phone Application

Identification of Plant Disease Using Smart Phone Application

A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

An ultra-low noise capacitance to voltage converter for sensor applications in 0.35  µm CMOS

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A Capacitive-to-Digital Converter With Automatic Range Adaptation for Readout Instrumentation

Cited by 18 publications

References 30 publications

Identification of Plant Disease Using Smart Phone Application

Identification of Plant Disease Using Smart Phone Application

A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

An ultra-low noise capacitance to voltage converter for sensor applications in 0.35 µm CMOS

Contact Info

Product

Resources

About

An ultra-low noise capacitance to voltage converter for sensor applications in 0.35  µm CMOS