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Smart CMOS Image Sensors and Related Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 7685

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Special Issue Editor

Dr. Renato Turchetta

E-Mail Website
Guest Editor

Imasenic S.L., Barcelona, Spain
Interests: CMOS image sensors

Special Issue Information

Dear Colleagues,

The field of CMOS image sensors is continuously growing, and so is the need for processing the data they generate. Making image sensors smart can be a must in many applications, and it is often a desired quality. This can be done in many ways, from the pixel to the periphery of the sensor. At the pixel level, event-driven pixels can be designed or in-/intra-pixel intelligence can be added, for example with a neuronal network. Data compression can also help, and in this case, low-energy solutions will be preferred. With the emergence of different types of sensors capable of providing in-depth information, 3D imaging becomes possible and the need for processing this type of data appears as well. Sensors where artificial intelligence is integrated as close as possible at the sensing place, and included in the pixel itself, are also demanded. Around this growing set of activities to develop smart sensors, there is also an overarching need for keeping power low, so that mobile applications can be addressed. Towards this goal, energy-harvesting sensors capable of generating at least part of their power needs are also demanded.

Contributions to these varied and ever expending research fields are invited for this Special Issue of the Sensors journal.

Dr. Renato Turchetta
Guest Editor

Manuscript Submission Information

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Keywords

CMOS image sensors
Stacked sensors
Artificial Intelligence
Event-driven image sensors
Compressed sensing
On-chip processing
3D and 2D imaging
Neuron networks
Low power
Energy-harvesting

Published Papers (4 papers)

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Research

18 pages, 800 KiB

Open AccessArticle

Low Memory Access Video Stabilization for Low-Cost Camera SoC

by Yun-Gu Lee

Sensors 2022, 22(6), 2341; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062341 - 18 Mar 2022

Cited by 1 | Viewed by 1529

Abstract

Video stabilization is one of the most important features in consumer cameras. Even simple video stabilization algorithms may need to access the frames several times to generate a stabilized output image, which places a significant burden on the camera hardware. This high-memory-access requirement makes it difficult to implement video stabilization in real time on low-cost camera SoC. Reduction of the memory usage is a critical issue in camera hardware. This paper presents a structure and layout method to efficiently implement video stabilization for low-end hardware devices in terms of shared memory access amount. The proposed method places sub-components of video stabilization in a parasitic form in other processing blocks, and the sub-components reuse data read from other processing blocks without directly accessing data in the shared memory. Although the proposed method is not superior to the state-of-the-art methods applied in post-processing in terms of video quality, it provides sufficient performance to lower the cost of camera hardware for the development of real-time devices. According to my analysis, the proposed one reduces the memory access amount by 21.1 times compared to the straightforward method. Full article

(This article belongs to the Special Issue Smart CMOS Image Sensors and Related Applications)

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19 pages, 4242 KiB

Open AccessArticle

Enhanced Channel Calibration for the Image Sensor of the TuMag Instrument

by Eduardo Magdaleno, Manuel Rodríguez Valido, David Hernández, María Balaguer, Basilio Ruiz Cobo, David Orozco Suárez, Daniel Álvarez García and Argelio Mauro González

Sensors 2022, 22(6), 2078; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062078 - 08 Mar 2022

Cited by 1 | Viewed by 1818

Abstract

The Sunrise missions consist of observing the magnetic field of the sun continuously for a few days from the stratosphere. In these missions, a balloon supporting a telescope and associated instrumentation, including a Tunable Magnetograph (TuMag), is lifted into the stratosphere. In the camera of this instrument, the image sensor sends its data to a Field Programmable Gate Array (FPGA) using eight transmission channels. These channels must be previously calibrated for a correct delivery of the image. For this mission, the FPGA has been exchanged for a newer and larger one, so the firmware has been adapted to the new device. In addition, the calibration algorithm has been parallelized as the main innovation of this work, taking advantage of the increase in logic resources of the new FPGA, in order to minimize the calibration time of the channels. The algorithm has been implemented specifically for this instrument without using the Input Serial Deserializer (ISERDES) Intellectual Property (IP), since this IP does not support the deserialization of the data sent by the image sensor to the FPGA. Full article

(This article belongs to the Special Issue Smart CMOS Image Sensors and Related Applications)

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13 pages, 3046 KiB

Open AccessArticle

Measurement of Corona Discharges under Variable Geometry, Frequency and Pressure Environment

by Pau Bas-Calopa, Jordi-Roger Riba and Manuel Moreno-Eguilaz

Sensors 2022, 22(5), 1856; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051856 - 26 Feb 2022

Cited by 9 | Viewed by 1970

Abstract

Aeronautical industry is evolving towards more electric aircrafts (MEA), which will require much more electrical power compared to conventional models. To satisfy this increasing power demand and stringent weight requirements, distribution voltages must be raised, which jointly with the low-pressure environment and high operating frequencies increase the risk of electrical discharges occurrence. Therefore, it is important to generate data to design insulation systems for these demanding applications. To this end, in this work a sphere-to-plane electrode configuration is tested for several sphere geometries (diameters ranging from 2 mm to 10 mm), frequencies of 50 Hz, 400 Hz and 800 Hz and pressures in the 20–100 kPa range, to cover most aircraft applications. The corona extinction voltage is experimentally determined by using a gas-filled tube solar blind ultraviolet (UV) sensor. In addition, a CMOS imaging sensor is used to locate the discharge points. Next, to gain further insight to the discharge conditions, the electric field strength is calculated using finite element method (FEM) simulations and fitted to equations based on Peek’s law. The results presented in this paper could be especially valuable to design aircraft electrical insulations as well as for high-voltage hardware manufacturers, since the results allow determining the electric field values at which the components can operate free of surface discharges for a wide altitude range. Full article

(This article belongs to the Special Issue Smart CMOS Image Sensors and Related Applications)

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17 pages, 3872 KiB

Open AccessCommunication

Characterization of the Relationship between the Loess Moisture and Image Grayscale Value

by Qingbing Liu, Jinge Wang, Hongwei Zheng, Tie Hu and Jie Zheng

Sensors 2021, 21(23), 7983; https://0-doi-org.brum.beds.ac.uk/10.3390/s21237983 - 30 Nov 2021

Cited by 3 | Viewed by 1591

Abstract

This paper presents a model for estimating the moisture of loess from an image grayscale value. A series of well-controlled air-dry tests were performed on saturated Malan loess, and the moisture content of the loess sample during the desiccation process was automatically recorded while the soil images were continually captured using a photogrammetric device equipped with a CMOS image sensor. By converting the red, green, and blue (RGB) image into a grayscale one, the relationship between the water content and grayscale value, referred to as the water content–gray value characteristic curve (WGCC), was obtained; the impacts of dry density, particle size distribution, and illuminance on WGCC were investigated. It is shown that the grayscale value increases as the water content decreases; based on the rate of increase of grayscale value, the WGCC can be segmented into three stages: slow-rise, rapid-rise, and asymptotically stable stages. The influences that dry density and particle size distribution have on WGCC are dependent on light reflection and transmission, and this dependence is closely related to soil water types and their relative proportion. Besides, the WGCC for a given soil sample is unique if normalized with illuminance. The mechanism behind the three stages of WGCC is discussed in terms of visible light reflection. A mathematical model was proposed to describe WGCC, and the physical meaning of the model parameters was interpreted. The proposed model is validated independently using another six different types of loess samples and is shown to match well the experimental data. The results of this study can provide a reference for the development of non-contact soil moisture monitoring methods as well as relevant sensors and instruments. Full article

(This article belongs to the Special Issue Smart CMOS Image Sensors and Related Applications)

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