Power Analysis and Conditional Expectation of CMOS Sensors

Faculty Research Day 2018: Doctoral Student Poster 3rd PlaceOur research currently focusing on image sensors predominantly the sensors implemented using CMOS (Complementary Metal Oxide Semiconductor) technology. These sensors designated as CMOS sensors which were introduced after CCD (Charge-coupled Devices) sensors since CCDs having some drawbacks in terms of its power and making cost compared to CMOS sensors. The most prominent feature of the CMOS sensors is that they can work at low voltage. CMOS sensors need only one supply voltage but CCDs require three to four which makes the cost of the CMOS sensor very low compared to CCD. In this context we concentrated on power consumption of CMOS sensors and corresponding regression analysis applied to obtain the linearity between the input voltage and the power consumed by the sensor in different technical environments. Further research includes the testing of these sensors in terms of their response with respect to the input voltage levels, temperature effects, noise and the conditional expectation among them. Along with that we are computing the parameters in order to characterize the sensor in according with the physical and the logical effects

A Noise Immune Technique to Suppress the Temporal Noise for Wide Dynamic Range CMOS Sensors

A CMOS image sensor architecture is presented that uses an extra level of parallelism and thermal and 1/f noise suppression techniques to achieve both low-light detection and a high frame rate. By adding the row-parallel readout ADCs, the conversion speed is improved by more than twice compared to the conventional top-bottom parallel ADC structure. The thermal and 1/f noise is reduced by combining the intrinsic oversampling of the incremental sigma-delta ADCs and the 1/f noise suppression through the source-follower inversion-to accumulation method. The chip contains 164 pads, including 24 LVDS drivers. Rows and columns follow the same readout paths. The pixels are surrounded by the pixel-bias circuits and by the switches for cycling the source follower of the pixels from inversion to accumulation for low-frequency noise reduction. The ADC is the key building block of the designed imager

Role of CMOS Image Sensors based Surveillance Systems in Demanding Fields

Our research currently focusing on image sensors predominantly the sensors implemented using CMOS (Complementary Metal Oxide Semiconductor) technology. Recent technology advances in CMOS image sensors (CIS) enable their utilization in the most demanding surveillance fields, especially visual surveillance and intrusion detection in intelligent surveillance systems, aerial surveillance in war zones, Earth environmental surveillance by the satellites in space monitoring, agricultural monitoring using wireless sensor networks and internet of things and driver assistance in automotive fields. We present an overview of CMOS image sensor-based surveillance applications over the last decade by tabulating the design characteristics related to image quality such as resolution, frame rate, dynamic range, signal-to-noise ratio, and also processing technology. Year wise usage of CIS models are represented

Vital Signs and Organs related Disease Diagnosis Systems using CMOS Image Sensors

According to the Center for Disease Control and Prevention (CDC), the average human life expectancy is 78.8 years. 3.2 million deaths are reported yearly due to heart disease, cancer, Alzheimer's disease, diabetes, and COVID-19. Diagnosing the disease is mandatory in the current way of living to avoid unfortunate deaths and maintain average life expectancy. CMOS image sensor (CIS) became a prominent technology in assisting the devices for monitoring and clinical diagnosis to treat diseases in the medical domain. To address the significance of CMOS image sensors' usage in disease diagnosis systems, this paper focuses on the CIS incorporated disease diagnosis systems related to vital organs of the human body like the heart, lungs, brain, eyes, intestines, bones, skin, blood, and bacteria cells causing diseases. The main objective is to evaluate the systems' capabilities and highlight the most potent ones with advantages, disadvantages, and accuracy, that are used in disease diagnosis. We used PRISMA workflow for study selection methodology, and the parameter-based evaluation is performed on disease diagnosis systems related to the human body's organs. The corresponding CIS models used in systems are mapped organ-wise and tabulated the data collected over the last decade

Evaluation of Temporal readout noise in low power CMOS Sensors

Our research currently focusing on image sensors predominantly the sensors implemented using CMOS (Complementary Metal Oxide Semiconductor) technology. These sensors designated as CMOS sensors which were introduced after CCD (Charge-coupled Devices) sensors since CCDs having some drawbacks in terms of its power and making cost compared to CMOS sensors. The most prominent feature of the CMOS sensors is that they can work at low voltage. CMOS sensors need only one supply voltage but CCDs require three to four which makes the cost of the CMOS sensor very low compared to CCD. CMOS image sensors in general have higher temporal noise, higher fixed pattern noise, higher dark current, smaller full well charge capacitance, and lower spectral response, they cannot provide the same wide dynamic range (DR) and superior signal to noise ratio (SNR) that the CCD image sensors have. The Temporal noise of low power CMOS Sensor is evaluated with respect to various pixel sizes and Pixel arrays and corresponding regression analysis applied to obtain the linearity between the input voltage and the power consumed by the sensor in different technical environments

CMOS Technology in Sensing Fields

The role of CMOS Image Sensors since their birth around the 1960s has been changing a lot. Unlike the past, current CMOS Image Sensors are becoming competitive with regard to Charged Couple Device (CCD) technology. They offer many advantages with respect to CCD, such as lower power consumption, lower voltage operation, on-chip functionality and lower cost. Nevertheless, they are still too noisy and less sensitive than CCDs. Noise and sensitivity are the key-factors to compete with industrial and scientific CCDs. It must be pointed out also that there are several kinds of CMOS Image sensors, each of them to satisfy the huge demand in different areas, such as Digital photography, industrial vision, medical and space applications, electrostatic sensing, automotive, instrumentation and 3D vision systems. In the wake of that, a lot of research has been carried out, focusing on problems to be solved such as sensitivity, noise, power consumption, voltage operation, speed imaging and dynamic range. In this paper, CMOS Image Sensors are reviewed, providing information on the latest advances achieved, their applications, the new challenges and their limitations

Pose Variance, Illuminations and Occlusions involved Driver Emotion Detection System

Monitoring the emotions of drivers are the key aspects while designing the advanced driver assistance systems (ADAS) in vehicles. To ensure the safety and track the possibility of the accidents, the emotion monitoring will play a key role in justifying the mental status of the driver. Recent developments in face expression recognition have brought the tremendous attention across the world due to its intellectual capabilities to track the facial expressions. Machine learning and deep learning technologies have helped a lot in developing an efficient face expression recognition systems. Two novel approaches using machine learning, deep learning algorithms and residual neural networks are proposed to monitor six class of expressions of the driver in different pose variations and occlusions. We obtained the better accuracies with these two novel approaches when compared to the state of art methods

CMOS Image Sensors in Surveillance System Applications

Recent technology advances in CMOS image sensors (CIS) enable their utilization in the most demanding of surveillance fields, especially visual surveillance and intrusion detection in intelligent surveillance systems, aerial surveillance in war zones, Earth environmental surveillance by satellites in space monitoring, agricultural monitoring using wireless sensor networks and internet of things and driver assistance in automotive fields. This paper presents an overview of CMOS image sensor-based surveillance applications over the last decade by tabulating the design characteristics related to image quality such as resolution, frame rate, dynamic range, signal-to-noise ratio, and also processing technology. Different models of CMOS image sensors used in all applications have been surveyed and tabulated for every year and application.https://doi.org/10.3390/s2102048

Hybrid CNN-SVM Classifier Framework for Driver Emotion Detection System

Many studies have proved that the driver’s emotions are the significant factors that manage the driver’s behavior, leading to severe vehicle collisions. The ADAS systems can assist various functions for proper driving and estimate drivers’ capability of stable driving behavior and road safety. Therefore, continuous monitoring of drivers’ emotions can help predict their behavior to avoid accidents. A novel hybrid network architecture using a deep neural network and support vector machine has been developed to predict between six and seven driver’s emotions in different poses, occlusions, and illumination conditions to achieve this goal. Our proposed model achieved better performance accuracy of 84.41%, 95.05%, 98.57%, and 98.64% for FER 2013, CK+, KDEF, and KMU-FED datasets, respectively