Extreme Early Image Recognition Using Event-Based Vision.

While deep learning algorithms have advanced to a great extent, they are all designed for frame-based imagers that capture images at a high frame rate, which leads to a high storage requirement, heavy computations, and very high power consumption. Unlike frame-based imagers, event-based imagers output asynchronous pixel events without the need for global exposure time, therefore lowering both power consumption and latency. In this paper, we propose an innovative image recognition technique that operates on image events rather than frame-based data, paving the way for a new paradigm of recognizing objects prior to image acquisition. To the best of our knowledge, this is the first time such a concept is introduced featuring not only extreme early image recognition but also reduced computational overhead, storage requirement, and power consumption. Our collected event-based dataset using CeleX imager and five public event-based datasets are used to prove this concept, and the testing metrics reflect how early the neural network (NN) detects an image before the full-frame image is captured. It is demonstrated that, on average for all the datasets, the proposed technique recognizes an image 38.7 ms before the first perfect event and 603.4 ms before the last event is received, which is a reduction of 34% and 69% of the time needed, respectively. Further, less processing is required as the image is recognized 9460 events earlier, which is 37% less than waiting for the first perfectly recognized image. An enhanced NN method is also introduced to reduce this time.

Performance Optimization of Wearable Printed Human Body Temperature Sensor Based on Silver Interdigitated Electrode and Carbon-Sensing Film.

The human body's temperature is one of the most important vital markers due to its ability to detect various diseases early. Accurate measurement of this parameter has received considerable interest in the healthcare sector. We present a novel study on the optimization of a temperature sensor based on silver interdigitated electrodes (IDEs) and carbon-sensing film. The sensor was developed on a flexible Kapton thin film first by inkjet printing the silver IDEs, followed by screen printing a sensing film made of carbon black. The IDE finger spacing and width of the carbon film were both optimized, which considerably improved the sensor's sensitivity throughout a wide temperature range that fully covers the temperature of human skin. The optimized sensor demonstrated an acceptable temperature coefficient of resistance (TCR) of 3.93 × 10-3 °C-1 for temperature sensing between 25 °C and 50 °C. The proposed sensor was tested on the human body to measure the temperature of various body parts, such as the forehead, neck, and palm. The sensor showed a consistent and reproducible temperature reading with a quick response and recovery time, exhibiting adequate capability to sense skin temperatures. This wearable sensor has the potential to be employed in a variety of applications, such as soft robotics, epidermal electronics, and soft human-machine interfaces.

State-of-the-Art Light to Digital Converter Circuits Applicable in Non-Invasive Health Monitoring Devices to Combat COVID-19 and Other Respiratory Illnesses: A Review.

In the past few years, a tremendous advancement in the outcome of biomedical circuits and systems has been reported. Unfortunately, at the time of the sudden outbreak of COVID-19, the electronic engineering researchers felt dearth on their side to combat the pandemic, as no such immediate cutting-edge solutions were ready to recognize the virus with some standard and smart electronic devices. Likely, in this paper, a detailed comparative and comprehensive study on circuit architectures of the biomedical devices is presented. Mostly, this study relates the industry standard circuit schemes applicable in non-invasive health monitoring to combat respiratory illnesses. The trending circuit architectural schemes casted-off to tapeout non-invasive health-care devices available in the past literature are meticulously and broadly discussed in this study. Further, the comprehensive comparison of the state of art of the device performance in terms of supply voltage, chip area, sensitivity, dynamic range, etc. is also shown in this paper. The inclusive design processes of the health monitoring devices from Lab to Industry is thoroughly discussed for the readers. The authors think, that this critical review summarising all the trending and most cited health-care devices in a single paper will alternately help the industrialists to adapt and modify the circuit architectures of the health monitoring devices more precisely and straightforwardly. Finally, the demand for health monitoring devices particularly responsible to detect respiratory illnesses, measuring blood pressure and heart-rate is growing widely in the market after the the incident of COVID-19 and other respiratory diseases.

Progressive Early Image Recognition for Wireless Vision Sensor Networks.

A wireless vision sensor network (WVSN) is built by using multiple image sensors connected wirelessly to a central server node performing video analysis, ultimately automating different tasks such as video surveillance. In such applications, a large deployment of sensors in the same way as Internet-of-Things (IoT) devices is required, leading to extreme requirements in terms of sensor cost, communication bandwidth and power consumption. To achieve the best possible trade-off, we propose in this paper a new concept that attempts to achieve image compression and early image recognition leading to lower bandwidth and smart image processing integrated at the sensing node. A WVSN implementation is proposed to save power consumption and bandwidth utilization by processing only part of the acquired image at the sensor node. A convolutional neural network is deployed at the central server node for the purpose of progressive image recognition. The proposed implementation is capable of achieving an average recognition accuracy of 88% with an average confidence probability of 83% for five subimages, while minimizing the overall power consumption at the sensor node as well as the bandwidth utilization between the sensor node and the central server node by 43% and 86%, respectively, compared to the traditional sensor node.

Hydration Assessment Using the Bio-Impedance Analysis Method.

Body hydration is considered one of the most important physiological parameters to measure and one of the most challenging. Current methods to assess hydration are invasive and require costly clinical settings. The bio-impedance analysis offers a noninvasive and inexpensive tool to assess hydration, and it can be designed to be used in wearable health devices. The use of wearable electronics in healthcare applications has received increased attention over the last decade. New, emerging medical devices feature continuous patient monitoring and data collection to provide suitable treatment and preventive actions. In this paper, a model of human skin is developed and simulated to be used as a guide to designing a dehydration monitoring system based on a bio-impedance analysis technique. The study investigates the effect of applying different frequencies on the dielectric parameters of the skin and the resulting measured impedance. Two different interdigitated electrode designs are presented, and a comparison of the measurements is presented. The rectangular IDE is printed and tested on subjects to validate the bio-impedance method and study the interpretation of its results. The proposed design offers a classification criterion that can be used to assess dehydration without the need for a complex mathematical model. Further clinical testing and data are needed to refine and finalize the criteria.

A 160 m visible light communication link using hybrid undersampled phase-frequency shift on-off keying and CMOS image sensor.

This work demonstrates a record 160 m long visible light communication (VLC) link using LED transmitters and an image sensor-based receiver, for distance-critical applications. This marks the longest VLC distance achieved to date, using a combination of novel modulation technique at the transmitter and an undersampling-based receiver incorporating adaptive threshold decisioning. Experimental results demonstrate low bit error rate (BER) performance at the achieved communication distance.

Recent Developments in Printing Flexible and Wearable Sensing Electronics for Healthcare Applications.

Wearable biosensors attract significant interest for their capabilities in real-time monitoring of wearers' health status, as well as the surrounding environment. Sensor patches are embedded onto the human epidermis accompanied by data readout and signal conditioning circuits with wireless communication modules for transmitting data to the computing devices. Wearable sensors designed for recognition of various biomarkers in human epidermis fluids, such as glucose, lactate, pH, cholesterol, etc., as well as physiological indicators, i.e., pulse rate, temperature, breath rate, respiration, alcohol, activity monitoring, etc., have potential applications both in medical diagnostics and fitness monitoring. The rapid developments in solution-based nanomaterials offered a promising perspective to the field of wearable sensors by enabling their cost-efficient manufacturing through printing on a wide range of flexible polymeric substrates. This review highlights the latest key developments made in the field of wearable sensors involving advanced nanomaterials, manufacturing processes, substrates, sensor type, sensing mechanism, and readout circuits, and ends with challenges in the future scope of the field. Sensors are categorized as biological and fluidic, mounted directly on the human body, or physiological, integrated onto wearable substrates/gadgets separately for monitoring of human-body-related analytes, as well as external stimuli. Special focus is given to printable materials and sensors, which are key enablers for wearable electronics.

All-Printed Human Activity Monitoring and Energy Harvesting Device for Internet of Thing Applications.

A self-powered device for human activity monitoring and energy harvesting for Internet of Things (IoT) devices is proposed. The self-powered device utilizes flexible Nano-generators (NGs), flexible diodes and off-the-shelf capacitors. During footsteps the NGs generate an AC voltage then it is converted into DC using rectifiers and the DC power is stored in a capacitor for powering the IoT devices. Polydimethylsiloxane (PDMS) and zinc stannate (ZnSnO3) composite is utilized for the NG active layer, indium tin oxide (ITO) and aluminum (Al) are used as the bottom and top electrodes, respectively. Four diodes are fabricated on the bottom electrode of the NG and connected in bridge rectifier configuration. A generated voltage of 18 Vpeak was achieved with a human footstep. The self-powered smart device also showed excellent robustness and stable energy scavenger from human footsteps. As an application we demonstrate human activity detection and energy harvesting for IoT devices.

A Low-Cost Strain Gauge Displacement Sensor Fabricated via Shadow Mask Printing.

This work presents a cost-effective shadow mask printing approach to fabricate flexible sensors. The liquid-state sensing material can be directly brushed on a flexible substrate through a shadow mask. The ink leakage issue which often occurs in printed electronics is addressed with a custom taping scheme. A simple thermal compression bonding approach is also proposed to package the functional area of the sensor. To verify the feasibility and robustness of the proposed fabrication approach, a prototyped strain gauge displacement sensor is fabricated using carbon ink as the sensing material and a flexible polyimide (PI) film as the substrate. Once the substrate is deformed, cracks in the solidified ink layer can cause an increased resistance in the conductive path, thus achieving function of stable displacement/strain sensing. As a demonstration for displacement sensing application, this sensor is evaluated by studying its real-time resistance response under both static and dynamic mechanical loading. The fabricated sensor shows a comparable performance (with a gauge factor of ~17.6) to those fabricated using costly lithography or inkjet printing schemes, while with a significantly lower production cost.

Residual interpolation for division of focal plane polarization image sensors.

Division of focal plane (DoFP) polarization image sensors capture polarization properties of light at every imaging frame. However, these imaging sensors capture only partial polarization information, resulting in reduced spatial resolution output and a varying instantaneous field of overview (IFoV). Interpolation methods are used to reduce the drawbacks and recover the missing polarization information. In this paper, we propose residual interpolation as an alternative to normal interpolation for division of focal plane polarization image sensors, where the residual is the difference between an observed and a tentatively estimated pixel value. Our results validate that our proposed algorithm using residual interpolation can give state-of-the-art performance over several previously published interpolation methods, namely bilinear, bicubic, spline and gradient-based interpolation. Visual image evaluation as well as mean square error analysis is applied to test images. For an outdoor polarized image of a car, residual interpolation has less mean square error and better visual evaluation results.

Patterned dual-layer achromatic micro-quarter-wave-retarder array for active polarization imaging.

In this paper, we present a liquid-crystal-polymer (LCP)-based dual-layer micro-quarter-wave-retarder (MQWR) array for active polarization image sensors. The proposed MQWRs, for the first time, enable the extraction of the incident light's circularly polarized components in the whole visible regime, which correspond to the fourth parameter of Stokes vector. Compared with the previous implementations, our proposed MQWRs feature high achromaticity, making their applications no longer limited to monochromatic illumination. In addition, the presented thin structure exhibits an overall thickness of 2.43µm, leading to greatly alleviated optical cross-talk between adjacent photo-sensing pixels. Moreover, the reported superior optical performance (e.g. minor transmittance, extinction ratio) validates our optical design and optimization of the proposed MQWRs. Furthermore, the demonstrated simple fabrication recipe offers a cost-effective solution for the monolithic integration between the proposed MQWR array and the commercial solid-state image sensors, which makes the multi-spectral full Stokes polarization imaging system on a single chip feasible.

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 µm CMOS process and the measurement result shows an ADC power consumption lower than 63.5 µW under 1.4 V power supply and 50 MHz clock frequency.

Two-step single slope/SAR ADC with error correction for CMOS image sensor.

Conventional two-step ADC for CMOS image sensor requires full resolution noise performance in the first stage single slope ADC, leading to high power consumption and large chip area. This paper presents an 11-bit two-step single slope/successive approximation register (SAR) ADC scheme for CMOS image sensor applications. The first stage single slope ADC generates a 3-bit data and 1 redundant bit. The redundant bit is combined with the following 8-bit SAR ADC output code using a proposed error correction algorithm. Instead of requiring full resolution noise performance, the first stage single slope circuit of the proposed ADC can tolerate up to 3.125% quantization noise. With the proposed error correction mechanism, the power consumption and chip area of the single slope ADC are significantly reduced. The prototype ADC is fabricated using 0.18 µ m CMOS technology. The chip area of the proposed ADC is 7 µ m × 500 µ m. The measurement results show that the energy efficiency figure-of-merit (FOM) of the proposed ADC core is only 125 pJ/sample under 1.4 V power supply and the chip area efficiency is 84 k µ m(2) · cycles/sample.

A Highly Integrated Lab-on-a-CMOS Platform for Real-Time Monitoring of E. Coli Growth Kinetics.

Existing miniaturized and cost-effective solutions for bacterial growth monitoring usually require offline incubators with constant temperature to culture the bio-samples prior to measurement. Such a separated sample preparation and detection scheme requires extensive human intervention, risks contamination, and suffers from poor temporal resolution. To achieve integrated sample preparation and real-time bacterial growth monitoring, this article presents a lab-on-a-CMOS platform incorporates an optical sensor array, temperature sensor array, micro-heaters, and readout circuits. Escherichia coli's (E. coli) optimum growth temperature of 37 °C is achieved through a heat regulation system consisting of two micro-heaters and an on-chip temperature sensor array. A photodiode array with an in-pixel capacitive trans-impedance amplifier to reduce inter-pixel cross-coupling is designed to extract the optical information during bacterial growth. To balance the footprint, power consumption, and quantization speed, a 10 b column successive-approximation register (SAR)/single-slope (SS) dual-mode analog-to-digital converter (ADC) is designed to digitize the temperature and optical signals. Fabricated in a standard 0.18 um CMOS process, the proposed platform can regulate the sample temperature to 37 +/- 0.2/0.3 °C within 32 mins. Enabled by an on-chip heat regulation system and photodetectors, the prototype demonstrates a real-time monitoring of bacterial growth kinetics and antibiotic responses. Minute-level temporal resolution is achieved as this proposed platform is free of extensive and time-consuming human intervention. The proposed platform can be viably used in contamination sensitive applications such as antibiotic tests, stem cell cultures, and organ-on-chips.

Advancing crop classification in smallholder agriculture: A multifaceted approach combining frequency-domain image co-registration, transformer-based parcel segmentation, and Bi-LSTM for crop classification.

Agricultural Remote Sensing has the potential to enhance agricultural monitoring in smallholder economies to mitigate losses. However, its widespread adoption faces challenges, such as diminishing farm sizes, lack of reliable data-sets and high cost related to commercial satellite imagery. This research focuses on opportunities, practices and novel approaches for effective utilization of remote sensing in agriculture applications for smallholder economies. The work entails insights from experiments using datasets representative of major crops during different growing seasons. We propose an optimized solution for addressing challenges associated with remote sensing-based crop mapping in smallholder agriculture farms. Open source tools and data are used for inter and intra-sensor image registration, with a root mean square error of 0.3 or less. We also propose and emphasize on the use of delineated vegetation parcels through Segment Anything Model for Geospatial (SAM-GEOs). Furthermore a Bidirectional-Long Short-Term Memory-based (Bi-LSTM) deep learning model is developed and trained for crop classification, achieving results with accuracy of more than 94% and 96% for validation sets of two data sets collected in the field, during 2 growing seasons.

Harnessing Green Electricity from Food: A Split Black Gram-Based Triboelectric Nanogenerator for a Self-Powered Autonomous Lighting System and Portable Electronics.

Triboelectric nanogenerators (TENGs) represent a promising solution to mounting environmental concerns associated with battery disposal amid the escalating demand for portable electronics. However, prevailing TENG fabrication predominantly relies on nonbiodegradable, nonbiocompatible, and synthetic materials, posing a grave ecological threat. To mitigate this, there is a pressing need to develop eco-friendly and green TENGs leveraging sustainable, naturally occurring materials. This study pioneers the use of split black gram (SBG) as a tribo-positive material for TENGs. SBG's effectiveness as a tribo-positive material stems from its abundance of oxygen-containing functional groups, as confirmed by FTIR analysis, facilitating electron donation during the triboelectric process. SBG offers compelling advantages, including widespread availability, cost-effectiveness, biodegradability, and hydrophobic and adhesive properties due to its richness in starch and protein, positioning it as an optimal choice for eco-conscious TENG manufacturing. The fabrication process of an SBG-TENG is not only economical and facile but also solvent-free, requiring no specialized tools. Demonstrating commendable performance, the SBG-TENG achieves a maximum power density of 15.36 µW/cm2 at 1 MΩ, with an open circuit voltage of 84 V and short circuit current of 28 µA, comparable to recent studies. In practical applications, the SBG-TENG seamlessly integrates with LEDs and portable electronic devices via a full bridge rectifier, successfully powering them postcapacitor charging. Moreover, an autonomous lighting system is developed by embedding the SBG-TENG in a foot mat, enabling wireless light control through human stepping on the mat, introducing power-saving functionality for residential and office environments. In essence, the introduction of the SBG-TENG not only delivers cost-effectiveness but also minimizes the environmental impact by harnessing sustainable energy from food sources.

Wearable Printed Temperature Sensors: Short Review on Latest Advances for Biomedical Applications.

The rapid growth in wearable biosensing devices is driven by the strong desire to monitor the human health data and to predict the symptoms of chronic diseases at an early stage. Different sensors are developed for continuous monitoring of various biomarkers through wearable and implantable sensing patches. Temperature sensor has proved to be an important physiological parameter amongst the various wearable biosensing patches. This paper highlights the recent progresses made in printing of functional nanomaterials for developing wearable temperature sensors on polymeric substrates. A special focus is given to the advanced functional nanomaterials as well as their deposition through printing technologies. The geometric resolutions, shape, physical and electrical characteristics as well as sensing properties using different materials are compared and summarized. Wearability is the main concern of these newly developed sensors, which is summarized by discussing representative examples. Finally, the challenges concerning the stability, repeatability, reliability, sensitivity, linearity, ageing, and large-scale manufacturing are discussed with future outlook of the wearable systems.

Metallic Micro-Nano Network-Based Soft Transparent Electrodes: Materials, Processes, and Applications.

Soft transparent electrodes (TEs) have received tremendous interest from academia and industry due to the rapid development of lightweight, transparent soft electronics. Metallic micro-nano networks (MMNNs) are a class of promising soft TEs that exhibit excellent optical and electrical properties, including low sheet resistance and high optical transmittance, as well as superior mechanical properties such as softness, robustness, and desirable stability. They are genuinely interesting alternatives to conventional conductive metal oxides, which are expensive to fabricate and have limited flexibility on soft surfaces. This review summarizes state-of-the-art research developments in MMNN-based soft TEs in terms of performance specifications, fabrication methods, and application areas. The review describes the implementation of MMNN-based soft TEs in optoelectronics, bioelectronics, tactile sensors, energy storage devices, and other applications. Finally, it presents a perspective on the technical difficulties and potential future possibilities for MMNN-based TE development.

An Efficient Memristor-Based Circuit Implementation of Squeeze-and-Excitation Fully Convolutional Neural Networks.

Recently, there has been a surge of interest in applying memristors to hardware implementations of deep neural networks due to various desirable properties of the memristor, such as nonvolativity, multivalue, and nanosize. Most existing neural network circuit designs, however, are based on generic frameworks that are not optimized for memristors. Furthermore, to the best of our knowledge, there are no existing efficient memristor-based implementations of complex neural network operators, such as deconvolutions and squeeze-and-excitation (SE) blocks, which are critical for achieving high accuracy in common medical image analysis applications, such as semantic segmentation. This article proposes convolution-kernel first (CKF), an efficient scheme for designing memristor-based fully convolutional neural networks (FCNs). Compared with existing neural network circuits, CKF enables effective parameter pruning, which significantly reduces circuit power consumption. Furthermore, CKF includes the novel, memristor-optimized implementations of deconvolution layers and SE blocks. Simulation results on real medical image segmentation tasks confirm that CKF obtains up to 56.2% reduction in terms of computations and 33.62-W reduction in terms of power consumption in the circuit after weight pruning while retaining high accuracy on the test set. Moreover, the pruning results can be applied directly to existing circuits without any modification for the corresponding system.

Fabrication of Low-Cost Resistance Temperature Detectors and Micro-Heaters by Electrohydrodynamic Printing.

EHD printing is an advanced deposition technology that is commonly utilized for the direct manufacture of electrical devices. In this study, meander-type resistive electrodes consisting of silver nanoparticles were printed directly on rigid glass and flexible polyethylene terephthalate (PET) substrates. High-resolution patterns of ≈50 µm linewidth were successfully printed on untreated surfaces utilizing a bigger nozzle of 100 µm inner diameter after improving the experimental settings. The manufactured electrodes were evaluated and used as Resistance Temperature Detectors (RTDs) and micro-heaters in a systematic manner. The temperature sensors performed well, with a Temperature Coefficient of Resistivity (TCRs) of 11.5 ×10-3/°C and 13.3 ×10-3/°C, for glass and PET substrates, respectively, throughout a wide temperature range of 100 °C and 90 °C. Furthermore, the RTDs had a quick response and recovery time, as well as minimal hysteresis. The electrodes' measured sensitivities as micro-heaters were 3.3 °C/V for glass and 6.8 °C/V for PET substrates, respectively. The RTDs were utilized for signal conditioning in a Wheatstone bridge circuit with a self-heating temperature of less than 1 °C as a practical demonstration. The micro-heaters have a lot of potential in the field of soft wearable electronics for biomedical applications, while the extremely sensitive RTDs have a lot of potential in industrial situations for temperature monitoring.