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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Phononic crystals (PnCs) exhibit acoustic properties that are not usually found in natural materials, which leads to the possibility of new devices for the complex manipulation of acoustic waves. In this article, a micron‐scale phononic waveguide constructed by line defects in PnCs to achieve on‐chip, tightly confined guiding, bending, and splitting of surface acoustic waves (SAWs) is reported. The PnC is made of a square lattice of periodic nickel pillars on a piezoelectric substrate. The PnC lattice constant, pillar diameter, and pillar height are set to 10, 7.5, and 3.2 µm, respectively, leading to a complete bandgap centered at 195 MHz. Interdigitated transducers are monolithically integrated on the same substrate for SAW excitation. The guiding, bending, and splitting of SAWs in the phononic waveguide are experimentally observed through measurement of the out‐of‐plane displacement fields using a scanning optical interferometer. The combination of destructive interference due to the Bragg bandgap and the interaction of the propagating wave with the pillars around the channel results in a tight confinement of the displacement field. The proposed phononic waveguides demonstrate the feasibility of precise local manipulation of SAW that is essential for emerging frontier applications, notably for phonon‐based classical and quantum information processing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this article, we present a CMOS hybrid magnetic field sensor for real-time motor speed monitoring for smart energy-efficient autonomous systems. The sensor system consists of a micro Hall effect sensor ($\mu $ HES) and a micro search coil magnetometer ($\mu $ SCM) using Taiwan Semiconductor Manufacturing Company (TSMC) 0.18- $\mu \text{m}$ 1P6M CMOS process with a die size of $1.5\times2$ mm. The hybrid sensor is realized completely by the CMOS process without postprocessing. A wide range of rotation speeds can be measured by the presented passive $\mu $ SCM without signal amplification (>600 r/min) and the combination of the $\mu $ SCM and $\mu $ HES on the same chip allows the low rotation speed range measurement (< 600 r/min). Furthermore, the $\mu $ SCM has zero offset and very low-temperature dependency. The magnetic field of a permanent magnet on the rotating shaft of the motor is measured by the $\mu $ SCM and $\mu $ HES to determine the rotation speed. The sensitivity of the $\mu $ HES (at 5-V bias voltage) and the $\mu $ SCM inside a uniform magnetic field are 113.6 and $11.3 \mu \text{V}$ /mT at 1 Hz without amplification, respectively. The noise equivalent magnetic induction (NEMI) of the $\mu $ SCM was calculated and verified experimentally by utilizing a zero-drift instrumentation amplifier. Furthermore, a simplified equivalent circuit model for the $\mu $ SCM is presented and verified experimentally. According to the equivalent circuit model, the linear frequency response region of the $\mu $ SCM is up to 20 kHz, which suits the controlling motors in smart autonomous systems and other low-frequency applications. [ABSTRACT FROM AUTHOR]

Brain‐inspired computation is a promising field toward the next generation of intelligent computing systems. To mimic the brain functions, this field requires devices that can support the goal of computing efficiency, size, and cost. Therefore, researchers are trying to fabricate soft and flexible devices using synthetically organic and inorganic materials like solid‐state or ionic liquids. Herein, a soft and flexible bioinspired neuromorphic computing device based on organic silk fibroin gel, which is robust under external forces, is proposed. Silk fibroin protein is composed of essential and nonessential amino acids. with special amount of positively charged glycine, alanine, which plays an essential role during ionic movement to perform neuromorphic computing. Detailed electrical characterization of the proposed (Cu/silk fibroin gel/Cu) discrete device confirms the synaptic behavior by applying different pulse amplitudes, pulse widths, and frequencies. In addition, the proposed device delivers stable performance during mechanical deformations. The analysis of the present results defines that the proposed device is a potential candidate for environmentally friendly wearable intelligent electronics. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) transceivers suffer from optical interference and ambient light saturation, which severely affect the reception of optical signals. In this paper, we discuss various techniques for the suppression of ambient light in electronic, optical, communication and wavelength domains with focus on VLC systems for vehicular communication. Designing transceivers for outdoor VLC systems is challenging due to the dynamic environment and the time-varying nature of interference from different types of ambient light sources. This is a unique study that addresses the problem of optical interference in multiple physical domains and offers qualitative insights in a systematic manner. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper examines the simulation, design, and fabrication of a nano-particle silver microheater. COMSOL Multiphysics is used to simulate the microheater of an area of 720 × 720 μm 2 . Different stages of the microheater fabrication process are discussed. The size of the cartridge used is 10 pL and the nozzle diameter was 50 μ m . The drop spacing was chosen to be 45 μ m after testing several different values. Controlled printing of Ag ink was reached by setting the tickle control frequency to 8 kHz and cartridge print height to 0.4 mm. The nozzle temperature was set at ambient temperature. The inkjet printed microheater is of same area and track and gap widths of 110 μ m . The measured resistance of the microheater, 119 Ω, is approximately equal to the simulated resistance of 100 Ω. It was observed that the temperature at the center of the heater reaches up to 250 °C. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this article, we present a novel electronic nose fabrication process based on highly programmable anodic aluminum oxide (AAO) nanoarchitectonics and ultrasonic spray pyrolysis (USP) deposition. Featuring an ultralow manufacturing cost, the deposited material’s morphology can be accurately controlled with fabricated general-purpose AAO template. Compared with nonstandard lithography-based template fabrication method, the need of complicated Bosch etching process and its associated complex process parameter tuning is eliminated. As a result, the cost-effective mass production of 3-D nanotemplate-based material and devices can be enabled. In addition, the target material’s limited coverage and time efficiency issues widely existing in the previous deposition methods are well-addressed by our customized USP deposition, especially for the 3-D nanotemplate with large surface-to-volume ratio, leading to significantly improved gas-sensing performance. Moreover, the proposed fabrication recipe, together with the adopted gas recognition algorithms based on linear discriminant analysis (LDA), is validated based on the reported extensive measurement results for five gas biomarkers widely exploited for patients’ exhaled gas-sensing and recognition applications. This shows great potential for the early disease diagnose of diabetes, breast cancer, acute lung injury, colon diseases, lung cancer, and so on. [ABSTRACT FROM AUTHOR]

This article presents a low-power column-parallel $\Sigma \Delta $ analog-to-digital converter (ADC) with shared operational transconductance amplifiers (OTAs) for CMOS image sensors (CISs). Through the proposed time division multiplexing of OTAs, the power consumption of the modulator achieves a reduction of about 40%. The proposed structure alleviates the layout requirement of OTAs, enabling implementation of a high-resolution low-power image sensor in advanced CMOS technology nodes. As the coupled noise introduced by the proposed column-shared scheme is suppressed to a lower level than the inherent crosstalk of adjacent pixels, the proposed structure only contributes a negligible inter-column coupled noise from the ADCs. Moreover, a compact digital decimation filter with a single-bit-bit-wise-inversion (BWI) topology is also proposed, which can reduce chip area significantly. The prototype sensor is fabricated in a 40-nm standard CMOS technology with 256 $\times256$ pixel array and 256 proposed column-parallel $\Sigma \Delta $ ADCs. Each $\Sigma \Delta $ ADC occupies a core area of 4.5 $\mu \text{m}\,\,\times $ 310 $\mu \text{m}$ , while consuming a power of 58.8 $\mu \text{W}$. The measured differential nonlinearity (DNL) and integral nonlinearity (INL) are +0.49/−0.65 LSB and +5.1/−4.8 LSB, respectively. Measurement results show a dynamic range of 79.9 dB and an effective-number-of-bit (ENOB) of 11.4 bit. This work achieves a figure of merit (FOM) of 97.2 fJ/step. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The recent emergence of new applications in the area of wireless video sensor network and ultra-low-power biomedical applications (such as the wireless camera pill) have created new design challenges and frontiers requiring extensive research work. In such applications, it is often required to capture a large amount of data and process them in real time while the hardware is constrained to take very little physical space and to consume very little power. This is only possible using custom single-chip solutions integrating image sensor and hardware-friendly image compression algorithms. This paper proposes an adaptive quantization scheme based on boundary adaptation procedure followed by an online quadrant tree decomposition processing enabling low power and yet robust and compact image compression processor integrated together with a digital CMOS image sensor. The image sensor chip has been implemented using 0.35-[micro]m CMOS technology and operates at 3.3 V. Simulation and experimental results show compression figures corresponding to 0.6-0.8 bit per pixel, while maintaining reasonable peak signal-to-noise ratio levels and very low operating power consumption. In addition, the proposed compression processor is expected to benefit significantly from higher resolution and Megapixels CMOS imaging technology.

A digital pixel sensor (DPS) array with a time-based analogue-to-digital conversion (ADC) technique featuring adaptive dynamic range and programmable pixel response is presented. Measurement results indicate a 100 dB dynamic range, a 41 s mean dark time, and an average current of 1.6 A per DPS.

A bagging ensemble consists of a set of classifiers trained independently and combined by a majority vote. Such a combination improves generalization performance but can require large amounts of memory and computation, a serious drawback for addressing portable real-time pattern recognition applications. We report here a compact three-dimensional (3-D) multiprecision very large-scale integration (VLSI) implementation of a bagging ensemble. In our circuit, individual classifiers are decision trees implemented as threshold networks-one layer of threshold logic units (TLUs) followed by combinatorial logic functions. The hardware was fabricated using 0.7-[micro]m CMOS technology and packaged using MCM-V micro-packaging technology. The 3-D chip implements up to 192 TLUs operating at a speed of up to 48GCPPS and implemented in a volume of (w x L x h) = (2 x 2 x 0.7) [cm.sup.3]. The 3-D circuit features a high level of programmability and flexibility offering the possibility to make an efficient use of the hardware resources in order to reduce the power consumption. Successful operation of the 3-D chip for various precisions and ensemble sizes is demonstrated through an electronic nose application. Index Terms--Bagging, decision trees, threshold networks, very large-scale integration (VLSI), three-dimensional (3-D) packaging technology.

In this brief, we analyzed the output ripple in a capacitively-coupled chopper-based instrumentation amplifier (CCIA). Our analysis found that the shape and amplitude of the output ripple of the CCIA are determined by five design parameters, namely amplifier offset, transconductance, chopping frequency, Miller compensation capacitance, and the nulling resistor. With different design specifications, the CCIA ripple appears in four different shapes, including triangular ripple, RC-settling ripple, square ripple, and step-change ripple. These findings are verified by the silicon results. As the cause of each ripple shape varies, this analysis serves as a guideline for designing practical chopper-based amplifiers and their corresponding ripple reduction techniques. [ABSTRACT FROM AUTHOR]

In this article, we report a wafer-level packaged Pirani vacuum gauge using the proprietary InvenSense CMOS MEMS technology. The micro Pirani vacuum gauge features three serpentine-shaped molybdenum thermistors on the suspended silicon-on-insulator (SOI) bridges, while the wiring gap of each serpentine-shaped silicon microbridge is 1.6 ${ {\mu }}\text{m}$. For the vacuum range of $5\times 10^{-{4}}$ –760 Torr, the CMOS MEMS Pirani gauge configured with a constant temperature interface circuit achieves a sensitivity of 0.414 V/Torr in a very fine vacuum regime, while its heating power is less than 21.3 mW. Moreover, the measured output of the micro Pirani gauge shows good agreement with a semi-empirical model, while the model predicts that the proposed Pirani gauge can measure a vacuum pressure as low as $2.6\times 10^{-{4}}$ Torr. The performance achieved by this Pirani vacuum gauge combined with its high level of integration makes it a promising Internet of Things (IoT) sensing node for vacuum monitoring in the industry. [ABSTRACT FROM AUTHOR]