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Sensor interfaces demand ever-increasing energy efficiency from analog-to-digital converters (ADCs). For sensors requiring a wide dynamic range (DR) (>12 bit), the incremental ADC (IADC) is becoming a popular choice. In this article, the design of an energy-efficient IADC with a 16-bit signal-to-noise and distortion ratio (SNDR) and 17-bit DR is described. The IADC implements a novel capacitor scaling technique, which reduces power consumption in the critical first operational transconductance amplifier (OTA) by up to 37%. The new technique can be combined with the extended counting (EC) scheme utilizing an in-loop 4-bit asynchronous successive approximation register (SAR) (ASAR) ADC. Using a three-phase operation, this IADC architecture further reduces the power while enabling wider signal bandwidth with a much lower oversampling ratio (OSR). Silicon measurements in a 0.18- $\mu $ m CMOS process show the IADC achieved a DR of 102.2 dB, within a bandwidth of 2.04 kHz, with power consumption of about $25~\mu $ W, leading to a state-of-the-art Schreier figure-of-merit (FoM) of 181.1 dB. [ABSTRACT FROM AUTHOR]

Analog-to-information converters (AICs) take advantage of the limited information bandwidth in high-frequency signals to improve the energy efficiency of front-end data converters. High-resolution image sensors often convey limited information due to the spatial redundancy between neighboring pixels. This paper proposes a mixed-signal AIC which compresses each nonoverlapping 4 × 4 pixel block in a 816 × 640 pixel prototype active-pixel sensor (APS) imager. It combines an energy-efficient charge-pump bit-image processor (BIP) with an area-efficient successive-approximation-register-single-slope (SAR-SS) hybrid analog-to-digital converter (ADC) via a charge-transfer-amplifier (CTA). The AIC is fully dynamic and consumes no static power. The ADC's capacitor array doubles as a computational device for parts of the compression algorithm which reduces its sampling rate by a factor of four. The compressed data contains direct edge information and can be decoded by a very simple receiver. The fabricated prototype consumes 12 pJ per pixel at 111 fps in the image compression mode and 48 pJ per pixel at 28.7 fps in raw data mode (9 b per pixel) under the same clock rate. To the best of our knowledge, this is the most energy-efficient compressive CMOS image sensor ever reported in the literature, thanks to the proposed AIC. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, we propose a low-power true single-phase-clock (TSPC) based domino logic circuit design. Compared to using three clock transistors in the conventional TSPC-based scheme, the proposed circuit only requires two transistors. As a result, the clock load capacitance is reduced, leading to low power consumption in the clock distribution network. A keeper design to solve charge sharing is also demonstrated. Simulation results using 90nm and 45nm CMOS technologies are provided and iscussed, respectively, which illustrate power saving as compared to conventional design not only when the input logic is active but also when the input logic is held to zero. [Copyright &y& Elsevier]

In this paper, we present a 4T low-power linear-output current-mediated CMOS APS imager, in which reset and read-out operations are carried-out simultaneously on two pixels of the same row. The proposed operating technique greatly simplifies the pixel architecture with only four transistors and two control signals required, while six transistors and four control lines are required by its current-mediated counterpart. The imager achieves fixed pattern noise (FPN) correction during pixel-readout and exhibits a power consumption which is independent of the imager array size, since only a single current source is solicited at any given time due to the array-level operating technique. A linearization circuit technique using the transistor's channel length modulation effect is employed enabling to double the linear range of the pixel's photon-to-output signal transfer function. Performance analysis and experimental results are presented for a 32\,\times\,32 image sensor array prototype, fabricated using AMS 0.35-\mum process. The pixel size is 6.5\,\times\,6.5 \mu\ m^2 with 22% fill-factor. The chip total power consumption is less than 1 mW, at 50 frames/s with a 3.3 V power supply. [ABSTRACT FROM AUTHOR]

• An analytical and data-driven model is developed to analyse the greenhouse. • Data-driven artificial neural network is used as the prediction model. • An RMPC based on the minimax approach is adopted. • RMPC results in superior temperature control and energy consumption. The greenhouse microclimate, especially temperature, is highly complex, and controlling it requires significant resources due to the greenhouses' inefficient design. The application of model predictive control is a promising strategy for temperature control and efficient greenhouse management. However, it does not account for the inaccuracies and uncertainties existing in the system, leading to sub-optimal temperatures. Therefore, this study proposes a comprehensive data-driven robust model predictive control framework for greenhouse temperature control and its energy utilisation assessment in the presence of uncertainties. First, an analytical model based on mass and energy balance and a data-driven model based on an artificial neural network is developed, and their prediction performance is compared. The artificial neural network demonstrates a higher prediction accuracy and is used as the system model in the proposed control framework. A robust model predictive control strategy, based on the minimax objective function and particle swarm optimisation algorithm, is developed to handle the uncertainties in the system. Results illustrate that in the presence of uncertainties, the robust model predictive control strategy outperforms the existing greenhouse climate management system and basic model predictive control with an RMSE of 0.32 °C and 0.60 °C for a two-day simulation period in winter and summer, respectively. Furthermore, the robust model predictive control strategy leads to an energy reduction of 9.67% and 23.61% in winter and summer. The proposed framework is flexible and general and can be applied to other greenhouses with different configurations and cultivated crops by fine-tuning it on the new data set. [ABSTRACT FROM AUTHOR]

This paper reports a global digital controller for multi-channel high density micro-stimulator applications. By centralizing the essential functions including stimulation timing controls, tissue-electrode impedance (TEI) monitoring and power supply modulation in the global digital controller (GDC), digital blocks in the local electrode driver channels (LEDCs) can be greatly simplified, resulting in compact electrode drivers suitable for high density intracellular stimulations. Based on the proposed column-parallel row-scanning (CPRS) stimulator topology, the 5-wire global control ensures customized per channel stimulation with minimal interfacing overhead. A 4-column GDC stimulator prototype is fabricated in a 0.18 µm CMOS process. It features flexible stimulation with a power consumption of 18 µW/column while enabling global power-supply modulation and real-time TEI monitoring for improved stimulation efficiency and safety. [ABSTRACT FROM AUTHOR]

With the global increase in food demand, closed and controlled greenhouses are an essential source for year-round crop production. Maintaining the optimum conditions inside the greenhouse throughout the year is critical to improving crop quality and yield. However, greenhouses consume more resources than other commercial buildings due to their inefficient operation and structure. Therefore, a data-driven model predictive control approach for a semi-closed greenhouse is proposed for temperature control and reducing energy consumption in this study. The proposed method consists of a multilayer perceptron model representing the greenhouse system integrated with an objective function and an optimization algorithm. The multilayer perceptron model is trained using historical data from the greenhouse with solar radiation, outside temperature, humidity difference, fan speed, HVAC control as the input parameters to predict the temperature. The greenhouse model's performance is evaluated under varying scenarios, such as increasing the prediction time step and changing the number of samples in the training data set. Results illustrated that the MPC approach had a better temperature control than the greenhouse adaptive control system for winter and summer with an RMSE value of 0.33 °C and 0.36 °C, respectively. Similarly, model predictive control resulted in an energy reduction of 7.70% for winter and 16.57% for the summer season. The proposed model predictive control framework is flexible and can be applied to other greenhouse systems by tuning the model on the new data set. • A model predictive control approach is adopted for a semi-closed greenhouse. • A multilayer perceptron model is used for modeling the dynamic system. • Not only temperature set-point tracking but also energy consumption is considered. • MPC results in superior temperature tracking and reduction in energy consumption. [ABSTRACT FROM AUTHOR]