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Stem diameter is a critical phenotypic parameter for maize, integral to yield prediction and lodging resistance assessment. Traditionally, the quantification of this parameter through manual measurement has been the norm, notwithstanding its tedious and laborious nature. To address these challenges, this study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter. This technology offers a practical solution for conducting rapid and non-destructive phenotyping. Firstly, RGB images, depth images, and 3D point clouds of maize stems were captured using an RGB-D camera, and precise alignment between the RGB and depth images was achieved. Subsequently, the contours of maize stems were delineated using 2D image processing techniques, followed by the extraction of the stem’s skeletal structure employing a thinning-based skeletonization algorithm. Furthermore, within the areas of interest on the maize stems, horizontal lines were constructed using points on the skeletal structure, resulting in 2D pixel coordinates at the intersections of these horizontal lines with the maize stem contours. Subsequently, a back-projection transformation from 2D pixel coordinates to 3D world coordinates was achieved by combining the depth data with the camera’s intrinsic parameters. The 3D world coordinates were then precisely mapped onto the 3D point cloud using rigid transformation techniques. Finally, the maize stem diameter was sensed and determined by calculating the Euclidean distance between pairs of 3D world coordinate points. The method demonstrated a Mean Absolute Percentage Error (MAPE) of 3.01%, a Mean Absolute Error (MAE) of 0.75 mm, a Root Mean Square Error (RMSE) of 1.07 mm, and a coefficient of determination (R²) of 0.96, ensuring accurate measurement of maize stem diameter. This research not only provides a new method of precise and efficient crop phenotypic analysis but also offers theoretical knowledge for the advancement of precision agriculture.

The target region and diameter of maize stems are important phenotyping parameters for evaluating crop vitality and estimating crop biomass. To address the issue that the target region and diameter of maize stems obtained after transplantation may not accurately reflect the true growth conditions of maize, a phenotyping monitoring technology based on an internal gradient algorithm is proposed for acquiring the target region and diameter of maize stems. Observations were conducted during the small bell stage of maize. First, color images of maize plants were captured by an Intel RealSense D435i camera. The color information in the color image was extracted by the hue saturation value (HSV) color space model. The maximum between-class variance (Otsu) algorithm was applied for image threshold segmentation to obtain the main stem of maize. Median filtering, image binarization, and morphological opening operations were then utilized to remove noise from the images. Subsequently, the morphological gradient algorithm was applied to acquire the target region of maize stems. The similarity between the three types of gradient images and the manually segmented image was evaluated by pixel ratio extraction and image quality assessment indicators. Evaluation results indicated that the internal gradient algorithm could more accurately obtain the target region of maize stems. Finally, a checkerboard was employed as a reference for measurement assistance, and the stem diameter of maize was calculated by the pinhole imaging principle. The mean absolute error of stem diameter was 1.92 mm, the mean absolute percentage error (MAPE) was 5.16%, and the root mean square error (RMSE) was 2.25 mm. The R² value was 0.79. With an R² greater than 0.7 and a MAPE within 6%, the phenotyping monitoring technology based on the internal gradient algorithm was proven to accurately measure the diameter of maize stems. The application of phenotyping monitoring technology based on the internal gradient algorithm in field conditions provides technological support for smart agriculture.

Solving the problem of the stem contour extraction of maize is difficult under open field conditions, and the stem diameter cannot be measured quickly and nondestructively. In this paper, maize at the small and large bell stages was the object of study. An adaptive threshold segmentation algorithm based on the color space model was proposed to obtain the stem contour and stem diameter of maize in the field. Firstly, 2D images of the maize stem in the field were captured with an RGB-D camera. Then, the images were processed by hue saturation value (HSV) color space. Next, the stem contour of the maize was extracted by maximum between-class variance (Otsu). Finally, the reference method was used to obtain the stem diameter of the maize. Scatter plots and Dice coefficients were used to compare the contour extraction effects of the HSV + fixed threshold algorithm, the HSV + Otsu algorithm, and the HSV + K-means algorithm. The results showed that the HSV + Otsu algorithm is the optimal choice for extracting the maize stem contour. The mean absolute error, mean absolute percentage error (MAPE), and root mean square error (RMSE) of the maize stem diameter at the small bell stage were 4.30 mm, 10.76%, and 5.29 mm, respectively. The mean absolute error, MAPE, and RMSE of the stem diameter of the maize at the large bell stage were 4.78 mm, 12.82%, and 5.48 mm, respectively. The MAPE was within 10–20%. The results showed that the HSV + Otsu algorithm could meet the requirements for stem diameter measurement and provide a reference for the acquisition of maize phenotypic parameters in the field. In the meantime, the acquisition of maize phenotypic parameters under open field conditions provides technical and data support for precision farming and plant breeding.

Network-on-Chips with simple topologies are widely used due to their scalability and high bandwidth. The transmission latency increases greatly with the number of on-chip nodes. A NoC, called single-cycle multi-hop asynchronous repeated traversal (SMART), is proposed to solve the problem by bypassing intermediate routers. However, the bypass setup request of SMART requires additional pipeline stages and wires. In this paper, we present a NoC with rapid bypass channels that integrates the bypass information into each flit. In the proposed NoC, all the bypass requests are delivered along with flits at the same time reducing the transmission latency. Besides, the bypass request is unicasted in our design instead of broadcasting in SMART leading to a great reduction in wire overhead. We evaluate the NoC in four synthetic traffic patterns. The result shows that the latency of our proposed NoC is 63.54% less than the 1-cycle NoC. Compared to SMART, more than 80% wire overhead and 27% latency are reduced.

Embedded processors are widely used in various systems working on different tasks with different workloads. A more complex micro-architecture leads to better peak performance and worse power consumption. Shutting down the units designed for performance enhancement could improve energy efficiency in low-workload scenarios. In this paper, we evaluated the energy distribution in various embedded processors. According to the analysis, pipeline registers and the dynamic branch predictor, which are employed for better peak performance, have great impacts on energy efficiency. Thus, we proposed an ultra-low-power processor with variable micro-architecture. The processor is based on a 4-stage pipeline core with a Gshare branch predictor, and all units work in high-performance mode. In normal mode, the Gshare predictor is shut down and Always-Not-Taken prediction is used. In low-power mode, some of the pipeline registers are bypassed to avoid unnecessary energy dissipation and improve executing efficiency. A mode register (MR) is designed to indicate current working mode. Switching between different modes is controlled by the software. The proposed core is implemented in 40 nm technology and simulated with the traces of 17 benchmarks in Embench. The average amounts of power consumed by the respective modes are 41.7 μW, 59.7 μW and 71.1 μW. The results show that normal mode (N-mode) and low-power mode (L-mode) consume 16.08% and 41.37% less power than high-performance mode (H-mode) on average. In best case scenarios, they could save 25.36% and 49.30% more power than H-mode. Considering the execution efficiency evaluated by instructions per cycle (IPC), the proposed processor consumes 7.78% or 51.57% less energy for each instruction than the baseline core. The area of the proposed processor is only 7.19% larger than the baseline core, and only 3.08% more power is consumed in H-mode.

A novel miniaturized, integrated whole-column imaging detection (WCID) system on a microchip is presented. In this system, a program controlled organic light emitting diode (OLED) array was used as a spatial-scanning light source, to achieve imaging by the time sequence of the excited fluorescence. By this mechanism, a photomultiplier tube (PMT) instead of a charge coupled detector (CCD) can be applied to the imaging. Unlike conventional systems, no lenses, fibers or any mechanical components are required either. The novel flat light source provides uniform excitation light without size limitations and outputs a stronger power by pulse driving. The scanning mode greatly reduced the power consumption of the light source, which is valuable for a portable system. Meanwhile, this novel simplified system has a broader linear range, higher sensitivity and higher efficiency in data collection. Isoelectric focusing of R-phycoerythrin (PE) and monitoring of the overall process with WCID were performed on this system. The limit of detection (LOD) was 38 ng mL−1 or 3.2 pg at 85 nL per column injection of PE. The system provides a technique for WCID capillary isoelectric focusing (cIEF) on chip and can be used for throughput analysis. [ABSTRACT FROM AUTHOR]

Lithium cobalt oxide was introduced into organic light-emitting diodes as the electron injection layer. The device with tris(8-hydroxyquinolato) aluminum doped with 10-(2-benzothiazolyl)-1,1,7,7-tetramethyl-2,3,6,7-tetrahydro-1H,5H,11H-[1]benzopyrano[6,7,8-ij]quinolizin-11-one as the light-emitting layer and LiCoO2 as the electron injection layer showed promising efficiency (10.74 cd/A at 11 V) and longer lifetime (2.8 times as much as LiF/Al control device). Lithium cobalt oxide proved to be thermally decomposed in vacuum to form lithium oxide, which was responsible for the enhanced electron injection. [ABSTRACT FROM AUTHOR]

An integrated and simplified microfluidic device using a 250 μm × 1-4 cm of organic light emitting diode (OLED) array as a two-dimensional light source for single-channel and multichannel whole-column imaging detection was developed. This fluorescence detection system was used for isoelectric focusing (IEF) of R-phycoerythrin in a microchip. The IEF conditions were optimized, and the total analysis time was extremely reduced to 30 s for 2-cm-long microchannels at 700 V/cm of electric field strength without the presence of electroosmotic flow. The compression of pH gradient caused by electrolytes drawing into the microchannels was efficiently restrained when 1% hydroxylpropylmethyl cellulose in 2% ampholyte was used as the carder for IEF. Under optimized IEF conditions, the detection limit of this system was ∼0.6 μg/mL or 45 pg at 75 nL/column injection of R-phycoerythrin. This OLED-induced fluorescence detection system for WCID provides a high-speed IEF technique with quantitative ability and the potential for high integration and throughput microchip systems. [ABSTRACT FROM AUTHOR]