A new trend in system-on-chip (SoC) design is chiplet-based IP reuse using 2.5-D integration. Complete electronic systems can be created through the integration of chiplets on an interposer, rather than through a monolithic flow. This approach expands access to a large catalog of off-the-shelf intellectual properties (IPs), allows reuse of them, and enables heterogeneous integration of blocks in different technologies. In this article, we present a highly integrated design flow that encompasses architecture, circuit, and package to build and simulate heterogeneous 2.5-D designs. Our target design is 64-core architecture based on Reduced Instruction Set Computer (RISC)-V processor. We first chipletize each IP by adding logical protocol translators and physical interface modules. We convert a given register transfer level (RTL) for 64-core processor into chiplets, which are enhanced with our centralized network-on-chip. Next, we use our tool to obtain physical layouts, which is subsequently used to synthesize chip-to-chip I/O drivers and these chiplets are placed/routed on a silicon interposer. Our package models are used to calculate power, performance, and area (PPA) and reliability of 2.5-D design. Our design space exploration (DSE) study shows that 2.5-D integration incurs 1.29x power and 2.19x area overheads compared with 2-D counterpart. Moreover, we perform DSE studies for power delivery scheme and interposer technology to investigate the tradeoffs in 2.5-D integrated chip (IC) designs.

A database is presented that allows the investigation of machine learning (ML) tools and techniques in the signal integrity (SI), power integrity (PI), and electromagnetic compatibility (EMC) domains. The database contains different types of printed circuit board (PCB)-based interconnects and corresponding frequency domain data from a physics-based (PB) tool and represent multiple electromagnetic (EM) aspects to SI and PI optimization. The interconnects have been used in the past by the authors to investigate ML techniques in SI and PI. However, many more tools and techniques can be developed and applied to these structures. The setup of the database, its data sets, and examples on how to apply ML techniques to the data will be discussed in detail. Overall 78961 variations of interconnects are presented. By making this database available we invite other researchers to apply and customize their ML techniques using our results. This provides the possibility to accelerate ML research in EMC engineering without the need to generate expensive data.

Machine learning algorithms are increasingly used to enhance agricultural productivity cost-effectively. A critical task in precision agriculture is locating a plant’s root collar. This is required for the site-specific fertilization of the plants. Though state-of-the-art machine learning models achieve stellar performance in object detection, they are often sensitive to noisy inputs and variation in environment settings. In this paper, we propose an innovative technique of smooth perturbations to improve the robustness of root collar detection tasks using the YOLOv5 neural network model. We train a YOLOv5 model on blueberry image data for root collar detection. A small amount noise is added as a smooth perturbation to the bounding box of dimensions 50× 50, and this perturbed image is fed for training. Furthermore, we introduce an additional test set that represents the out-of-distribution (O.O.D.) case by applying Gaussian blur on test images to simulate particle situation. We use three different image datasets to train our model, the (i) Estonian blueberry, (ii) Serbian blueberry image, and (iii) public dataset sourced from Roboflow datasets, of sample size 118, 2779, and 2993, respectively. We achieve an overall precision of 0.886 on perturbed blueberry images compared to 0.871 on original (unperturbed) images for the O.O.D. test set. Similarly, our smooth perturbation training has achieved an mAP of 0.828, which significantly increases against the result of normal training, which only reaches 0.794. The result proves that our proposed smooth perturbation is an effective method to increase the robustness and generalizability of the object detection task.

The greenhouse effect is a severe global problem

In precision agriculture (PA), monitoring individual plant health is crucial for optimizing yields and minimizing resources. The normalized difference vegetation index (NDVI), a widely used health indicator, typically relies on expensive multispectral cameras. This study introduces a method for predicting the NDVI of blueberry plants using RGB images and deep learning, offering a cost-effective alternative. To identify individual plant bushes, K-means and Gaussian Mixture Model (GMM) clustering were applied. RGB images were transformed into the HSL (hue, saturation, lightness) color space, and the hue channel was constrained using percentiles to exclude extreme values while preserving relevant plant hues. Further refinement was achieved through adaptive pixel-to-pixel distance filtering combined with the Davies–Bouldin Index (DBI) to eliminate pixels deviating from the compact cluster structure. This enhanced clustering accuracy and enabled precise NDVI calculations. A convolutional neural network (CNN) was trained and tested to predict NDVI-based health indices. The model achieved strong performance with mean squared losses of 0.0074, 0.0044, and 0.0021 for training, validation, and test datasets, respectively. The test dataset also yielded a mean absolute error of 0.0369 and a mean percentage error of 4.5851. These results demonstrate the NDVI prediction method’s potential for cost-effective, real-time plant health assessment, particularly in agrobotics.

Imaging methods based on the scattering of atmospheric muons use the natural flux of muons produced in the atmosphere. Muon tomography (MT) has emerged as a promising technology in a variety of applications, from detecting contraband in border security to studying the interior of natural or human-made structures, detecting nuclear waste, and monitoring hazards. Current muon imaging techniques have many challenges, e.g., low muon flux, poor spatial resolution, and uncertainty in muon tracks. Conventional reconstruction algorithms, e.g., PoCA (Point of Closest Approach) suffer from insufficient precision of object localization. In this paper, to improve the precision of object locations, we propose three novel computationally efficient and reliable machine learning models (ML) for constructing binary maps of image locations. ML models are trained on positive and negative imprints of objects to predict the binary map of the object locations. We train our ML models separately on straight muon rays, scattered muon rays, and a combination of straight and scattered muon rays. The combination of straight and scattered muon-rays gives a dual view of the object locations. Our proposed novel ML model architectures, along with combined trained data, solve the hard inverse problem of object detection and location efficiently. Our experimental results validate our method compared to the standard algorithm.