Abstract

In this paper, we determine the computational complexity of recognizing two graph classes, grounded 𝖫-graphs and stabbable grid intersection graphs. An 𝖫-shape is made by joining the bottom end-point of a vertical (|) segment to the left end-point of a horizontal (−) segment. The top end- point of the vertical segment is known as the anchor of the 𝖫-shape. Grounded 𝖫-graphs are the intersection graphs of 𝖫-shapes such that all the 𝖫-shapes’ anchors lie on the same horizontal line. We show that recognizing grounded 𝖫-graphs is 𝖭𝖯-complete. This answers an open question asked by Jelínek & Töpfer (Electron. J. Comb., 2019). Grid intersection graphs are the intersection graphs of axis-parallel line segments in which two vertical (similarly, two horizontal) segments cannot intersect. We say that a (not necessarily axis-parallel) straight line 𝓁 stabs a segment 𝑠, if 𝑠 intersects 𝓁. A graph 𝐺 is a stabbable grid intersection graph (S TAB GIG) if there is a grid intersection representation of 𝐺 in which the same line stabs all its segments. We show that recognizing S TAB GIG graphs is 𝖭𝖯-complete, even on a restricted class of graphs. This answers an open question asked by Chaplick et al. (Order, 2018).

Abstract

In this article, an incoherent beam combination of higher-order Gaussian beams through atmospheric turbulence is studied. An analytical expression of the combined intensity and spot size of higher-order Gaussian beams such as Hermite Gaussian (HG), Laguerre Gaussian (LG), and Bessel Gaussian (BG) are derived. The performance of these higher-order Gaussian beams is analyzed in various modes including the effect of beam wander, jitter, bore-sight error, Strehl ratio, and Visibility. A series of analytical simulations shows the intensity variation of 19 higher-order combined beams. Spot size, peak, and average intensity comparisons are made between various modes of higher-order Gaussian beam combinations. It is seen that the spot size of the combined beam increases rapidly in a higher mode of HG and LG beam. We evaluate the efficiency of combining beams at different distances, noting that it increases with higher mode orders and reaches its maximum with the 𝐻𝐺22 mode. Additionally, we explore the performance of higher-order Gaussian beam combinations under varying ground turbulence conditions. We observe that higher modes such as 𝐻𝐺22 and 𝐿𝐺22 are more susceptible to strong turbulence compared to lower modes.

Abstract

In this letter, we have modeled and analyzed the impact of alignment errors in fiber lasers and collimating lens arrays on incoherent beam combination systems. The combined intensity expression is calculated considering different errors, and efficiency comparisons are made between 7 and 61 laser beam arrays. The results show that the misalignment error also increases with an increase in the number of beam arrays, leading to a decrease in the combination efficiency. We observed that to achieve more than 80% combination efficiency, maximum error tolerances should be 3 µm, 0.3 mm, and 4 mrad for displacement, focal and tilt errors, respectively. The findings presented in this letter have practical relevance for enhancing the design, operation, and optimization of beam combination systems.

Abstract

Human posture analysis has gained a significant research interest in the recent times. It helps in many applications such as gait analysis for detecting neurological disorders, fall detection of elderly people, and continuous monitoring of severely ill patients. Camera-based vision systems are commonly employed for detecting human postures; however, they cause concerns over the subjects' privacy. To address this challenge, we present a millimeter wave (mmWave) radar-based, truly non-contact, non-intrusive, and privacy-conscious posture detection and classification system in this research. The proposed system utilizes three-dimensional point cloud data of the subject to comprehensively classify body postures, capturing intricate real-time details. In this work, we also present a custom-designed Convolutional Neural Network (CNN) and its comparison with other models, which are conventionally used for posture classification. We also demonstrate the hardware implementation of the proposed system and present the measurement results using Texas Instruments' IWR1843BOOST radar module. The proposed CNN model achieves an accuracy of 97.10% while classifying standing, sitting, lying and bending postures.

Abstract

This paper presents the design and implementation of a hardware system for real-time object detection and range estimation utilizing Frequency-Modulated Continuous Wave (FMCW) millimeter wave radar signals, which are commonly used in Advance Driver Assistance Systems (ADAS) and robotic applications. The proposed system utilizes the Fast Fourier Transform (FFT) algorithm to process the FMCW radar signal for estimating the range of an object. For developing a resource-efficient and low latency range-estimation hardware, this paper also presents a comparative analysis for the implementation of three popular FFT architectures (iterative Radix-2, iterative Radix-4, and pipelined Radix-2) on FPGA. Based on the presented analysis the lowest latency range-estimation architecture has been chosen for FFT implementation and a system prototype is developed as a proof-of-concept by integrating the commercially available Texas Instruments' 77 GHz AWR1642BOOST FMCW radar module with a FPGA to host the chosen FFT architecture. Measurement results of the proposed system hardware are also presented in this paper, which shows >99.21% range-estimation accuracy.

Abstract

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Abstract

Sugarcane holds a critical position in global agriculture, serving as a basis for the sugar and bioenergy sectors. The integration of remote sensing technologies and sophisticated machine learning approaches and related models has revolutionized sugarcane research. These tools ofer efcient, noninvasive, and large-scale assessment methods. This review highlights the utilization of satellite imagery and sensor data, encompassing RGB, multispectral, hyperspectral, and unmanned aerial vehicles (UAVs) in sugarcane agriculture. It addresses crop identifcation, pest and disease management, yield and acreage estimation, modeling, phenotypic measurement, and their impact on empowering farmers with insights for optimal irrigation, fertilizer application, and overall crop management. These advancements signifcantly increase productivity and foster environmental sustainability. The review had dual aims: (1) consolidate RS data applications in India’s sugarcane research and development, and (2) examine the pros and cons of RS and AI methods in sugarcane farming. The review employed prominent bibliographic databases—google scholar, scopus, researchgate, and web of science—along with pertinent research articles on RS and AI applications in sugarcane, and comprehensive data on sensors and UAVs retrieved from these databases. The study concludes that AI-driven crop RS stands as an efective method for monitoring and managing sugarcane, contributing signifcantly to improving yield and quality, while simultaneously ofering substantial benefts in social, economic, and environmental realms. However, challenges in the sugar industry, such as adapting technology, high initial costs, climate impact, communication, policy, and regulation, must be addressed.

Abstract

Coastal waters are complex, dynamic, and sensitive, and any change in the system impacts the marine environment and life. Coastal water quality has been decreasing due to the incursion of anthropogenic derived waste and toxins into the ocean. This study investigates water quality along the Kollam coast of Kerala State, India, using Sentinel-2 Multispectral Imager (MSI) data for the period of 2019–2022. Four key water quality parameters, chlorophyll (Chl-a), total suspended matter (TSM), turbidity, and coloured dissolved organic matter (CDOM), were analysed for seasonal variations and driving factors. The study highlights the potential of web-based platforms like Google Earth Engine for facilitating large-scale water quality assessments. The results reveal a distinct seasonal pattern in all parameters, primarily influenced by monsoonal riverine discharge and anthropogenic activities as contributing factors to water quality degradation. Overall, the study emphasises the need for comprehensive monitoring and management strate- gies to ensure the long-term sustainability of the coastal ecosystem.

Abstract

We focus on the problem of LiDAR point cloud based loop detection (or Finding) and closure (LDC) for mobile robots. State-of-the-art (SOTA) methods directly generate learned embeddings from a given point cloud, require large data augmentation, and are not robust to wide viewpoint variations in 6 Degrees-of-Freedom (DOF). Moreover, the absence of strong priors in an unstructured point cloud leads to highly inaccurate LDC. In this original approach, we propose independent roll and pitch canonicalization of point clouds using a common dominant ground plane. We discretize the canonicalized point clouds along the axis perpendicular to the ground plane leads to images simi- lar to digital elevation maps (DEMs), which expose strong spatial priors in the scene. Our experiments show that LDC based on learnt embeddings from such DEMs is not only data efficient but also significantly more robust, and generalizable than the current SOTA. We report an (aver- age precision for loop detection, mean absolute transla- tion/rotation error) improvement of (8.4, 16.7/5.43)% on the KITTI08 sequence, and (11.0, 34.0/25.4)% on GPR10 sequence, over the current SOTA. To further test the ro- bustness of our technique on point clouds in 6-DOF motion we create and opensource a custom dataset called Lidar- UrbanFly Dataset (LUF) which consists of point clouds ob- tained from a LiDAR mounted on a quadrotor. More details on our website https://gsc2001.github.io/FinderNet/

Abstract

This work introduces Talk2BEV, a large vision-language model (LVLM) interface for bird’s-eye view (BEV) maps in autonomous driving contexts. While existing perception systems for autonomous driving scenarios have largely focused on a pre-defined (closed) set of object categories and driving scenarios, Talk2BEV blends recent advances in general-purpose language and vision models with BEV-structured map representations, eliminating the need for task-specific models. This enables a single system to cater to a variety of autonomous driving tasks encompassing visual and spatial reasoning, predicting the intents of traffic actors, and decision-making based on visual cues. We extensively evaluate Talk2BEV on a large number of scene understanding tasks that rely on both the ability to interpret free-form natural language queries and in grounding these queries to the visual context embedded into the language-enhanced BEV map. To enable further research in LVLMs for autonomous driving scenarios, we develop and release Talk2BEV-Bench, a benchmark encompassing 1000 human-annotated BEV scenarios, with more than 20,000 questions and ground-truth responses from the NuScenes dataset.