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As urbanization increases, streetlights have become significant consumers of electrical power, making it imperative to develop effective control methods for sustainability. This paper offers a comprehensive review on control methods of smart streetlight systems, setting itself apart by introducing a novel light scheme framework that provides a structured classification of various light control patterns, thus filling an existing gap in the literature. Unlike previous studies, this work dives into the technical specifics of individual research papers and methodologies, ranging from basic to advanced control methods like computer vision and deep learning, while also assessing the energy consumption associated with each approach. Additionally, the paper expands the discussion to explore alternative functionalities for streetlights, such as serving as communication networks, environmental monitors, and electric vehicle charging stations. This multidisciplinary research aims to be a pivotal resource for both academics and industry professionals, laying the groundwork for future innovation and sustainable solutions in urban lighting.

Convolutional Neural Networks (CNNs) have been incredibly effective for object detection tasks. YOLOv4 is a state-of-the-art object detection algorithm designed for embedded systems. It is based on YOLOv3 and has improved accuracy, speed, and robustness. However, deploying CNNs on embedded systems such as Field Programmable Gate Arrays (FPGAs) is difficult due to their limited resources. To address this issue, FPGA-based CNN architectures have been developed to improve the resource utilization of CNNs, resulting in improved accuracy and speed. This paper examines the use of General Matrix Multiplication Operations (GEMM) to accelerate the execution of YOLOv4 on embedded systems. It reviews the most recent GEMM implementations and evaluates their accuracy and robustness. It also discusses the challenges of deploying YOLOv4 on autonomous vehicle datasets. Finally, the paper presents a case study demonstrating the successful implementation of YOLOv4 on an Intel Arria 10 embedded system using GEMM.

La maladie de Still de l´adulte (MSA) et les lymphomes sont des pathologies dont la présentation clinique et même histopathologique est très proche. L′association des deux pathologies est rarement rapportée dans la littérature. Nous rapportons une observation de maladie de Still de l′ adulte diagnostiquée chez une patiente de 26 ans antérieurement traitée pour LMNH à grandes cellules B par chimiothérapie et auto-greffe de cellules souches hématopoïétiques avec rémission complète. Les cas de MSA associés aux Lymphomes sont rares et dans tous les cas le diagnostic de MSA avait précédé celui de Lymphome. Notre observation est particulière par la succession LMNH puis MSA et soulève plusieurs hypothèses sur les liens entre ces deux pathologies. Les liens entre maladies dysimmunitaires et hémopathies lymphoïdes ont largement été prouvés, qu′ il s′agisse de l′ évolution de maladies auto-immunes vers un lymphome ou de manifestations dysimmunitaires survenant au cours de ce dernier. Notre cas illustre la difficulté de distinction entre ces entités.

Simultaneous Localization And Mapping (SLAM) algorithms are being used in many robotic applications and autonomous navigation systems. The FastSLAM2.0 addresses an issue of the SLAM problem and allows a robot to navigate in an unknown environment. Several works have presented many algorithmic optimizations to reduce the computational complexity of such algorithm. In this paper, a GPGPU (general-purpose computing on graphics processing units) is exploited to achieve a parallel implementation of the FastSLAM2.0. The GPGPU acceleration is done using two different implementations for parallel programming. The first implementation used OpenGL shading language which is based on the characteristics of graphics hardwares. The second implementation used OpenCL which allows hardware acceleration across heterogeneous architectures. We also explored the impact of the two approaches on the the resulting GPGPU implementation. A comparison related to processing-time and localization accuracy is made using a real indoor dataset. Our results show a significant speedup of the GPGPU implementation over a Quad-Core CPU. We show also that, by adopting the same optimization methodology using the two approachs, the OpenCL implementation is faster and suitable for GPGPU accelerated SLAM algorithms.

SLAM algorithm permits the robot to cartography the desired environment while positioning it in space. It is a more efficient system and more accredited by autonomous vehicle navigation and robotic application in the ongoing research. Except it did not adopt any complete end-to-end hardware implementation yet. Our work aims to a hardware/software optimization of an expensive computational time functional block of monocular ORB-SLAM2. Through this, we attempt to implement the proposed optimization in FPGA-based heterogeneous embedded architecture that shows attractive results. Toward this, we adopt a comparative study with other heterogeneous architecture including powerful embedded GPGPU (NVIDIA Tegra TX1) and high-end GPU (NVIDIA GeForce 920MX). The implementation is achieved using high-level synthesis-based OpenCL for FPGA and CUDA for NVIDIA targeted boards.

As urbanization increases, streetlights have become significant consumers of electrical power, making it imperative to develop effective control methods for sustainability. This paper offers a comprehensive review on control methods of smart streetlight systems, setting itself apart by introducing a novel light scheme framework that provides a structured classification of various light control patterns, thus filling an existing gap in the literature. Unlike previous studies, this work dives into the technical specifics of individual research papers and methodologies, ranging from basic to advanced control methods like computer vision and deep learning, while also assessing the energy consumption associated with each approach. Additionally, the paper expands the discussion to explore alternative functionalities for streetlights, such as serving as communication networks, environmental monitors, and electric vehicle charging stations. This multidisciplinary research aims to be a pivotal resource for both academics and industry professionals, laying the groundwork for future innovation and sustainable solutions in urban lighting. [ABSTRACT FROM AUTHOR]