Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks

A detailed environment perception is a crucial component of automated vehicles. However, to deal with the amount of perceived information, we also require segmentation strategies. Based on a grid map environment representation, well-suited for sensor fusion, free-space estimation and machine learning, we detect and classify objects using deep convolutional neural networks. As input for our networks we use a multi-layer grid map efficiently encoding 3D range sensor information. The inference output consists of a list of rotated bounding boxes with associated semantic classes. We conduct extensive ablation studies, highlight important design considerations when using grid maps and evaluate our models on the KITTI Bird's Eye View benchmark. Qualitative and quantitative benchmark results show that we achieve robust detection and state of the art accuracy solely using top-view grid maps from range sensor data.Comment: 6 pages, 4 tables, 4 figure

Special section on smart grids: A hub of interdisciplinary research : IEEE ACCESS Special section editorial smart grids: A hub of interdisciplinary research

International audienceThe smart grid is an important hub of interdisciplinary research where researchers from different areas of science and technology combine their efforts to enhance the traditional electrical power grid. Due to these efforts, the traditional electrical grid is now evolving. The envisioned smart grid will bring social, environmental, ethical, legal and economic benefits. Smart grid systems increasingly involve machine-to-machine communication as well as human-to-human, or simple information retrieval. Thus, the dimensionality of the system is massive. The smart grid is the combination of different technologies, including control system theory, communication networks, pervasive computing , embedded sensing devices, electric vehicles, smart cities, renewable energy sources, Internet of Things, wireless sensor networks, cyber physical systems, and green communication. Due to these diverse activities and significant attention from researchers, education activities in the smart grid area are also growing. The smart grid is designed to replace the traditional electrical power grid. The envisioned smart grid typically consists of three networks: Home Area Networks (HANs), Neighborhood Area Networks (NANs), and Wide Area Networks (WANs). HANs connect the devices within the premises of the consumer and connect smart meters, Plug-in Electric Vehicles (PEVs), and distributed renewable energy sources. NANs connect multiple HANs and communicate the collected information to a network gateway. WANs serve as the communication backbone. Communication technologies play a vital role in the successful operation of smart grid. These communication technologies can be adopted based upon the specific features required by HANs, NANs, and WANs. Both wired and the wireless communication technologies can be used in the smart grid [1]. However, wireless communication technologies are suitable for many smart grid applications due to the continuous development in the wireless research domain. One drawback of wireless communication technologies is the limited availability of radio spectrum. The use of cognitive radio in smart grid communication will be helpful to break the spectrum gridlock through advanced radio design and operating in multiple settings, such as underlay, overlay, and interweave [2]. The smart grid is the combination of diverse sets of facilities and technologies. Thus, the monitoring and control of transmission lines, distribution facilities, energy generation plants, and as well as video monitoring of consumer premises can be conducted through the use of wireless sensor networks [3]–[6]. In remote sites and places where human intervention is not possible, wireless sensor and actuator networks can be useful for the successful smart grid operation [7], [8]. Since wireless sensor networks operate on the Industrial, Scientific, and Medical (ISM) band, the spectrum might get congested due to overlaid deployment of wireless sensor networks in the same premises. Thus, to deal with this spectrum congestion challenge, cognitive radio sensor networks can be used in smart grid environments [9], [10]. The objective of this Special Section in IEEE ACCESS is to showcase the most recent advances in the interdisciplinary research areas encompassing the smart grid. This Special Section brings together researchers from diverse fields and specializations, such as communications engineering, computer science, electrical and electronics engineering, educators, mathematicians and specialists in areas related to smart grids. In this Special Section, we invited researchers from academia, industry, and government to discuss challenging ideas, novel research contributions, demonstration results, and standardization efforts on the smart grid and related areas. This Special Section is a collection of eleven articles. These articles are grouped into the following four areas: (a) Reliability, security, and privacy for smart grid, (b), Demand response management, understanding customer behavior, and social networking applications for smart grid, (c) Smart cities, renewable energy, and green smart grid, and (d) Communication technologies, control and management for the smart grid

A Data Collecting Strategy for Farmland WSNs using a Mobile Sink

To the characteristics of large number of sensor nodes, wide area and unbalanced energy consumption in farmland Wireless Sensor Networks, an efficient data collection strategy (GCMS) based on grid clustering and a mobile sink is proposed. Firstly, cluster is divided based on virtual grid, and the cluster head is selected by considering node position and residual energy. Then, an optimal mobile path and residence time allocation mechanism for mobile sink are proposed. Finally, GCMS is simulated and compared with LEACH and GRDG. Simulation results show that GCMS can significantly prolong the network lifetime and increase the amount of data collection, especially suitable for large-scale farmland Wireless Sensor Networks

A Grid-Based Framework for Pervasive Healthcare using Wireless Sensor Networks: A Case for Developing Nations

The advent of new emerging technologies such as ubiquitous mobile computing, telemedicine, Internet, grid computing, embedded systems and wireless sensor networks (WSN) offers great potentials to radically alter the existing modes of healthcare administration and delivery in developing nations. The phenomenon of 'standalone hospitals' and a healthcare delivery service paradigm that always requires a physical ono-on-one contact between the patient and the medical expert even for cases of simple diagnosis, needs to be radically altered. In this study, we present the design of a national healthcare grid infrastructure, based on a novel integration of wireless sensor networks and wireless grid computing, for the purpose of inter-hospital collaboration and pervasive real-time monitoring of healthcare patients. This holds the promise of improving the nature of healthcare delivery services in developing nations

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

Sensor Event Processing on Grid

Wireless sensor networks are increasingly being deployed in many important applications. For sharing huge amount of sensor data efficiently with diverse users, an information dissemination mechanism is very necessary and important component. In this paper, we have proposed an efficient architecture integrated with sensor network and Grid technology. To disseminate the sensed data to users geographically distributed, an experimental method using Data Grid on pub/sub (publish/subscription) is designed for a u-Healthcare application and its performance is evaluated for various predicate cases