Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits. Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos. To this end, we introduce a new benchmark dataset for research use containing over 600,000 labeled digits cropped from Street View images. We then demonstrate the difficulty of recognizing these digits when the problem is approached with hand-designed features. Finally, we employ variants of two recently proposed unsupervised feature learning methods and find that they are convincingly superior on our benchmarks.

/pdf/reading-digits-in-natural-images-with-unsupervised-feature-3ckuq1665o.pdf

Reading Digits in Natural Images with Unsupervised Feature Learning

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

IEEE Transactions on Pattern Analysis and Machine Intelligence

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. We define novel 3D detection and tracking metrics. We also provide careful dataset analysis as well as baselines for lidar and image based detection and tracking. Data, development kit and more information are available online.

nuScenes: A multimodal dataset for autonomous driving

Long-range communication technologies are increasingly used due to the internet of things (IoT) deployments and their ability to perform for long-range and low power while providing a multiyear battery life. In this paper, we study security management, counter management, message acknowledgment, and vulnerabilities of LoRaWAN. We propose a jamming attack to interfere or disrupt the LoRa network by using software-defined radio-based universal software radio peripheral (USRP), GNU radio, and LoRa devices. Our analysis shows that LoRa transmission gets affected because of the coexistence of other LoRa transmission. While the impact of the interference and the effect of the imperfect orthogonality of spreading factor (SF) much depend upon the distance between LoRa communicating devices and the interference source.

Impact of Interference on LoRaWAN Link Performance

With digital TV switchover and using cognitive radio technology, a spectrum of unoccupied TV channels, called TV white spaces (TVWS), can be used for different applications. Radio signals in these bands travel farther and penetrate obstacles more effectively than those in cellular and 2.4 GHz band owing to the lower frequency. With the growth in wireless services and higher data rate applications, the demand for spectrum is also increasing. Studies show that such licensed spectrum is underutilized. This chapter covers the study on TVWS, its architecture, and protocols, TVWS over low-power wide-area networks, along with its applications and future challenges and opportunities.

TV white spaces for low-power wide-area networks

Leveraging on recent TV white space communications developments in regulations, standards initiatives and technology, this paper considers a suitable next generation network comprising of two primary users (PUs) that compete to offer a service to a group of secondary users (SUs) in the form of mesh routers that belong to different entrepreneurs participating in a noncooperative TV white space trading. From a game theoretic perspective the non-cooperativeinteractionofthePUsisviewedasapricingproblemwherein each PU strives to maximize its own profit. Subsequently the problem is formulated as a Bertrand game in an oligopolistic market where the PUs are playerswhoareresponsibleforsellingTVwhitespectruminthemarketwhile the SUs are the players who are the buyers of theTVwhite spectrum.The PUs strategise by way of price adjustment, so much such that SUs tend to favour the lowest price when buying. The inter- operator agreements are based on the delay and throughput QoS performance optimization metrics respectively. A performance evaluation of both models is comparatively performed with regards to parameters such as cost, generated revenue, profit, best response in

/pdf/optimisation-of-a-tv-white-space-broadband-market-model-for-1igshaims5.pdf

Optimisation of a TV White Space Broadband Market Model for Rural Entrepreneurs

Recently the Internet of Vehicles (IoV) has become a promising research area in the field of the Internet of Things (IoT), which enables vehicles to communicate and exchange real-time information with each other, as well as with infrastructure, people, and other sensors and actuators through various communication interfaces. The realization of IoV networks faces various communication and networking challenges to meet stringent requirements of low latency, dynamic topology, high data-rate connectivity, resource allocation, multiple access, and QoS. Advances in information-centric networks (ICN), edge computing (EC), and artificial intelligence (AI) will transform and help to realize the Intelligent Internet of Vehicles (IIoV). Information-centric networks have emerged as a paradigm promising to cope with the limitations of the current host-based network architecture (TCP/IP-based networks) by providing mobility support, efficient content distribution, scalability and security based on content names, regardless of their location. Edge computing (EC), on the other hand, is a key paradigm to provide computation, storage and other cloud services in close proximity to where they are requested, thus enabling the support of real-time services. It is promising for computation-intensive applications, such as autonomous and cooperative driving, and to alleviate storage burdens (by caching). AI has recently emerged as a powerful tool to break through obstacles in various research areas including that of intelligent transport systems (ITS). ITS are smart enough to make decisions based on the status of a great variety of inputs. The convergence of ICN and EC with AI empowerment will bring new opportunities while also raising not-yet-explored obstacles to realize Intelligent IoV. In this paper, we discuss the applicability of AI techniques in solving challenging vehicular problems and enhancing the learning capacity of edge devices and ICN networks. A comprehensive review is provided of utilizing intelligence in EC and ICN to address current challenges in their application to IIoV. In particular, we focus on intelligent edge computing and networking, offloading, intelligent mobility-aware caching and forwarding and overall network performance. Furthermore, we discuss potential solutions to the presented issues. Finally, we highlight potential research directions which may illuminate efforts to develop new intelligent IoV applications.

/pdf/convergence-of-information-centric-networks-and-edge-q7bopr6p.pdf

Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

Remote locations in rural areas can benefit from any system that would provide some form of connectivity. In a previous work we described a low cost architecture that, thanks to the use of the LoRa technology, allowed long links using low energy and a cheap infrastructure. In this work we extend those results by adding the possibility to include generic external sources of data using an MQTT based interface. More specifically, we describe a voice messaging system that allows users that cannot read or write to send voice based messages. We describe how the system was integrated into the existing platform and present some performance results. We consider that these results are promising and provide a tool that can offer a useful service at a low cost.

Marco Zennaro

Papers

Impact of Interference on LoRaWAN Link Performance

TV white spaces for low-power wide-area networks

Optimisation of a TV White Space Broadband Market Model for Rural Entrepreneurs

Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

Adding voice messages to a low-cost long-range data messaging system