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Abstract: This paper describes a real case implementation of an automatic pedestrian-detection solution, implemented in the city of Aveiro, Portugal, using affordable LiDAR technology and open, publicly available, pedestrian-detection frameworks based on machine-learning algorithms. The presented solution makes it possible to anonymously identify pedestrians, and extract associated information such as position, walking velocity and direction in certain areas of interest such as pedestrian crossings or other points of interest in a smart-city context. All data computation (3D point-cloud processing) is performed at edge nodes, consisting of NVIDIA Jetson Nano and Xavier platforms, which ingest 3D point clouds from Velodyne VLP-16 LiDARs. High-performance real-time computation is possible at these edge nodes through CUDA-enabled GPU-accelerated computations. The MQTT protocol is used to interconnect publishers (edge nodes) with consumers (the smartcity platform). The results show that using currently affordable LiDAR sensors in a smart-city context, despite the advertising characteristics referring to having a range of up to 100 m, presents great challenges for the automatic detection of objects at these distances. The authors were able to efficiently detect pedestrians up to 15 m away, depending on the sensor height and tilt. Based on the implementation challenges, the authors present usage recommendations to get the most out of the used technologies.

Dissertação apresentada à Escola Superior de Tecnologia do Instituto Politécnico de Castelo Branco para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Desenvolvimento de Software Sistemas Interativos

“This is a pre-copyedited version of a contribution published in Marques P., Radwan A., Mumtaz S., Noguet D., Rodriguez J., Gundlach M. (eds) Cognitive Radio Oriented Wireless Networks published by Springer. The definitive authenticated version is available online via https://doi.org/10.1007/978-3-319-76207-4_23 "

This paper presents the development of a bin-picking solution based on low-cost vision systems for the manipulation of automotive electrical connectors using machine learning techniques. The automotive sector has always been in a state of constant growth and change, which also implies constant challenges in the wire harnesses sector, and the emerging growth of electric cars is proof of this and represents a challenge for the industry. Traditionally, this sector is based on strong human work manufacturing and the need arises to make the digital transition, supported in the context of Industry 4.0, allowing the automation of processes and freeing operators for other activities with more added value. Depending on the car model and its feature packs, a connector can interface with a different number of wires, but the connector holes are the same. Holes not connected with wires need to be sealed, mainly to guarantee the tightness of the cable. Seals are inserted manually or, more recently, through robotic stations. Due to the huge variety of references and connector configurations, layout errors sometimes occur during seal insertion due to changed references or problems with the seal insertion machine. Consequently, faulty connectors are dumped into boxes, piling up different types of references. These connectors are not trash and need to be reused. This article proposes a bin-picking solution for classification, selection and separation, using a two-finger gripper, of these connectors for reuse in a new operation of removal and insertion of seals. Connectors are identified through a 3D vision system, consisting of an Intel RealSense camera for object depth information and the YOLOv5 algorithm for object classification. The advantage of this approach over other solutions is the ability to accurately detect and grasp small objects through a low-cost 3D camera even when the image resolution is low, benefiting from the power of machine learning algorithms.

Dissertação apresentada à Escola Superior de Tecnologia do Instituto Politécnico de Castelo Branco para obtenção do grau de Mestre em Desenvolvimento de Software e Sistemas Interativos.

With the massive deployment of broadband access to the end-users, the continuous improvement of the hardware capabilities of end devices and better video compression techniques, acceptable conditions have been met to unleash over-the-top bandwidth demanding and time-stringent P2P applications, such as multiview real-time media distribution. Such applications enable the transmission of multiple views of the same scene, providing consumers with a more immersive visual experience. This article proposes an architecture to distribute multiview real-time media content using a hybrid DVB-T2, client-server and P2P paradigms, supported by an also novel QoS solution. The approach minimizes packet delay, interar- rival jitter, inter-ISP traffic and traffic at the ISP core network, which are some of the main drawbacks of P2P networks, whilst still meeting stringent QoS demands. The proposed architecture uses DVB-T2 to distribute a self-contained and fully decodable base-layer video signal, assumed to be always available to the end-user, and an IP network to distribute in parallel - with increased delay - additional IP video streams. The result is a decoded video quality that adapts to individual end-user conditions and maxi- mizes viewing experience. To achieve its target goal this architecture: defines new services for the ISP’s services network and new roles for the ISP core, edge and border routers; makes use of pure IP mul- ticast transmission at the ISP’s core network, greatly minimizing bandwidth consumption; constructs a geographically contained P2P network that uses P2P application-level multicast trees to assist the dis- tribution of the IP video streams at the ISP access networks, greatly reducing inter-ISP traffic, and; de- scribes a novel QoS control architecture that takes advantage of the Internet resource over-provisioning techniques to meet stringent QoS demands in a scalable manner. The proposed architecture has been im- plemented in both real test bed implementation and ns-2 simulations. Results have shown a highly scal- able P2P overlay construction algorithm, with very fast computation of application-level multicast trees (in the order of milliseconds), and efficient reaction to peer-churn with no perceptually annoying impair- ments noticed. Furthermore, enormous bandwidth savings are achieved at the ISP core network, which considerable lower management and investment costs in infrastructure. The QoS based results have also shown that the proposed approach effectively deploys a fast and scalable resource and admission control mechanism, considerably lowering signalling events using a per-class over-provisioning approach thus preventing per-flow QoS reservation signalling messages. Moreover, it is aware of network link resources in real-time and supports for service differentiation and network convergence by guaranteeing that each admitted traffic flow receives the contracted QoS. Finally, the proposed architecture for Multiview Real- Time Media Distribution for Next Generation Networks, as a component for a large project demonstrator, has been evaluated by an independent panel of experts following ITU recommendations, obtaining an excellent evaluation as computed by Mean Opinion Score.

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“© © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”

The concept of smart cities grew with the need to rethink the use of urban spaces based on the constant technological advances and respecting sustainability. Today the urbanism and the methodologies to think about the city are changing, as citizens want more access to digital information on almost everything. Therefore, cities need to be planned and equipped with infrastructures that enable connectivity between the citizens’ devices and the digital information. This challenge raises technological problems, such as traffic management, in an attempt to guarantee fair network access to all users. Solutions based on wireless resource management and self-organizing networks are key when design the connectivity for these smart cities. This paper presents a study on forecasting the daily load of Wi-Fi city hotspots, taking also in consideration the weather conditions. This is particularly interesting to predict the network load and resource requirements needed to ensure proper quality of service is provided to the hotspot users. The study was performed in a Wi-Fi hotspot located in the city of Castelo Branco, Portugal. The results show the ARIMA model is capable of identifying and forecasting seasonality events for one week in advance including its capability to correlate the number of hotspot users with weather conditions.