Hugo Marques

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 smart-city 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.

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.

New emerging applications for small unmanned aerial vehicles (UAVs) are attracting attention from key stakeholders, including the European Commission, where the U-space initiative is a clear example of the momentum being gathered. Cellular networks offer wide area, high speed, and secure wireless connectivity, which can enhance control and safety for UAV operations in beyond visual line-of-sight (BVLOS) scenarios. This paper describes the work undertaken to assess if existing commercial cellular networks can ensure high data connectivity in low altitude and BVLOS UAV operational scenarios throughout different times-of-day. Dedicated radio probes have been developed to monitor the aerial cellular network performance for different heights and propagation conditions. Evaluations have been performed in two separated outdoor aerial testbeds and the collected results hint for a promising off-the-shelf service guarantee in low altitudes and inspire for a disruptive "drone-as-a-service" business model.

To gain competitive advantage in today's mobile market, cellular network testing, monitoring and improving customer experience is crucial. Today independent benchmarking companies are hired by mobile operators to run drive tests in a certain geographical areas. The high cost for running these tests results in a low frequency of execution, typically this benchmarking is executed no more than two or three times per year, which is not sufficient to follow the dynamics of an LTE network in a dense urban area. The majority of the drive testing costs come from the car, driver, and the in-car technician. Another approach is to take advantage of existing transportation companies to carry on network benchmarking services to Mobile Network Operators. Unattended measurement nodes can be deployed in existing transportation fleets without the need for dedicated field personnel, reducing the cost of testing up to 70%. This demo uses nodes placed in buses, available in several cities in Europe, to create and validate an automatic LTE network benchmark. The tool allows an easy comparative analyses of mobile network quality of Service and quality of experience parameters based on the operators raw data.