Abstract
In wireless networking R&D we typically depend on experimentation to further evaluate a solution, as simulation is inherently a simplification of the real-world. However, experimentation is limited in aspects where simulation excels, such as repeatability and reproducibility. Real wireless experiments are hardly repeatable. Given the same input they can produce very different output results, since wireless communications are influenced by external random phenomena such as noise, interference, and multipath. Real experiments are also difficult to reproduce due to testbed operational constraints and availability. We have previously proposed the Trace-based Simulation (TS) approach, which uses the TraceBasedPropagationLossModel to successfully reproduce past experiments. Yet, in its current version, the TraceBasedPropagationLossModel only supports point-to-point scenarios. In this paper, we introduce a new version of the model that supports Multiple Access wireless scenarios. To validate the new version of the model, the network throughput was measured in a laboratory testbed. The experimental results were then compared to the network throughput achieved using the ns-3 trace-based simulation and a pure ns-3 simulation, confirming the TS approach is valid for multiple access scenarios too.

Abstract
The availability of low cost networked wireless devices and video cameras is enabling wireless video sensor networks (WVSNs), which can be used in scenarios such as healthcare, agriculture, smart cities, intelligent transportation systems, and surveillance. These scenarios typically require that each node sends a video stream to a server located in the cloud. The IEEE 802.11 is considered a suitable technology for transmitting video wirelessly, as it supports high data rates. However, when using a multi-hop topology to extend the IEEE 802.11 coverage, the IEEE 802.11-based WVSNs suffer from three problems: low network capacity, throughput unfairness, and energy inefficiency. To overcome these problems, we propose a holistic solution, named Green wiReless vidEo sENsor NEtworks uSing out-of-band Signalling (GREENNESS). GREENNESS combines a node polling mechanism with the use of out-of-band signaling over a low power radio to signal when a video sensor should switch ON and OFF its IEEE 802.11 interface, thus saving energy. The results obtained for random network topologies show that GREENNESS can achieve energy savings up to 92%, and improve network capacity and throughput fairness when compared to state of the art CSMA/CA-based WVSN solutions.

Abstract
The support of video in the learning environment is nowadays used to many ends, either for demonstration, research or share. It is intended to reinforce the space before and after class and introduce a new dynamic and interaction in the classroom itself. Pedagogical innovation may be achieved by different approaches to motivate students and obtain better results. This paper presents a revision of the literature about the potential of using video annotation in the education context, specifically in the domain of Physics, using an open source annotation tool. The creation of audiovisual references, either for quick access to parts of organized video annotated content by the teacher, knowledge building or revision by and for other students is analyzed. This study is complemented with a testbed, showing the potential of using audiovisual annotated content, within a k-12 context. Students were invited to select video content, annotate, organize and publish the annotations, which could support the learning process in the domain of Physics. Results show that most of the aspects under analysis received a positive evaluation. The only exception relates to the capacity of the approach to motivated students to the study of Physics, as most of the students did not see this methodology as a motivating means.

Abstract
Process variations in MOS manufacturing processes tend to increase within each new technology node, leading to lower production yields, particularly those of analogue circuits, which are also sensitive to external perturbations that might force there performance to drift beyond there admissible tolerance margins. This issue is addressed here with the design of a calibratable N-Integer phase-locked loop (PLL) based frequency synthesizer. In the approach being proposed, a statistical analysis is carried out to know the sensitivity of the PLL performance to expected process, temperature, and voltage variations. A training data set is then identified and used to define the calibration law and the required calibration algorithm. Simulation results are presented that confirm its validity.

Abstract
Panoramic or aerial images can be acquired with some easiness and cover vast tracts of territory to be used in fire detection. The analysis of these images, in particular based on color and threshold indices, can be very interesting computationally when applied in real time systems and collected, for example, through drones or watchtowers. This paper presents a solution designated Color Algorithm for Flame Exposure (CAFE), which significantly improves an existing method (cf. Forest Fire Detection Index - FFDI) in flame detection, based on daylight images, in mixed Mediterranean landscape, containing vegetation, buildings, burning areas, land, etc. The CAFE approach, presented, adds a parameterizable transformation of the image into the Lab color space. This approach was tested in four distinct scenarios, significantly reducing false positives and maintaining an equivalent level of false negatives when compared to the FFDI approach.

Abstract
Location data plays an important role in several applications embedded in our digital living. These applications, usually, take advantage of Global Positioning System (GPS). However, GPS is not targeted for indoor location, therefore this paper presents an alternative system, based on Bluetooth Low Energy (BLE) beacons that together with bluetooth-enabled Smartphones, allows the development of low cost and accurate location-aware applications for indoor scenarios. The paper describes the challenges associated with the system deployment and presents algorithms to improve the distance estimation process as the user moves around the smart space. The evaluation performed shows that this approach has good results on noise reduction and movement adaptation allowing a close tracking of the indoor user position. © Springer International Publishing AG, part of Springer Nature 2018.