Abstract
Localization and Mapping of autonomous robots in an harsh and unstable environment such as a steep slope vineyard is a challenging research topic. Dead Reckoning systems can fail due to the harsh conditions of the terrain, and the Global Position System can be affected by noise or even be unavailable. Agriculture is moving towards precision agriculture, with advanced monitoring systems and wireless sensor networks. These systems and wireless sensors are installed in the crop field and can be considered relevant landmarks for robot localization. In this paper the distance accuracy provided by bluetooth based sensors is deeply studied and characterized. It is considered a multi antenna receiver bluetooth system and obtained the transfer functions (from Received Signal Strength Indication (RSSI) to distance estimation) for each set of antenna and sensors. The performance of this technology is compared against Time-of-flight based technologies (Pozyx). The obtained results show that the agricultural wireless sensors can be used as redundant artificial landmarks for localization purposes. Besides, the RSSI characterization allowed to improve the previous results of our Beacon Mapping Procedure (BMP) required for accurate and reliable localization systems. © 2018 IEEE.

Abstract
Interest in autonomous vehicles has steadily increased in recent years. A number of tasks, like lane tracking, semaphore detection and decoding, are key features for a self-driving robot. This paper presents a path detection and tracking algorithm using the Inverse Perspective Mapping and Hough Transform methods compounded with real-time vision techniques and a semaphore recognition system based on color segmentation. An evaluation of the proposed algorithm is performed and a comparison between the results using real-time techniques is also presented. The suggested architecture has been put to test on autonomous driving robot who competed in the Portuguese autonomous vehicle competition called "Festival Nacional de Robotica". The overall process of the lane tracking algorithm, takes about 1.4 ms per image, almost 60 times faster than the first algorithm tested and a good accuracy, showing a translation error below 0.03m and a rotation error below 5 degrees. Regarding the real-time semaphore recognition, it takes about 0.35 ms to detect a semaphore and has achieved a perfect score in the laboratory tests performed.

Abstract
Unique Objects (UNOs) are relevant for real-world applications such as anti-counterfeiting systems. In this work, cork is demonstrated as a UNO, part of the Physical Unclonability and Disorder (PUD) system. An adequate measurement kit (illumination device) and recognition method are also devised and evaluated. Natural hills and valleys of the cork are enhanced using the illumination device and the overall robustness of the recognition application inherent to UNOs is presented. The lighting device is based on grazing light and the recognition task is based on a local feature detector and descriptor called ORB - Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The performance evaluation utilizes a private cork database (1500 photos of 500 cork stoppers) and three public iris databases. In the tests carried out on the illumination device, the results clearly show the success of capturing stable/repeatable features needed for the recognition task in the cork database. This achievement is also reflected in the perfect recognition score achieved in the cork database, in the intra-distance measure mu(intra), which gives the notion of average noise between measures, and in the inter-distance mu(inter) which provides hints about the randomness/uniqueness of a cork. Regarding the recognition application, its effectiveness is further tested using the iris databases. Regardless of the fact that the recognition algorithm was not designed for the iris recognition problem, the results show that the proposed approach is capable of competing with the techniques found in the literature specially designed for iris recognition. Furthermore, the evaluation shows that the three requirements that constitute a UNO (Disorder, Operability, and Unclonability) are fulfilled, thus supporting the main assertion of this work: that cork is a UNO.

Abstract
Using 2D images in authentication systems raises the question of spoof attacks: is it possible to deceive an authentication system using fake models possessing identical visual properties of the genuine one? In this work, an anti-spoofing method approach for a wine anti-counterfeiting system is presented. The proposed method relies in two different color spaces: CIE L*u*v* and, to distinguish between a genuine instance and a spoof attack. To evaluate the proposed strategy, two databases were used: a private database, with photos/2D attacks of cork stoppers, created for this work; and the public Replay-Attack database that is used for face spoofing detection methods testing. The results on the private database show that the anti-spoofing approach is able to distinguish with high accuracy a real photo from an attack. Regarding the public database, the results were obtained with existing methods, as the best HTER results using a single frame approach. © 2018, Springer Nature Switzerland AG.

Abstract
Content Based Image Retrieval (CBIR) is an important field of research targeting different applications. The one presented in this work results from an identified need for a wine anti-counterfeiting scheme based on photos of cork stoppers already proposed in previous work. A photo of each cork stopper is used to register a wine bottle during the bottling process. To verify the genuineness of the product a user captures a photo of the stopper; that photo is then compared with the entirety of the database and all the relevant information is retrieved to the user. This approach can be a very slow process, becoming more time consuming with each increase of the database. Aiming to solve this problem, a CBIR system for wine anti-counterfeiting purposes based on cork stopper photos is presented. The feature extraction is achieved using Oriented FAST and Rotated BRIEF (ORB), selecting the "best" keypoints (according to the Harris corner measure) per region. The regions are delimited by concentric circumferences calculated using the radius of the identified cork. The number of regions (N-R), along with the number of selected keypoints (N-p) per region, define the number of ORB descriptors needed for indexing the image in the database. To measure the similarity between the query image and the database a score is calculated based on N-R and N-P Finally, the best N images according to the calculated score are retrieved. To evaluate the proposed approach 2 datasets (natural cork and agglomerate cork dataset) were created, totaling 1500 photos, and 4 different smartphone rear cameras were used. The recall results range from 75.0% to 99.5% in the natural dataset and from 86.0% to 99.0% in the agglomerate dataset. The average time for the retrieval process is about 9.74ms in a database populated with 100 photos. Critical analysis on the recall rate and its time tradeoff is also discussed. The results show that the proposed CBIR is adequate for its application - wine anti- counterfeiting.

Abstract
Innovation has a cost. It is often the case that museums trying to innovate, simply cannot afford what it takes to build truly memorable exhibitions. Lack of human resources and high-tech equipment makes it hard to create quality contents that could be shown to the general public. Nevertheless, universities' museums are usually embedded in an environment that has the potential to provide all the tools and human resources required. The only thing needed is to establish a proper strategy and an interaction facilitator platform - U.OpenLab - which enables creating, building and sharing knowledge about the museums' collections and the academic population. This will make it easier to distribute the aforementioned knowledge to the general public, in a truly sustainable, systematic, integrated and articulated manner. In this paper we are going to present the University of Porto (U.Porto) OpenLab prototype that is being built as the stepping stone of this project, providing students with learning in a project environment.