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- Hypermedia APIs for the Web of ThingsPublication . Martins, Jaime; Mazayev, Andriy; Correia, NoéliaThe Web of Things is a new and emerging concept that defines how the Internet of Things can be connected using common Web technologies, by standardizing device interactions on upper-layer protocols. Even for devices that can only communicate using proprietary vendor technologies, upper-layer protocols can generally provide the necessary contact points for a high degree of interoperability. One of the major development issues for this new concept is creating efficient hypermedia-enriched application programming interfaces (APIs) that can map physical Things into virtual ones, exposing their properties and functionality to others. This paper does an in-depth comparison of the following six hypermedia APIs: 1) the JSON Hypertext Application Language from IETF; 2) the Media Types for Hypertext Sensor Markup from IETF; 3) the Constrained RESTful Application Language from IETF'; 4) the Web Thing Model from Evrythng; 5) the Web of Things Specification from W3C; and 6) the Web Thing API from Mozilla.
- Luminance, colour, viewpoint and border enhanced disparity energy modelPublication . Martins, Jaime; Rodrigues, Joao; du Buf, J. M. H.The visual cortex is able to extract disparity information through the use of binocular cells. This process is reflected by the Disparity Energy Model, which describes the role and functioning of simple and complex binocular neuron populations, and how they are able to extract disparity. This model uses explicit cell parameters to mathematically determine preferred cell disparities, like spatial frequencies, orientations, binocular phases and receptive field positions. However, the brain cannot access such explicit cell parameters; it must rely on cell responses. In this article, we implemented a trained binocular neuronal population, which encodes disparity information implicitly. This allows the population to learn how to decode disparities, in a similar way to how our visual system could have developed this ability during evolution. At the same time, responses of monocular simple and complex cells can also encode line and edge information, which is useful for refining disparities at object borders. The brain should then be able, starting from a low-level disparity draft, to integrate all information, including colour and viewpoint perspective, in order to propagate better estimates to higher cortical areas.
- GACN: Self-clustering genetic algorithm for constrained networksPublication . A. Martins, J.; Mazayev, Andriy; Correia, Noélia; Schutz, G.; Barradas, A.Extending the lifespan of a wireless sensor network is a complex problem that involves several factors, ranging from device hardware capacity (batteries, processing capabilities, and radio efficiency) to the chosen software stack, which is often unaccounted for by the previous approaches. This letter proposes a genetic algorithm-based clustering optimization method for constrained networks that significantly improves the previous state-of-the-art results, while accounting for the specificities of the Internet engineering task force, Constrained RESTful Environment (CoRE), standards for data transmission and specifically relying on CoRE interfaces, which fit this purpose very well.
- Resource design in constrained networks for network lifetime increasePublication . Correia, Noélia; Mazayev, Andriy; Schutz, G.; A. Martins, J.; Barradas, A.As constrained "things" become increasingly integrated with the Internet and accessible for interactive communication, energy efficient ways to collect, aggregate, and share data over such constrained networks are needed. In this paper, we propose the use of constrained RESTful environments interfaces to build resource collections having a network lifetime increase in mind. More specifically, based on existing atomic resources, collections are created/designed to become available as new resources, which can be observed. Such resource design should not only match client's interests, but also increase network lifetime as much as possible. For this to happen, energy consumption should be balanced/fair among nodes so that node depletion is delayed. When compared with previous approaches, results show that energy efficiency and network lifetime can be increased while reducing control/registration messages, which are used to set up or change observations.
- An integrated framework for combining gist vision with object segregation categorisation and recognitionPublication . Rodrigues, J. M. F.; Almeida, D.; Martins, Jaime; Lam, RobertoThere are roughly two processing systems: (1) very fast gist vision of entire scenes, completely bottom-up and data driven, and (2) Focus-of-Attention (FoA) with sequential screening of specific image regions and objects. The latter system has to be sequential because unnormalised input objects must be matched against normalised templates of canonical object views stored in memory, which involves dynamic routing of features in the visual pathways.
- Cortical multiscale line-edge disparity modelPublication . Rodrigues, J. M. F.; Martins, Jaime; Lam, Roberto; du Buf, J. M. H.Most biological approaches to disparity extraction rely on the disparity energy model (DEM). In this paper we present an alternative approach which can complement the DEM model. This approach is based on the multiscale coding of lines and edges, because surface structures are composed of lines and edges and contours of objects often cause edges against their background. We show that the line/edge approach can be used to create a 3D wireframe representation of a scene and the objects therein. It can also significantly improve the accuracy of the DEM model, such that our biological models can compete with some state-of-the-art algorithms from computer vision.
- Interoperability in IoT through the semantic profiling of objectsPublication . Mazayev, Andriy; Martins, Jaime; Correia, NoéliaThe emergence of smarter and broader people-oriented IoT applications and services requires interoperability at both data and knowledge levels. However, although some semantic IoT architectures have been proposed, achieving a high degree of interoperability requires dealing with a sea of non-integrated data, scattered across vertical silos. Also, these architectures do not fit into the machine-to-machine requirements, as data annotation has no knowledge on object interactions behind arriving data. This paper presents a vision of how to overcome these issues. More specifically, the semantic profiling of objects, through CoRE related standards, is envisaged as the key for data integration, allowing more powerful data annotation, validation, and reasoning. These are the key blocks for the development of intelligent applications.
- Low-cost natural interface based on head movementsPublication . Martins, Joao M. S.; Rodrigues, Joao; Martins, Jaime; Velasco, C; Weber, G; Barroso, J; Mohamad, Y; Paredes, HSometimes people look for freedom in the virtual world. However, not all have the possibility to interact with a computer in the same way. Nowadays, almost every job requires interaction with computerized systems, so people with physical impairments do not have the same freedom to control a mouse, a keyboard or a touchscreen. In the last years, some of the government programs to help people with reduced mobility suffered a lot with the global economic crisis and some of those programs were even cut down to reduce costs. This paper focuses on the development of a touchless human-computer interface, which allows anyone to control a computer without using a keyboard, mouse or touchscreen. By reusing Microsoft Kinect sensors from old videogames consoles, a cost-reduced, easy to use, and open-source interface was developed, allowing control of a computer using only the head, eyes or mouth movements, with the possibility of complementary sound commands. There are already available similar commercial solutions, but they are so expensive that their price tends to be a real obstacle in their purchase; on the other hand, free solutions usually do not offer the freedom that people with reduced mobility need. The present solution tries to address these drawbacks. (C) 2015 Published by Elsevier B.V.
- Semantic web thing architecturePublication . Mazayev, Andriy; Martins, Jaime; Correia, NoéliaAs the Internet of Things evolves and matures, the number of connected devices and the amount of generated data grows exponentially. Integrative standards and API design patterns are required to deal with this fast growth, while easing machine to machine communication and promoting ubiquitous computing. This paper discusses the W3C Web of Things model that is currently in the process of standardization, and presents our overview and implementation of this model.
- SpectraNet–53: A deep residual learning architecture for predicting soluble solids content with VIS–NIR spectroscopyPublication . A. Martins, J.; Guerra, Rui Manuel Farinha das Neves; Pires, R.; Antunes, M.D.; Panagopoulos, T.; Brázio, A.; Afonso, A.M.; Silva, L.; Lucas, M.R.; Cavaco, A.M.This work presents a new deep learning architecture, SpectraNet-53, for quantitative analysis of fruit spectra, optimized for predicting Soluble Solids Content (SSC, in Brix). The novelty of this approach resides in being an architecture trainable on a very small dataset, while keeping a performance level on-par or above Partial Least Squares (PLS), a time-proven machine learning method in the field of spectroscopy. SpectraNet-53 performance is assessed by determining the SSC of 616 Citrus sinensi L. Osbeck 'Newhall' oranges, from two Algarve (Portugal) orchards, spanning two consecutive years, and under different edaphoclimatic conditions. This dataset consists of short-wave near-infrared spectroscopic (SW-NIRS) data, and was acquired with a portable spectrometer, in the visible to near infrared region, on-tree and without temperature equalization. SpectraNet-53 results are compared to a similar state-of-the-art architecture, DeepSpectra, as well as PLS, and thoroughly assessed on 15 internal validation sets (where the training and test data were sampled from the same orchard or year) and on 28 external validation sets (training/test data sampled from different orchards/years). SpectraNet-53 was able to achieve better performance than DeepSpectra and PLS in several metrics, and is especially robust to training overfit. For external validation results, on average, SpectraNet-53 was 3.1% better than PLS on RMSEP (1.16 vs. 1.20 Brix), 11.6% better in SDR (1.22 vs. 1.10), and 28.0% better in R2 (0.40 vs. 0.31).