A QoS Solution for Three-Dimensional Full-HD H.264/MVC Video Transmission over IP Networks

Tridimensional video streaming has recently drawn significant attention from users and content providers. This has led to an implementation of 3D transmission over IP networks that extend the legacy 2D solution to support multiple views within each image. More recently, a Multiview Video Coding amendment of the H.264 standard has been approved which, among other applications, is being used on the encoding of 3D content in Blu-ray discs. This latter solution has shown to be capable of improving the compression ratio when compared with the Side-by-Side encoding, by exploring inter-view redundancies. In this paper, we evaluate the challenges of encoding and transmitting 3D content in MVC, for Full HD content distribution over IP networks. We also design and evaluate a Weighted RED queuing mechanism for QoS capable networks based on the MVC structure is capable of improving the quality of the received video

A subjective quality estimation tool for the evaluation of video communication systems

This paper presents a quantification tool for the evaluation of scalable and non-scalable video communication systems. The proposed mechanism estimates the subjective quality of experience (QoE) of a human viewer according to the temporal resolution, the spatial resolution and the Root Mean Square of the Error (RMSE) between the original image and the encoded one. According to these three dimensions of quality it enables an encoder and server to search for the best combination of each of these scalability factors in order to deliver the best quality. The proposed quantification tool was obtained through subjective tests using a panel of evaluators and a new methodology which have shown good correlation factors between measurement data and estimating functions

Anomaly detection of consumption in Hotel Units: A case study comparing isolation forest and variational autoencoder algorithms

Buildings are responsible for a high percentage of global energy consumption, and thus, the improvement of their efficiency can positively impact not only the costs to the companies they house, but also at a global level. One way to reduce that impact is to constantly monitor the consumption levels of these buildings and to quickly act when unjustified levels are detected. Currently, a variety of sensor networks can be deployed to constantly monitor many variables associated with these buildings, including distinct types of meters, air temperature, solar radiation, etc. However, as consumption is highly dependent on occupancy and environmental variables, the identification of anomalous consumption levels is a challenging task. This study focuses on the implementation of an intelligent system, capable of performing the early detection of anomalous sequences of values in consumption time series applied to distinct hotel unit meters. The development of the system was performed in several steps, which resulted in the implementation of several modules. An initial (i) Exploratory Data Analysis (EDA) phase was made to analyze the data, including the consumption datasets of electricity, water, and gas, obtained over several years. The results of the EDA were used to implement a (ii) data correction module, capable of dealing with the transmission losses and erroneous values identified during the EDA’s phase. Then, a (iii) comparative study was performed between a machine learning (ML) algorithm and a deep learning (DL) one, respectively, the isolation forest (IF) and a variational autoencoder (VAE). The study was made, taking into consideration a (iv) proposed performance metric for anomaly detection algorithms in unsupervised time series, also considering computational requirements and adaptability to different types of data. (v) The results show that the IF algorithm is a better solution for the presented problem, since it is easily adaptable to different sources of data, to different combinations of features, and has lower computational complexity. This allows its deployment without major computational requirements, high knowledge, and data history, whilst also being less prone to problems with missing data. As a global outcome, an architecture of a platform is proposed that encompasses the mentioned modules. The platform represents a running system, performing continuous detection and quickly alerting hotel managers about possible anomalous consumption levels, allowing them to take more timely measures to investigate and solve the associated causes.info:eu-repo/semantics/publishedVersio

Developments of serious games in education

As Human Computer Interaction technologies evolve, they are supporting the generation of innovative solutions in a broad range of domains. Among them, Serious Games are defined as new type of computer game that is capable of stimulating users to learn, by playing and competing against themselves, against other users or against a computer application. While it could be applied to a broad range of fields and ages, these games are becoming especially relevant in educational contexts and for the most recent generation of students that is growing in a new technological environment, very different from the one we had some years ago. However, in order to become fully accepted as a teaching/learning tool in both formal and informal contexts, this technology has still to overcome several challenges. Given these considerations, this chapter makes a state-of-the-art review of several works that were done in this field, followed by the description of two real world projects, helping to understand the applicability of this technology, but also its inherent challenges.info:eu-repo/semantics/publishedVersio

GyGSLA: A portable glove system for learning sign language alphabet

The communication between people with normal hearing with those having hearing or speech impairment is difficult. Learning a new alphabet is not always easy, especially when it is a sign language alphabet, which requires both hand skills and practice. This paper presents the GyGSLA system, standing as a completely portable setup created to help inexperienced people in the process of learning a new sign language alphabet. To achieve it, a computer/mobile game-interface and an hardware device, a wearable glove, were developed. When interacting with the computer or mobile device, using the wearable glove, the user is asked to represent alphabet letters and digits, by replicating the hand and fingers positions shown in a screen. The glove then sends the hand and fingers positions to the computer/mobile device using a wireless interface, which interprets the letter or digit that is being done by the user, and gives it a corresponding score. The system was tested with three completely inexperience sign language subjects, achieving a 76% average recognition ratio for the Portuguese sign language alphabet.info:eu-repo/semantics/publishedVersio

Role of the Hippocampal Formation in Navigation from a Simultaneous Location and Mapping Perspective

The research of the brain has led to many questions, with most of them still not having a definitive answer. One of those questions is about how the brain acts when we navigate a new space. Inside the Temporal Lobe’s Hippocampal structure, specific types of neurons and neuronal structures are responsible to identify spatial elements. To recognize spaces, these cells require data, which is obtained from the subject’s senses. It is important to understand how these features are captured, processed, encoded and how the Hippocampus, and its neighboring elements, use the information to help in the navigation and mapping of a place. A specific type of neurons seems to support an animals location and spatial mapping, on other areas of research, discrete global grid systems are used to increase the independence of the autonomous vehicles, allowing the indexing of assets across the globe by partitioning the earth into grids that take into account the heterogeneity of the scales of the associated geospatial data. In this context, the main objective of this chapter is to make an analysis about the biological and technical aspects of navigation by establishing a bridge between the Hippocampus and Simultaneous Localization and Mapping (SLAM) methods

Utilização do sistema de desenvolvimento Little Giant

Relatório de estágio PRODEP III - Medida 4.3 - Acção de Formação n.º 4Estágio realizado no INESC e orient. Prof. Raúl VidalRelatório de estágio curricular da LEEC 1992/1993A informação relativa à instituição de acolhimento de estágio foi fornecida pelo SR

A Gray Code model for the encoding of grid cells in the Entorhinal Cortex

In the brains of humans and mammals, the formation of episodic memories results from the association between objects, space and time. Both the hippocampus and the entorhinal cortex have shown to play an essential role in the formation of these memories. The hippocampus may be considered as an indexer of the pattern of neocortical activity produced by an episode, while the entorhinal cortex is characterized as performing time and space integration, conveying that information to the hippocampus, in the form of grid cells. Although these grid cells are biological based, non-biological grid cells are used in discrete global grid systems that currently support the indexing of assets across the globe, allowing a more adequate partitioning of the Earth into logical structures that take into account the heterogeneity of the scales of the associated geospatial data. The reasons that led to the definition of these grid systems at macro-levels may have led to the formation of similar structures inside our brains. In this paper, we investigate a representation that unifies these views, creating new types of Gray encodings for both one- and two-dimensional spaces. We start by defining a multilayer ternary encoder based on an equilateral triangular coordinate system. After defining a space filling method for this two-dimensional architecture, two Gray codes for one-dimensional signals are defined, for both circular/periodic and non-circular representations of signals. An algorithm is defined to build the two-dimensional Gray encoding of grid cells, which is then successfully applied in a navigation system of a robot, generating patterns of grid cells similar with the ones observed in neuroscience.info:eu-repo/semantics/publishedVersio

Improving energy efficiency in smart-houses by optimizing electrical loads management

In this work, the Genetic Algorithm is explored for solving a predictive based demand side management problem (a combinatorial optimization problem) and the main measures lbr performance evaluation are evaluated. In this context, we propose a smart energy scheduling approach for household appliances in real-time to achieve minimum consumption costs and a reduction in peak load. We consider a scenario of selfconsumption where the surplus from local power generation can be sold to the grid, and the existence of appliances that can be shiftable from peak hours to off-peak hours. Results confirm the importance of the tuning procedure and the structure of the genome and algorithm's operators determine the performance of such type of meta-heuristics. This fact is more decisive when there are several operational constraints on the system, as for example short-term optimal scheduling decision, time constraints and power limitations. Details about the scheduling problem, comparison strategies, metrics, and results are provided.European Union under the FEDER (Fundo Europeu de Desenvolvimento Regional) programEuropean Union (EU) [0076_AGERAR_6_E]FCT, through IDMEC under LAETA [UID/EMS/50022/2019