Fast Image Recovery Using Variable Splitting and Constrained Optimization

We propose a new fast algorithm for solving one of the standard formulations of image restoration and reconstruction which consists of an unconstrained optimization problem where the objective includes an $\ell_2$ data-fidelity term and a non-smooth regularizer. This formulation allows both wavelet-based (with orthogonal or frame-based representations) regularization or total-variation regularization. Our approach is based on a variable splitting to obtain an equivalent constrained optimization formulation, which is then addressed with an augmented Lagrangian method. The proposed algorithm is an instance of the so-called "alternating direction method of multipliers", for which convergence has been proved. Experiments on a set of image restoration and reconstruction benchmark problems show that the proposed algorithm is faster than the current state of the art methods.Comment: Submitted; 11 pages, 7 figures, 6 table

An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems

We propose a new fast algorithm for solving one of the standard approaches to ill-posed linear inverse problems (IPLIP), where a (possibly non-smooth) regularizer is minimized under the constraint that the solution explains the observations sufficiently well. Although the regularizer and constraint are usually convex, several particular features of these problems (huge dimensionality, non-smoothness) preclude the use of off-the-shelf optimization tools and have stimulated a considerable amount of research. In this paper, we propose a new efficient algorithm to handle one class of constrained problems (often known as basis pursuit denoising) tailored to image recovery applications. The proposed algorithm, which belongs to the family of augmented Lagrangian methods, can be used to deal with a variety of imaging IPLIP, including deconvolution and reconstruction from compressive observations (such as MRI), using either total-variation or wavelet-based (or, more generally, frame-based) regularization. The proposed algorithm is an instance of the so-called "alternating direction method of multipliers", for which convergence sufficient conditions are known; we show that these conditions are satisfied by the proposed algorithm. Experiments on a set of image restoration and reconstruction benchmark problems show that the proposed algorithm is a strong contender for the state-of-the-art.Comment: 13 pages, 8 figure, 8 tables. Submitted to the IEEE Transactions on Image Processin

Constructing networks of defects with scalar fields

We propose a new way to build networks of defects. The idea takes advantage of the deformation procedure recently employed to describe defect structures, which we use to construct networks, spread from small rudimentary networks that appear in simple models of scalar fields.Comment: 5 pages, 4 figures, version with new title, motivations and references, to appear in PL

An energy management platform for public buildings

This paper describes the development and implementation of an electronic platform for energy management in public buildings. The developed platform prototype is based on the installation of a network of wireless sensors using the emerging Long Range (LoRa) low power long-range wireless network technology. This network is used to collect sensor data, which is stored online and manipulated to extract knowledge and generate actions toward energy saving solutions. In this process, gamification approaches were used to motivate changes in the users' behavior towards more sustainable actions in public buildings. These actions and the associated processes can be implemented as public services, and they can be replicated to different public buildings, contributing to a more energy-sustainable world. The developed platform allows the monitoring and management of the heating/cooling, electric power consumption, and lighting levels. In order to validate the proposed electronic platform, sensor information was collected in the context of a university campus, which was used as an application scenario in public buildings.info:eu-repo/semantics/publishedVersio

Numerical solution of linear models in economics: The SP-DG model revisited

In general, complex and large dimensional models are needed to solve real economic problems. Due to these characteristics, there is either no analytical solution for them or they are not attainable. As a result, solutions can be only obtained through numerical methods. Thus, the growing importance of computers in Economics is not surprising. This paper focuses on an implementation of the SP-DG model, using Matlab,developed by the students as part of the Computational Economics course. We also discuss some of our teaching/learning experience within the course, given for the first time in the FEP Doctoral Programme in Economics.SP-DG Model, Output, Inflation, Numerical Simulation, Teaching of Economics

IoT and blockchain paradigms for EV charging system

In this research work, we apply the Internet of Things (IoT) paradigm with a decentralized blockchain approach to handle the electric vehicle (EV) charging process in shared spaces, such as condominiums. A mobile app handles the user authentication mechanism to initiate the EV charging process, where a set of sensors are used for measuring energy consumption, and based on a microcontroller, establish data communication with the mobile app. A blockchain handles financial transitions, and this approach can be replicated to other EV charging scenarios, such as public charging systems in a city, where the mobile device provides an authentication mechanism. A user interface was developed to visualize transactions, gather users’ preferences, and handle power charging limitations due to the usage of a shared infrastructure. The developed approach was tested in a shared space with three EVs using a charging infrastructure for a period of 3.5 months.info:eu-repo/semantics/publishedVersio

Smart home power management system for electric vehicle battery charger and electrical appliance control

This paper presents a power management system (PMS) designed for smart homes aiming to deal with the new challenges imposed by the proliferation of plug-in electric vehicles (EVs) and their coexistence with other residential electrical appliances. The PMS is based on a hybrid wireless network architecture composed by a local hub/gateway and several Bluetooth Low Energy (BLE) and Wi-Fi sensor/actuator devices. These wireless devices are used to transfer information inside the smart home using the MQTT (Message Queuing Telemetry Transport) protocol. Based on the proposed solution, the current consumption of the EV battery charger and other residential electrical appliances are dynamically monitored and controlled by using a configurable algorithm, ensuring that the total current consumption does not cause the tripping of the home circuit breaker. An Android client application allows the user to monitor and configure the system operation in real-time, a developed Wi Fi smart plug permits to measure the RMS values of current of the connected electrical appliance and change its state of operation remotely, and an EV battery charger may be controlled in terms of operating power according to set-points received from the Android client application. Experimental tests are used to evaluate the quality of service provided by the developed smart home platform in terms of communication delay and reliability. An experimental validation for different conditions of operation of the proposed smart home PMS concerning the power operation of the EV battery charger with the proposed control algorithm is also presented.info:eu-repo/semantics/acceptedVersio

Vertical movement patterns and ontogenetic niche expansion in the Tiger Shark, Galeocerdo cuvier

Sharks are top predators in many marine ecosystems and can impact community dynamics, yet many shark populations are undergoing severe declines primarily due to overfishing. Obtaining species-specific knowledge on shark spatial ecology is important to implement adequate management strategies for the effective conservation of these taxa. This is particularly relevant concerning highly-mobile species that use wide home ranges comprising coastal and oceanic habitats, such as tiger sharks, Galeocerdo cuvier. We deployed satellite tags in 20 juvenile tiger sharks off northeastern Brazil to assess the effect of intrinsic and extrinsic factors on depth and temperature usage. Sharks were tracked for a total of 1184 d and used waters up to 1112 m in depth. The minimum temperature recorded equaled 4 degrees C. All sharks had a clear preference for surface (< 5 m) waters but variability in depth usage was observed as some sharks used mostly shallow (< 60 m) waters whereas others made frequent incursions into greater depths. A diel behavioral shift was detected, with sharks spending considerably more time in surface (< 10 m) waters during the night. Moreover, a clear ontogenetic expansion in the vertical range of tiger shark habitat was observed, with generalized linear models estimating a similar to 4-fold increase in maximum diving depth from 150- to 300-cm size-classes. The time spent in the upper 5 m of the water column did not vary ontogenetically but shark size was the most important factor explaining the utilization of deeper water layers. Young-of-the-year tiger sharks seem to associate with shallow, neritic habitats but they progressively move into deeper oceanic habitats as they grow larger. Such an early plasticity in habitat use could endow tiger sharks with access to previously unavailable prey, thus contributing to a wider ecological niche.State Government of Pernambuco; Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior [BJT/A049/2013]; Fundacao para a Ciencia e Tecnologia, Portugal [SFRH/BD/37065/2007]info:eu-repo/semantics/publishedVersio

CHARACTERIZATION OF EQUILIBRIUM CONDITIONS OF ADSORBED SILICAGEL/WATER BED ACCORDING TO DUBININASTAKHOV AND FREUNDLICH

Systems of adsorption have been studied as an alternative for the cooling systems for saving electrical energy. The main advantage is the heat as the driving sources, for example, hot water or waste heat, widely used in the industries, and solar energy. The pair adsorbent/ adsorbate determines the behavior of these systems. Therefore, the knowledge of the equilibrium conditions between the adsorbent and the adsorbate is very important. The pair silica gel/water has the advantage of exploiting low-temperature heat sources. In this paper, the equilibrium conditions of the pair silica gel/water were investigated and the data were used to identify the coefficients of Dubinin-Astakhov equation and Freundlich equation. The experiments consisted of measuring temperature and pressure for different adsorbed mass of water in the adsorbent (silica gel). The amount of adsorbed mass (kg) per adsorbent mass (kg) used were: 0.007, 0.013, 0.024, 0.047, 0.092, 0.162 and 0.209. Both equations showed good agreement with experimental data, the coefficients of regression (R2) were 0.991 on the Dubinin-Astakhov equation and 0.993 for the Freundlich equation

Study of Compression Statistics and Prediction of Rate-Distortion Curves for Video Texture

Encoding textural content remains a challenge for current standardised video codecs. It is therefore beneficial to understand video textures in terms of both their spatio-temporal characteristics and their encoding statistics in order to optimize encoding performance. In this paper, we analyse the spatio-temporal features and statistics of video textures, explore the rate-quality performance of different texture types and investigate models to mathematically describe them. For all considered theoretical models, we employ machine-learning regression to predict the rate-quality curves based solely on selected spatio-temporal features extracted from uncompressed content. All experiments were performed on homogeneous video textures to ensure validity of the observations. The results of the regression indicate that using an exponential model we can more accurately predict the expected rate-quality curve (with a mean Bj{\o}ntegaard Delta rate of 0.46% over the considered dataset) while maintaining a low relative complexity. This is expected to be adopted by in the loop processes for faster encoding decisions such as rate-distortion optimisation, adaptive quantization, partitioning, etc.Comment: 17 page