Browsing by Author "Caldeira, J.M.L.P."
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- Accept mobile: a mobile tool for the SINMETRO accept information systemPublication . Caldeira, J.M.L.P.; Dias, Edgar; Paulo, Bruno; Neves, Paulo AlexandreIncreasing demand of mobile applications for on-site data acquisition pushes the development of flexible and easy to use mobile tools, with great advantages over the traditional computer-based approaches. The Accept System from SINMETRO allows data gathering for quality control, in the form of Inspection sheets. Such tool allows quality assurance by monitoring some samples of a given material, for instance milk, wine, and even maintenance management. This paper presents a mobile application in the Accept System that allows a Personal Digital Assistant (PDA) device to perform data gathering based on XML Inspection templates. Using .NET Compact Framework through C#, and database the technologies SQL Server and SQL Server CE, we developed Accept Mobile. Accept Mobile uses the Remote Data Access (RDA) mechanism to send data over to the server through a synchronization service, while also providing the needed support for disconnected operation. We prove that the mobile application is very convenient and provides enough functionality for the user to dismiss the portable computer, although the main application was never developed with mobility concerns in mind.
- An Eco-Energetic performance comparison of dehumidification systems in High-Moisture indoor environmentsPublication . Santos, Alexandre F.; Gaspar, Pedro Dinis; Souza, Heraldo J.L.; Caldeira, J.M.L.P.; Soares, V.N.G.J.This study discusses the choice of dehumidification systems for high-moisture indoor environments, such as indoor swimming pools, supported by an eco-energetic performance comparison. Initially, the causes of the high relative humidity and condensation in these spaces are reported, as well as the available dehumidification technologies. Two different solutions are described: desiccant wheel dehumidification and re-cooling. The energy demand required by a refrigeration system is lower than the desiccant wheel; however, the former system requires less maintenance and does not require refrigerant fluid. An eco-energetic comparison is performed between the two systems in two countries with different energy matrices (Brazil and USA). In Brazil, the desiccant wheel is the best choice for the past 10 years, with a predicted 351,520 kgCO2 of CO2 emissions, which is 38% lower than the refrigeration system. In the USA, the best option is the refrigeration system (1,463,350 kgCO2), a 12% more efficient option than desiccant wheels. This model can be considered for energy and CO2 emissions assessment, predicting which system has better energy efficiency and lower environmental impact, depending on the refrigerant type, location and environmental conditions.
- Artificial intelligence decision support system based on artificial neural networks to predict the commercialization time by the evolution of peach qualityPublication . Ananias, Estevão; Gaspar, Pedro Dinis; Soares, V. N.G.J; Caldeira, J.M.L.P.Climacteric fruit such as peaches are stored in cold chambers after harvest and usually are maintained there until the desired ripening is reached to direct these fruit to market. Producers, food industries and or traders have difficulties in defining the period when fruit are at the highest level of quality desired by consumers in terms of the physical-chemical parameters (hardness –H–, soluble solids content –SSC–, and acidity –Ac–). The evolution of peach quality in terms of these parameters depends directly on storage temperature –T– and relative humidity –RH–, as well on the storage duration –t–. This paper describes an Artificial Intelligence (AI) Decision Support System (DSS) designed to predict the evolution of the quality of peaches, namely the storage time required before commercialization as well as the late commercialization time. The peaches quality is stated in terms of the values of SSC, H and Ac that consumers most like for the storage T and RH. An Artificial neuronal network (ANN) is proposed to provide this prediction. The training and validation of the ANN were conducted with experimental data acquired in three different farmers’ cold storage facilities. A user interface was developed to provide an expedited and simple prediction of the marketable time of peaches, considering the storage temperature, relative humidity, and initial physical and chemical parameters. This AI DSS may help the vegetable sector (logistics and retailers), especially smaller neighborhood grocery stores, define the marketable period of fruit. It will contribute with advantages and benefits for all parties—producers, traders, retailers, and consumers—by being able to provide fruit at the highest quality and reducing waste in the process. In this sense, the ANN DSS proposed in this study contributes to new AI-based solutions for smart cities.
- Autonomous robot path construction prototype using wireless sensor networksPublication . Amaro, José Paulo; Caldeira, J.M.L.P.; Soares, V.N.G.J.; Dias, João A.The use of wireless sensor networks (WSN) can be a valuable contribution in disaster situations or life-threatening exploration. Using wireless mobile robots, it is possible to explore vast areas without human intervention. However, the wireless network coverage that can keep mobile robots connected to the base station / gateway is a major limitation. With this in mind it was created a prototype of an extensible WSN using mobile robot nodes that cooperate amongst themselves. The strategy adopted in this project proposes using three types of nodes: master node, static node, and robot node. Three different algorithms were also developed and proposed: Received Signal Strength Indication (RSSI) Request; Automovement; Robot Cooperation and Response to Static Node. The performance evaluation of the prototype was carried out using a real-world testbed with each developed algorithm. The results achieved were very promising to continue the evolution of the prototype.
- A biosensor and data presentation solution for body sensor networksPublication . Neves, Paulo Alexandre; Caldeira, J.M.L.P.; Mendes, Américo; Pereira, Orlando; Rodrigues, JoelA Body Sensor Network can sense health parameters directly on the patient’s body, allowing 24/7 monitoring in an unobtrusive way. Several tiny sensors collect and route data to a special sink node. A new intra-vaginal biosensor was developed to study the relation between temperature variations and women health conditions, such as ovulation period, among others. We present a biosensor prototype and some initial results on real scenarios with a woman. One of the main issues in a body sensor network is the transformation of the sensor raw data into meaningful medical data for medical staff. Several approaches exist, from mobile device-based approaches to more powerful hardware such as a personal computer. This paper presents our current work in body sensor networks, namely a prototype for intra-vaginal temperature monitoring with initial results, and a mobile tool for data presentation of a three-tier body sensor network. The gathered results demonstrate the feasibility of the approach, contributing to the widespread application of body sensor networks.
- Bird deterrent solutions for crop protection: approaches, challenges, and opportunitiesPublication . Micaelo, Eduardo B.; Lourenço, Leonardo G.P.S.; Gaspar, Pedro Dinis; Caldeira, J.M.L.P.; Soares, V.N.G J.Weeds, pathogens, and animal pests are among the pests that pose a threat to the productivity of crops meant for human consumption. Bird-caused crop losses pose a serious and costly challenge for farmers. This work presents a survey on bird deterrent solutions for crop protection. It first introduces the related concepts. Then, it provides an extensive review and categorization of existing methods, techniques, and related studies. Further, their strengths and limitations are discussed. Based on this review, current gaps are identified, and strategies for future research are proposed.
- Body sensor network mobile solutions for biofeedback monitoringPublication . Pereira, Orlando; Caldeira, J.M.L.P.; Rodrigues, JoelBody sensor networks (BSN) appeared as an application of Wireless Sensor Networks (WSN) to medicine and biofeedback. Such networks feature smart sensors (biosensors) that capture bio-physiological parameters from people and can offer an easy way for data collection. BSNs also need suitable interfaces for data processing, presentation, and storage for latter retrieval. As a result, Bluetooth technology can be used to communicate with several more powerful and graphical user interface (GUI)-enabled devices such as mobile phones or regular computers. Taking into account that people currently use mobile and smart phones, it offers a good opportunity to propose a suitable mobile system for BSN networks. This paper presents a BSN mobile solution for biofeedback monitoring using the four major smart phone platforms: Symbian, Windows Mobile, Android, and iPhone. As case study, a sensing health with intelligence modularity, mobility and experimental reusability (SHIMMER) platform with a core-body temperature sensor enabled to construct the BSN was used. The four mobile applications were evaluated and validated, and are ready for use.
- Comparison of on-policy deep reinforcement learning A2C with off-policy DQN in irrigation optimization : a case study at a site in PortugalPublication . Alibabaei, Khadijeh; Gaspar, Pedro Dinis; Assunção, Eduardo; Alirezazadeh, Saeid; Lima, Tânia M.; Soares, V.N.GJ.; Caldeira, J.M.L.P.Precision irrigation and optimization of water use have become essential factors in agricul- ture because water is critical for crop growth. The proper management of an irrigation system should enable the farmer to use water efficiently to increase productivity, reduce production costs, and maxi- mize the return on investment. Efficient water application techniques are essential prerequisites for sustainable agricultural development based on the conservation of water resources and preservation of the environment. In a previous work, an off-policy deep reinforcement learning model, Deep Q-Network, was implemented to optimize irrigation. The performance of the model was tested for tomato crop at a site in Portugal. In this paper, an on-policy model, Advantage Actor–Critic, is implemented to compare irrigation scheduling with Deep Q-Network for the same tomato crop. The results show that the on-policy model Advantage Actor–Critic reduced water consumption by 20% compared to Deep Q-Network with a slight change in the net reward. These models can be developed to be applied to other cultures with high production in Portugal, such as fruit, cereals, and wine, which also have large water requirements.
- Computational simulation of an agricultural robotic rover for weed control and fallen fruit collection : algorithms for image detection and recognition and systems control, regulation and commandPublication . Ribeiro, João P.L.; Gaspar, Pedro Dinis; Soares, V.N.G.J.; Caldeira, J.M.L.P.The continuous rise in the world’s population has increased the need for food, resulting in a rise of agricultural holdings to ensure the supply of these goods directly to the populations and indirectly to all processing industries in the food business. This situation has led agriculture to reinvent itself and introduce new technics and tools to ensure tighter control of the crops and increase yields in food production. However, the lack of labor coupled with the evolution of weeds resistant to herbicides created a crisis in agricultural food production. However, with the growing evolution in electronics, automation, and robotics, new paths are emerging to solve these problems. A robotic rover was designed to optimize the tasks of weed control and collection of fallen fruits of an orchard. In weed control, a localized spraying system is proposed, therefore reducing the amount of applied herbicides. With fruit collection, it is possible to direct fallen fruits for animal feeding and possible to reduce microbial activity on the next campaign crops, therefore avoiding damage. This study proposes the simulation of this robotic rover on robotic simulation software. It also proposes the replication of a similar environment of an orchard to generate an algorithm that controls the rover on the tasks of localized spraying and fallen fruit collection. Creating and testing these algorithms by using a robotic simulator speed up and ease the evaluation of different scenarios and hypotheses, with the added benefit of being able to test two tasks simultaneously. This method also allows greater freedom and creativity because there are no concerns about hardware damage. It should also be noted that development costs are very low.
- Computer vision for reducing food waste in an institutional canteen: A literature review and performance analysisPublication . Correia, Ana; Aidos, Clara; Caldeira, J.M.L.P.; Soares, V.N.G.J.Food waste in today's society has been the subject of growing interest and discussion, given its economic, environmental, social, and nutritional implications. Although food waste is present throughout the food supply chain, in developed countries it tends to be higher in the final stages of consumption (e.g., households and food services). This study focuses on institutional canteens, where food waste includes prepared meals that have not been sold (i.e., leftovers), as well as food served that is left on plates after the meal has been consumed (i.e., scraps). It presents a first step towards developing a prototype/solution based on computer vision techniques to identify and quantify food waste in an institutional canteen. It begins by introducing the related concepts. It then surveys the state-of-the-art and categorizes existing solutions, presenting their main characteristics, strengths, and limitations. Inception-V3 and ResNet-50 are identified as the most promising computer vision techniques, and their performance has been evaluated. Information is also provided on open questions and research directions in this area