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- Digital to analog converter based on pulse-width addingPublication . Viegas, Vítor; Pereira, José Miguel Costa DiasThis paper presents a digital-to-analog converter (DAC) based on the pulse width created naturally by the binary counting sequence. The concept involves combining the counting impulses of the bits to form a pulse-width modulated signal, where the mean voltage is proportional to the input digital code. We propose a circuit capable of achieving this using general-purpose components. Although our design targets 8 bits, it is scalable for any number of nibbles. The paper details simulations conducted to verify the proper functioning of the circuit and to evaluate its performance. Tests were performed to determine the static characteristics of the converter, measure its differential nonlinearity (DNL), and observe its step response. In static terms, the converter exhibited negligible gain and offset errors, with a DNL below 1 LSB (least significant bit). The converter operated correctly without any missing codes. In dynamic terms, the converter demonstrated a bandwidth of 10 kHz and behaved like a second-order low-pass filter with critical damping.
- Editorial: IoT, UAV, BCI empowered deep learning models in precision agriculturePublication . Lian, Jian; Dias Pereira, José Miguel Costa
- A flexible, low-cost and algorithm-independent calibrator for automated blood pressure measuring devicesPublication . Dias Pereira, José Miguel Costa; Ribeiro, Gonçalo; Postolache, OctavianArterial hypertension is one of the most important public health problems, especially in developed countries. The quality and calibration of blood pressure (BP) equipment used for non-invasive blood pressure (NIBP) measurement are essential to obtain accurate data that support correct medical diagnostics. This paper includes the hardware and software description of a flexible, low-cost and algorithm-independent calibrator prototype that can be used for the static and dynamic calibration of automated blood pressure measuring devices (ABPMDs). In the context of this paper, the meaning of calibrator flexibility is mainly related to its ability to adapt or change easily in response to different situations in terms of the calibration of ABPMDs that can use a variety of calibration settings without the need to use specific oscillometric curves from different ABPMD manufacturers. The hardware part of the calibrator includes mainly an electro-pneumatic regulator, used to generate dynamic pressure signals with arbitrary waveforms, amplitudes and frequencies, a pressure sensor, remotely connected through a pneumatic tube to the blood pressure (BP) cuff, a blood pressure release valve and analog conditioning circuits, plus the A/D converter. The software part of the calibrator, mainly developed in LabVIEW 20, enables the simulation of oscillometric pressure pulses with different envelope profiles and the implementation of the main algorithms that are typically used to evaluate systolic, diastolic and mean arterial pressure values. Simulation and experimental results that were obtained validate the theoretical expectations and show a very acceptable level of accuracy and performance of the presented NIBP calibrator prototype. The prototype calibration results were also validated using a certified NIBP calibrator that is frequently used in clinical environments.
- A novel AI approach for assessing stress levels in patients with type 2 Diabetes Mellitus based on the acquisition of physiological parameters acquired during daily lifePublication . Ribeiro, Gonçalo; Monge, João; Postolache, Octavian; Dias Pereira, José Miguel CostaStress is the inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues, such as cardiovascular illnesses and diabetes. Various stress meters have been suggested in the past, along with diverse approaches for its estimation. However, in the case of more serious health issues, such as hypertension and diabetes, the results can be significantly improved. This study presents the design and implementation of a distributed wearable-sensor computing platform with multiple channels. The platform aims to estimate the stress levels in diabetes patients by utilizing a fuzzy logic algorithm that is based on the assessment of several physiological indicators. Additionally, a mobile application was created to monitor the users’ stress levels and integrate data on their blood pressure and blood glucose levels. To obtain better performance metrics, validation experiments were carried out using a medical database containing data from 128 patients with chronic diabetes, and the initial results are presented in this study.
- Securing IoT sensors using sharding-based blockchain network technology integration: a systematic reviewPublication . Aslam, Ammad; Postolache, Octavian; Oliveira, Sancho; Dias Pereira, José Miguel CostaSharding is an emerging blockchain technology that is used extensively in several fields such as finance, reputation systems, the IoT, and others because of its ability to secure and increase the number of transactions every second. In sharding-based technology, the blockchain is divided into several sub-chains, also known as shards, that enhance the network throughput. This paper aims to examine the impact of integrating shardingbased blockchain network technology in securing IoT sensors, which is further used for environmental monitoring. In this paper, the idea of integrating sharding-based blockchain technology is proposed, along with its advantages and disadvantages, by conducting a systematic literature review of studies based on sharding-based blockchain technology in recent years. Based on the research findings, sharding-based technology is beneficial in securing IoT systems by improving security, access, and transaction rates. The findings also suggest several issues, such as cross-shard transactions, synchronization issues, and the concentration of stakes. With an increased focus on showcasing the important trade-offs, this paper also offers several recommendations for further research on the implementation of blockchain network technology for securing IoT sensors with applications in environment monitoring. These valuable insights are further effective in facilitating informed decisions while integrating sharding-based technology in developing more secure and efficient decentralized networks for internet data centers (IDCs), and monitoring the environment by picking out key points of the data.
- Pressure sensors: working principles of static and dynamic calibrationPublication . Pereira, José Miguel Costa DiasThis paper starts with an overview of the main principles used for pressure measurements, focusing on their usage in industrial applications’ domains. Then, the importance of calibration procedures, namely, static and dynamic calibration of pressure sensors, is analyzed. Regarding calibration, it is important to note that there are several applications where the pressure signals to be measured can have large variations in short periods of time. In industrial applications, particularly in continuous production processes, generally, dynamic pressure measurements are less common; however, they are still required in several cases, such as control loops that are very sensitive to pressure variations, even if the frequencies of those variations are in the range of a few tens of hertz, or even lower. The last part of the paper presents the hardware and software of a flexible and low-cost static and dynamic pressure calibrator that also presents the capability to generate arbitrary waveform pressure signals for calibration and testing purposes. The proposed calibrator also includes the following advantages: remote pressure sensing capabilities that can be used to minimize calibration errors, such as those associated with capillary effects and pressure leakages; portability; and low cost. The paper ends with some experimental results obtained with the proposed calibrator.
- AI-powered solution for plant disease detection in viticulturePublication . Madeira, Miguel; Porfírio, Rui; Santos, Pedro Albuquerque; Madeira, Rui NevesIn an era dominated by the intersection of advanced technology and traditional industries, the domain of agriculture is on the verge of a revolutionary transformation. This article introduces a solution for vineyard producers, harnessing satellite imagery, weather data, and deep learning (DL) to identify vineyard diseases robustly. This solution, designed for proactive plant health management, stands as a transformative tool towards digital viticulture. Such tools transition from luxuries to essentials as vineyards confront evolving challenges like climate change and new pathogens. Our research builds on the hypothesis that customising deep learning architectures for specific tasks is crucial in enhancing their effectiveness. We contribute by introducing a tailored convolutional neural network (CNN) architecture, developed specifically for the classification of plant diseases using vineyard imagery. The experimental results demonstrate that our custom CNN architecture exhibits performance on par with established state-of-the-art models like ResNet50 and MobileNetV2, underscoring the value of specialized solutions in addressing the unique challenges of viticulture. This paper introduces an overview of the solution’s architecture, presents the implementation of DL modules with their corresponding results, and describes use case scenarios.
- Strawberry plant as a biomonitor of trace metal air pollution: a citizen science approach in an urban-industrial area near Lisbon, PortugalPublication . Gamelas ou Carla A. Gamelas, Carla; Canha, Nuno; Justino, Ana R.; Nunes, Alexandre; Nunes, Sandra; Dionísio, Isabel; Kertesz, Zsofia; Almeida, Susana MartaA biomonitoring study of air pollution was developed in an urban-industrial area (Seixal, Portugal) using leaves of strawberry plants (Fragaria × ananassa Duchesne ex Rozier) as biomonitors to identify the main sources and hotspots of air pollution in the study area. The distribution of exposed strawberry plants in the area was based on a citizen science approach, where residents were invited to have the plants exposed outside their homes. Samples were collected from a total of 49 different locations, and their chemical composition was analyzed for 22 chemical elements using X-ray Fluorescence spectrometry. Source apportionment tools, such as enrichment factors and principal component analysis (PCA), were used to identify three different sources, one geogenic and two anthropogenic (steel industry and traffic), besides plant major nutrients. The spatial distribution of elemental concentrations allowed the identification of the main pollution hotspots in the study area. The reliability of using strawberry leaves as biomonitors of air pollution was evaluated by comparing them with the performance of transplanted lichens by regression analysis, and a significant relation was found for Fe, Pb, Ti, and Zn, although with a different accumulation degree for the two biomonitors. Furthermore, by applying PCA to the lichen results, the same pollution sources were identified.
- IoT, UAV, BCI empowered deep learning models in precision agriculturePublication . Lian, Jian; Pereira, José Miguel Costa Dias
- Transmissores pneumáticosPublication . V. Dionísio, R.