Measuring cortisol levels as a stress biomarker is essential in many medical conditions associated with a high risk of metabolic syndromes such as anxiety and cardiovascular diseases, among others. One technology that has a growing interest in recent years is fiber optic biosensors that enable ultrasensitive cortisol detection. Such interest is allied with progress being achieved in basic interrogation, accuracy improvements, and novel applications. The development of improved cortisol monitoring, with a simplified manufacturing process, high reproducibility, and low cost, are challenges that these sensing mechanisms still face, and for which solutions are still needed. In this paper, a comprehensive characterization of a D-shaped fiber optic immunosensor for cortisol detection based on surface plasmon resonance (SPR) enabled by gold coating is reported. Specifically, the sensor instrumentation and fabrication processes are discussed in detail, and a simulation with its complete mathematical formalism is also presented. Moreover, experimental cortisol detection tests were performed for a detection range of 0.01 to 100 ng/mL, attaining a logarithmic sensitivity of 0.65 ± 0.02 nm/log(ng/mL) with a limit of detection (LOD) of 1.46 ng/mL. Additionally, an investigation of signal processing is also discussed, with the main issues addressed in order to highlight the best way to extract the sensing information from the spectra measured with a D-shaped sensor.

Series elastic actuators (SEAs) can provide low output impedance, bandwidth close to the human movement and direct measurement of torque through the spring deflection. These advantages enable the application of SEAs in human active orthosis and exoskeletons. However, conventional technologies to measure the spring deflection are bulky, inhibit natural pattern of movement or are sensitive to misalignments. This paper presents the application of polymer optical fiber (POF) as a sensor to measure the spring deflection to overcome some of the issues of conventional technologies, since it is compact, lightweight and have electromagnetic immunity. Furthermore, the spring is employed to validate a torque sensor based on POF stress-optic effects. Results show high linearity of both sensors and mean squared errors below the encoder resolution employed as reference on dynamic measurements.

A theoretical study and an experimental evaluation on the performance of the long-range (LoRa) technology are presented in this paper. After an instructive mathematical review of important concepts, we assess the LoRa performance in additive white Gaussian noise (AWGN) channels through a numerical model, to elucidate its communication capabilities in negative signal-to-noise ratio (SNR) conditions and, as consequence, in the long distances promised by such low power and narrowband technology. Because of the significance of forward error correction codes in the performance of modern systems, we propose an analytical bit-error-rate (BER) expression that considers the influence of LoRa code rate (CR) parameter. The agreement between the theoretical and the numerical results of a Hamming coded system validated the closed-form BER, evaluated in terms of SNR in AWGN channels. Moreover, an experimental setup with off-the-shelf LoRa wide area network (LoRaWAN) equipment was prepared to demonstrate the feasibility of this promising technology, as well the impact of the CR parameter on the packet error rate (PER), in a link with a single end-device. As expected, communication in urban and open areas with distances up to ≈3 and ≈10 km, respectively, were achieved, according to measurements of the average received signal indicators around -100 dBm for an average SNR ≈5 dB. The usefulness of the practical analysis was confirmed by the agreement between the theoretical and the measured noise floors, and by the verification of the substantial impact of the parameter CR on the system PER.

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This paper proposes an approach to process the response of a distributed temperature sensor using a nonlinear autoregressive with external input neural network. The developed model is composed of three steps: extraction of characteristics, regression, and reconstruction of the signal. Such an approach is robust because it does not require knowledge of the characteristics of the signal; it has a reduction of data to be processed, resulting in a low processing time, besides the simultaneous improvement of spatial resolution and temperature. We obtain total correction of the temperature resolution and spatial resolution of 5 cm of the sensor.

The contingency analysis of power systems represents a critical part of security monitoring, required to maintain power system reliability. In general, traditional N-1 contingency analysis methods simulate a few outages, due to the high computational costs involved. Thus, they may fail to identify some critical contingencies that can lead to cascading failures. This paper proposes a new approach to N-1 contingency analysis of electric transmission systems, based on network centrality measures. The proposed method evaluates all possible transmission line outages in a very short computational time, and it requires only topological information. Results are shown for two electric power systems: ITAIPU 11 bus and IEEE 39 bus. Comparisons between the results obtained by the proposed method and traditional ones show the accuracy of the proposed method to identify critical buses and transmission lines, in local and global context, even in absence of electrical information. The proposed method is interesting as a direct and fast tool applied to the pre-analysis process, since topological network behavior is verified. Thus, pre-analysis provides a prior response to the system operator of the points by which the electrical power system analysis should begin, in order to ensure safe operational state of the system.