Bluetooth Low Energy (BLE) is a wireless protocol well suited for ultralow-power sensors running on small batteries. It is optimized for low power communication and is not compatible with the original Bluetooth, referred to as Bluetooth Basic Rate (BR)/Enhanced Data Rate (EDR). BLE is described as a new protocol in the official Bluetooth 4.0 specification. It reuses many parts of the Bluetooth BR hardware and software stack to enable dual mode devices supporting Bluetooth BR/EDR and BLE. To design energy-efficient devices, the protocol provides a number of parameters that need to be optimized within an energy, latency and throughput design space. To minimize power consumption, the protocol parameters have to be optimized for a given application. Therefore, an energy-model that can predict the energy consumption of a BLE-based wireless device for different parameter value settings, is needed. As BLE differs from Bluetooth BR significantly, models for Bluetooth BR cannot be easily applied to the BLE protocol. Since the last one year, there have been a couple of proposals on energy models for BLE. However, none of them can model all the operating modes of the protocol. This paper presents a precise energy model of the BLE protocol, that allows the computation of a device's power consumption in all possible operating modes. To the best of our knowledge, our proposed model is not only one of the most accurate ones known so far (because it accounts for all protocol parameters), but it is also the only one that models all the operating modes of BLE. Furthermore, we present a sensitivity analysis of the different parameters on the energy consumption and evaluate the accuracy of the model using both discrete event simulation and actual measurements. Based on this model, guidelines for system designers are presented, that help choosing the right parameters for optimizing the energy consumption for a given application.
This article presents the design, construction, and evaluation of an easy-to-build textile pressure resistive sensor created from low-cost conventional anti-static sheets and conductive woven fabrics. The sensor can be built quickly using standard household tools, and its thinness makes it especially suitable for wearable applications. Five sensors constructed under such conditions were evaluated, presenting a stable and linear characteristic in the range 1 to 70 kPa. The linear response was modeled and fitted for each sensor individually for comparison purposes, confirming a low variability due to the simple manufacturing process. Besides, the recovery times of the sensors were measured for pressures in the linear range, observing, for example, an average time of 1 s between the moment in which a pressure of 8 kPa was no longer applied, and the resistance variation at the 90% of its nominal value. Finally, we evaluated the proposed sensor design on a classroom application consisting of a smart glove that measured the pressure applied by each finger. From the evaluated characteristics, we concluded that the proposed design is suitable for didactic, healthcare and lifestyle applications in which the sensing of pressure variations, e.g., for activity assessment, is more valuable than accurate pressure sensing.
This article presents a parametric study of a fully 3D-printed hemispherical dielectric resonator antenna (DRA) using low loss dielectric filament and high-conductive filaments jointly with a low-cost customized dual-extruding 3D printer. The parametric study consisted in the design and evaluation of five different hemispherical DRA topologies with different internal shapes and the same overall size, in which the printing infill percentage of the DRA was reduced. A 3D-printed metallic cap was included in the antenna to compensate for the resonant frequency shift in order to maintain its original dimensions. Measurement results show that all evaluated antennas kept the same resonant frequencies and similar radiation patterns while reducing the overall weight of the topology in 22% of the nominal weight.INDEX TERMS 3D-printing, conductive filaments, dielectric resonator antennas, dielectric filaments.
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This article shows how to create a robust thermal face recognition system based on the FaceNet architecture. We propose a method for generating thermal images to create a thermal face database with six different attributes (frown, glasses, rotation, normal, vocal, and smile) based on various deep learning models. First, we use StyleCLIP, which oversees manipulating the latent space of the input visible image to add the desired attributes to the visible face. Second, we use the GANs N’ Roses (GNR) model, a multimodal image-to-image framework. It uses maps of style and content to generate thermal imaging from visible images, using generative adversarial approaches. Using the proposed generator system, we create a database of synthetic thermal faces composed of more than 100k images corresponding to 3227 individuals. When trained and tested using the synthetic database, the Thermal-FaceNet model obtained a 99.98% accuracy. Furthermore, when tested with a real database, the accuracy was more than 98%, validating the proposed thermal images generator system.
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