DSpace Collection:https://hdl.handle.net/10171/232032024-03-29T06:13:44Z2024-03-29T06:13:44Z0.5 V and 0.43 pJ/bit capacitive sensor interface for passive wireless sensor systems.https://hdl.handle.net/10171/692242024-03-11T06:07:10Z2015-09-01T00:00:00ZTitle: 0.5 V and 0.43 pJ/bit capacitive sensor interface for passive wireless sensor systems.
Abstract: This paper presents an ultra low-power and low-voltage pulse-width modulation
based ratiometric capacitive sensor interface. The interface was designed and fabricated in a
standard 90 nm CMOS 1P9M technology. The measurements show an effective resolution
of 10 bits using 0.5 V of supply voltage. The active occupied area is only 0.0045 mm2
and
the Figure of Merit (FOM), which takes into account the energy required per conversion
bit, is 0.43 pJ/bit. Furthermore, the results show low sensitivity to PVT variations due to
the proposed ratiometric architecture. In addition, the sensor interface was connected to
a commercial pressure transducer and the measurements of the resulting complete pressure
sensor show a FOM of 0.226 pJ/bit with an effective linear resolution of 7.64 bits. The results
validate the use of the proposed interface as part of a pressure sensor, and its low-power
and low-voltage characteristics make it suitable for wireless sensor networks and low power
consumer electronics.2015-09-01T00:00:00ZSemi-passive UHF RFID sensor tags: A comprehensive reviewhttps://hdl.handle.net/10171/691022024-03-11T06:06:47Z2023-01-01T00:00:00ZTitle: Semi-passive UHF RFID sensor tags: A comprehensive review
Abstract: This paper presents a comprehensive overview and analysis of the state-of-the-art (SoA) in semi-passive or Battery-Assisted (BAP) Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) sensor tags compliant with EPC Global G2/ISO-18000C. These tags operate on the same communication principle as fully passive sensor tags but incorporate a battery or an energy harvesting module. This additional power source extends communication ranges and enables power demanding applications using low-power microcontrollers (MCUs) and higher-end sensors. This article also analyzes various key features, including tag integrated circuit (IC) architecture, types of energy harvesting modules, and communication range. The main conclusions are threefold. Firstly, selecting the appropriate tag IC requires a careful analysis of its features such as sensitivity, sensor interfaces, or data logging capabilities. For instance, among the solutions examined in the SoA, half of them opted for a tag IC capable of MCU communication via SPI or I2C buses. Secondly, it is essential to assess both the forward and backward communication links to leverage the sensitivity of the tag IC in BAP mode. Interestingly, only one-third of the SoA solutions achieved the theoretical communication range anticipated by the sensitivity of the tag IC. Finally, an energy budget analysis is required to ensure that the energy generation suffices to meet the energy requirements of the tag. While most solutions rely on batteries as the energy source and analyze battery lifespan, only a few studies employing energy harvesters conduct an energy budget analysis due to the additional complexity involved.2023-01-01T00:00:00ZChipless RFID tag implementation and machine-learning workflow for robust identificationhttps://hdl.handle.net/10171/687962024-02-12T06:06:58Z2023-12-01T00:00:00ZTitle: Chipless RFID tag implementation and machine-learning workflow for robust identification
Abstract: In this work, we describe a complete step-by-step workflow to apply machine-learning (ML) classification for chipless radio-frequency identification (RFID) tag identification, covering: 1) the tag implementation criteria for circular ring resonator (CRR) and square ring resonator (SRR) arrays for ML interoperability; 2) the data collection procedure to get a sufficiently representative dataset of real measurements; 3) the ML techniques to visualize the data and reduce its dimensionality; 4) the evaluation of the ML classifier to ensure high-accuracy predictions on new measurements; and 5) a thresholding scheme to increase the certainty of the predictions. The differences in the tags' frequency responses are maximized by optimizing the Hamming distance between the tag identifiers (IDs) and by controlling each resonator array's radar cross section (RCS) level. We show that the proposed workflow achieves perfect accuracy for the identification of four tags at a fixed distance of 160 cm. We also evaluate the performance of the proposed workflow to identify up to 16 tags within a flexible range (up to 140 cm), showcasing the tradeoff between the number of tags that can be correctly classified based on the reading range.2023-12-01T00:00:00ZDesign and manufacturing of shin pads with multi-material additive manufactured features for football playershttps://hdl.handle.net/10171/683582024-01-22T06:05:23Z2019-03-01T00:00:00ZTitle: Design and manufacturing of shin pads with multi-material additive manufactured features for football players2019-03-01T00:00:00Z