This paper presents the use of radio-frequency identification (RFID) technology for the real-time remote monitoring of body temperature, while an associated program can determine the location of the body carrying the respective sensor. The RFID chip’s internal integrated temperature sensor is used for both the human-body temperature detection and as a measurement device, while using radio-frequency communication to broadcast the temperature information. The adopted RFID location technology makes use of reference tags together with a nearest neighbor localization algorithm and a multiple-antenna time-division multiplexing location system. A graphical user interface (GUI) was developed for collecting temperature and location data for the data fusion by using RFID protocols. With a puppy as test object, temperature detection and localization experiments were carried out. The measured results show that the applied method, when using a mercury thermometer for comparison in terms of measuring the temperature of the dog, has a good consistency, with an average temperature error of 0.283 °C. When using the associated program over the area of 12.25 m², the average location error is of 0.461 m, which verifies the feasibility of the sensor-carrier location by using the proposed program.

In the field of near-infrared weak light detection, an InP/InGaAs single-photon avalanche diode (SPAD) is preferred due to the advantages of high sensitivity, low cost and room-temperature operation. To properly simulate and optimize the SPAD’s front-end circuit, a comprehensive and compact behavior model of the InP/InGaAs SPAD is normally required to accurately describe the statistical behavior of the detectors. In this paper, an InP/InGaAs SPAD analytical model is constructed, which not only includes the direct current (DC) and alternating current (AC) behavior simulating the avalanche and quenching processes, but also describes the dark count, after-pulsing and photon detection efficiency. For dark count noise, three important generation mechanisms are considered, including thermal generation, trap-assisted tunneling and band-to-band tunneling. The model described by the Verilog-A hardware description language (HDL) can be directly implemented in the commercial circuit simulator. A gated mode, passive quenching and recharging circuit is used to simulate and verify the developed model. The simulation results are in good agreement with the reported test data, demonstrating the accuracy of the developed InP/InGaAs SPAD model.

As a crucial part in sewage, high-salt wastewater contains abundant corrosive components and heavy metal elements, which makes it difficult for invasive monitoring. Therefore, based on coupling Radio Frequency Identification(RFID) tags, this paper proposes a non-invasive monitoring method for concentration of inorganic salt in sewage. In this method, Cole-Cole model and Slit Cylindrical Capacitance(SCC) model are combined to explore the relationship between capacitance of RFID and concentration of inorganic salt in sewage pipeline. A New Efficient Levenberg-Marquardt(NELM) algorithm is used for regression between relative permittivity and concentration of inorganic salt categorically. Then, measurement data is processed under modified relative permittivity mixing rule of electrolyte and solvent. Finally the concentration of each component in the multi-component inorganic salt wastewater are obtained. Experimental results show that the maximal relative error (MRE) and average relative error(ARE) of single inorganic salt solution are 1.1% and 0.5%, while mixed solution are 2.6% and 1.89%. Such results indicate significant improvement comparing with previous works. Great potential of RFID in working sewage monitoring is sufficiently confirmed.

In this brief, an efficient training method for memristor-based array (crossbar) with one transistor and one memristor (1T1M) synapse is proposed, which enables parallel update of memristor-based arrays trained by stochastic gradient descent within two steps. Voltage ThrEshold Adaptive Memristor (VTEAM) model is utilized to describe memristor characteristics for simulations. On this basis, circuit parameters optimization method compensating the asymmetric and nonlinear weight update is provided for better training results. The effectiveness of proposed training method is evaluated on OR, AND functions and digit recognition task. Simulation results demonstrate the robustness of proposed training method to electrical noise and imperfections of memristors.

A near-infrared (NIR)-enhanced single-photon avalanche diode (SPAD) with a retrograded NM/XP junction for an automotive LiDAR was designed based on CSMC 0.18 µm BCD technology. A 3 µm depth NM/XP junction embedded in a lightly doped deep p-well (DP) improves the absorption efficiency in the NIR regime; the photo-generated electrons generated in the depletion region are efficiently collected into the central multiplication region by a drift process, and then the impact ionization is triggered by the strong field, resulting in a high photon detection efficiency (PDE). Additionally, the deep NM/XP junction and the buried layer effectively isolate the dark noise originating from the interface and the substrate. The SPAD was initially simulated by numerical calculation, and then was evaluated with active quench/reset electronics in a circuit simulator. The results revealed that the SPAD with an active area of 314µm² achieves a PDE of 16.2% at 905 nm and a dark count rate (DCR) of 1.46Hz/µm², with an excess bias of 5 V at room temperature. The designed SPAD is well suited for the low-cost, miniaturized automotive LiDAR.