This paper presents a multi-sensing pixel capable of sensing ion concentration, temperature, moisture and light from the same spatial point. The presented pixel is composed of an ISFET chemical sensor, a light sensor, a moisture sensor and a temperature sensor, all implemented in standard CMOS technology in a compact pixel. All sensing modalities are acquired simultaneously and modulated onto a single waveform using multi-frequency PWM modulation, overcoming the associated transmission overhead. Furthermore, ISFET non-idealities such as trapped charge are compensated through a global DAC, balancing the input linear OTA, while the light sensor dark current is mitigated in-pixel by subtracting its influence using a reference photodiode. The proposed pixel occupies 50x50 µm 2 , and it is integrated as part of a 4x4 multi-sensing array, along with on-chip biasing, instrumentation and digital control. To the best of our knowledge, this architecture represents the first multisensing architecture capable of extracting four features from the same spatial point simultaneously, enabling ISFET-based platforms to sense both the chemical signal and the external conditions influencing its measurement.
Ultra-wideband (UWB) wireless indoor positioning systems rely on time of flight (TOF) to estimate distances but can be biased and miscalculated due to non-line-of-sight (NLOS) transmission channels in complex environments. Therefore, to remove errors, several machine learning techniques have been proposed for identifying NLOS signals from Channel Impulse Responses (CIRs). However, as CIR signals could be heavily influenced by various environments, current NLOS classifiers are not universal to provide satisfactory accuracy for new scenarios and require detailed measurements on a large number of CIRs for training. Hence, we propose a generalization method based on data augmentation via noise injection and transfer learning to allow the deep neural network (DNN) trained under a lab condition to be applied to various and even harsh practical scenarios with the need to measure massive training data minimized. This paper presents the first demonstration that it is effective to utilize a lab-based pre-trained DNN for real-world transfer and white Gaussian noise data augmentation for ML-based NLOS identification on UWB CIRs to address the problem when it is not feasible to measure sufficient training data. Our testing results show that in two scenarios, corridor and parking lot, with only 50 CIR signals as the training set, the accuracy of the NLOS identification model after applying the proposed method is increased from 84.4% to 98.8% and from 81.1% to 97.1%, respectively.Impact Statement-In this paper, we propose a robust and data-efficient DNN-based method for identifying non-line-of-sight (NLOS) signals within ultra-wideband (UWB) indoor positioning signals to overcome distance estimation errors. For applications in a new environment or generalization across multiple environments, the need for sufficient data to train the DNN model can be largely lowered and higher accuracy can be offered. Furthermore, with our approach, the realization of accurate NLOS identification becomes possible in some harsh scenarios where collecting a large amount of data is costly, timeconsuming, or even impossible. In addition, we have investigated the possibility of applying noise injection to augment channel impulse response signals (CIRs) and to deal with environmental This work has been partly sponsored by the IoT Superproject, a strategic initiative of the
Background Patients with severe bone fractures and complex bone deformities are treated by orthopedic surgeons with external fixation for several months. During this long treatment period, there is a high risk of inflammation and infection at the superficial skin area (pin site). This can develop into a devastating, sometimes fatal, and always costly condition of deep bone infection. Objective For pin site infection surveillance, thermography technology could be the solution to build an objective and continuous home-based remote monitoring tool to avoid frequent nursing care and hospital visits. However, future studies of infection monitoring require a preliminary step to automate the process of locating and detecting the pin sites in thermal images reliably for temperature measurement, and this step is the aim of this study. Methods This study presents an automatic approach for identifying and annotating pin sites on visible images using bounding boxes and transferring them to the corresponding thermal images for temperature measurement. The pin site is detected by applying deep learning-based object detection architecture YOLOv5 with a novel loss evaluation and regression method, control distance intersection over union. Furthermore, we address detecting pin sites in a practical environment (home setting) accurately through transfer learning. Results and conclusion The proposed model offers the pin site detection in 1.8 ms with a high precision of 0.98 and enables temperature information extraction. Our work for automatic pin site annotation on thermography paves the way for future research on infection assessment on thermography.
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