A CMOS Integrator-Based Clock-Free Time-to-Digital Converter for Home-Monitoring LiDAR Sensors

This paper presents a nine-bit integrator-based time-to-digital converter (I-TDC) realized in a 180 nm CMOS technology for the applications of indoor home-monitoring light detection and ranging (LiDAR) sensors. The proposed I-TDC exploits a clock-free configuration so as to discard clock-related dynamic power consumption and some notorious issues such as skew, glitch, and synchronization. It consists of a one-dimensional (1D) flash TDC to generate coarse-control codes and an integrator with a peak detection and hold (PDH) circuit to produce fine-control codes.

A thermometer-to-binary converter is added to the 1D flash TDC, yielding four-bit coarse codes so that the measured detection range can be represented by nine-bit digital codes in total. Test chips of the proposed I-TDC demonstrate the measured results of the 53 dB dynamic range, i.e., the maximum detection range of 33.6 m and the minimum range of 7.5 cm. The chip core occupies the area of 0.14 × 1.4 mm², with the power dissipation of 1.6 mW from a single 1.2-V supply.

When we touch an object, thermosensation allows us to perceive not only the temperature but also wetness and types of materials with different thermophysical properties (i.e., thermal conductivity and heat capacity) of objects. Emulation of such sensory abilities is Gentaur WiFi Ultra Low Datalogging Monitoring Thermometer
important in robots, wearables, and haptic interfaces, but it is challenging because they are not directly perceptible sensations but rather learned abilities via sensory experiences. Emulating the thermosensation of human skin, we introduce an artificial thermosensation based on an intelligent thermo-/calorimeter (TCM) that can objectively differentiate types of contact materials and solvents with different thermophysical properties.

We demonstrate a TCM based on pyroresistive composites with ultrahigh sensitivity (11.2% °C^-1) and high accuracy (<0.1 °C) by precisely controlling the melt-induced volume expansion of a semicrystalline polymer, as well as the negative temperature coefficient of reduced graphene oxide. In addition, the ultrathin TCM with coplanar electrode design shows deformation-insensitive temperature sensing, facilitating wearable skin temperature monitoring with accuracy higher than a commercial thermometer.

Moreover, the TCM with a high pyroresistivity can objectively differentiate types of contact materials and solvents with different thermophysical properties. In a proof-of-principle application, our intelligent TCM, coupled with a machine-learning algorithm, enables objective evaluation of the thermal attributes (coolness and wetness) of skincare products.

Patient-reported outcome measures (PROM) for monitoring treatment toxicity improve quality of life (QoL) and clinical outcomes. However, no such PROMs exist for sub-Saharan African cancer patients. We aimed to validate the Patient Reported Symptoms-South Africa (PRS-SA) survey, a novel PROM for measuring distress and chemotherapy-related symptoms in South African cancer patients. We enrolled patients at the oncology clinic at Charlotte Maxeke Hospital, Johannesburg. At three separate visits, participants simultaneously completed the PRS-SA survey and several previously validated questionnaires.

We constructed a receiver operator characteristics curve for distress levels predicting a Hospital Anxiety and Depression Scale (HADS) score ≥15. We evaluated construct validity for symptom items by comparing severity to the EORTC Core Quality of Life Questionnaire (QLQ-C30) summary score (Pearson correlation tests) and ECOG performance status (Mann-Whitney U tests).

We assessed symptom item responsiveness by comparing change in severity to change in QLQ-C30 summary score and comparing standardized mean scores with negative, no, or positive change on the Global Impression of Change (GIC) questionnaire (Jockheere-Terpstra trend test). Overall, 196 participants with solid tumors completed instruments.

A distress score of 4 had 82% sensitivity and 55% specificity for clinical depression/anxiety. All symptom items showed construct validity by association with either QLQ-C30 score or performance status (highest p = 0.03). All but cough showed responsiveness to change in QLQ-C30 score (highest p = 0.045). In South African cancer patients, the PRS-SA’s stress scale behaves similarly to the distress thermometer in other populations, and the symptom items demonstrated construct validity and responsiveness. Of note, 46% and 74% of participants who completed the PRS-SA in English or isiZulu, respectively, required assistance reading half or more of the instrument.

The most effective methods of preventing COVID-19 infection include maintaining physical distancing and wearing a face mask while in close contact with people in public places. However, densely populated areas have a greater incidence of COVID-19 dissemination, which is caused by people who do not comply with standard operating procedures (SOPs). This paper presents a prototype called PADDIE-C19 (Physical Distancing Device with Edge Computing for COVID-19) to implement the physical distancing monitoring based on a low-cost edge computing device. The PADDIE-C19 provides real-time results and responses, as well as notifications and warnings to anyone who violates the 1-m physical distance rule.

In addition, PADDIE-C19 includes temperature screening using an MLX90614 thermometer and ultrasonic sensors to restrict the number of people on specified premises. The Neural Network Processor (KPU) in Grove Artificial Intelligence Hardware Attached on Top (AI HAT), an edge computing unit, is used to accelerate the neural network model on person detection and achieve up to 18 frames per second (FPS).

The results show that the accuracy of person detection with Grove AI HAT could achieve 74.65% and the average absolute error between measured and actual physical distance is 8.95 cm. Furthermore, the accuracy of the MLX90614 thermometer is guaranteed to have less than 0.5 °C value difference from the more common Fluke 59 thermometer. Experimental results also proved that when cloud computing is compared to edge computing, the Grove AI HAT achieves the average performance of 18 FPS for a person detector (model) with an average 56 ms execution time in different networks, regardless of the network connection type or speed.