Comparative Analysis of Audio Processing Techniques on Doppler Radar Signature of Human Walking Motion Using CNN Models.

Artificial intelligence (AI) radar technology offers several advantages over other technologies, including low cost, privacy assurance, high accuracy, and environmental resilience. One challenge faced by AI radar technology is the high cost of equipment and the lack of radar datasets for deep-learning model training. Moreover, conventional radar signal processing methods have the obstacles of poor resolution or complex computation. Therefore, this paper discusses an innovative approach in the integration of radar technology and machine learning for effective surveillance systems that can surpass the aforementioned limitations. This approach is detailed into three steps: signal acquisition, signal processing, and feature-based classification. A hardware prototype of the signal acquisition circuitry was designed for a Continuous Wave (CW) K-24 GHz frequency band radar sensor. The collected radar motion data was categorized into non-human motion, human walking, and human walking without arm swing. Three signal processing techniques, namely short-time Fourier transform (STFT), mel spectrogram, and mel frequency cepstral coefficients (MFCCs), were employed. The latter two are typically used for audio processing, but in this study, they were proposed to obtain micro-Doppler spectrograms for all motion data. The obtained micro-Doppler spectrograms were then fed to a simplified 2D convolutional neural networks (CNNs) architecture for feature extraction and classification. Additionally, artificial neural networks (ANNs) and 1D CNN models were implemented for comparative analysis on various aspects. The experimental results demonstrated that the 2D CNN model trained on the MFCC feature outperformed the other two methods. The accuracy rate of the object classification models trained on micro-Doppler features was 97.93%, indicating the effectiveness of the proposed approach.

Bioimpedance-Measurement-Based Non-Invasive Method for In Ovo Chicken Egg Sexing.

Day-old male chick culling is one of the world's most inhumane problems in the poultry industry. Every year, seven billion male chicks are slaughtered in laying-hen hatcheries due to their higher feed exchange rate, lower management than female chicks, and higher production costs. This study describes a novel non-invasive method for determining the gender of chicken eggs. During the incubation period of fourteen days, four electrodes were attached to each egg for data collection. On the last day of incubation, a standard polymerase chain reaction (PCR)-based chicken gender determination protocol was applied to the eggs to obtain the gender information. A relationship was built between the collected data and the egg's gender, and it was discovered to have a reliable connection, indicating that the chicken egg gender can be determined by measuring the impedance data of the eggs on day 9 of incubation with the four electrodes set and using the self-normalization technique. This is a groundbreaking discovery, demonstrating that impedance spectroscopy can be used to sex chicken eggs before they hatch, relieving the poultry industry of such an ethical burden.

A New Biorecognition-Element-Free IDµE Sensor for the Identification and Quantification of E. coli.

The label-free biosensor has emerged as an effective tool for the purpose of early detection of causative pathogens such as Escherichia coli as a preventive measure. In this study, a biorecognition-element-free interdigitated microelectrode (IDµE) sensor is designed and developed with this in mind, with good reliability and affordability. Results show that the designed sensor can identify E. coli with good selectivity using an impedance and capacitance of 7.69 MHz. At its optimum impedance of 1.3 kHz, the IDµE sensor can reliably quantify E. coli in a range of measurement (103.2~106 cfu/mL), linearity (R2 = 0.97), sensitivity (18.15 kΩ/log (cfu/mL)), and limit of detection (103.2 cfu/mL). In summary, the IDµE sensor developed possesses high potential for industrial and clinical applications.

Noninvasive and real-time in vivo characterization of Inflammation skin. A feasibility of animal study.

BACKGROUND: Inflammatory skin diseases were the most common problem in dermatology. This study aimed to develop a circuit by using a simple method for noninvasive, objective, and real-time skin inflammation screening. MATERIALS AND METHODS: Sprague-Dawley rats were used in this study. The rats were chemically induced to suffer from skin inflammation at the back of their left-hand side while the right-hand side of their back remained untreated serving as a control. Impedance (Z) spectrum of the rat's skin was recorded. RESULTS: Two characteristic frequencies (4.5 and 48.3 kHz) were found. At the two frequencies, the impedance of inflammatory skin tissue (ZIST ) was found to be significantly (P < .05) smaller than that of normal healthy skin tissue (ZNHST ). Moreover, the ratio of the impedance measured at 4.5 kHz (Zf = 4 .5 kHz ) to the impedance measured at 48.3 kHz (Zf = 48.3 kHz ), that is, Zf = 4.5 kHz /Zf = 48.3 kHz , was capable of skin inflammation screening. It was observed that the inflammatory skin tissue (IST) had the smaller value of Zf = 4 .5 kHz /Zf = 48.3 kHz (value < 8.5) and normal healthy skin tissue (NHST) had the higher value of Zf = 4 .5 kHz /Zf = 48.3 kHz (value ≈ 10) which almost remained constant. CONCLUSION: A circuit was developed which was used for measuring the skin impedance accurately at the two characteristic frequencies for skin inflammation screening.

An Immunosensor for the Detection of ULBP2 Biomarker.

Pancreatic cancer (PC) is a global health problem that features a very high mortality rate. The UL16 binding protein 2 (ULBP2) is a new biomarker for PC detection. This study develops a simple, reliable, and inexpensive immunosensor for the detection of the ULBP2 antigen while also investigating the effects of an array configuration of connected sensors and zinc oxide (ZnO) nanoparticles on the immunosensor's sensitivity. The ULBP2 antibody was immobilized onto the screen-printed carbon electrode (SPCE) surfaces of three different sensors: a simple SPCE (ULBP2-SPCE); an SPCE array, which is a series of identical SPCE connected to each other at different arrangements of rows and columns (ULBP2-SPCE-1x2 and ULBP2-SPCE-1x3); and an SPCE combined with ZnO nanoparticles (ULBP2-ZnO/SPCE). Impedance spectrum measurements for the immunosensors to ULBP2 antigen were conducted and compared. According to the result, the array configurations (ULBP2-SPCE-1x2 and ULBP2-SPCE-1x3) show an improvement of sensitivity compared to the ULBP2-SPCE alone, but the improvement is not as significant as that of the ULBP2-ZnO/SPCE configuration (ULBP2-ZnO/SPCE > ULBP2-SPCE: 18 times larger). The ULBP2-ZnO/SPCE immunosensor has a low limit of detection (1 pg/mL) and a high sensitivity (332.2 Ω/Log(pg/mL)), excellent linearity (R2 = 0.98), good repeatability (coefficients of variation = 5.03%), and is stable in long-term storage (retaining 95% activity after 28 days storage). In an array configuration, the immunosensor has an increased signal-to-noise ratio (ULBP2-SPCE-1x3 > ULBP2-SPCE: 1.5-fold) and sensitivity (ULBP2-SPCE-1x3 > ULBP2-SPCE: 2.6-fold). In conclusion, either the modification with ZnO nanoparticles onto the sensor or the use of an array configuration of sensors can enhance the immunosensor's sensitivity. In this study, the best immunosensor for detecting ULBP2 antigens is the ULBP2-ZnO/SPCE immunosensor.

A Reusable Mask for Coronavirus Disease 2019 (COVID-19).

The outbreak of Novel Coronavirus is causing an intensely feared globally. World Health Organization has even declared that it is a global health emergency. The simplest method to limit the spread of this new virus and for people to protect themselves as well as the others is to wear a mask in crowded places. The sudden increase demand on face mask has caused manufacturers the inability to not provide enough products in a short time and the situation properly will stay the same for a period of time. In this article, we aim to give an idea on how to save the number of face masks used but still provides the same protective values using a Cardiopulmonary resuscitation (CPR) mask and a common surgical facemask.