Array of Biomimetic Hair Sensor Dedicated for Flow Pattern Recognition

Flow sensor arrays can be used to extract features from flow fields rather than averaging or providing local measurements provided the sensors in the array structure can be interrogated individually. This paper addresses the latest developments in fabrication and array interfacing of biomimetic artificial air-flow sensors. Hair flow sensors in wafer level arrays have been successfully fabricated using SOI wafers with deep trench isolation structures. Using a Frequency Division Multi¬plexing (FDM) technique, we were able to simultaneously measure flow signals at multiple sensor positions. By virtue of FDM, once signals are retrieved from all individual array elements, spatio-temporal flow patterns can be reconstructed, while few system interconnects are required

A two-stage power amplifier design for ultra-wideband applications

In this paper, a two-stage 0.18 μm CMOS power amplifier (PA) for ultra-wideband (UWB) 3 to 5 GHz based on common source inductive degeneration with an auxiliary amplifier is proposed. In this proposal, an auxiliary amplifier is used to place the 2nd harmonic in the core amplified in order to make up for the gain progression phenomena at the main amplifier output node. Simulation results show a power gain of 16 dB with a gain flatness of 0.4 dB and an input 1 dB compression of about -5 dBm from 3 to 5 GHz using a 1.8 V power supply consuming 25 mW. Power added efficiency (PAE) of around 47% at 4 GHz with 50 Ω load impedance was also observed

Cardiac arrhythmias classification using photoplethysmography database

Abstract Worldwide, Cardiovascular Diseases (CVDs) are the leading cause of death. Patients at high cardiovascular risk require long-term follow-up for early CVDs detection. Generally, cardiac arrhythmia detection through the electrocardiogram (ECG) signal has been the basis of many studies. This technique does not provide sufficient information in addition to a high false alarm potential. In addition, the electrodes used to record the ECG signal are not suitable for long-term monitoring. Recently, the photoplethysmogram (PPG) signal has attracted great interest among scientists as it provides a non-invasive, inexpensive, and convenient source of information related to cardiac activity. In this paper, the PPG signal (online database Physio Net Challenge 2015) is used to classify different cardiac arrhythmias, namely, tachycardia, bradycardia, ventricular tachycardia, and ventricular flutter/fibrillation. The PPG signals are pre-processed and analyzed utilizing various signal-processing techniques to eliminate noise and artifacts, which forms a stage of signal preparation prior to the feature extraction process. A set of 41 PPG features is used for cardiac arrhythmias' classification through the application of four machine-learning techniques, namely, Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNNs), and Ensembles. Principal Component Analysis (PCA) technique is used for dimensionality reduction and feature extraction while preserving the most important information in the data. The results show a high-throughput evaluation with an accuracy of 98.4% for the KNN technique with a sensitivity of 98.3%, 95%, 96.8%, and 99.7% for bradycardia, tachycardia, ventricular flutter/fibrillation, and ventricular tachycardia, respectively. The outcomes of this work provide a tool to correlate the properties of the PPG signal with cardiac arrhythmias and thus the early diagnosis and treatment of CVDs

Spatio-temporal flow pattern observations using bio-inspired hair flow sensors

In nature, sensing is a fundamental property of virtually all living creatures. For many insects airflow patterns, as observed by means of their hair-sensors, carry highly valuable information exposing the sources of these flows. Flow-sensor arrays can be used to extract spatio-temporal flow fields rather than average or local flow quantities. Here we investigate the possibility to measure spatio-temporal airflow fields generated by a pulsed-like airflow by means of our artificial hair-sensors and hair-sensor arrays. The measurements show the hair-sensors’ ability to follow the changes in the flow field with sufficient temporal and spatial resolution. Such a system can be used for reconstructing hydrodynamic images of moving bodies to be implemented in guiding robots purposes even in total darkness

AUTOMATIC DETECTION OF PNEUMONIA USING CONCATENATED CONVOLUTIONAL NEURAL NETWORK

Pneumonia is a life-threatening disease and early detection can save lives, many automated systems have contributed to the detection of this disease and currently deep learning models have become one of the most widely used models for building these systems. In this study, two deep learning models are combined: DenseNet169 and pre-activation ResNet models, and used for automatic detection of pneumonia. DenseNet169 model is an extension of the ResNet model, while the second is a modified version the ResNet model, these models achieved good results in the field of medical imaging. Two methods are used to deal with the problem of unbalanced data: class weight, which enables to control the percentage of data to be used from the original data for each class of data, while the other method is resampling, in which modified images are produced with an equal distribution using data augmentation. The performance of the proposed model is evaluated using a balanced dataset consists of 5856 images. Achieved results were promising compared to several previous studies. The model achieved a precision value of 98%, an area under curve (AUC) based on ROC of 97%, and a loss value of 0.23. [JJCIT 2023; 9(2.000): 118-136

Modeling of a square-shape ZnO, ZnS and AlN membrane for mems capacitive pressure-sensor applications

In this paper, mathematical modeling and simulation of a MEMS-based clamped square-shape membrane for capacitive pressure sensors have been performed. Three types of membrane materials were investigated (i.e. Zinc Oxide (ZnO), Zinc Sulfide (ZnS) and Aluminum Nitride (AlN)). Various performance parameters such as capacitance changes, deflection, nonlinearity, the sensitivity of the membrane structure for different materials and film-thicknesses have been considered using the Finite Element Method (FEM) and analytically determined using the FORTRAN environment. The simulation model outperforms in terms of the effective capacitance value. The results show that the membrane deflection is linearly related to the applied pressure. The ZnS membrane provides a capacitance of 0.023 pico-Farad at 25 kPa with a 42.5% relative capacitance changes to reference capacitance. Additionally, the results show that for ZnO and AlN membranes the deflection with no thermal stress is higher than that with thermal stress. However, an opposite behavior for the ZnS membrane structure has been observed. The mechanical and capacitance sensitivities are affected by the membrane thickness as the capacitance changes are inversely proportional to the membrane thickness. Such results open possibilities to utilize various materials for pressure sensor applications by means of the capacitance-based detection technique

Improved Small-Signal Characteristics of Infrared 850 nm Top-Emitting Vertical-Cavity Lasers

High-speed infrared vertical-cavity surface-emitting laser diodes (VCSELs) with Cu-plated heat sinks were fabricated and tested. VCSELs with 10 mm aperture diameter and 4 mm of electroplated copper demonstrated a -3dB modulation bandwidth (f-3dB) of 14 GHz and a resonance frequency (fR) of 9.5 GHz at a bias current density (Jbias) of only 4.3 kA/cm2, which corresponds to an improved f-3dB2/Jbias ratio of 44 GHz2/kA/cm2. At higher and lower bias current densities, the f-3dB2/ Jbias ratio decreased to about 30 GHz2/kA/cm2 and 18 GHz2/kA/cm2, respectively. Examination of the analogue modulation response demonstrated that the presented VCSELs displayed a steady f-3dB/ fR ratio of 1.41±10% over the whole range of the bias current (1.3Ith to 6.2Ith). The devices also demonstrated a maximum modulation bandwidth (f-3dB max) of more than 16 GHz at a bias current less than the industrial bias current standard for reliability by 25%

HAIR-BASED FLOW-SENSING INSPIRED BY THE CRICKET CERCAL SYSTEM

International audienceMicro electro mechanical system (MEMS) offers exciting possibilities for the fabrication of bioinspired mechanosensors. Over the last years we have been working on cricket inspired hair-sensor arrays for spatio-temporal flow-field observations (i.e., flow-cameras) and source localization. Whereas making flow-sensors as energy efficient as cricket hair-sensors appears to be a real challenge, we have managed to fabricate hair-sensors with sub-millimeter per second flow sensing thresholds, use them in lateral line experiments, address them individually while in arrays, track transient flows, quantify viscous coupling effects and use parametric effects to achieve sharp filtering and amplification. In this research insect biologists and engineers have been working in close collaboration, generating a bidirectional flow of information and knowledge, beneficial to both. For example where the engineering has greatly benefitted from the insights derived from biology and biophysical models, the biologists have taken advantage of MEMS structures allowing for experiments that are hard to do on living materialRead More: http://www.worldscientific.com/doi/abs/10.1142/9789814354936_003