Low Temperature-InAs Compounds as Terahertz Emitters and Detectors

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Temperature-Dependent Resistive Properties of Vanadium Pentoxide/Vanadium Multi-Layer Thin Films for Microbolometer & Antenna-Coupled Microbolometer Applications

In this study, vanadium oxide (VxOy) semiconducting resistive thermometer thin films were developed, and their temperature-dependent resistive behavior was examined. Multilayers of 5-nm-thick vanadium pentoxide (V2O5) and 5-nm-thick vanadium (V) films were alternately sputter-deposited, at room temperature, to form 105-nm-thick VxOy films, which were post-deposition annealed at 300 °C in O2 and N2 atmospheres for 30 and 40 min. The synthesized VxOy thin films were then patterned into resistive thermometer structures, and their resistance versus temperature (R-T) characteristics were measured. Samples annealed in O2 achieved temperature coefficients of resistance (TCRs) of −3.0036 and −2.4964%/K at resistivity values of 0.01477 and 0.00819 Ω·cm, respectively. Samples annealed in N2 achieved TCRs of −3.18 and −1.1181%/K at resistivity values of 0.04718 and 0.002527 Ω·cm, respectively. The developed thermometer thin films had TCR/resistivity properties suitable for microbolometer and antenna-coupled microbolometer applications. The employed multilayer synthesis technique was shown to be effective in tuning the TCR/resistivity properties of the thin films by varying the annealing conditions

Optical and Temperature-Dependent Electrical Properties of Ge_1–xPb_xO_y Thin Films for Microbolometer Applications

In this study, an experimental investigation was conducted to explore Ge1–xPbxOy as a candidate material for temperature-sensing layers in uncooled microbolometers. RF and DC sputtering techniques were used to deposit Ge1–xPbxOy thin films with various oxygen concentrations on silicon substrates at room temperature. The composition of the samples was experimentally analyzed using energy dispersive X-ray spectroscopy (EDX) that showed various oxygen concentrations. Atomic force microscopy (AFM) analysis showed excellent average surface roughness ranging from 0.6995 to 0.8660 nm. Increasing the concentration of oxygen up to 31% improved the thermoelectric and optical characteristics of the prepared Ge1–xPbxOy thin films. The highest temperature coefficient of resistance (TCR) of the fabricated samples was −3.85%/K for the Ge0.94Pb0.06O0.31 thin film. By using Essential McLeod software, optical simulation of the thin film samples was performed to assess the highest absorptance of the cavity microbolometer structure, which was 81.88% for the Ge0.94Pb0.06O0.31 thin film

Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis

Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient’s life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors

A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images

International audienceThe Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently

Hydrothermal growth optimization of vertically aligned ZnO nanowire arrays and their dye-sensitized solar cell performance under air/oxygen environments

In this work, the hydrothermal growth parameters of vertically aligned ZnO nanowire arrays on FTO substrates were optimized for best surface coverage and minimal stacking and entanglement between nanowires for dye-sensitized solar cell (DSSC) application. The concentration of seed layer solution was found to be essential in reducing the stacking effect of adjacent nanowires. The growth temperature study has revealed a remarkable existence of a temperature window in the range 90 °C–110 °C, beyond which the nanowire lengths start to decline. We attributed the nanowire length regression above 110 °C to the enhanced homogeneous nucleations in the growth solution. Nanowire entanglement started to be observable at ∼100 °C for a growth duration of 2 h. DSSCs were fabricated using the grown ZnO nanowire arrays that are post-annealed under air and pure oxygen gas environments. Oxygen post-annealing resulted in a remarkable decrease in the open circuit voltage (V _OC ), which we attributed to the large impact of band bending

Isolated Convolutional-Neural-Network-Based Deep-Feature Extraction for Brain Tumor Classification Using Shallow Classifier

In today’s world, a brain tumor is one of the most serious diseases. If it is detected at an advanced stage, it might lead to a very limited survival rate. Therefore, brain tumor classification is crucial for appropriate therapeutic planning to improve patient life quality. This research investigates a deep-feature-trained brain tumor detection and differentiation model using classical/linear machine learning classifiers (MLCs). In this study, transfer learning is used to obtain deep brain magnetic resonance imaging (MRI) scan features from a constructed convolutional neural network (CNN). First, multiple layers (19, 22, and 25) of isolated CNNs are constructed and trained to evaluate the performance. The developed CNN models are then utilized for training the multiple MLCs by extracting deep features via transfer learning. The available brain MRI datasets are employed to validate the proposed approach. The deep features of pre-trained models are also extracted to evaluate and compare their performance with the proposed approach. The proposed CNN deep-feature-trained support vector machine model yielded higher accuracy than other commonly used pre-trained deep-feature MLC training models. The presented approach detects and distinguishes brain tumors with 98% accuracy. It also has a good classification rate (97.2%) for an unknown dataset not used to train the model. Following extensive testing and analysis, the suggested technique might be helpful in assisting doctors in diagnosing brain tumors

Diagnostic Validity and Reliability of Low-Dose Prospective ECG-Triggering Cardiac CT in Preoperative Assessment of Complex Congenital Heart Diseases (CHDs)

For the precise preoperative evaluation of complex congenital heart diseases (CHDs) with reduced radiation dose exposure, we assessed the diagnostic validity and reliability of low-dose prospective ECG-gated cardiac CT (CCT). Forty-two individuals with complex CHDs who underwent preoperative CCT as part of a prospective study were included. Each CCT image was examined independently by two radiologists. The primary reference for assessing the diagnostic validity of the CCT was the post-operative data. Infants and neonates were the most common age group suffering from complex CHDs. The mean volume of the CT dose index was 1.44 ± 0.47 mGy, the mean value of the dose-length product was 14.13 ± 5.4 mGy*cm, and the mean value of the effective radiation dose was 0.58 ± 0.13 mSv. The sensitivity, specificity, PPV, NPV, and accuracy of the low-dose prospective ECG-gated CCT for identifying complex CHDs were 95.6%, 98%, 97%, 97%, and 97% for reader 1 and 92.6%, 97%, 95.5%, 95.1%, and 95.2% for reader 2, respectively. The overall inter-reader agreement for interpreting the cardiac CCTs was good (κ = 0.74). According to the results of our investigation, low-dose prospective ECG-gated CCT is a useful and trustworthy method for assessing coronary arteries and making a precise preoperative diagnosis of complex CHDs

Rapid Room-Temperature Synthesis of Mesoporous TiO2 Sub-Microspheres and Their Enhanced Light Harvesting in Dye-Sensitized Solar Cells

Submicron sized mesoporous spheres of TiO2 have been a potential alternative to overcome the light scattering limitations of TiO2 nanoparticles in dye-sensitized solar cells (DSSCs). Currently available methods for the growth of mesoporous TiO2 sub-microspheres involve long and relatively high temperature multi-stage protocols. In this work, TiO2 mesoporous sub-microspheres composed of ~5 nm anatase nanocrystallites were successfully synthesized using a rapid one-pot room-temperature CTAB-based solvothermal synthesis. X-Ray Diffraction (XRD) showed that the grown structures have pure anatase phase. Transmission electron microscopy (TEM) revealed that by reducing the surfactant/precursor concentration ratio, the morphology could be tuned from monodispersed nanoparticles into sub-micron sized mesoporous beads with controllable sizes (50–200 nm) and with good monodispersity as well. The growth mechanism is explained in terms of the competition between homogeneous nucleation/growth events versus surface energy induced agglomeration in a non-micelle CTAB-based soft templating environment. Further, dye-sensitized solar cells (DSSCs) were fabricated using the synthesized samples and characterized for their current-voltage characteristics. Interestingly, the DSSC prepared with 200 nm TiO2 sub-microspheres, with reduced surface area, has shown close efficiency (5.65%) to that of DSSC based on monodispersed 20 nm nanoparticles (5.79%). The results show that light scattering caused by the agglomerated sub-micron spheres could compensate for the larger surface areas provided by monodispersed nanoparticles