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Red blood cells (RBCs) become sickle‐shaped and stiff under hypoxia as a consequence of hemoglobin (Hb) polymerization in sickle cell anemia. Distinguishing between sickle cell disease and trait is crucial during the diagnosis of sickle cell disease. While genetic analysis or high‐performance liquid chromatography (HPLC) can accurately differentiate between these two genotypes, these tests are unsuitable for field use. Here, we report a novel microscopy‐based diagnostic test called ShapeDx™ to distinguish between disease and trait blood in less than 1 h. This is achieved by mixing an unknown blood sample with low and high concentrations of a chemical oxygen scavenger and thereby subjecting the blood to slow and fast hypoxia, respectively. The different rates of Hb polymerization resulting from slow and fast hypoxia lead to two distinct RBC shape distributions in the same blood sample, which allows us to identify it as healthy, trait, or disease. The controlled hypoxic environment necessary for differential Hb polymerization is generated using an imaging microchamber, which also reduces the sickling time of trait blood from several hours to just 30 min. In a single‐blinded proof‐of‐concept study conducted on a small cohort of clinical samples, the results of the ShapeDx™ test were 100% concordant with HPLC results. Additionally, our field studies have demonstrated that ShapeDx™ is the first reported microscopy test capable of distinguishing between sickle cell disease and trait samples in resource‐limited settings with the same accuracy as a gold standard test. [ABSTRACT FROM AUTHOR]

Sound Source Localization (SSL) systems focus on finding the direction of a sound source. Sound source localization is an essential feature in robots and humanoids. Research is being done for two decades to optimize SSL techniques and enhance their accuracy. Presented in this review we have categorized various proposed SSL techniques into four main types. Out of which one type is SSL that is based on conventional algorithms like Generalized Cross Correlation (GCC), Multiple Signal Classification (MUSIC), Time Difference of Arrival (TDOA), etc. using multiple microphones array configurations. The second type involves techniques based on binaural signal processing using conventional algorithms (GCC, MUSIC, TDOA). SSL techniques that fall under the third and fourth types of categories are developed recently in the last decade with the rise of Convolutional Neural Network (CNN) algorithms. The third type of SSL technique makes use of multiple microphone array configurations using CNN and the fourth type involves the most recently evolved technique based on binaural signal processing using CNN. The different SSL techniques based on multiaural and binaural signals using the conventional algorithm as well as CNN are presented in this review. The review paper provides an overview of SSL systems in terms of the number of microphones used, layouts of microphonic arrays, algorithms to perform SSL and localization in 3D space (azimuth, elevation and distance). From the review we found that out of all SSL techniques, CNN is the emerging and optimized one. By using CNN in SSL systems, the least error rate of 0.1 % was achieved. [ABSTRACT FROM AUTHOR]

Electromyography (EMG) is a technique for recording biomedical electrical signals obtained from the neuromuscular activities. These signals are used to monitor medical abnormalities and activation levels, and also to analyze the biomechanics of any animal movements. In this article, we provide a short review of EMG signal acquisition and processing techniques. The average efficiency of capture of EMG signals with current technologies is around 70%. Once the signal is captured, signal processing algorithms then determine the recognition accuracy, with which signals are decoded for their corresponding purpose (e.g., moving robotic arm, speech recognition, gait analysis). The recognition accuracy can go as high as 99.8%. The accuracy with which the EMG signal is decoded has already crossed 99%, and with improvements in deep learning technology, there is a large scope for improvement in the design hardware that can efficiently capture EMG signals. [ABSTRACT FROM AUTHOR]

We present a Nuclear Magnetic Resonance (NMR) compatible platform for the automated real-time monitoring of biochemical reactions using a flow shuttling configuration. This platform requires a working sample volume of ∼11 mL and it can circulate samples with a flow rate of 28 mL/min, which makes it suitable to be used for real-time monitoring of biochemical reactions. Another advantage of the proposed low-cost platform is the high spectral resolution. As a proof of concept, we acquire 1 H NMR spectra of waste orange peel, bioprocessed using Trichoderma reesei fungus, and demonstrate the real-time measurement capability of the platform. The measurement is performed over more than 60 h, with a spectrum acquired every 7 min, such that over 510 data points are collected without user intervention. The designed system offers high resolution, automation, low user intervention, and, therefore, time-efficient measurement per sample. [ABSTRACT FROM AUTHOR]

Technological advancements in wearable devices have revolutionized smart shoes. Smart shoes are sometimes referred to as intelligent shoes or computer-based shoes. They are capable of recognizing and recording data from day-to-day activities by the user. Such smart shoes are designed with sensors, vibrating motors, GPS, wireless systems, and various other sensors/actuators for the comfort and benefit of the wearer. In the current manuscript, we are reviewing various technologies that are implemented in smart shoes. [ABSTRACT FROM AUTHOR]

Airborne diseases cause detriment in the human life. Many were found in history such as TB, SARS, MERS and recently COVID 19. These diseases hit the dead rate and crushes health and wealth of the world population. Mostly, airborne diseases will spread rapidly in crowdy places. Especially, in case of COVID-19, Wearing mask and monitoring body temperature by individual is good solution to prevent rapid spread of the disease. So, keeping safety measures in monitoring face mask and body temperature is crucial in crowdy places such as Airports, railway stations, Bus Stations, malls, temples, etc. is obligatory. With a focus on emphasizing the health of the people we proposed integrated system that monitors body temperature and face mask of each individual and open/close pathway gate to allow after knee verification. Proposed Prototype uses Raspberry pi to monitor Face mask using CNN and Arduino to monitor body temperature and enable motor drivers to open or close the pathway Gate. Efficiency and loss of Proposed CNN model was trained and tested with multiple epoches. [ABSTRACT FROM AUTHOR]

Aim:This study article's major goal is to classify facial expressions more accurately by utilizing Convolutional Neural Networks (CNNs) in contrast to AlexNet. Materials and Methods: The data of the facial expressions is taken from the FER2013 available on kaggle. The Convolutional neural network (CNN) is the algorithm most common method of visual analysis with best accuracy and Alexney is also an algorithm used here to compare the accuracy of novel image classification. Results: The Convolution neural network (CNN) produces 82% accuracy in predicting facial expressions on the dataset, whereas AlexNet produces 76% accuracy. Convolutional neural network (CNN) is better than AlexNet. The study groups differ from one another in a statistically significant way (p <0.05). Conclusion: Convolutional Neural Network provides better outcomes in accuracy rate when compared to AlexNet for predicting facial expressions. [ABSTRACT FROM AUTHOR]

Aim: The aim of this research article is classification of facial expression recognition Convolutional Neural Network (CNN) compared with ResNet yielded better accuracy. Materials and Methods: The data of the facial expressions is taken from the FER2013 available on kaggle. The convolutional neural network (CNN) is the most widely used algorithm for image analysis with best accuracy and ResNet is also an algorithm used here to compare the accuracy of Novel image classification. Results: The Convolution neural network (CNN) produces 82.14% accuracy in predicting facial expressions on the dataset, whereas ResNet produces 78.01% accuracy. Convolutional neural network (CNN) is better than ResNet. With (p<0.05), there is a statistically significant difference between the research groups. Conclusion: Convolutional Neural Network provides better outcomes in accuracy rate when compared to ResNet for predicting facial expressions. [ABSTRACT FROM AUTHOR]