Smartphone-Based Voice Wellness Index Application for Dysphonia Screening and Assessment: Development and Reliability.

OBJECTIVE: This study aimed to develop a Voice Wellness Index (VWI) application combining the acoustic voice quality index (AVQI) and glottal function index (GFI) data and to evaluate its reliability in quantitative voice assessment and normal versus pathological voice differentiation. STUDY DESIGN: Cross-sectional study. METHODS: A total of 135 adult participants (86 patients with voice disorders and 49 patients with normal voices) were included in this study. Five iOS and Android smartphones with the "Voice Wellness Index" app installed were used to estimate VWI. The VWI data obtained using smartphones were compared with VWI measurements computed from voice recordings collected from a reference studio microphone. The diagnostic efficacy of VWI in differentiating between normal and disordered voices was assessed using receiver operating characteristics (ROC). RESULTS: With a Cronbach's alpha of 0.972 and an ICC of 0.972 (0.964-0.979), the VWI scores of the individual smartphones demonstrated remarkable inter-smartphone agreement and reliability. The VWI data obtained from different smartphones and a studio microphone showed nearly perfect direct linear correlations (r = 0.993-0.998). Depending on the individual smartphone device used, the cutoff scores of VWI related to differentiating between normal and pathological voice groups were calculated as 5.6-6.0 with the best balance between sensitivity (94.10-95.15%) and specificity (93.68-95.72%), The diagnostic accuracy was excellent in all cases, with an area under the curve (AUC) of 0.970-0.974. CONCLUSION: The "Voice Wellness Index" application is an accurate and reliable tool for voice quality measurement and normal versus pathological voice screening and has considerable potential to be used by healthcare professionals and patients for voice assessment.

Reliability of Universal-Platform-Based Voice Screen Application in AVQI Measurements Captured with Different Smartphones.

The aim of the study was to develop a universal-platform-based (UPB) application suitable for different smartphones for estimation of the Acoustic Voice Quality Index (AVQI) and evaluate its reliability in AVQI measurements and normal and pathological voice differentiation. Our study group consisted of 135 adult individuals, including 49 with normal voices and 86 patients with pathological voices. The developed UPB "Voice Screen" application installed on five iOS and Android smartphones was used for AVQI estimation. The AVQI measures calculated from voice recordings obtained from a reference studio microphone were compared with AVQI results obtained using smartphones. The diagnostic accuracy of differentiating normal and pathological voices was evaluated by applying receiver-operating characteristics. One-way ANOVA analysis did not detect statistically significant differences between mean AVQI scores revealed using a studio microphone and different smartphones (F = 0.759; p = 0.58). Almost perfect direct linear correlations (r = 0.991-0.987) were observed between the AVQI results obtained with a studio microphone and different smartphones. An acceptable level of precision of the AVQI in discriminating between normal and pathological voices was yielded, with areas under the curve (AUC) displaying 0.834-0.862. There were no statistically significant differences between the AUCs (p > 0.05) obtained from studio and smartphones' microphones. The significant difference revealed between the AUCs was only 0.028. The UPB "Voice Screen" application represented an accurate and robust tool for voice quality measurements and normal vs. pathological voice screening purposes, demonstrating the potential to be used by patients and clinicians for voice assessment, employing both iOS and Android smartphones.

Agrast-6: Abridged VGG-Based Reflected Lightweight Architecture for Binary Segmentation of Depth Images Captured by Kinect.

Binary object segmentation is a sub-area of semantic segmentation that could be used for a variety of applications. Semantic segmentation models could be applied to solve binary segmentation problems by introducing only two classes, but the models to solve this problem are more complex than actually required. This leads to very long training times, since there are usually tens of millions of parameters to learn in this category of convolutional neural networks (CNNs). This article introduces a novel abridged VGG-16 and SegNet-inspired reflected architecture adapted for binary segmentation tasks. The architecture has 27 times fewer parameters than SegNet but yields 86% segmentation cross-intersection accuracy and 93% binary accuracy. The proposed architecture is evaluated on a large dataset of depth images collected using the Kinect device, achieving an accuracy of 99.25% in human body shape segmentation and 87% in gender recognition tasks.

Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images.

The identification of human activities from videos is important for many applications. For such a task, three-dimensional (3D) depth images or image sequences (videos) can be used, which represent the positioning information of the objects in a 3D scene obtained from depth sensors. This paper presents a framework to create foreground-background masks from depth images for human body segmentation. The framework can be used to speed up the manual depth image annotation process with no semantics known beforehand and can apply segmentation using a performant algorithm while the user only adjusts the parameters, or corrects the automatic segmentation results, or gives it hints by drawing a boundary of the desired object. The approach has been tested using two different datasets with a human in a real-world closed environment. The solution has provided promising results in terms of reducing the manual segmentation time from the perspective of the processing time as well as the human input time.

Eight Element Side Edged Framed MIMO Antenna Array for Future 5G Smart Phones.

This paper presents a novel design of a Multiple Input Multiple Output (MIMO) antenna system for next generation sub 6 GHz 5G and beyond mobile terminals. The proposed system is composed of a main board and two side boards. To make the design cost-effective, FR4 is used as a substrate. The design is based on a unit monopole antenna etched at the side substrate. The single element is resonating at 3.5 GHz attaining a 10 dB bandwidth of 200 MHz and a 6 dB bandwidth of 400 MHz. The single element is then transformed into an MIMO array of 8-elements with an overall dimension of 150 mm × 75 mm × 7 mm, providing pattern diversity characteristics and isolation better than -12 dB for any two radiating elements. A number of studies such as effects of human hand on the system that includes single hand mode and dual mode scenarios and the effects of Liquid Crystal Display (LCD) over the principal performance parameters of the system are presented. The envelop correlation coefficient (ECC) is computed for all the scenarios and it is found that ECC is less than 0.1 for any case and maximum channel capacity is 38.5 bps/Hz within the band of interest. The main advantage of the proposed design over available designs in the literature is that almost all of the main substrate is empty providing wide space for different sensors, systems, and mobile technology components. A brief literature comparison of the proposed system is also presented. To validate the proposed model, a prototype is fabricated and results are presented. This design can be applied on higher frequencies to future micromachines for on chip communications using same theocratical approach as the space for higher frequencies in mmwave spectrum has been reserved. The simulated results are in an excellent agreement with the measured results. All the main performance parameters of the design are calculated and compared with the measured results wherever possible.

An Image Encryption Scheme Based on Block Scrambling, Modified Zigzag Transformation and Key Generation Using Enhanced Logistic-Tent Map.

Nowadays, the images are transferred through open channels that are subject to potential attacks, so the exchange of image data requires additional security in many fields, such as medical, military, banking, etc. The security factors are essential in preventing the system from brute force and differential attacks. We propose an Enhanced Logistic Map (ELM) while using chaotic maps and simple encryption techniques, such as block scrambling, modified zigzag transformation for encryption phases, including permutation, diffusion, and key stream generation to withstand the attacks. The results of encryption are evaluated while using the histogram, correlation analysis, Number of Pixel Change Rate (NPCR), Unified Average Change Intensity (UACI), Peak-Signal-to-Noise Ratio (PSNR), and entropy. Our results demonstrate the security, reliability, efficiency, and flexibility of the proposed method.

Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies.

The current era demands high quality software in a limited time period to achieve new goals and heights. To meet user requirements, the source codes undergo frequent modifications which can generate the bad smells in software that deteriorate the quality and reliability of software. Source code of the open source software is easily accessible by any developer, thus frequently modifiable. In this paper, we have proposed a mathematical model to predict the bad smells using the concept of entropy as defined by the Information Theory. Open-source software Apache Abdera is taken into consideration for calculating the bad smells. Bad smells are collected using a detection tool from sub components of the Apache Abdera project, and different measures of entropy (Shannon, Rényi and Tsallis entropy). By applying non-linear regression techniques, the bad smells that can arise in the future versions of software are predicted based on the observed bad smells and entropy measures. The proposed model has been validated using goodness of fit parameters (prediction error, bias, variation, and Root Mean Squared Prediction Error (RMSPE)). The values of model performance statistics ( R 2 , adjusted R 2 , Mean Square Error (MSE) and standard error) also justify the proposed model. We have compared the results of the prediction model with the observed results on real data. The results of the model might be helpful for software development industries and future researchers.

Subjective visual vertical assessment with mobile virtual reality system.

BACKGROUND AND OBJECTIVE: The subjective visual vertical (SVV) is a measure of a subject's perceived verticality, and a sensitive test of vestibular dysfunction. Despite this, and consequent upon technical and logistical limitations, SVV has not entered mainstream clinical practice. The aim of the study was to develop a mobile virtual reality based system for SVV test, evaluate the suitability of different controllers and assess the system's usability in practical settings. MATERIALS AND METHODS: In this study, we describe a novel virtual reality based system that has been developed to test SVV using integrated software and hardware, and report normative values across healthy population. Participants wore a mobile virtual reality headset in order to observe a 3D stimulus presented across separate conditions - static, dynamic and an immersive real-world ("boat in the sea") SVV tests. The virtual reality environment was controlled by the tester using a Bluetooth connected controllers. Participants controlled the movement of a vertical arrow using either a gesture control armband or a general-purpose gamepad, to indicate perceived verticality. We wanted to compare 2 different methods for object control in the system, determine normal values and compare them with literature data, to evaluate the developed system with the help of the system usability scale questionnaire and evaluate possible virtually induced dizziness with the help of subjective visual analog scale. RESULTS: There were no statistically significant differences in SVV values during static, dynamic and virtual reality stimulus conditions, obtained using the two different controllers and the results are compared to those previously reported in the literature using alternative methodologies. The SUS scores for the system were high, with a median of 82.5 for the Myo controller and of 95.0 for the Gamepad controller, representing a statistically significant difference between the two controllers (P<0.01). The median of virtual reality-induced dizziness for both devices was 0.7. CONCLUSIONS: The mobile virtual reality based system for implementation of subjective visual vertical test, is accurate and applicable in the clinical environment. The gamepad-based virtual object control method was preferred by the users. The tests were well tolerated with low dizziness scores in the majority of patients.