An Ensemble Network for High-Accuracy and Long-Term Forecasting of Icing on Wind Turbines.

Freezing of wind turbines causes loss of wind-generated power. Forecasting or prediction of icing on wind turbine blades based on SCADA sensor data allows taking appropriate actions before icing occurs. This paper presents a newly developed deep learning network model named PCTG (Parallel CNN-TCN GRU) for the purpose of high-accuracy and long-term prediction of icing on wind turbine blades.

A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids.

This paper provides a review of various machine learning approaches that have appeared in the literature aimed at individualizing or personalizing the amplification settings of hearing aids. After stating the limitations associated with the current one-size-fits-all settings of hearing aid prescriptions, a spectrum of studies in engineering and hearing science are discussed. These studies involve making adjustments to prescriptive values in order to enable preferred and individualized settings for a hearing aid user in an audio environment of interest to that user.

Improving Recognition of Defective Epoxy Images in Integrated Circuit Manufacturing by Data Augmentation.

This paper discusses the problem of recognizing defective epoxy drop images for the purpose of performing vision-based die attachment inspection in integrated circuit (IC) manufacturing based on deep neural networks. Two supervised and two unsupervised recognition models are considered. The supervised models examined are an autoencoder (AE) network together with a multi-layer perceptron network (MLP) and a VGG16 network, while the unsupervised models examined are an autoencoder (AE) network together with k-means clustering and a VGG16 network together with k-means clustering.

Generating Defective Epoxy Drop Images for Die Attachment in Integrated Circuit Manufacturing via Enhanced Loss Function CycleGAN.

In integrated circuit manufacturing, defects in epoxy drops for die attachments are required to be identified during production. Modern identification techniques based on vision-based deep neural networks require the availability of a very large number of defect and non-defect epoxy drop images. In practice, however, very few defective epoxy drop images are available.

Personalization of Hearing Aid Fitting Based on Adaptive Dynamic Range Optimization.

Adaptive dynamic range optimization (ADRO) is a hearing aid fitting rationale which involves adjusting the gains in a number of frequency bands by using a series of rules. The rules reflect the comparison of the estimated percentile occurrences of the sound levels with the audibility and comfort hearing levels of a person suffering from hearing loss. In the study reported in this paper, a previously developed machine learning method was utilized to personalize the ADRO fitting in order to provide an improved hearing experience as compared to the standard ADRO hearing aid fitting.

Smartphone-Based Hearing Aid Compression and Noise Reduction.

This paper presents an assistive hearing smartphone app mimicking the two main functions of hearing aids, consisting of compression and noise reduction. The app is designed to run in real time on smartphones or tablets. Appropriate levels of amplification or gain are activated by selecting a filter from a filter bank for six audio environment situations covering three sound pressure levels of speech and two sound pressure levels of noise.

A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods.

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement.

Image Segmentation Using Deep Learning: A Survey.

Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others, and numerous segmentation algorithms are found in the literature. Against this backdrop, the broad success of deep learning (DL) has prompted the development of new image segmentation approaches leveraging DL models. We provide a comprehensive review of this recent literature, covering the spectrum of pioneering efforts in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multiscale and pyramid-based approaches, recurrent networks, visual attention models, and generative models in adversarial settings.

Real-Time Moving Object Detection in High-Resolution Video Sensing.

This paper addresses real-time moving object detection with high accuracy in high-resolution video frames. A previously developed framework for moving object detection is modified to enable real-time processing of high-resolution images. First, a computationally efficient method is employed, which detects moving regions on a resized image while maintaining moving regions on the original image with mapping coordinates.

C-MHAD: Continuous Multimodal Human Action Dataset of Simultaneous Video and Inertial Sensing.

Existing public domain multi-modal datasets for human action recognition only include actions of interest that have already been segmented from action streams. These datasets cannot be used to study a more realistic action recognition scenario where actions of interest occur randomly and continuously among actions of non-interest or no actions. It is more challenging to recognize actions of interest in continuous action streams since the starts and ends of these actions are not known and need to be determined in an on-the-fly manner.

Fusion of Video and Inertial Sensing for Deep Learning-Based Human Action Recognition.

This paper presents the simultaneous utilization of video images and inertial signals that are captured at the same time via a video camera and a wearable inertial sensor within a fusion framework in order to achieve a more robust human action recognition compared to the situations when each sensing modality is used individually. The data captured by these sensors are turned into 3D video images and 2D inertial images that are then fed as inputs into a 3D convolutional neural network and a 2D convolutional neural network, respectively, for recognizing actions. Two types of fusion are considered-Decision-level fusion and feature-level fusion.

Integrating Signal Processing Modules of Hearing Aids into a Real-Time Smartphone App.

This paper presents the integration of three major modules of the signal processing pipeline that go into a typical digital hearing aid as a real-time smartphone app. These modules include voice activity detection, noise reduction, and compression. The steps taken to allow the real-time implementation of this integration or signal processing pipeline are discussed.

A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection.

This paper presents a smartphone app that performs real-time voice activity detection based on convolutional neural network. Real-time implementation issues are discussed showing how the slow inference time associated with convolutional neural networks is addressed. The developed smartphone app is meant to act as a switch for noise reduction in the signal processing pipelines of hearing devices, enabling noise estimation or classification to be conducted in noise-only parts of noisy speech signals.

A mobile computer aided system for optic nerve head detection.

Background And Objective: The detection of optic nerve head (ONH) in retinal fundus images plays a key role in identifying Diabetic Retinopathy (DR) as well as other abnormal conditions in eye examinations. This paper presents a method and its associated software towards the development of an Android smartphone app based on a previously developed ONH detection algorithm. The development of this app and the use of the d-Eye lens which can be snapped onto a smartphone provide a mobile and cost-effective computer-aided diagnosis (CAD) system in ophthalmology.

Smartphone-based real-time speech enhancement for improving hearing aids speech perception.

In this paper, the development of a speech processing pipeline on smartphones for hearing aid devices (HADs) is presented. This pipeline is used for noise suppression and speech enhancement (SE) to improve speech quality and intelligibility. The proposed method is implemented to run in real-time on Android smartphones.

Automatic switching between noise classification and speech enhancement for hearing aid devices.

This paper presents a voice activity detector (VAD) for automatic switching between a noise classifier and a speech enhancer as part of the signal processing pipeline of hearing aid devices. The developed VAD consists of a computationally efficient feature extractor and a random forest classifier. Previously used signal features as well as two newly introduced signal features are extracted and fed into the classifier to perform automatic switching.

Smartphone-based noise adaptive speech enhancement for hearing aid applications.

It is well established that the presence of environmental noises has a negative impact on the performance of hearing aid devices. This paper addresses a noise adaptive speech enhancement solution for the purpose of improving the performance of hearing aid devices in noisy environments. Depending on three noise types of babble, machinery, and driving car, the parameters of a recently developed speech enhancement algorithm are appropriately adjusted to gain improved speech understanding performance in noisy environments.

A medication adherence monitoring system for pill bottles based on a wearable inertial sensor.

This paper presents a medication adherence monitoring system for pill bottles based on a wearable inertial sensor. Signal templates corresponding to the two actions of twist-cap and hand-to-mouth are created using a camera-assisted training phase. The act of pill intake is then identified by performing a moving window dynamic time warping in real-time between signal templates and the signals acquired by the wearable inertial sensor.

Home-based Senior Fitness Test measurement system using collaborative inertial and depth sensors.

This paper presents a home-based Senior Fitness Test (SFT) measurement system by using an inertial sensor and a depth camera in a collaborative way. The depth camera is used to monitor the correct pose of a subject for a fitness test and any deviation from the correct pose while the inertial sensor is used to measure the number of a fitness test action performed by the subject within the time duration specified by the fitness protocol. The results indicate that this collaborative approach leads to high success rates in providing the SFT measurements under realistic conditions.

A multi-band environment-adaptive approach to noise suppression for cochlear implants.

This paper presents an improved environment-adaptive noise suppression solution for the cochlear implants speech processing pipeline. This improvement is achieved by using a multi-band data-driven approach in place of a previously developed single-band data-driven approach. Seven commonly encountered noisy environments of street, car, restaurant, mall, bus, pub and train are considered to quantify the improvement.

Real-time implementation of cochlear implant speech processing pipeline on smartphones.

This paper presents the real-time implementation of an adaptive speech processing pipeline for cochlear implants on the smartphone platform. The pipeline is capable of real-time classification of background noise environment and automated tuning of a noise suppression component based upon the detected background noise environment. This pipeline was previously implemented on the FDA-approved PDA platform for cochlear implant studies.

Spatial Mutual Information as Similarity Measure for 3-D Brain Image Registration.

Information theoretic-based similarity measures, in particular mutual information, are widely used for intermodal/intersubject 3-D brain image registration. However, conventional mutual information does not consider spatial dependency between adjacent voxels in images, thus reducing its efficacy as a similarity measure in image registration. This paper first presents a review of the existing attempts to incorporate spatial dependency into the computation of mutual information (MI).

Environment-adaptive speech enhancement for bilateral cochlear implants using a single processor.

A computationally efficient speech enhancement pipeline in noisy environments based on a single-processor implementation is developed for utilization in bilateral cochlear implant systems. A two-channel joint objective function is defined and a closed form solution is obtained based on the weighted-Euclidean distortion measure. The computational efficiency and no need for synchronization aspects of this pipeline make it a suitable solution for real-time deployment.

Real-time dual-microphone noise classification for environment-adaptive pipelines of cochlear implants.

This paper presents an improved noise classification in environment-adaptive speech processing pipelines of cochlear implants. This improvement is achieved by using a dual-microphone and by using a computationally efficient feature-level combination approach to achieve real-time operation. A new measure named Suppression Advantage is also defined in order to quantify the noise suppression improvement of an entire pipeline due to noise classification.