EHNet: Efficient Hybrid Network with Dual Attention for Image Deblurring.

The motion of an object or camera platform makes the acquired image blurred. This degradation is a major reason to obtain a poor-quality image from an imaging sensor. Therefore, developing an efficient deep-learning-based image processing method to remove the blur artifact is desirable.

Multi-Branch Network for Color Image Denoising Using Dilated Convolution and Attention Mechanisms.

Image denoising is regarded as an ill-posed problem in computer vision tasks that removes additive noise from imaging sensors. Recently, several convolution neural network-based image-denoising methods have achieved remarkable advances. However, it is difficult for a simple denoising network to recover aesthetically pleasing images owing to the complexity of image content.

CNN-Based Facial Expression Recognition with Simultaneous Consideration of Inter-Class and Intra-Class Variations.

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function.

Low-Light Image Enhancement Using Hybrid Deep-Learning and Mixed-Norm Loss Functions.

This study introduces a low-light image enhancement method using a hybrid deep-learning network and mixed-norm loss functions, in which the network consists of a decomposition-net, illuminance enhance-net, and chroma-net. To consider the correlation between R, G, and B channels, YCbCr channels converted from the RGB channels are used for training and restoration processes. With the luminance, the decomposition-net aims to decouple the reflectance and illuminance and to train the reflectance, leading to a more accurate feature map with noise reduction.

Classification of Handwashing and Similar Activities.

In this paper, an ensemble gentle boost decision tree classification algorithm is trained to classify handwashing from similar activities such as applying lotion to hands. Data is collected using a 3-axis accelerometer and gyroscope worn on the wrist. First, the data collection procedure is described.

Fall Prediction in People with Parkinson's Disease.

A preliminary study result predicting fall events in patients with Parkinson's disease (PD) by using a simple motion sensor is described in this paper. Causes of falls in people with PD can be postural instability, freezing of gait, festinating gait, dyskinesias, visuospatial dysfunction, orthostatic hypotension, and posture problems. This study uses only one motion sensor in collecting data.

Garment Integrated Spinal Posture Detection Using Wearable Magnetic Sensors.

Spine Curvature Disorder (SCD) is a medical condition that affects the shape of the spine. Methods of monitoring SCDs involve visual inspection followed by X-rays and measurements. Once a patient is diagnosed with SCD and treatment or therapy is implemented, progress is tracked by exposing the patient to multiple periodic X-rays to determine the spine responses to treatments or therapies.

Training a Neural Network for Vocal Stereotypy Detection.

This system was developed to detect and diagnose vocal stereotypies made by non-verbal autistic children. Vocal stereotypies are loud non-speech vocalizations made by these children.The system discussed in this paper uses a deep learning neural network to detect these vocalizations.

Adaptive Image Rendering Using a Nonlinear Mapping-Function-Based Retinex Model.

This paper introduces an adaptive image rendering using a parametric nonlinear mapping-function-based on the retinex model in a low-light source. For this study, only a luminance channel was used to estimate the reflectance component of an observed low-light image, therefore halo artifacts coming from the use of the multiple center/surround Gaussian filters were reduced. A new nonlinear mapping function that incorporates the statistics of the luminance and the estimated reflectance in the reconstruction process is proposed.

Vocal Stereotypy Detection: An Initial Step to Understanding Emotions of Children with Autism Spectrum Disorder.

A system has been developed to automatically record and detect behavioral patterns and vocal stereotypy which is also known as vocal stimming, a non-verbal vocalization often observed in children with Autism Spectrum Disorder (ASD). The system incorporates audio, video and wearable accelerometer based sensors. Microphones and video camera were used to collect data and were used for analysis.

Coherence resonance in bursting neural networks.

Synchronized neural bursts are one of the most noticeable dynamic features of neural networks, being essential for various phenomena in neuroscience, yet their complex dynamics are not well understood. With extrinsic electrical and optical manipulations on cultured neural networks, we demonstrate that the regularity (or randomness) of burst sequences is in many cases determined by a (few) low-dimensional attractor(s) working under strong neural noise. Moreover, there is an optimal level of noise strength at which the regularity of the interburst interval sequence becomes maximal-a phenomenon of coherence resonance.

Circadian waves of cytosolic calcium concentration and long-range network connections in rat suprachiasmatic nucleus.

The suprachiasmatic nucleus (SCN) is the master clock in mammals governing the daily physiological and behavioral rhythms. It is composed of thousands of clock cells with their own intrinsic periods varying over a wide range (20-28 h). Despite this heterogeneity, an intact SCN maintains a coherent 24 h periodic rhythm through some cell-to-cell coupling mechanisms.

Semi-supervised event detection using higher order statistics for multidimensional time series accelerometer data.

In this study, we target to automatically detect stereotypical behavioral patterns (stereotypy) and self-injurious behaviors (SIB) of Autistic children which can lead to critical damages or wounds as they tend to repeatedly harm oneself. Our custom designed accelerometer based wearable sensors are placed at wrists, ankles and upper body to detect stereotypy and SIB. The analysis was done on four children diagnosed with ASD who showed repeated behaviors that involve part of the body such as flapping arms, body rocking and self-injurious behaviors such as punching their face, or hitting their legs.

Automatic characterization and detection of behavioral patterns using linear predictive coding of accelerometer sensor data.

In this study, we target to automatically detect behavioral patterns of patients with autism. Many stereotypical behavioral patterns may hinder their learning ability as a child and patterns such as self-injurious behaviors (SIB) can lead to critical damages or wounds as they tend to repeatedly harm one single location. Our custom designed accelerometer based wearable sensor can be placed at various locations of the body to detect stereotypical self-stimulatory behaviors (stereotypy) and self-injurious behaviors of patients with Autism Spectrum Disorder (ASD).

Intracellular calcium spikes in rat suprachiasmatic nucleus neurons induced by BAPTA-based calcium dyes.

Background: Circadian rhythms in spontaneous action potential (AP) firing frequencies and in cytosolic free calcium concentrations have been reported for mammalian circadian pacemaker neurons located within the hypothalamic suprachiasmatic nucleus (SCN). Also reported is the existence of "Ca(2+) spikes" (i.e.

Optimal sensor location for body sensor network to detect self-stimulatory behaviors of children with Autism Spectrum Disorder.

In this study, we investigate various locations of sensor positions to detect stereotypical self-stimulatory behavioral patterns of children with Autism Spectrum Disorder (ASD). The study is focused on finding optimal detection performance based on sensor location and number of sensors. To perform this study, we developed a wearable sensor system that uses a 3 axis accelerometer.

Classification of continuously executed early morning activities using wearable wireless sensors.

In this paper, we study the personal monitoring system that classifies the continuously executed early morning activities of daily living. The system is intended to assist those with cognitive impairments due to traumatic brain injuries. The system can be used to help therapists in hospitals or could be deployed in one's home to track and monitor the activities executed by the recovering patients.