Diagnosis of hearing impairment based on wavelet transformation and machine learning approach

Hearing impairment has become the most widespread sensory disorder in the world, obstructing human-to-human communication and comprehension. The EEG-based brain-computer interface (BCI) technology may be an important solution to rehabilitating their hearing capacity for people who are unable to sustain verbal contact and behavioral response by sound stimulation. Auditory evoked potentials (AEPs) are a kind of EEG signal produced by an acoustic stimulus from the brain scalp. This study aims to develop an intelligent hearing level assessment technique using AEP signals to address these concerns. First, we convert the raw AEP signals into the time–frequency image using the continuous wavelet transform (CWT). Then, the Support vector machine (SVM) approach is used for classifying the time–frequency images. This study uses the reputed publicly available dataset to check the validation of the proposed approach. This approach achieves a maximum of 95.21% classification accuracy, which clearly indicates that the approach provides a very encouraging performance for detecting the AEPs responses in determining human auditory level

Sea-Floor Power Generation System

Ocean currents represent a potentially significant, currently untapped, resource of energy. The total worldwide power in ocean currents has been estimated to be about 5,000 GW, with power densities of up to 15 kW/m2. In the paper we describe a micro sea-floor power generation system being designed and developed at Electrical Energy System Lab, Memorial University of Newfoundland. The ocean current data around Newfoundland shows a significantly low non-tidal ocean current speed (in the range of 0.11-0.15m/s) at various depths. We would like to extract few watts electrical from the sea-floor ocean current. The produced power is required for the data processing computer and signal conditioning circuits of sea-floor instrumentation. The proposed power generation system will consist of a spiral shape drag type turbine rotor, a low rpm generator, batteries for energy storage, a controlled DC-DC converter, instrumentation and a micro controller based control system for the turbine. This paper describes progress made so far. We present some ocean current data, design and lab test results of our first proto-type sea-floor power generation system

Pathogenic Fusarium oxysporum f. sp. cepae growing inside onion bulbs emits volatile organic compounds that correlate with the extent of infection

Diseases develop during the storage of onions. To minimize losses, new methods are needed to identify diseased bulbs early in storage. Volatile organic compounds (VOCs), the respiration rate, weight loss, and the dry matter content were investigated for 1-7 weeks post inoculation of bulbs with water (control) and two strains (Fox006 or Fox260) of Fusarium oxysporum f. sp. cepae. Photos, multispectral image analysis, and real-time polymerase chain reaction (PCR) showed no infection in the control onions, weak pathogenic infection in Fox006-onions, and strong pathogenic infection in Fox260-onions at week 7 post inoculation. Infected bulbs exhibited increased respiration rate, increased VOC emission rate, and increased weight loss. The control and Fox006-onions did not respond to inoculation and had similar reaction pattern. Forty-three different VOCs were measured, of which 17 compounds had sulfur in their chemical structure. 1-Propanethiol, methyl propyl sulfide, and styrene were emitted in high concentrations and were positively correlated with the extent of infection (r = 0.82 - 0.89). Therefore, these compounds were the most promising volatile markers of Fusarium basal rot infection. For the first time, we show that the extent of fungal infection determined by real-time PCR in onion bulbs is related with VOC emission.Peer reviewe

Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach

Hearing loss has become the world's most widespread sensory impairment. The applicability of a traditional hearing test is limited as it allows the subject to provide a direct response. The main aim of this study is to build an intelligent hearing level evaluation method using possible auditory evoked signals (AEPs). AEP responses are subjected to fixed acoustic stimulation strength for usual auditory and abnormal ear subjects to detect the hearing disorder. In this paper, the AEP responses have been captured from the sixteen subjects when the subject hears the auditory stimulus in the left or right ear. Then, the features have extracted with the help of Fast Fourier Transform (FFT), Power Spectral Density (PSD), Spectral Centroids, Standard Deviation algorithms. To classify the extracted features, the Support Vector Machine (SVM) approach using Radial Basis Kernel Function (RBF) has been used. Finally, the performance of the classifier in terms of accuracy, confusion matrix, true positive and false negative rate, precision, recall, and Cohen-Kappa-Score have been evaluated. The maximum classification accuracy of the developed SVM model with FFT feature was observed 95.29% (10 s time windows) which clearly indicates that the method provides a very encouraging performance for detecting the AEPs responses.

Rice disease identification through leaf image and iot based smart rice fieldmonitoring system

Rice disease identification in early-stage, proper medication in case of disease affection and managing irrigation at the appropriate time is the most consequential phenomena to increase the production level of the rice. In this paper, a novel technique to diagnose the rice diseases and smart medication prescription system have been proposed. Furthermore, the internet of things (IoT) based smart rice field monitoring system has also been proposed. To identify rice diseases, the leaf image dataset (consists of healthy and three different diseases) has been analyzed through the convolutional neural network (CNN). The obtained rice disease diagnosis accuracy of the proposed system was 98.7%. In a real-time system, the leaf image data has been collected remotely using Raspberry Pi and the data has been sent to a server to be tested by a trained CNN model. Some sensors including soil moisture sensor, pressure sensor, humidity sensor, and temperature sensor have been implanted in the targeted field which aims to record the current scenario of the rice field and send the sensors data to the server. On a web page, proper medications have been displayed if any rice disease identified. Moreover, the user may monitor his field remotely which facilitates irrigation in opportune time

An intelligent system for hearing disorder detection using auditory evoked potential (AEP) signals

Approximately 466 million people have different kinds of hearing impairment in the world, and 34 million are children. Brain-computer interfaces (BCIs) are systems that link the human brain to external technology, which is the best way to address these concerns. Auditory evoked potentials (AEPs) are a type of EEG signal which has been commonly employed to detect early hearing disorder

A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline

The auditory evoked potential (AEP) has been considered a standard clinical instrument for hearing and neurological evaluation. Although several approaches for learning EEG signal characteristics have been established earlier, the hybridization concept has rarely been explored to produce novel representations of AEP features and achieve further performance enhancement for AEP signals. Moreover, the classification of auditory attention within a concise time interval is still facing some challenges. To address this concern, this study has proposed a hybridization scheme, represented as a hybrid convoluted k-nearest neighbour (CKNN) algorithm, consisting of concatenating the convolutional layer of CNN with k-nearest neighbour (k-NN) classifier. The proposed architecture helps in improving the accuracy of KNN from 83.23% to 92.26% with a 3-second decision window. The effect of several concise decision windows is also investigated in this analysis. The proposed architecture is validated by a publicly benchmark AEP dataset, and the outcomes indicate that the CKNN significantly outperforms other state-of-the-art techniques with a concise decision window. The proposed framework shows superior performance in a concise decision window that can be effectively used for early hearing deficiency diagnosis. This paper also presents several discoveries that could be helpful to the neurological community

Investigation of Time-Domain and Frequency-Domain Based Features to Classify the EEG Auditory Evoked Potentials (AEPs) Responses

The auditory evoked potentials (AEPs) are a kind of electroencephalographic (EEG) signal that is produced by an acoustic stimulus from the region of the brain. The people who are unable to maintain the verbal communication and behavioral response through the sound stimulation, EEG based brain-computer interface (BCI) technology could be an effective alternative to rehabilitate their hearing ability. In this paper, the AEP responses of three distinct English words namely bed, please and sad have been recognized. The EEG features in terms of Fast Fourier Transform (FFT), power spectral density (PSD), spectral centroids, standard deviation, Log energy entropy, mean, skewness, kurtosis has been selected as a feature extraction method. Support Vector Machine (SVM), Linear discriminant analysis (LDA) and K-Nearest Neighbors (K-NN) have been employed to classify the extracted features. Among all these features, power spectral density with SVM classification has achieved the best accuracy. Different performance measures were evaluated to identify the best set of features as well as model. The best classification accuracy was demonstrated by the developed SVM model was observed as 82.86% which clearly indicates that the method provides a very encouraging performance for detecting the AEPs responses

Effect of ultrasound-assisted freezing on the physico-chemical properties and volatile compounds of red radish

Power ultrasound, which can enhance nucleation rate and crystal growth rate, can also affect the physico-chemical properties of immersion frozen products. In this study, the influence of slow freezing (SF), immersion freezing (IF) and ultrasound-assisted freezing (UAF) on physico-chemical properties and volatile compounds of red radish was investigated. Results showed that ultrasound application significantly improved the freezing rate; the freezing time of ultrasound application at 0.26 W/cm was shorten by 14% and 90%, compared to IF and SF, respectively. UAF products showed significant (p < 0.05) reduction in drip loss and phytonutrients (anthocyanins, vitamin C and phenolics) loss. Compared to SF products, IF and UAF products showed better textural preservation and higher calcium content. The radish tissues exhibited better cellular structures under ultrasonic power intensities of 0.17 and 0.26 W/cm with less cell separation and disruption. Volatile compound data revealed that radish aromatic profile was also affected in the freezing process

EEG mechanism interaction to evaluate vehicle’s driver microsleep

Microsleep or more commonly known as momentary uncontrollable fall asleep in a very short period of time usually occurs between one second to fifteen seconds. In Malaysia, one of the factors that contribute to accidents is due to the microsleep factor when the driver is driving without them being aware. This factor also often occurs when driving in a tired state and traveling too long distance. Weather factors can also contribute to microsleep. Therefore, in this research, a system has been developed to detect frequency waves from the brain based on signals from electroencephalogram (EEG) electrodes to prevent drivers from experiencing microsleep and getting involved in accidents. To conduct this research, five subjects of different ages and gender were selected to collect their brainwave data using the NeuroSky Mindwave Mobile Headset device and the EegID Record application in two different situations, namely by driving the simulation in a challenging condition for 30 minutes and the second situation is by driving the simulation in a relaxed condition for 30 minutes. In addition, the use of MATLAB in this research is to pre-process the wave signal to remove unwanted noise interference. Then, a bandpass filter is used to classify and separate the signal into Theta, Alpha, and Beta waves. These three waves will be analyzed and studied based on the age and gender differences of the subjects. After the spectrum of the wave is drawn to trigger the alarm system and the steering vibration motor if microsleep is detected for some period of one to 3 seconds