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In mammography diagnosis systems, high False Negative Rate (FNR) has always been a significant problem since a false negative answer may lead to a patient's death. This paper is directed towards the development of a novel Computer-aided Diagnosis (CADx) system for the diagnosis of breast masses. It aims at intensifying the performance of CADx algorithms as(More)
Classification of breast abnormalities such as masses is a challenging task for radiologists. Computer-aided Diagnosis (CADx) technology may enhance the performance of radiologists by assisting them in classifying patterns into benign and malignant categories. Although Neural Networks (NN) such as Multilayer Perceptron (MLP) have drawbacks, namely long(More)
This paper presents improvements made to the previously developed noise classification path of the environment-adaptive cochlear implant speech processing pipeline. These improvements consist of the utilization of subband noise features together with a random forest tree classifier. Three commonly encountered noise environments of babble, street, and(More)
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,(More)
This paper presents the real-time implementation and field testing of an app running on smartphones for classifying noise signals involving subband features and a random forest classifier. This app is compared to a previously developed app utilizing mel-frequency cepstral coefficients features and a Gaussian mixture model classifier. The real-time(More)
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(More)
This paper presents the steps one needs to take in order to run a signal processing algorithm designed in Simulink on the ARM processor of smartphones. The steps are conveyed by transitioning two signal processing application examples from Simulink to smartphone. The application examples involve background noise classification and lane departure detection.(More)
This paper features the novel three stage CBIR system using different orders of Zernike moments. In the first stage, segmentation is done using various image processing techniques. In the second stage, two sets of features each containing 32 Zernike moments and one more set containing 62 Zernike moments with different orders are extracted from the images.(More)