A network gateway is a mechanism which provides protocol translation and/or validation of network traffic using the metadata contained in network packets. For media applications such as Voice-over-IP, the portion of the packets containing speech data cannot be verified and can provide a means of maliciously transporting code or sensitive data undetected. One solution to this problem is through Voice Activity Detection (VAD). Many VAD’s rely on time-domain features and simple thresholds for efficient speech detection however this doesn’t say much about the signal being passed. More sophisticated methods employ machine learning algorithms, but train on specific noises intended for a target environment. Validating speech under a variety of unknown conditions must be possible; as well as differentiating between speech and nonspeech data embedded within the packets. A real-time speech detection method is proposed that relies only on a clean speech model for detection. Through the use of Gammatone filter bank processing, the Cepstrum and several frequency domain features are used to train a One-Class Support Vector Machine which provides a clean-speech model irrespective of environmental noise. A Wiener filter is used to provide improved operation for harsh noise environments. Greater than 90% detection accuracy is achieved for clean speech with approximately 70% accuracy for SNR as low as 5dB

Strawberry is an important cash crop in California, and its supply accounts for 80% of the US market [2]. However, in current practice, strawberries are picked manually, which is very labor-intensive and time-consuming. In addition, the farmers need to hire an appropriate number of laborers to harvest the berries based on the estimated volume. When overestimating the yield, it will cause a waste of human resources, while underestimating the yield will cause the loss of the strawberry harvest [3]. Therefore, accurately estimating harvest volume in the field is important to farmers. This paper focuses on an image-based solution to detect strawberries in the field by using the traditional computer vision technique and deep learning method. When strawberries are in different growth stages, there are considerable differences in their color. Therefore, various color spaces are first studied in this work, and the most effective color components are used in detecting strawberries and differentiating mature and immature strawberries. In some color channels such as the R color channel from the RGB color model, Hue color channel from the HSV color model, 'a' color channel from the Lab color model, the pixels belonging to ripe strawberries are clearly distinguished from the background pixels. Thus, the color-based K-mean cluster algorithm to detect red strawberries will be exploited. Finally, it achieves a 90.5% truth-positive rate for detecting red strawberries. For detecting the unripe strawberry, this thesis first trained the Support Vector Machine classifier based on the HOG feature. After optimizing the classifier through hard negative mining, the truth-positive rate reached 81.11%. Finally, when exploring the deep learning model, two detectors based on different pre-trained models were trained using TensorFlow Object Detection API with the acceleration of Amazon Web Services' GPU instance. When detecting in a single strawberry plant image, they have achieved truth-positive rates of 89.2% and 92.3%, respectively; while in the strawberry field image with multiple plants, they have reached 85.5% and 86.3%.

We propose a method of adaptive image denoising using a support vector machine (SVM) classifier to select between multiple well-performing contemporary denoising algorithms for each pixel of a noisy image. We begin by proposing a simple method for realistically generating noisy images, and also describe a number of novel and pre-existing features based on seam energy, local colour, and saliency which are used as classifier inputs. Our SVM strategic image denoising (SVMSID) results demonstrate better image quality than either candidate denoising algorithm for images of moderate noise level, as measured using the perceptually-based quaternion structural similarity image metric (QSSIM). We also demonstrate a modified training point selection method to improve robustness across many noise levels, and propose various extensions to SVMSID for further exploration. / Thesis / Master of Applied Science (MASc)

Heart disease is the leading cause of death for men and women in the United States. Deaths from cardiovascular disease jumped globally from 12.1 million in 1990 to 20.5 million in 2021, according to a new report from the World Heart Federation. The Electrocardiogram (ECG, or EKG) is a non-invasive and efficient test that records the electrical activities of a human heart. In recent years, various approaches based on computational intelligence have been developed and successfully applied to automatic detection of cardiac arrhythmia on ECG signals. In this thesis, we study the application of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for identification of cardiac irregularities. The two methods are tested on ECG signals with six different heartbeat conditions in the MIT- BIH Arrhythmia database. Computer simulation results show both methods are highly effective with detection rates of close to 98% and 99%, respectively.

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