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Articles 1 - 5 of 5
Full-Text Articles in Signal Processing
Re-Design Of Precision Signal Conditioning Circuit For Detecting Schumann Resonance, Rohith Bikkina
Re-Design Of Precision Signal Conditioning Circuit For Detecting Schumann Resonance, Rohith Bikkina
Graduate Theses, Dissertations, and Problem Reports
Extremely low frequencies signals are waves between 3 to 30Hz and corresponding wavelengths between 10,000 to 100,000 kilometers. The specific signals used here are generated from lightning and are excited at frequencies around 8Hz, 14Hz, 20Hz. These are often called Schumann Resonance frequencies. Several stations have been built around the world for identifying ELF waves. All of those required a sparsely populated area that was far away from electric power lines because of interference from electric noise at 50 Hz and 60Hz. This project develops and tests an amplifier and filter circuit that should assist in identifying the Schumann Resonance …
Analysis Of Millimeter-Wave Networks: Blockage, Antenna Directivity, Macrodiversity, And Interference, Enass Hriba
Analysis Of Millimeter-Wave Networks: Blockage, Antenna Directivity, Macrodiversity, And Interference, Enass Hriba
Graduate Theses, Dissertations, and Problem Reports
Due to its potential to support high data rates at low latency with reasonable interference isolation because of signal blockage at these frequencies, millimeter-wave (mmWave) communications has emerged as a promising solution for next-generation wireless networks. MmWave systems are characterized by the use of highly directional antennas and susceptibility to signal blockage by buildings and other obstructions, which significantly alter the propagation environment. The received power of each transmission depends on the direction the corresponding antennas point and whether the signal’s path is line-of-sight (LOS), non-LOS (i.e., partially blocked), or completely blocked. A key challenge in modeling blocking in mmWave …
Real Vs Fake Faces: Deepfakes And Face Morphing, Jacob L. Dameron
Real Vs Fake Faces: Deepfakes And Face Morphing, Jacob L. Dameron
Graduate Theses, Dissertations, and Problem Reports
The ability to determine the legitimacy of a person’s face in images and video can be important for many applications ranging from social media to border security. From a biometrics perspective, altering one’s appearance to look like a target identity is a direct method of attack against the security of facial recognition systems. Defending against such attacks requires the ability to recognize them as a separate identity from their target. Alternatively, a forensics perspective may view this as a forgery of digital media. Detecting such forgeries requires the ability to detect artifacts not commonly seen in genuine media. This work …
Integration Of Deep Hashing And Channel Coding For Biometric Security And Biometric Retrieval, Veeru Talreja
Integration Of Deep Hashing And Channel Coding For Biometric Security And Biometric Retrieval, Veeru Talreja
Graduate Theses, Dissertations, and Problem Reports
In the last few years, the research growth in many research and commercial fields are due to the adoption of state of the art deep learning techniques. The same applies to even biometrics and biometric security. Additionally, there has been a rise in the development of deep learning techniques used for approximate nearest neighbor (ANN) search for retrieval on multi-modal datasets. These deep learning techniques knows as deep hashing (DH) integrate feature learning and hash coding into an end-to-end trainable framework. Motivated by these factors, this dissertation considers the integration of deep hashing and channel coding for biometric security and …
Deep Models For Improving The Performance And Reliability Of Person Recognition, Sobhan Soleymani
Deep Models For Improving The Performance And Reliability Of Person Recognition, Sobhan Soleymani
Graduate Theses, Dissertations, and Problem Reports
Deep models have provided high accuracy for different applications such as person recognition, image segmentation, image captioning, scene description, and action recognition. In this dissertation, we study the deep learning models and their application in improving the performance and reliability of person recognition. This dissertation focuses on five aspects of person recognition: (1) multimodal person recognition, (2) quality-aware multi-sample person recognition, (3) text-independent speaker verification, (4) adversarial iris examples, and (5) morphed face images. First, we discuss the application of multimodal networks consisting of face, iris, fingerprint, and speech modalities in person recognition. We propose multi-stream convolutional neural network architectures …