Open Access. Powered by Scholars. Published by Universities.®
![Digital Commons Network](http://assets.bepress.com/20200205/img/dcn/DCsunburst.png)
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Machine learning (2)
- Adaptive beamformer (ABF) (1)
- Array signal processing (1)
- Automaton (1)
- Autonomy (1)
-
- DAST (1)
- Decentralized learning (1)
- Dominant mode rejection (DMR) (1)
- Eigendecomposition (1)
- Fair machine learning (1)
- Federated learning (1)
- Information theory (1)
- IoT (1)
- Neuromorphic computing (1)
- Photovoltaic/Solar (1)
- Probabilistic inference (1)
- Random matrix theory (RMT) (1)
- SCADA (1)
- Sample covariance matrix (SCM) (1)
- Signal processing (1)
- Spiking neural networks (1)
- Trojan backdoors (1)
- Publication
Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Performance Analysis Of The Dominant Mode Rejection Beamformer, Enlong Hu
Performance Analysis Of The Dominant Mode Rejection Beamformer, Enlong Hu
Dissertations
In array signal processing over challenging environments, due to the non-stationarity nature of data, it is difficult to obtain enough number of data snapshots to construct an adaptive beamformer (ABF) for detecting weak signal embedded in strong interferences. One type of adaptive method targeting for such applications is the dominant mode rejection (DMR) method, which uses a reshaped eigen-decomposition of sample covariance matrix (SCM) to define a subspace containing the dominant interferers to be rejected, thereby allowing it to detect weak signal in the presence of strong interferences. The DMR weight vector takes a form similar to the adaptive minimum …
Local Learning Algorithms For Stochastic Spiking Neural Networks, Bleema Rosenfeld
Local Learning Algorithms For Stochastic Spiking Neural Networks, Bleema Rosenfeld
Dissertations
This dissertation focuses on the development of machine learning algorithms for spiking neural networks, with an emphasis on local three-factor learning rules that are in keeping with the constraints imposed by current neuromorphic hardware. Spiking neural networks (SNNs) are an alternative to artificial neural networks (ANNs) that follow a similar graphical structure but use a processing paradigm more closely modeled after the biological brain in an effort to harness its low power processing capability. SNNs use an event based processing scheme which leads to significant power savings when implemented in dedicated neuromorphic hardware such as Intel’s Loihi chip.
This work …
Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness, Nicholas Furth
Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness, Nicholas Furth
Theses
Machine learning models have been shown to be vulnerable against various backdoor and data poisoning attacks that adversely affect model behavior. Additionally, these attacks have been shown to make unfair predictions with respect to certain protected features. In federated learning, multiple local models contribute to a single global model communicating only using local gradients, the issue of attacks become more prevalent and complex. Previously published works revolve around solving these issues both individually and jointly. However, there has been little study on the effects of attacks against model fairness. Demonstrated in this work, a flexible attack, which we call Un-Fair …
Design And Implementation Of Photovoltaic Energy Harvesting Automaton, Iskandar Askarov
Design And Implementation Of Photovoltaic Energy Harvesting Automaton, Iskandar Askarov
Theses
Global domestic electricity consumption has been rapidly increasing in the past three decades. In fact, from 1990 to 2020, consumption has more than doubled from 10,120 TWh to 23,177 TWh [1]. Moreover, consumers have been turning more towards clean, renewable energy sources such as Photovoltaic. According to International Energy Agency, global Solar power generation alone in 2019 has reached almost 3% [4] of the electricity supply. Even though the efficiency of photovoltaic panels has been growing, presently, the highest efficiency solar panels available to an average consumer range only from 20%-22% [14]. Many research papers have been published to increase …