As interest grows in developing devices to harvest energy from ocean waves, tidal currents, and offshore wind, concerns over possible environmental effects from such devices also grow. One such concern is over the induced electric fields and the generated magnetic fields from generators and their associated submarine power cables in...
The Gulf of Alaska (GOA) is home to the most productive fisheries in the world. In 2019, 2.2 million metric tons of fish were shipped from Alaska to destinations all over the world (NOAA Fisheries, 2019). From 2014-2016 and, more recently, in 2019 the largest heatwaves in recorded history caused...
The Internet of Things (IoT) paradigm brought an ever-increasing dependence on low-power devices to collect sensor data and transmit that information to the cloud, placing greater demand on connectivity and lifespan. In response, rapid worldwide innovation demonstrates the trade-offs in processing, communication, and energy consumption with diverse approaches to low-power...
We consider the problem of finding unknown patterns that are recurring across multiple sets. For example, finding multiple objects that are present in multiple images or a short DNA code that is repeated across multiple DNA sequences. We first consider a simple problem of finding a single unknown pattern in...
Efficient time-series analysis can impact multiple application domains such as motif discovery in gene analysis or music data, extracting spectro-temporal patterns in acoustic scene analysis, or annotating and classifying electrical bio-signals (such as ECG, EEG, and EMG) for medical applications.
Time-series analysis involves a variety of tasks.
To predict future...
Currently, a popular approach to image classification uses the deep Transformer architecture. In a Transformer, the attention mechanism enables the model to learn efficiently with fewer computational resources than the convolutional neural networks (CNNs). In this thesis, we study the sparse attention mechanism widely used in the Transformers developed specifically...
Novelty detection plays an important role in machine learning and signal processing. This
project studies novelty detection in a new setting where the data object is represented as
a bag of instances and associated with multiple class labels, referred to as multi-instance
multi-label (MIML) learning. Contrary to the common assumption...
A fully integrated CMOS latched comparator is presented for use as a wake-up circuit that is attached to an RF energy harvester in a battery free wireless sensor network. The system consumes less than 36nA static current at 20°C and dissipates 2pJ of energy per conversion. The comparator comprises of...
Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a good fit for many applications, especially in the cases where complete ground truth data is not available for offline training. In addition, batch methods do...
Phase Locked Loops(PLLs) are an integral part of almost every electronic system. Systems involving low frequency clocks often require PLLs with low bandwidth. The area occupied by the large loop filter capacitor and resistor in a low bandwidth PLL design makes the realization of traditional charge-pump PLL architecture impractical on...