High-Confidence Data-Driven Ambiguity Sets for Time-Varying Linear Systems

This paper builds Wasserstein ambiguity sets for the unknown probability distribution of dynamic random variables leveraging noisy partial-state observations. The constructed ambiguity sets contain the true distribution of the data with quantifiable probability and can be exploited to formulate robust stochastic optimization problems with out...

Unrolling of Simplicial ElasticNet for Edge Flow Signal Reconstruction

The edge flow reconstruction task consists of retreiving edge flow signals from corrupted or incomplete measurements. This is typically solved by a regularized optimization problem on higher-order networks such as simplicial complexes and the corresponding regularizers are chosen based on prior knowledge. Tailoring this prior to the setting...

A Highly Linear Receiver Using Parallel Preselect Filter for 5G Microcell Base Station Applications

By introducing three different techniques, this article, for the first time, presents a wideband highly linear receiver (RX) capable of handling blocking scenarios in fifth-generation (5G) microcell base station applications. First, a parallel preselect filter is introduced to satisfy the base station co-location blocking requirements. Next,...

Comparison of Semi-Empirical Noise Models with Flyover Measurements of Operating Aircraft

The contribution of the engine and the airframe to the total noise generated by an aircraft varies with the operating conditions. Semi-empirical models are able to account for such variations but require detailed engine and airframe data as input that is not readily available for most aircraft types and operations. This hinders the validation of...

Nessy: A Neuro-Symbolic System for Label Noise Reduction

Noisy labels represent one of the key issues in supervised machine learning. Existing work for label noise reduction mainly takes a probabilistic approach that infers true labels from data distributions in low-level feature spaces. Such an approach is not only limited by its capability to learn high-quality data representations, but also by...

A new detection method for noisy channels with time-varying offset

We consider noisy communications and storage systems that are hampered by varying offset of unknown magnitude such as low-frequency signals of unknown amplitude added to the sent signal. We study and analyze a new detection method whose error performance is independent of both unknown base offset and offset's slew rate. The new method...

A 76.5-92.6 GHz CMOS LNA Using Two-Port kQ-Product Theory for Transformer Design

This letter presents a convenient approach based on the two-port kQ-product theory to analyze the influence of interwinding capacitive coupling on the efficiency of the transformer. It is demonstrated that a transformer with proper size can benefit from the interwinding capacitive coupling to maximize its efficiency at a desired frequency....

Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control

Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model. This paper presents a new Instrumental Variable (IV) approach to Data-enabled Predictive Control (DeePC) that results in favorable noise mitigation properties, and demonstrates the direct equivalence...

Gas Turbines Power Regulation Subject to Actuator Constraints, Disturbances and Measurement Noises

An accurate understanding of the output power functionality in gas turbines concerning the fuel, inlet valve status and other parameters can provide a general overview of the turbine's performance. In this study, the problem of practical power regulation in gas turbines is investigated in the simultaneous presence of actuator constraints,...

Detecting Socially Significant Music Events using Temporally Noisy Labels

In this paper, we focus on event detection over the timeline of a music track. Such technology is motivated by the need for innovative applications such as searching, non-linearaccess and recommendation. Event detection over the timeline requires time-code level labels in order to train machine learning dels. We use timed comments from...

A Low-Cost Robust Distributed Linearly Constrained Beamformer for Wireless Acoustic Sensor Networks with Arbitrary Topology

We propose a new robust distributed linearly constrained beamformer which utilizes a set of linear equality constraints to reduce the cross power spectral density matrix to a block-diagonal form. The proposed beamformer has a convenient objective function for use in arbitrary distributed network topologies while having identical performance...

Biometric palmprint verification: A dynamical system approach

Most of the existing techniques for palmprint recognition rely on metrics, typically based on static functions, which evaluate the distance between a pair of features. In this paper, we propose a new technique for palmprint verification based on a dynamical system approach for principal palm lines matching. The proposed dynamic algorithm is...