Federated Learning (FL) enables decentralized Machine Learning (ML), focusing on preserving data privacy, but faces a unique set of optimization challenges, such as dealing with non-IID data, communication overhead, and client drift. Adaptive optimiz...
Effective and reliable load forecasting is an important basis for power system planning and operation decisions. Its forecasting accuracy directly affects the safety and economy of the operation of the power system. However, attaining the desired poi...
This paper develops a framework to track the trajectory of a target in 2D by considering a moving ownship able to measure bearing measurements. Notably, the framework allows one to incorporate additional information (e.g., obtained via intelligence)...
Variational inference provides a way to approximate probability densities through optimization. It does so by optimizing an upper or a lower bound of the likelihood of the observed data (the evidence). The classic variational inference approach sugge...
Personnel selection represents a two-sided matching problem in which firms compete for qualified candidates by designing job-offer packages. While traditional models assume fully rational agents, real-world decision-makers often face bounded rational...
The Information Bottleneck (IB) framework offers a theoretically optimal approach to data modeling, although it is often intractable. Recent efforts have optimized supervised deep neural networks (DNNs) using a variational upper bound on the IB objec...
This paper addresses a problem of optimal error quantification in the framework of robust control theory in the 𝓁1 setup. The upper bounds of biased external disturbance and the gains of coprime factor perturbations in a discrete-time linear...
The problem of data exchange between multiple nodes with storage and communication capabilities models several current multi-user communication problems like Coded Caching, Data Shuffling, Coded Computing, etc. The goal in such problems is to design...
We consider the lossless compression bound of any individual data sequence. Conceptually, its Kolmogorov complexity is such a bound yet uncomputable. According to Shannon’s source coding theorem, the average compression bound is nH, where n is...
Modeling of key variable data needs to consider the complex characteristics of systems in the catalytic cracking unit (CCU) of petroleum refining process, such as slow time-varying behavior, complex dynamic properties, distributed traits, and unknown...
In this study, a bound-constrained optimization algorithm is applied for estimating physiological data (pulse and breathing rate) of human body using 60 GHz Doppler radar, by detecting displacements induced by breathing and the heartbeat of a human s...
We illustrate how computer-aided methods can be used to investigate the fundamental limits of the caching systems, which are significantly different from the conventional analytical approach usually seen in the information theory literature. The line...
Sequential change-point detection when the distribution parameters are unknown is a fundamental problem in statistics and machine learning. When the post-change parameters are unknown, we consider a set of detection procedures based on sequential lik...
Kernel methods have been used for turning linear learning algorithms into nonlinear ones. These nonlinear algorithms measure distances between data points by the distance in the kernel-induced feature space. In lossy data compression, the optimal tra...
This paper proposes an upper bound for mine productivity (useful for long-term planning) and also a simple truck dispatch rule (useful for short-term operations) that demonstrates how tight the upper bound can be using a simulation. It also proposes...
The B-spline function representation is commonly used for data approximation and trajectory definition, but filter-based methods for nonlinear weighted least squares (NWLS) approximation are restricted to a bounded definition range. We present an alg...
This work focuses on the development of a Lyapunov-based economic model predictive control (LEMPC) scheme that utilizes recurrent neural networks (RNNs) with an online update to optimize the economic benefits of switched non-linear systems subject to...
The retransmission threshold in wireless sensor networks is critical to the latency of data delivery in the networks. However, existing works on data transmission in sensor networks did not consider the optimization of the retransmission threshold, a...
In this paper, the inverse problem of identifying the source term of the time fractional diffusion-wave equation is studied. This problem is ill-posed, i.e., the solution (if it exists) does not depend on the measurable data. Under the priori bound c...
Video delivery is exploiting 5G networks to enable higher server consolidation and deployment flexibility. Performance optimization is also a key target in such network systems. We present a multi-objective optimization framework for service function...
This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scat...
Increasing importance in the field of artificial intelligence has led to huge progress in remote sensing. Deep learning approaches have made tremendous progress in hyperspectral image (HSI) classification. However, the complexity in classifying the H...
Federated learning (FL) is a distributed machine learning approach that can enable Internet of Things (IoT) edge devices to collaboratively learn a machine learning model without explicitly sharing local data in order to achieve data clustering, pred...
What is the value of just a few bits to a guesser? We study this problem in a setup where Alice wishes to guess an independent and identically distributed (i.i.d.) random vector and can procure a fixed number of k information bits from Bob, who has o...
In this article, we design and evaluate several algorithms for the computation of the optimal Rice coding parameter. We conjecture that the optimal Rice coding parameter can be bounded and verify this conjecture through numerical experiments using re...
A private compression design problem is studied, where an encoder observes useful data Y, wishes to compress them using variable-length code, and communicates them through an unsecured channel. Since Y are correlated with the private attribute X, the...
The optimal allocation of limited resources along with output target setting are critical in pursuing the sustainability and competitiveness of organizations. The process of resource distribution is usually implemented through a central unit that rou...
In this paper, a dynamic multi-task scheduling prototype is proposed to improve the limited resource utilization in the vehicular networks (VNET) assisted by mobile edge computing (MEC). To ensure quality of service (QoS) and meet the growing data de...
Deterministic forecasting models have been used through the years to provide accurate predictive outputs in order to efficiently integrate wind power into power systems. However, such models do not provide information on the uncertainty of the predic...
Computer-aided methods, based on the entropic linear program framework, have been shown to be effective in assisting the study of information theoretic fundamental limits of information systems. One key element that significantly impacts their comput...
Unmanned Aerial Vehicles (UAVs) play an important role in applications such as data collection and target reconnaissance. An accurate and optimal path can effectively increase the mission success rate in the case of small UAVs. Although path planning...
In this paper, we derive lower and upper bounds on the OPTA of a two-user multi-input multi-output (MIMO) causal encoding and causal decoding problem. Each user’s source model is described by a multidimensional Markov source driven by additive...
Considering the effect of the utility harmonic impedance variations on harmonic responsibility, a method based on piecewise bound constrained optimization is proposed in this paper to evaluate the load harmonic responsibilities. The wavelet packet tr...
The Gini index, a widely used economic inequality measure, is computed using data whose designs involve clustering and stratification, generally known as complex household surveys. Under complex household survey, we develop two novel procedures for e...
Age of information (AoI) is an emerging network metric that measures information freshness from an application layer perspective. It can evaluate the timeliness of information in industrial wireless sensor networks (IWSNs). Previous research has prim...
A robust continuous-time linear programming problem is formulated and solved numerically in this paper. The data occurring in the continuous-time linear programming problem are assumed to be uncertain. In this paper, the uncertainty is treated by fol...
In recent years, data-driven deep learning has yielded fruitful results in synthetic aperture radar (SAR) ship detection; weakly supervised learning methods based on horizontal bounding boxes (HBBs) train oriented bounding box (OBB) detectors using H...
A solid-phase extraction (SPE) technique was developed and optimised for isolation and concentration of extractable and bound phenolic acids from germinated spelt seeds, for analysis by liquid chromatography–mass spectrometry. Samples initially under...
Nowadays, rural power supply in China plays an important role in restricting the economic development and improvement of residential living standards. In this study, an interval full-infinite programming rural energy model (IFIP-REM) was developed fo...
Mobile edge computing (MEC) has become more popular both in academia and industry. Currently, with the help of edge servers and cloud servers, it is one of the substantial technologies to overcome the latency between cloud server and wireless device,...
In Long Range Wide Area Network (LoRaWAN), the data rate of the devices can be adjusted to optimize the throughput by changing the spreading factor. However, the adaptive data rate has to be carefully utilized because the collision probability, which...
The Gaussian mixture model (GMM) is commonly used to model the heavy tail of the ground-based augmentation system (GBAS) range error distribution. In practice, Gaussian over-bounding based on a GMM is used to over-bound the heavy tail of the ranging...
In recent years, tools from information theory have played an increasingly prevalent role in statistical machine learning. In addition to developing efficient, computationally feasible algorithms for analyzing complex datasets, it is of theoretical i...
This paper proposes the lower-bounded Fréchet–log-logistic distribution (LFLD), a probability model designed for robust flood frequency analysis (FFA). The LFLD addresses key limitations of traditional distributions (e.g., generalized ex...
In this paper, a globally optimal algorithm based on a maximum feasible subsystem framework is proposed for robust pairwise registration of point cloud data. Registration is formulated as a branch-and-bound problem with mixed-integer linear programmi...
This paper investigates the large time behavior of solutions to the 2D micropolar equations with partial dissipation. When there is mixed velocity dissipation and positive angular viscosity, we obtain the optimal decay estimates to the global solutio...
Dark ships, vessels deliberately disabling their AIS signals, constitute a grave maritime safety hazard, with detection efforts hindered by issues like over-reliance on AIS, inadequate surveillance coverage, and significant mismatch rates. This paper...
For space-based gravitational wave detection, a laser interferometric measurement system composed of a three-spacecraft formation offers the most rewarding bandwidth of astrophysical sources. There are no oscillators available that are stable enough...
In this paper, we study the finite element method of the Navier–Stokes equations with the initial data belonging to the L2 space for all time t>0. Due to the poor smoothness of the initial data, the solution of the problem is singular, altho...
When confronted with massive data streams, summarizing data with dimension reduction methods such as PCA raises theoretical and algorithmic pitfalls. A principal curve acts as a nonlinear generalization of PCA, and the present paper proposes a novel...
of 12