This article focuses on the adaptive Kalman filtering problem for linear systems with unknown covariances of both dynamic multiplicative noise (multiplicative measurement noise) and additive noises (additive process and measurement noises). A recursive-noise adaptive Kalman filter is proposed to estimate both states and covariances of noises by using the variational Bayesian (VB) inference and an indirect method. First, we characterize inverse Wishart priors for both measurement noise covariance and process noise covariance and employ the Student’s t -distribution to represent the likelihood function, which is non-Gaussian and affected by mixing multiplicative noise and additive measurement noise. Then, an adaptive Kalman filtering for recursive both noise covariance matrices and dynamic state is proposed following VB inference. Performance analysis for VB procedures and the proposed filter is provided to ensure the convergence and stability. A target tracking example is provided to validate the effectiveness of the proposed filtering algorithm.

The paper considers target tracking system with multiplicative noise. Gaussian white, colored and non-Gaussian multiplicative noise are taken into account. We present optimal filtering algorithms to settle tracking system contaminated by Gaussian white and colored multiplicative noise and a suboptimal filtering algorithm to tackle the non-Gaussian case. The proposed optimal filtering algorithms are derived by using projection theorem and the suboptimal filtering algorithm is deduced by applying variational Bayesian inference. Simulation results verify the effectiveness of these presented algorithms.

This paper considers estimation algorithms for linear singular systems with random uncertain parameter matrices. We propose an optimal filtering algorithm and an optimal fixed-interval smoothing algorithm to filter out artifacts, such as noise, from the parameter matrix by using the projection theorem and an equivalent transformation. A performance analysis and simulation results verify the proposed algorithms.

Serverless computing has rapidly prospered as a new cloud computing paradigm with agile scalability, pay-as-you-go pricing, and ease-to-use features for Machine Learning (ML) inference tasks. Users package their ML code into lightweight serverless functions and execute them using containers. Unfortunately, a notorious problem, called cold-starts, hinders serverless computing from providing low-latency function executions. To mitigate cold-starts, pre-warming, which keeps containers warm predictively, has been widely accepted by academia and industry. However, pre-warming fails to eliminate the unique latency incurred by loading ML artifacts. We observed that for ML inference functions, the loading of libraries and models takes significantly more time than container warming. Consequently, pre-warming alone is not enough to mitigate the ML inference function's cold-starts. This paper introduces InstaInfer, an opportunistic preloading technique to achieve instant inference by eliminating the latency associated with loading ML artifacts, thereby achieving minimal time cost in function execution. InstaInfer fully utilizes the memory of warmed containers to preload the function's libraries and model, striking a balance between maximum acceleration and resource wastage. We design InstaInfer to be transparent to providers and compatible with existing pre-warming solutions. Experiments on OpenWhisk with real-world workloads show that InstaInfer reduces up to 93% loading latency and achieves up to 8× speedup compared to state-of-the-art pre-warming solutions.

Respiratory diseases are leading causes of death worldwide, and failure to detect diseases at an early stage can threaten people’s lives. Previous research has pointed out that deep learning and machine learning are valid alternative strategies to detect respiratory diseases without the presence of a doctor. Thus, it is worthwhile to develop an automatic respiratory disease detection system. This paper proposes a deep neural network with a blocking variable, namely Blnet, to classify respiratory sound, which integrates the strength of the ResNet, GoogleNet, and the self-attention mechanism. To solve the non-IID data problem, a two-stage training process with the blocking variable was developed. In addition, the mix-up data augmentation within the clusters was used to address the imbalanced data problem. The performance of the Blnet was tested on the ICBHI 2017 data, and the model achieved 79.13% specificity and 66.31% sensitivity, with an average score of 72.72%, which is a 4.22% improvement in the average score and a 12.61% improvement in sensitivity over the state-of-the-art results.