Sparse Bayesian learning (SBL) is an advanced statistical framework that dominantly enhances the sparse features of targets of interest in radar imagery. A widely adopted strategy for posterior approximation in SBL is the variational Bayesian (VB) inference, which circumvents the intractable high-dimensional integrals involved in direct posterior computation and yields closed-form expressions for the posterior moments. However, conventional VB inference suffers from computationally expensive matrix inversions, particularly in high-resolution radar imaging scenarios. To this end, a matrix-inversion-free SBL (MIF-SBL) method via improved VB inference is proposed. Within this framework, the sparsity-inducing hierarchical scheme based on the scaled Gaussian mixture model is retained, while explicit matrix inversion is replaced by solving a series of linear problems, which are efficiently handled via matrix–matrix multiplications. The proposed method reduces the computational cost by eliminating the need for direct matrix inversion, especially for the reconstruction of high-resolution radar imagery. Extensive experiments on both simulated and real radar datasets demonstrate that the proposed approach achieves improved computational efficiency while maintaining reconstruction accuracy comparable to conventional SBL.

Conventional statistical-driven synthetic aperture radar (SAR) imaging algorithms can only encode a single and/or static prior, leading to that limited features can be accessed quantitatively. To this end, a novel multi-task learning framework is proposed by devising a divide-and-conquer variational Bayesian (DC-VB) inference, so that elaborated features of interests can be exploited for a high-resolution SAR imagery. Specifically, a flexible generalized Gaussian distribution (GGD) and a customized hybrid probability distribution are introduced and employed for the priors of features of interests. To resolve the resultant complicated Bayesian inference, splitted random variables are incorporated, so that the joint posterior problem can be decomposed into multiple local problems that are easy to be solved. Simultaneously, dual variables are established for residual errors of the decomposition. To guarantee a global solution for the image of the target of interests, the data augmentation is employed to coordinate multiple local solutions. Therefore, the intended Bayesian inference works in a divide-and-conquer manner, which is superior in quantization of multiple features of high-resolution SAR imagery. It is capable of incorporating multiple priors in a fully-statistical probability and guaranteeing closed-form solutions of posterior distributions. Unavoidable propagation errors can be minimized in the DC-VB process. Raw SAR data is applied to validate the effectiveness of the proposed algorithm. Comparisons with conventions show the superiority in terms of qualitative and quantitative aspects.

This paper investigates a bi-static integrated sensing and communication (ISAC) system for multi-target scenarios using impulse radio ultra-wideband (IR-UWB) signals, which offer fine temporal resolution, low power consumption, and strong resistance to multipath interference. Two typical modulation schemes, namely pulse position modulation (PPM) and binary phase shift keying (BPSK), are considered for communication over the delay and phase domains, respectively. An innovative differential decoupling strategy is proposed, which eliminates the need for pilot symbols by leveraging the known starting symbol position. The sensing performance under various modulation and demodulation schemes is analyzed and compared with the conventional pilot-based (time-delay) decoupling strategy under current UWB standards. A key contribution of this work is the development of a unified analytical framework based on the Fisher information matrix (FIM), which characterizes the fundamental coupling between communication and sensing in both delay and Doppler domains. This coupling is examined through the singularity structure of the FIM, providing new insights into the joint performance limits of UWB-ISAC systems. Performance evaluation is conducted using the Cramer-Rao Lower Bound (CRLB) for sensing and the data transmission rate for communication, offering theoretical insights into choosing suitable data signal processing methods in real-world applications.

Due to the advantages in hardware implementation and spectrum efficiency, the integration of sensing and commu-nications (ISAC) has been attractive so far. In this paper, the inherent tradeoffs between sensing and communications (S&C) in frequency division multiplexing and waveform integration systems are analyzed, based on the high time resolution impulse radio ultra wideband (IR-UWB) signals. More specifically, we introduce two resource allocation schemes from the power and bandwidth perspective, to show the corresponding performance metrics including the Cramer Rao Bound (CRB) and Shannon limits for S&C. Lastly, the latest UWB standards are introduced as constraints, to perform evaluations.

Ultra wideband (UWB) systems have received great interest again due to the high range resolution, flexible data transmission capability, and low power consumption. In this paper, we develop a practical asynchronous integrated sensing and communication (ISAC) system using impulse radio UWB signals. This system operates within a joint mono-bistatic sensing network, accommodating multiple static and dynamic targets. To achieve simultaneous communication and target sensing, a reliable soft information based decouple solution is proposed to perform data demodulation in the typical UWB modulation waveforms. The data transmission can benefit from proper channel sensing, to about 2-3 dB gain, by exploiting the multipath components for demodulation. In addition to the demodulated data bits at the bi-static receiver, the environmental target distance and Doppler shift can also be achieved at both mono- and bi-static receivers, respectively. We then evaluate the sensing capability of the ISAC UWB system, by extensive simulations and practical experiments. The target tracking accuracy can be achieved within 20 cm at over 80% confidence, with commercial UWB devices according to practical measurements.

In this paper, we construct a totally asynchronous integrated sensing and communication (ISAC) system using impulse radio ultra-wideband (IR-UWB) signals. In a typical bistatic network in the presence of several static and dynamic targets, both line-of-sight (LOS) and multi-path components (MPCs) are considered. The transmitted data symbols demodulation and the Doppler measurement of targets within the environment could be achieved simultaneously, using a low complexity sequential estimation algorithm. In addition, the data transmission could achieve about 1.5 dB gain from the channel sensing, due to the fact that the extra MPC energy could be collected for data demodulation in this framework.

The integrated sensing and communication (ISAC) where both sensing and communication may share resources from multiple domains, has become a key point in future 6G wireless networks. One fundamental problem to ISAC is to discuss the essential tradeoff between sensing and communications. In this paper, we try to exploit this issue using the estimation theory, since transmitted symbols and channel characterizations are essentially unknown parameters to be jointly estimated. The Fisher information matrix (FIM) and Cramer Rao lower bound (CRB) are both used to give the ISAC performance benchmarks. The impulse radio ultra wideband (IR-UWB) signals are adopted for the case study. We first give the general models on the UWB ISAC system. We then compare the CRBs of sensing only, to ISAC systems, to illustrate the information coupling between sensing and communications. After proper decoupling operations such as pilots, we can see the inevitable performance degradation in ISAC, where uncertainties are essentially introduced by transmitted symbols. Furthermore, we also perform the energy optimization between pilots and data bits, to verify the tradeoff between sensing and communications.

Ultra wideband (UWB) signals, with their high time resolution, large bandwidth, and low energy consumption, hold great promise as candidates for future integrated sensing and communication (ISAC) systems. In this article, we attempt to explore the tradeoff between communication and sensing by estimating the unknown parameters of transmitted symbols and channel characteristics. We utilize the Fisher Information Matrix (FIM) and Cramr-Rao Bound (CRB) to quantify the performance. Firstly, due to the coupling between channel parameters and transmitted symbols, a differential decoupling approach is proposed. Subsequently, we conducted a comparative analysis of sensing performance across various decoupling methods. We can see that the transmitted symbols have noticeable degrading effects under typical channel parameters. Numerical results are provided, to show the decouple performance degradation on different decoupling schemes.

Due to the high time resolution, large bandwidth and low energy consumption, impulse radio ultra wideband (IRUWB) signals have been promising candidates for the forthcoming integrated sensing and communication (ISAC) systems. In this paper, we employ the parameter estimation theory as a methodology to demonstrate the inherent interdependence between channel parameters and transmitted symbols. Specifically, we introduce the Equivalent Fisher Information Matrix (EFIM) to show the implicit coupling between these two functions. According to proper decoupling operations such as pilots, we can see that the estimation performance of typical channel parameters, such as the propagation delay and Doppler shift of every multipath component (MPC) will be evidently degraded due to the unknown transmitted symbols. Finally, the influence on sensing performance of two main stream IR-UWB signal modulation schemes-the pulse position modulation (PPM) and binary phase shift keying (BPSK) in the ISAC system are then evaluated for decoupling analysis.

The Markowitz model, a Nobel Prize winning model for portfolio analysis, paves the theoretical foundation in finance for modern investment. However, it remains a challenging problem in the high frequency trading (HFT) era to find a more time efficient solution for portfolio analysis, especially when considering circumstances with the dynamic fluctuation of stock prices and the desire to pursue contradictory objectives for less risk but more return. In this paper, we establish a recurrent neural network model to address this challenging problem in runtime. Rigorous theoretical analysis on the convergence and the optimality of portfolio optimization are presented. Numerical experiments are conducted based on real data from Dow Jones Industrial Average (DJIA) components and the results reveal that the proposed solution is superior to DJIA index in terms of higher investment returns and lower risks.

Verifying the user identity of wearable devices is crucial for system security, especially before sensitive operations like making financial payments. A PPG-based two-factor authentication can be a promising solution with widely deployed PPG (Photoplethysmography) sensors within wearable devices. Our observations find PPG readings reveal a significant relevance to the user’s hand motions, i.e., gestures, while the user’s heartbeat characteristics and wearing habits are also implicitly related, which can be utilized for user authentication. In this paper, we design G-PPG, a gesture-related PPG-based two-factor authentication mechanism that can non-intrusively validate the user’s identity. In G-PPG, gesture detection and segmentation and a specific feature set are proposed for accurate gesture-related PPG characteristic extraction. Moreover, an adaptive update scheme is proposed for the high accuracy of long-term authentication. Our experiments among 15 participants demonstrate that G-PPG can achieve a 90% accuracy in the long-term study.

Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited. Thus, new approaches are necessary to explore the genetic architecture of insulin resistance. To that end, we generated an iPSC library across the spectrum of insulin sensitivity in humans. RNA-seq based analysis of 310 induced pluripotent stem cell (iPSC) clones derived from 100 individuals allowed us to identify differentially expressed genes between insulin resistant and sensitive iPSC lines. Analysis of the co-expression architecture uncovered several insulin sensitivity-relevant gene sub-networks, and predictive network modeling identified a set of key driver genes that regulate these co-expression modules. Functional validation in human adipocytes and skeletal muscle cells (SKMCs) confirmed the relevance of the key driver candidate genes for insulin responsiveness.