Jun Jason Zhang

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We integrate multipath exploitation with adaptive waveform design in order to increase the tracking performance of a vehicle moving in urban terrain. Mitigation of both clutter and strong multipath returns can result in increased target detection. However, exploiting multiple bounces from obstacles such as buildings can be shown to increase radar coverage(More)
Current DBS therapy delivers a train of electrical pulses at set stimulation parameters. This open-loop design is effective for movement disorders, but therapy may be further optimized by a closed loop design. The technology to record biosignals has outpaced our understanding of their relationship to the clinical state of the whole person. Neuronal(More)
Probability hypothesis density (PHD) filtering, implemented using particle filters, is a Bayesian technique used to non-linearly track multiple objects. In this paper, we propose a new approach based on PHD particle filters (PHD-PF) to automatically track the number of magnetoencephalography (MEG) neural dipole sources and their unknown states. In(More)
We investigate a characterization of underwater acoustic signals using extracted time-scale features of the propagation channel model for medium-to-high frequency range. The underwater environment over these frequencies causes multipath and Doppler scale changes on the transmitted signal. This is the result of the time-varying nature of the channel and also(More)
Multiple-input, multiple-output (MIMO) radar systems have gained significant attention as they can enhance target detection, identification and parameter estimation performance. In this paper, we consider the problem of optimizing the target tracking performance of a widely-separated MIMO radar system by scheduling the transmitter sensors and adaptively(More)
This paper presents a method for representation and classification of microscopic tissue images using the shear let transform. The objective is to automatically process biopsy tissue images and assist pathologists in analyzing carcinoma cells, e.g. differentiating between benign and malignant cells in breast tissues. Compared with wavelet filters such as(More)
We propose new Bayesian algorithms to automatically track current dipole sources of neural activity in real time. We integrate multiple particle filters to track the dynamic parameters of a known number of dipole sources, resulting in reducing the computational intensity incurred due to the large number of sensors required to observe magnetoencephalography(More)
Sequential Monte Carlo particle filters (PFs) are useful for estimating nonlinear non-Gaussian dynamic system parameters. As these algorithms are recursive, their real-time implementation can be computationally complex. In this paper, we analyze the bottlenecks in existing parallel PF algorithms, and we propose a new approach that integrates parallel PFs(More)