Depth from Defocus via Active Quasi-random Point Projections

Depth sensing has many practical applications in vision-relatedtasks. While many different depth measurement techniques existand depth camera technologies are constantly being advanced, activedepth sensing still rely on specialized hardware that are highlycomplex and costly. Motivated by this, we present a novel techniquefor inferring depth measurements via depth from defocus usingactive quasi-random point projection patterns. A quasi-randompoint projection pattern is projected onto the scene of interest, andeach projection point in the image captured by a camera is analysedusing a calibration model to estimate the depth at that point.The proposed method has a relatively simple setup, consisting of acamera and a projector, and enables depth inference from a singlecapture. Furthermore, the use of a quasi-random projection patterncan allow us to leverage compressive sensing theory to producefull depth maps in future applications. Experimental resultsshow the proposed system has strong potential for enabling activedepth sensing in a simple, efficient manner

Pharmacogenomics of neuroimmune interactions in human psychiatric disorders

There is bidirectional communication between the brain and the immune system. Overproduction of interleukin-1β (IL-1β) leads to systemic inflammatory response syndrome (SIRS). The crucial role of IL-1β in inflammation has been highlighted by studies p

Large System Analysis of Downlink MISO-NOMA System via Regularized Zero-Forcing Precoding with Imperfect CSIT

This letter studies the multiple-input single-output (MISO) non-orthogonal multiple-access (NOMA) downlink using regularized zero-forcing (RZF) precoding with imperfect channel state information (CSI). We first propose a new user scheduling scheme based on imperfect CSI and a model to characterize the channel correlation between the weak and strong users. Then we derive an approximate expression of the ergodic sum-rate using large-system random matrix theory. This approximation permits us to derive the optimal power allocation scheme that satisfies the rate requirement of the weak users. Simulation results are presented to confirm the accuracy of the approximation and reveal the relationships between the ergodic sum-rate, the channel correlation, and other system parameters

Secure Localization and Velocity Estimation in Mobile IoT Networks with Malicious Attacks

IEEE Secure localization and velocity estimation are of great importance in Internet of Things (IoT) applications and are particularly challenging in the presence of malicious attacks. The problem becomes even more challenging in practical scenarios in which attack information is unknown and anchor node location uncertainties occur due to node mobility and falsification of malicious nodes. This challenging problem is investigated in this paper. With reasonable assumptions on the attack model and uncertainties, the secure localization and velocity estimation problem is formulated as an intractable maximum a posterior (MAP) problem. A variational-message-passing (VMP) based algorithm is proposed to approximate the true posterior distribution iteratively and find the closed-form estimates of the location and velocity securely. The identification of malicious nodes is also achieved in the meantime. The convergence of the proposed VMP-based algorithm is also discussed. Numerical simulations are finally conducted and the results show the VMP-based joint localization and velocity estimation algorithm can approach the Bayesian Cramer Rao bound and is superior to other secure algorithms

Use of functional MRI to evaluate correlation between acupoints and brain cortex activites: comparison between conventional and electrical acupuncture

The use of acupuncture therapy in various functional disorders goes back several thousand years in China. Recendy, acupuncture becomes a 'hot' topic in the functional Magnetic Resonance (MR) imaging research studies [1-4]. A majority of these research projects is to study die correlation between the acupuncture points (acupoints) and die corresponding brain cortices, either by conventional acupuncture, electro-acupuncture or laser acupuncture. Cho et al reported mat by stimulating die vision-related acupoints (BL60, BL65, BL66, BL67) at the lateral side of die foot, neural response (i.e. high signal) was obtained at die visual cortex [1]. In this study, we verified Cho et al acupoints using conventional acupuncture and electro-acupuncture.published_or_final_versio

Robust Localization for Mixed LOS/NLOS Environments With Anchor Uncertainties

Localization is particularly challenging when the environment has mixed line-of-sight (LOS) and non-LOS paths and even more challenging if the anchors’ positions are also uncertain. In the situations in which the parameters of the LOS-NLOS propagation error model and the channel states are unknown and uncertainties for the anchors exist, the likelihood function of a localizing node is computationally intractable. In this paper, assuming the knowledge of the prior distributions of the error model parameters and that of the channel states, we formulate the localization problem as the maximization problem of the posterior distribution of the localizing node. Then we apply variational distributions and importance sampling to approximate the true posterior distributions and estimate the target’s location using an asymptotic minimum mean-square-error (MMSE) estimator. Furthermore, we analyze the convergence and complexity of the proposed variational Bayesian localization (VBL) algorithm. Computer simulation results demonstrate that the proposed algorithm can approach the performance of the Bayesian Cramer-Rao bound (BCRB) and outperforms conventional algorithm