Local convexity inspired low-complexity non-coherent signal detector for nano-scale molecular communications

Molecular communications via diffusion (MCvD) represents a relatively new area of wireless data transfer with especially attractive characteristics for nanoscale applications. Due to the nature of diffusive propagation, one of the key challenges is to mitigate inter-symbol interference (ISI) that results from the long tail of channel response. Traditional coherent detectors rely on accurate channel estimations and incur a high computational complexity. Both of these constraints make coherent detection unrealistic for MCvD systems. In this paper, we propose a low-complexity and noncoherent signal detector, which exploits essentially the local convexity of the diffusive channel response. A threshold estimation mechanism is proposed to detect signals blindly, which can also adapt to channel variations. Compared to other noncoherent detectors, the proposed algorithm is capable of operating at high data rates and suppressing ISI from a large number of previous symbols. Numerical results demonstrate that not only is the ISI effectively suppressed, but the complexity is also reduced by only requiring summation operations. As a result, the proposed noncoherent scheme will provide the necessary potential to low-complexity molecular communications, especially for nanoscale applications with a limited computation and energy budget

Low-complexity non-coherent signal detection for nano-scale molecular communications

Nano-scale molecular communication is a viable way of exchanging information between nano-machines. In this letter, a low-complexity and non-coherent signal detection technique is proposed to mitigate the intersymbol-interference (ISI) and additive noise. In contrast to existing coherent detection methods of high complexity, the proposed non-coherent signal detector is more practical when the channel conditions are hard to acquire accurately or hidden from the receiver. The proposed scheme employs the concentration difference to detect the ISI corrupted signals and we demonstrate that it can suppress the ISI effectively. The concentration difference is a stable characteristic, irrespective of the diffusion channel conditions. In terms of complexity, by excluding matrix operations or likelihood calculations, the new detection scheme is particularly suitable for nano-scale molecular communication systems with a small energy budget or limited computation resource

A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking

Maneuvering target tracking is a challenging problem for sensor systems because of the unpredictability of the targets' motions. This paper proposes a novel data-driven method for learning the dynamical motion model of a target. Non-parametric Gaussian process regression (GPR) is used to learn a target's naturally shift invariant motion (NSIM) behavior, which is translationally invariant and does not need to be constantly updated as the target moves. The learned Gaussian processes (GPs) can be applied to track targets within different surveillance regions from the surveillance region of the training data by being incorporated into the particle filter (PF) implementation. The performance of our proposed approach is evaluated over different maneuvering scenarios by being compared with commonly used interacting multiple model (IMM)-PF methods and provides around $90\%$ performance improvement for a multi-target tracking (MTT) highly maneuvering scenario.Comment: 11 pages, 10 figure

CircularRNA_0119872 regulates the microRNA-582- 3p/E2F transcription factor 3 pathway to promote the progression of malignant melanoma

OBJECTIVES: Malignant melanoma (MM) is an invasive tumor that poses a threat to patient health. Circular RNAs (circRNAs) are important regulators of MM carcinogenesis. In this study, we investigated the expression characteristics and biological functions of, and mechanism underlying, circ_0119872 expression in MM. METHODS: Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was employed to examine the circ_0119872, microRNA (miR)-582-3p, and E2F transcription factor 3 (E2F3) mRNA expression levels in MM tissues and cell lines. Western blotting was performed to quantify E2F3 protein expression. MM cells with circ_0119872 knockdown were established, and cell counting kit 8 (CCK-8) and transwell assays were utilized to examine the function of circ_0119872 and its effects on the malignant characteristics of MM cells. The MiRDB and TargetScan databases were used to predict the target genes of miR-582-3p. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used to explore the biological functions of the target genes of miR582-3p. Additionally, a dual-luciferase reporter gene experiment was performed to verify the targeting relationship between circ_0119872 and miR-582-3p as well as that between miR-582-3p and E2F3. RESULTS: Circ_0119872 was remarkably upregulated in MM tissues and cell lines. Circ_0119872 knockdown suppressed the cell proliferation and metastasis In addition, miR-582-3p was identified as a downstream target of circ_0119872. The target genes of miR-193a-3p are involved in melanogenesis and cancer-related signaling pathways. Mechanistically, circ_0119872 facilitated MM progression by adsorbing miR-582-3p and upregulating E2F3 expression. CONCLUSION: Circ_0119872 is an oncogenic circRNA that participates in the promotion of MM progression by regulating the miR-582-3p/E2F3 axis

A Gaussian process based method for multiple model tracking

Manoeuvring target tracking faces the challenge caused by the target motion model uncertainty, i.e., unknown model types or uncertain model parameters. Multiple-model (MM) methods have been generally considered to deal with this challenge, in which a bank of elemental filters is run simultaneously to provide a joint decision and estimation of motion model and localisation. However, if the uncertainty of the target trajectory increases, such as the target moves under mixed manoeuvring behaviours with time-varying parameters, more filters with different model assumptions have to be taken into account to match the motion of the target, which may lead to prohibitive computational complexity. To address this problem, we establish a training based algorithm which can learn the actual motion model as a Gaussian process (GP) regression. Then, by integrating the trained GP into the particle filter (PF), a GP-PF based tracking method is developed to track the manoeuvring targets in real-Time. Monte Carlo experiments show that the proposed method had much lower tracking root mean square error (RMSE) and robustness compared with the most commonly used MM methods

Adaptive kernel Kalman filter

Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of the predictive and posterior probability density function (pdf). This paper introduces a Bayesian filter called the adaptive kernel Kalman filter (AKKF). The AKKF approximates the arbitrary predictive and posterior pdf of hidden states using the kernel mean embedding (KME) in reproducing kernel Hilbert space (RKHS). In parallel with the KME, some particles in the data space are used to capture the properties of the dynamic system model. Specifically, particles are generated and updated in the data space. Moreover, the corresponding kernel weight means vector and covariance matrix associated with the particles' kernel feature mappings are predicted and updated in the RKHS based on the kernel Kalman rule (KKR). Simulation results are presented to confirm the improved performance of our approach with significantly reduced numbers of particles by comparing with the unscented Kalman filter (UKF), particle filter (PF), and Gaussian particle filter (GPF). For example, compared with the GPF, the AKKF provides around 50% logarithmic mean square error (LMSE) tracking performance improvement in the bearing-only tracking (BOT) system when using 50 particles