Video modeling via implicit motion representations

Video modeling refers to the development of analytical representations for explaining the intensity distribution in video signals. Based on the analytical representation, we can develop algorithms for accomplishing particular video-related tasks. Therefore video modeling provides us a foundation to bridge video data and related-tasks. Although there are many video models proposed in the past decades, the rise of new applications calls for more efficient and accurate video modeling approaches.;Most existing video modeling approaches are based on explicit motion representations, where motion information is explicitly expressed by correspondence-based representations (i.e., motion velocity or displacement). Although it is conceptually simple, the limitations of those representations and the suboptimum of motion estimation techniques can degrade such video modeling approaches, especially for handling complex motion or non-ideal observation video data. In this thesis, we propose to investigate video modeling without explicit motion representation. Motion information is implicitly embedded into the spatio-temporal dependency among pixels or patches instead of being explicitly described by motion vectors.;Firstly, we propose a parametric model based on a spatio-temporal adaptive localized learning (STALL). We formulate video modeling as a linear regression problem, in which motion information is embedded within the regression coefficients. The coefficients are adaptively learned within a local space-time window based on LMMSE criterion. Incorporating a spatio-temporal resampling and a Bayesian fusion scheme, we can enhance the modeling capability of STALL on more general videos. Under the framework of STALL, we can develop video processing algorithms for a variety of applications by adjusting model parameters (i.e., the size and topology of model support and training window). We apply STALL on three video processing problems. The simulation results show that motion information can be efficiently exploited by our implicit motion representation and the resampling and fusion do help to enhance the modeling capability of STALL.;Secondly, we propose a nonparametric video modeling approach, which is not dependent on explicit motion estimation. Assuming the video sequence is composed of many overlapping space-time patches, we propose to embed motion-related information into the relationships among video patches and develop a generic sparsity-based prior for typical video sequences. First, we extend block matching to more general kNN-based patch clustering, which provides an implicit and distributed representation for motion information. We propose to enforce the sparsity constraint on a higher-dimensional data array signal, which is generated by packing the patches in the similar patch set. Then we solve the inference problem by updating the kNN array and the wanted signal iteratively. Finally, we present a Bayesian fusion approach to fuse multiple-hypothesis inferences. Simulation results in video error concealment, denoising, and deartifacting are reported to demonstrate its modeling capability.;Finally, we summarize the proposed two video modeling approaches. We also point out the perspectives of implicit motion representations in applications ranging from low to high level problems

Effect of user mobility and channel fading on the outage performance of UAV communications

Many wireless networks operate in a mobile environment with randomly moving user terminals. This letter analytically characterizes the impact of ground user mobility, propagation environment and channel fading on the outage performance of unmanned aerial vehicle (UAV) communications. Closed-form expressions for the outage probability using the random waypoint model for ground user mobility, UAV channel models for different propagation environments and the Nakagamim model for fading channels are derived. Furthermore, the outage analysis takes into account the effect of co-channel interference by both the stationary and mobile users. Numerical results are presented to demonstrate the interplay between the communication performance and the system parameters

Optimum deployment of multiple UAVs for coverage area maximization in the presence of co-channel interference

The use of unmanned aerial vehicle (UAV) as aerial base stations can provide wireless communication services in the form of UAV-based small cells (USCs). Thus, the major design challenge that needs to be addressed is the coverage maximization of such USCs in the presence of co-channel interference generated by multiple UAVs operating within a specific target area. Consequently, the efficient deployment strategy is imperative for USCs while optimizing the coverage area performance to compensate the impact of interference. To this end, this paper presents a coordinated multi-UAV strategy in two scenarios. In the first scenario, symmetric placement of UAVs is assumed at a common optimal altitude and transmit power. In the second scenario, asymmetric deployment of UAVs with different altitudes and transmit powers is assumed. Then, the coverage area performance is investigated as a function of separation distance between UAVs which are deployed in a certain geographical area to satisfy a target signal-to-interference-plus-noise ratio (SINR) at the cell boundary. Finally, the system-level performance of a boundary user is studied in terms of the coverage probability. Numerical results unveils that the SINR threshold, the separation distance, and the number of UAVs and their formations should be carefully selected to achieve the maximum coverage area inside and to reduce the unnecessary expansion outside the target area. Thus, this paper provides important design guidelines for the deployment of multiple UAVs in presence of co-channel interference

Molecular basis for heat desensitization of TRPV1 ion channels.

The transient receptor potential vanilloid 1 (TRPV1) ion channel is a prototypical molecular sensor for noxious heat in mammals. Its role in sustained heat response remains poorly understood, because rapid heat-induced desensitization (Dh) follows tightly heat-induced activation (Ah). To understand the physiological role and structural basis of Dh, we carried out a comparative study of TRPV1 channels in mouse (mV1) and those in platypus (pV1), which naturally lacks Dh. Here we show that a temperature-sensitive interaction between the N- and C-terminal domains of mV1 but not pV1 drives a conformational rearrangement in the pore leading to Dh. We further show that knock-in mice expressing pV1 sensed heat normally but suffered scald damages in a hot environment. Our findings suggest that Dh evolved late during evolution as a protective mechanism and a delicate balance between Ah and Dh is crucial for mammals to sense and respond to noxious heat

Performance analysis of hybrid UAV networks for probabilistic content caching

Caching content in small-cell networks can reduce the traffic congestion in backhaul. In this article, we develop a hybrid caching network comprising of unmanned aerial vehicles (UAVs) and ground small-cell base stations (SBSs), where UAVs are preferred because of their flexibility and elevated platform for line of sight. First, we derive the association probability for the ground user affiliated with a UAV and ground SBS. Then, we derive the successful content delivery probability by considering both the intercell and intracell interference. We also analyze the energy efficiency of the hybrid network and compare it with the separate UAV and ground networks. We further propose the caching scheme to improve the successful content delivery by managing the content popularity, where the part of the caching capacity in each UAV and ground SBS is reserved to store the most popular content (MPC), while the remaining stores less popular contents. Numerical results unveil that the proposed caching scheme has an improvement of 26.6% in content delivery performance over the MPC caching, which overlooks the impact of content diversity during caching

Robust Blind Equalization for NB-IoT Driven by QAM Signals

The expansion of data coverage and the accuracy of decoding of the narrowband-internet of things (NB-IOT) mainly depend on the quality of channel equalizers. Without using training sequences, blind equalization is an effective method to overcome adverse effects in the internet of things (IoT). The constant modulus algorithm (CMA) has become a favorite blind equalization algorithm due to its least mean square (LMS)-like complexity and desirable robustness property. However, the transmission of high-order quadrature amplitude modulation (QAM) signals in the IoT can degrade its performance and the convergence speed. This paper investigates a family of modified constant modulus algorithms for blind equalization of IoT using high-order QAM. Our theoretical analysis for the first time illustrates that the classical CMA has the problem of artificial error using high-order QAM signals. In order to effectively deal with these issues, a modified constant modulus algorithm (MCMA) is proposed to decrease the modulus matched error, which can efficiently suppress the artificial error and misadjustment at the expense of reduced sample usage rate. Moreover, a generalized form of the MCMA (GMCMA) is developed to improve the sample usage rate and guarantee the desirable equalization performance. Two modified Newton methods (MNMs) for the proposed MCMA and GMCMA are constructed to obtain the optimal equalizer. Theoretical proofs are presented to show the fast convergence speed of the two MNMs. Numerical results show that our methods outperform other methods in terms of equalization performance and convergence speed

Chinese-to-English phonetic transfer of Chinese university EFL students

Phonetic transfer is defined as an L1 influence on the acquisition of L2 phonetics. Previous studies have investigated phonetic transfer in the area of articulation, but the effects of L1 on L2 pronunciation measured by speech recognition technology have been under-researched. This study aims to address the issue by focusing on a sample of 676 Chinese university ESL students. Drawing on quantitative data, it examined whether the participants applied phonetic transfer to ESL learning and what factors might have influenced the results of phonetic transfer. We assumed that Chinese-to-English phonetic transfer occurs but that the extent of the transfer would be small because Chinese and English belong to different language families. However, findings from this study confirm that Chinese-to-English phonetic transfer occurs and the extent is large. The findings regarding high transferability might be attributed to spelling through phonics and the nature of pronunciation acquisition