Figure 1: Standard 3D body estimation methods predict bodies that may be inconsistent with the 3D scene even though the results may look reasonable from the camera viewpoint. To address this, we exploit the 3D scene structure and introduce scene constraints for contact and inter-penetration. From left to right: (1) RGB image (top) and 3D scene reconstruction (bottom), (2) overlay of estimated bodies on the original RGB image without (yellow) and with (gray) scene constraints, 3D rendering of both the body and the scene from (3) camera view, (4) top view and (5) side view. AbstractTo understand and analyze human behavior, we need to capture humans moving in, and interacting with, the world. Most existing methods perform 3D human pose estimation without explicitly considering the scene. We observe however that the world constrains the body and vice-versa. To motivate this, we show that current 3D human pose estimation methods produce results that are not consistent with the 3D scene. Our key contribution is to exploit static 3D scene structure to better estimate human pose from monocular images. The method enforces Proximal Relationships with Object eXclusion and is called PROX. To test this, we collect a new dataset composed of 12 different 3D scenes and RGB sequences of 20 subjects moving in and interacting with the scenes. We represent human pose using the 3D human body model SMPL-X and extend SMPLify-X to estimate body pose using scene constraints. We make use of the 3D scene information by formulating two main constraints. The inter-penetration constraint penalizes intersection be-tween the body model and the surrounding 3D scene. The contact constraint encourages specific parts of the body to be in contact with scene surfaces if they are close enough in distance and orientation. For quantitative evaluation we capture a separate dataset with 180 RGB frames in which the ground-truth body pose is estimated using a motion capture system. We show quantitatively that introducing scene constraints significantly reduces 3D joint error and vertex error. Our code and data are available for research at https://prox.is.tue.mpg.de.

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Finite-state Markov chain (FSMC) models have often been used to characterize the wireless channel. The fitting is typically performed by partitioning the range of the received signal-to-noise ratio (SNR) into a set of intervals (states). Different partitioning criteria have been proposed in the literature, but none of them was targeted to facilitating the analysis of the packet delay and loss performance over the wireless link. In this paper, we propose a new partitioning approach that results in an FSMC model with tractable queueing performance. Our approach utilizes Jake's level-crossing analysis, the distribution of the received SNR, and the elegant analytical structure of Mitra's producer-consumer fluid queueing model. An algorithm is provided for computing the various parameters of the model, which are then used in deriving closed-form expressions for the effective bandwidth (EB) subject to packet loss and delay constraints. Resource allocation based on the EB is key to improving the perceived capacity of the wireless medium. Numerical investigations are carried out to study the interactions among various key parameters, verify the adequacy of the analysis, and study the impact of error control parameters on the allocated bandwidth for guaranteed packet loss and delay performance.

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The perceived video quality in a wireless streaming application strongly depends on the channel's dynamics and the fluctuations of the source bit rate. In this paper, we introduce two channel-adaptive rate control schemes for slowly and fast varying channels, respectively. Both schemes account for the playback buffer occupancy in the joint optimization of the source rate and channel-code forward error correction parameters. For the first scheme, we assume that the channel state does not change during the transmission of a video frame. We optimize the channel-code parameters and maximize the per-frame source rate subject to satisfying a constraint on the probability of delivering the next video frame within a buffer-occupancy-dependent critical time ( ). For the second scheme, we allow the channel state to change within the frame delivery period, and we compute the optimal system parameters and maximize the source rate while satisfying a constraint on the mean frame delivery time. Our schemes aim at maintaining the occupancy of the playback buffer around a predefined threshold value, hence ensuring continuous video playback. Simulation and numerical investigations are carried out to study the interactions among various key parameters and verify the adequacy of the analysis.Index Terms-Adaptive forward error correction (FEC), channel-code optimization, playback buffer control, source rate control, wireless channels.

Internet of things IoT is playing a remarkable role in the advancement of many fields such as healthcare, smart grids, supply chain management, etc. It also eases people's daily lives and enhances their interaction with each other as well as with their surroundings and the environment in a broader scope. IoT performs this role utilizing devices and sensors of different shapes and sizes ranging from small embedded sensors and wearable devices all the way to automated systems. However, IoT networks are growing in size, complexity, and number of connected devices. As a result, many challenges and problems arise such as security, authenticity, reliability, and scalability. Based on that and taking into account the anticipated evolution of the IoT, it is extremely vital not only to maintain but to increase confidence in and reliance on IoT systems by tackling the aforementioned issues. The emergence of blockchain opened the door to solve some challenges related to IoT networks. Blockchain characteristics such as security, transparency, reliability, and traceability make it the perfect candidate to improve IoT systems, solve their problems, and support their future expansion. This paper demonstrates the major challenges facing IoT systems and blockchain's proposed role in solving them. It also evaluates the position of current researches in the field of merging blockchain with IoT networks and the latest implementation stages. Additionally, it discusses the issues related to the IoT-blockchain integration itself. Finally, this research proposes an architectural design to integrate IoT with blockchain in two layers using dew and cloudlet computing. Our aim is to benefit from blockchain features and services to guarantee a decentralized data storage and processing and address security and anonymity challenges and achieve transparency and efficient authentication service.