This paper focuses on the problem of learning 6- DOF grasping with a parallel jaw gripper in simulation. Our key idea is constraining and regularizing grasping interaction learning through 3D geometry prediction. We introduce a deep geometry-aware grasping network (DGGN) that decomposes the learning into two steps. First, we learn to build mental geometry-aware representation by reconstructing the scene (i.e., 3D occupancy grid) from RGBD input via generative 3D shape modeling. Second, we learn to predict grasping outcome with its internal geometry-aware representation. The learned outcome prediction model is used to sequentially propose grasping solutions via analysis-by-synthesis optimization. Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations. This dataset includes 101 everyday objects spread across 7 categories, additionally, we propose a data augmentation strategy for effective learning; (3) We demonstrate that the learned geometry-aware representation leads to about 10% relative performance improvement over the baseline CNN on grasping objects from our dataset. (4) We further demonstrate that the model generalizes to novel viewpoints and object instances.

The quality of service inWireless Multimedia Sensor Networks (WMSN) is related to packet loss rate. Recently different studies have been done on developing efficient protocols in the transport layer for controlling packet loss in WMSN. However, all of these protocols are independent of the characteristics of multimedia content. In this paper, a novel transport layer protocol, called User Datagram Dispatcher Protocol (UDDP), is proposed to minimize the packet loss ratio in WMSN by considering traffic characteristics, the inter-arrival pattern of packets and packet priority. UDDP is a new cross-layer transport layer that uses information of MAC and application layers to distribute packet arrivals in a GOP. The experimental results show that our protocol provides performance improvement in terms of high video quality, packet loss, energy conservation and delay compared to other protocols such as CCF and PCCP.

Using Peer-to-Peer (P2P) overlay networks have become a progressively popular approach for streaming live media over the Internet due to their deployment simplicity and scalability. In this paper, we propose a new hybrid push-pull live P2P video streaming protocol called PPM that combines the benefits of pull and push mechanisms for video delivery. Our main goal is to minimize the network end-to-end delay compared to the pure mesh networks. The PPM consists of two phases; Pull-based and Push-based. In the first phase, a new peer joins to the network based on a pull-based mechanism. In the second phase, a parent node based on the peers' overlay hop count in the mesh topology is selected. Then, a dynamic tree is constructed to push the high priority video frames to the children of the selected parent. Using OMNET++ as the simulation platform, we show that beside significant improvement on the end-to-end delay, PPM achieves lower visual distortion compared to the pure mesh networks. Moreover, the simulation results confirm superiority of the PPM in comparison with the popular mesh-based P2P streaming systems.

The main source of energy consumption in the current MAC protocols for wireless sensor networks is idle listening. To mitigate this problem, duty cycling is used. However, it increases data delivery latency. In this paper, we propose an Interference-aware duty-cycle MAC (IMAC) protocol for wireless sensor networks that uses cross-layer information to reserve multiple paths for each source and send data packets along them efficiently. IMAC also handles the existing interference between these paths such that data packets can be delivered in the minimum required number of cycles. Simulation results in ns-2 show that the proposed algorithm has an average reduction of 49% in data delivery latency compared to a current solution called RMAC.

Error control is an important mechanism for providing robust multimedia communication in wireless sensor networks. Although there have been several research works in analysis of error control mechanisms in wireless multimedia networks and wireless sensor networks, but none of them are directly applicable to the wireless multimedia sensor networks (WMSNs) which has resource and performance constraints of WSNs as well as QoS requirements of multimedia communications. In this paper, we comprehensively evaluate the performance of several error control mechanisms in WMSNs. The results of our analysis provide an extensive comparison between automatic repeat request (ARQ), forward error correction (FEC), and hybrid FEC/ARQ error control mechanisms in terms of frame loss rate, frame peak signal-to-noise ratio (PSNR), and energy efficiency.

In this paper, we propose a new algorithm for occlusion handling for object tracking in the crowded video scenes. The algorithm exploits the properties of undecimated wavelet packet transform (UWPT) coefficients and texture analysis to track arbitrary objects. The algorithm is initialized by the user through specifying a region around the object of interest at the reference frame. Then, coefficients of the UWPT of the region construct a Feature Vector (FV) for every pixel in that region. Optimal search for the best match is then performed by using the generated FVs inside an adaptive search window. Adaptation of the search window is achieved by inter- frame texture analysis to find the direction and speed of the object motion. This temporal texture analysis also assists in tracking of the object under partial or short-term full occlusion. Experimental results show a good performance for occlusion handling for object tracking in crowded scenes, in particular crowds on stairs in airports or train stations.

This paper presents a shape prediction algorithm in a noisy video sequence based on pixel representation in the undecimated wavelet domain. In our algorithm for tracking of user-defined shapes in a noisy sequence of images, the amplitude of coefficients in the best basis tree expansion of the undecimated wavelet packet transform are used as feature vectors (FVs). FVs robustness against noise has been achieved through inherent denoising and edge component separation in the best basis selection algorithm. The algorithm uses these FVs to track the pixels of small square blocks located at the vicinity of the object boundary. Searching for the best-matched block has been performed using conventional block matching algorithm in the wavelet domain. Our experimental results show that the algorithm is robust to noise in case of object's shape translation, rotation and/or scaling and can be used to track both rigid and non-rigid shapes in image sequences

This paper describes a new real-time algorithm for tracking user-selected objects in a sequence of images based on a new block matching algorithm in wavelet domain. In our algorithm, the amplitude of coefficients in the best basis tree expansion of undecimated wavelet packet transform is used as the feature vectors (FVs). Real-time object tracking have been achieved using a new search technique for finding the best match among FVs of the reference block and FVs of the search area in the wavelet domain. Our experimental results show that the algorithm is robust to various object deformations and noisy video sequences.