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Distributed video coding in wireless multimedia sensor network for multimedia broadcasting
Recently the development of Distributed Video Coding (DVC) has provided the promising theory
support to realize the infrastructure of Wireless Multimedia Sensor Network (WMSN), which composed of autonomous hardware for capturing and transmission of quality audio-visual content. The implementation of DVC in WMSN can better solve the problem of energy constraint of the sensor nodes due to the benefit of lower computational encoder in DVC. In this paper, a practical DVC scheme, pixel-domain Wyner-Ziv(PDWZ) video
coding, with slice structure and adaptive rate selection(ARS) is proposed to solve the certain problems when applying DVC into WMSN. Firstly, the proposed slice structure in PDWZ has extended the feasibility of PDWZ to work with any interleaver size used in Slepian-wolf turbo codec for heterogeneous applications. Meanwhile,
based on the slice structure, an adaptive code rate selection has been proposed aiming at reduce the system delay occurred in feedback request. The simulation results clearly showed the enhancement in R-D performance and perceptual quality. It also can be observed that system delay caused by frequent feedback is greatly reduced, which gives a promising support for WMSN with low latency and facilitates the QoS management
Effect of oil palm empty fruit bunches (OPEFB) fibers to the compressive strength and water absorption of concrete
Growing popularity based on environmentally-friendly, low cost and lightweight building materials in the construction industry has led to a need to examine how these characteristics can be achieved and at the same time giving the benefit to the environment and maintain the material requirements based on the standards required. Recycling of waste generated from industrial and agricultural activities as measures of building materials is not only a viable solution to the problem of pollution but also to produce an economic design of building
Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data
Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future
Hierarchical Attention Network for Visually-aware Food Recommendation
Food recommender systems play an important role in assisting users to
identify the desired food to eat. Deciding what food to eat is a complex and
multi-faceted process, which is influenced by many factors such as the
ingredients, appearance of the recipe, the user's personal preference on food,
and various contexts like what had been eaten in the past meals. In this work,
we formulate the food recommendation problem as predicting user preference on
recipes based on three key factors that determine a user's choice on food,
namely, 1) the user's (and other users') history; 2) the ingredients of a
recipe; and 3) the descriptive image of a recipe. To address this challenging
problem, we develop a dedicated neural network based solution Hierarchical
Attention based Food Recommendation (HAFR) which is capable of: 1) capturing
the collaborative filtering effect like what similar users tend to eat; 2)
inferring a user's preference at the ingredient level; and 3) learning user
preference from the recipe's visual images. To evaluate our proposed method, we
construct a large-scale dataset consisting of millions of ratings from
AllRecipes.com. Extensive experiments show that our method outperforms several
competing recommender solutions like Factorization Machine and Visual Bayesian
Personalized Ranking with an average improvement of 12%, offering promising
results in predicting user preference for food. Codes and dataset will be
released upon acceptance
Unsupervised Segmentation of Action Segments in Egocentric Videos using Gaze
Unsupervised segmentation of action segments in egocentric videos is a
desirable feature in tasks such as activity recognition and content-based video
retrieval. Reducing the search space into a finite set of action segments
facilitates a faster and less noisy matching. However, there exist a
substantial gap in machine understanding of natural temporal cuts during a
continuous human activity. This work reports on a novel gaze-based approach for
segmenting action segments in videos captured using an egocentric camera. Gaze
is used to locate the region-of-interest inside a frame. By tracking two simple
motion-based parameters inside successive regions-of-interest, we discover a
finite set of temporal cuts. We present several results using combinations (of
the two parameters) on a dataset, i.e., BRISGAZE-ACTIONS. The dataset contains
egocentric videos depicting several daily-living activities. The quality of the
temporal cuts is further improved by implementing two entropy measures.Comment: To appear in 2017 IEEE International Conference On Signal and Image
Processing Application
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