Abstract

The fifth generation (5G) networks employing mil- limeter wave (mmwave) technology will gain the advantages of short distance directive propagation and more spectrum for communication. Device-to-Device communication (D2D) when integrated with mmWave technology can reach the demand for high capacity. In this paper, resource allocation for underlay D2D in outdoor mmWave scenario is presented. We emphasize on fair distribution of resources in a cell while maximizing the spectral efficiency. Also, we discuss the aspects in ad-hoc and cellular communication using the same scheme based on the requirement. The problem is formulated mathematically to maximize sum rate and an approach for resource allocation is proposed. This work aims to achieve higher system capacity with the proposed resource allocation scheme that does not have exclusivity in resource allocation and allows more than one user to share the same resource block without degrading the system spectral efficiency. However, to reduce the complexity, an upper limit on the number of users per resource block is imposed which is adaptive to the traffic environment. Results show that the proposed scheme can significantly enhance the system performance in terms of fairness and spectral efficiency.

Abstract

In this research paper several interesting themes related to Hopfield network are explored and concrete results are derived. For instance, convergence in partial parallel mode of operation, convergence when synaptic weight matrix, W is row diagonally dominant case. Also, structured recurrent Hopfield network with companion matrix as W is studied. The dynamics when the activation function is a step function, the state space is asymmetric hypercube are also studied. Finally several experimental results are derived.

Abstract

We describe a fully Bayesian approach to grapheme-to-phoneme conversion based on the joint-sequence model (JSM). Usually, standard smoothed n-gram language models (LM, e.g. Kneser-Ney) are used with JSMs to model graphone sequences (joint grapheme-phoneme pairs). However, we take a Bayesian approach using a hierarchical Pitman-Yor-Process LM. This provides an elegant alternative to using smoothing techniques to avoid over-training. No held-out sets and complex parameter tuning is necessary, and several convergence problems encountered in the discounted Expectation-Maximization (as used in the smoothed JSMs) are avoided. Every step is modeled by weighted finite state transducers and implemented with standard operations from the OpenFST toolkit. We evaluate our model on a standard data set (CMUdict), where it gives comparable results to the previously reported smoothed JSMs in terms of phoneme-error rate while requiring a much smaller training/testing time. Most importantly, our model can be used in a Bayesian framework and for (partly) un-supervised training.

Abstract

A new method of obtaining exact solution for the rate matrix R in the Matrix-Analytic method in case of the phase state of dimension two is proposed. The method is based on symbolic solution of the determinental polynomial equation, and obtaining a linear matrix equation for the unknown rate matrix R by Cayley–Hamilton theorem. The method is applied to analyze the EnergyPerformance tradeoff of an Internet-of-Things device. A new randomized regime switching scheme is proposed, which, as it is shown by means of numerical experiment, provides significant decrease of energy consumption of the system under study.

Abstract

Embodiments of a method are disclosed for computing trust index among multiple entities associated with a resource marketplace. The method includes receiving multiple inputs including interaction attributes, attribute importance factors, references to databases, and multiple entities associated with the resource marketplace. The method also includes creating a weighted-interaction graph based on the received inputs. The weighted-interaction graph includes multiple vertices representing the entities. The method further includes performing a topology-specific analysis of the weighted-interaction graph. The method furthermore includes computing Euclidean distances for each pair of vertices in the weighted-interaction graph based on the performed analysis. The method also includes determining a trust index for a first entity in the received multiple entities based on the computed Euclidean distances. The trust index includes ranking of at least one of the multiple entities with respect to the first entity. The ranking is inversely proportional to the computed Euclidean distances.

Abstract

The task of automatic image annotation involves assigning relevant multiple labels/tags to query images based on their visual content. One of the key challenge in multi-label image annotation task is the class imbalance problem where frequently occurring labels suppress the participation of rarely occurring labels. In this paper, we propose to exploit the multi-scale behavior in hypergraph heat diffusion framework for the automatic image annotation task. The proposed novel technique enables to model the higher order relationship among images in the feature space and provides a multi-scale label diffusion mechanism to address the class imbalance problem in the data.

Abstract

The disclosed embodiments illustrate methods and systems for processing crowd-sensed data. The method includes receiving the crowd-sensed data from a mobile device associated with a user. The crowd-sensed data corresponds to metadata of an event pertaining to an aberration in at least one of a public service, a public infrastructure, a private service, or a private infrastructure. Thereafter, the event may be prioritized based at least on a type of the event, a measure of impact of the event, or a measure of urgency to resolve the event. Further, a notification of the event may be transmitted to an organization responsible to at least resolve the event, based on the prioritizing, wherein the notification comprises at least the metadata.

Abstract

We present an algorithm for automatic blur detection of document images using a novel approach based on edge intensity profiles. Our main insight is that the edge profiles are a strong indicator of the blur present in the image, with steep profiles implying sharper regions and gradual profiles implying blurred regions. Our approach first retrieves the profiles for each point of intensity transition (each edge point) along the gradient and then uses them to output a quantitative measure indicating the extent of blur in the input image. The real time performance of the proposed approach makes it suitable for most applications. Additionally, our method works for both hand written and digital documents and is agnostic to the font types and sizes, which gives it a major advantage over the currently prevalent learning based approaches. Extensive quantitative and qualitative experiments over two different datasets show that our method outperforms almost all algorithms in current state of the art by a significant margin, especially in cross dataset experiments.

Abstract

Vertical video editing is the process of digitally editing the image within the frame as opposed to horizontal video editing, which arranges the shots along a timeline. Vertical editing can be a time-consuming and error-prone process when using manual key-framing and simple interpolation. In this paper, we present a general framework for automatically computing a variety of cinematically plausible shots from a single input video suitable to the special case of live performances. Drawing on working practices in traditional cinematography, the system acts as a virtual camera assistant to the film editor, who can call novel shots in the edit room with a combination of high-level instructions and manually selected keyframes.

Abstract

An examination of object recognition challenge leaderboards (ILSVRC, PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small differences amongst themselves in terms of error rate/mAP. To better differentiate the top performers, additional criteria are required. Moreover, the (test) images, on which the performance scores are based, predominantly contain fully visible objects. Therefore, ‘harder’ test images, mimicking the challenging conditions (e.g. occlusion) in which humans routinely recognize objects, need to be utilized for benchmarking. To address the concerns mentioned above, we make two contributions. First, we systematically vary the level of local object-part content, global detail and spatial context in images from PASCAL VOC 2010 to create a new benchmarking dataset dubbed PPSS-12. Second, we propose an object-part based benchmarking procedure which quantifies classifiers’ robustness to a range of visibility and contextual settings. The benchmarking procedure relies on a semantic similarity measure that naturally addresses potential semantic granularity differences between the category labels in training and test datasets, thus eliminating manual mapping. We use our procedure on the PPSS-12 dataset to benchmark top-performing classifiers trained on the ILSVRC-2012 dataset. Our results show that the proposed benchmarking procedure enables additional differentiation among state-of-the-art object classifiers in terms of their ability to handle missing content and insufficient object detail. Given this capability for additional differentiation, our approach can potentially supplement existing benchmarking procedures used in object recognition challenge leaderboards.