Qualcomm

About: Qualcomm is a company organization based out in Farnborough, United Kingdom. It is known for research contribution in the topics: Wireless & Signal. The organization has 19408 authors who have published 38405 publications receiving 804693 citations. The organization is also known as: Qualcomm Incorporated & Qualcomm, Inc..

On-line behavioral analysis engine in mobile device with multiple analyzer model providers

Abstract: Methods, systems and devices for generating data models in a client-cloud communication system may include applying machine learning techniques to generate a first family of classifier models that describe a cloud corpus of behavior vectors. Such vectors may be analyzed to identify factors in the first family of classifier models that have the highest probability of enabling a mobile device to better determine whether a mobile device behavior is malicious or benign. Based on this analysis, a second family of classifier models may be generated that identify significantly fewer factors and data points as being relevant for enabling the mobile device to better determine whether the mobile device behavior is malicious or benign based on the determined factors. A mobile device classifier module based on the second family of classifier models may be generated and made available for download by mobile devices, including devices contributing behavior vectors.

Apparatus and Methods for Locating, Tracking and/or Recovering a Wireless Communication Device

Abstract: Methods, systems, devices and computer program products are provided for locating, tracking and/or recovering a wireless communication device that has been misplaced, lost or stolen. In addition, the aspects provide for tracking or surveillance of the location or user in instances in which the wireless device has been loaned or is being used for covert surveillance. The aspects include communicating a locating state code to the targeted device, which detects the code and executes one or more routines that are associated with the respective code. In this regard, the targeted device is capable of carrying out different routines or sequences of actions depending on the state of the device, such as a misplaced state, a lost state, a stolen state or the like, which is based on the respective code.

Windowed Decoding of Protograph-Based LDPC Convolutional Codes Over Erasure Channels

Abstract: We consider a windowed decoding scheme for LDPC convolutional codes that is based on the belief-propagation (BP) algorithm. We discuss the advantages of this decoding scheme and identify certain characteristics of LDPC convolutional code ensembles that exhibit good performance with the windowed decoder. We will consider the performance of these ensembles and codes over erasure channels with and without memory. We show that the structure of LDPC convolutional code ensembles is suitable to obtain performance close to the theoretical limits over the memoryless erasure channel, both for the BP decoder and windowed decoding. However, the same structure imposes limitations on the performance over erasure channels with memory.

Variable wireless power transmission

Abstract: Exemplary embodiments are directed to variable power wireless power transmission. A method may include conveying wireless power to a device at a first power level during a time period. The method may further include conveying wireless power to one or more other devices at a second, different power level during another time period.

Scene Text Detection via Connected Component Clustering and Nontext Filtering

Abstract: In this paper, we present a new scene text detection algorithm based on two machine learning classifiers: one allows us to generate candidate word regions and the other filters out nontext ones. To be precise, we extract connected components (CCs) in images by using the maximally stable extremal region algorithm. These extracted CCs are partitioned into clusters so that we can generate candidate regions. Unlike conventional methods relying on heuristic rules in clustering, we train an AdaBoost classifier that determines the adjacency relationship and cluster CCs by using their pairwise relations. Then we normalize candidate word regions and determine whether each region contains text or not. Since the scale, skew, and color of each candidate can be estimated from CCs, we develop a text/nontext classifier for normalized images. This classifier is based on multilayer perceptrons and we can control recall and precision rates with a single free parameter. Finally, we extend our approach to exploit multichannel information. Experimental results on ICDAR 2005 and 2011 robust reading competition datasets show that our method yields the state-of-the-art performance both in speed and accuracy.