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..

Local area network assisted positioning

Abstract: A system and method of locating a position of a wireless device in range of one or more base stations. Three signals are received that each contain a unique identifier for a base station. An estimate of the distance between the wireless device and each base station is performed. Previously determined locations for each base station are referenced. At least one of the three base stations is capable of communication to remote locations and unavailable to the wireless device for communication to remote locations.

Method and apparatus for providing secure processing and data storage for a wireless communication device

Abstract: Techniques for providing secure processing and data storage for a wireless communication device. In one specific design, a remote terminal includes a data processing unit, a main processor, and a secure unit. The data processing unit processes data for a communication over a wireless link. The main processor provides control for the remote terminal. The secure unit includes a secure processor that performs the secure processing for the remote terminal (e.g., using public-key cryptography) and a memory that provides secure storage of data (e.g., electronics funds, personal data, certificates, and so on). The secure processor may include an embedded ROM that stores program instructions and parameters used for the secure processing. For enhanced security, the secure processor and memory may be implemented within a single integrated circuit. Messaging and data may be exchanged with the secure unit via a single entry point provided by a bus.

Wireless tracking device

Abstract: An apparatus and method for a tracking device to inconspicuously track a person to be monitored (such as a child or at-risk adult) are presented. Some embodiments of the present invention combine a positioning receiver (e.g., a GPS receiver) and a locking mechanism to act as a tracking device, which attaches to an article of clothing or fabric wearable by the person to be monitored. Some embodiments of the present invention keep a positioning receiver and a radio frequency identification tag (RFID tag) or other RF tag, which are physically separate but in RF proximity of each other.

Secure bootstrapping for wireless communications

Abstract: A mutual authentication method is provided for securely agreeing application-security keys with mobile terminals supporting legacy Subscriber Identity Modules (e.g., GSM SIM and CDMA2000 R-UIM, which do not support 3G AKA mechanisms). A challenge-response key exchange is implemented between a bootstrapping server function (BSF) and mobile terminal (MT). The BSF generates an authentication challenge and sends it to the MT under a server-authenticated public key mechanism. The MT receives the challenge and determines whether it originates from the BSF based on a bootstrapping server certificate. The MT formulates a response to the authentication challenge based on keys derived from the authentication challenge and a pre-shared secret key. The BSF receives the authentication response and verifies whether it originates from the MT. Once verified, the BSF and MT independently calculate an application security key that the BSF sends to a requesting network application function to establish secure communications with the MT.

Scene classification with semantic Fisher vectors

Abstract: With the help of a convolutional neural network (CNN) trained to recognize objects, a scene image is represented as a bag of semantics (BoS). This involves classifying image patches using the network and considering the class posterior probability vectors as locally extracted semantic descriptors. The image BoS is summarized using a Fisher vector (FV) embedding that exploits the properties of the space of these descriptors. The resulting representation is referred to as a semantic Fisher vector. Two implementations of a semantic FV are investigated. First involves modeling the BoS with a Dirichlet Mixture and computing the Fisher gradients for this model. Due to the difficulty of mixture modeling on a non-Euclidean probability simplex, this approach is shown to be unsuccessful. A second implementation is derived using the interpretation of semantic descriptors as parameters of a multinomial distribution. Like the parameters of any exponential family, these can be projected into their natural parameter space. For a CNN, this is shown equivalent to using inputs of its soft-max layer as patch descriptors. A semantic FV is then computed as a Gaussian Mixture FV in the space of these natural parameters. This representation is shown to outperform other alternatives such as FVs of features from the intermediate CNN layers or a classifier obtained by adapting (fine-tuning) the CNN. The proposed FV represents an embedding for object classification probabilities. As an image representation, therefore, it is complementary to the features obtained from a scene classification CNN. A combination of the two representations is shown to achieve state-of-the-art results on MIT Indoor scenes and SUN datasets.