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..

Data transmission with spatial spreading in a MIMO communication system

Abstract: For data transmission with spatial spreading, a transmitting entity (1) encodes and modulates each data packet to obtain a corresponding data symbol block, (2) multiplexes data symbol blocks onto NS data symbol streams for transmission on NS transmission channels of a MIMO channel, (3) spatially spreads the NS data symbol streams with steering matrices, and (4) spatially processes NS spread symbol streams for full-CSI transmission on NS eigenmodes or partial-CSI transmission on NS spatial channels of the MIMO channel. A receiving entity (1) obtains NR received symbol streams via NR receive antennas, (2) performs receiver spatial processing for full-CSI or partial-CSI transmission to obtain NS detected symbol streams, (3) spatially despreads the NS detected symbol streams with the same steering matrices used by the transmitting entity to obtain NS recovered symbol streams, and (4) demodulates and decodes each recovered symbol block to obtain a corresponding decoded data packet.

A Compact Deep Learning Model for Robust Facial Expression Recognition

Abstract: In this paper, we propose a compact frame-based facial expression recognition framework for facial expression recognition which achieves very competitive performance with respect to state-of-the-art methods while using much less parameters. The proposed framework is extended to a frame-to-sequence approach by exploiting temporal information with gated recurrent units. In addition, we develop an illumination augmentation scheme to alleviate the overfitting problem when training the deep networks with hybrid data sources. Finally, we demonstrate the performance improvement by using the proposed technique on some public datasets.

Learning speech models for mobile device users

Abstract: Techniques are provided to recognize a speaker's voice. In one embodiment, received audio data may be separated into a plurality of signals. For each signal, the signal may be associated with value/s for one or more features (e.g., Mel-Frequency Cepstral coefficients). The received data may be clustered (e.g., by clustering features associated with the signals). A predominate voice cluster may be identified and associated with a user. A speech model (e.g., a Gaussian Mixture Model or Hidden Markov Model) may be trained based on data associated with the predominate cluster. A received audio signal may then be processed using the speech model to, e.g.,: determine who was speaking; determine whether the user was speaking; determining whether anyone was speaking; and/or determine what words were said. A context of the device or the user may then be inferred based at least partly on the processed signal.

Deriving and Validating User Experience Model for DASH Video Streaming

Abstract: Ever since video compression and streaming techniques have been introduced, measurement of perceived video quality has been a non-trivial task. Dynamic adaptive streaming (DASH) over hypertext transfer protocol, is a new worldwide standard for adaptive streaming of video. DASH has introduced an additional level of complexity for measuring perceived video quality, as it varies the video bit rate and quality. In this paper, we study the perceived video quality using DASH. We investigate three factors which impact user perceived video quality: 1) initial delay; 2) stall (frame freezing); and 3) bit rate (frame quality) fluctuation. For each factor, we explore multiple dimensions that can have different effects on perceived quality. For example, in the case of the factor stall, while most previous research have studied how stall duration correlates with user experience, we also consider how the stalls are distributed together with the amount of motion in the video content, since we believe they may also impact user perceived quality. We conduct extensive subjective tests in which a group of subjects provide subjective evaluation while watching DASH videos with one or more artifacts occurring. Based on the subjective tests, we first derive impairment functions which can quantitatively measure the impairment of each factor, and then combine these impairment functions together to formulate an overall user experience model for any DASH video. We validate with high accuracy the user experience model, and demonstrate its applicability to long videos.

Dual-mode amplifier with high efficiency and high linearity

Abstract: An amplifier circuit having a high linearity mode of operation and a high efficiency mode of operation. The amplifier circuit comprises an amplifier having a variable active device periphery and a variable supply voltage; and a control circuit, coupled to the amplifier, for decreasing the variable active device periphery and increasing the variable supply voltage when in the high linearity mode of operation, and for increasing the variable active device periphery and decreasing the variable supply voltage when in the high efficiency mode of operation.