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..

Method and apparatus of power control in a CDMA dispatch system

Abstract: In a dispatch system, power of the forward link broadcast channel transmitted from a base station is controlled such that the power level is the minimum necessary and such that transmission of the forward link broadcast channel is terminated if no remote unit (10) is located within the coverage area of the base station (32). In addition to transmitting a forward link broadcast signal, the base station monitors an access channel. A first remote unit (10) in the base station coverage area receives and decodes the forward link broadcast signal. The first remote unit (10) determines a signal quality of said forward link broadcast signal. If the signal quality is unacceptable, the remote unit transmits a power request access message. The base station responds to the request by increasing the transmission power of forward link broadcast channel (70). If no requests are received at the base station, the forward link broadcast channel is slowly decreased to a minimum (54). Transmission of the forward link broadcast channel may be terminated completely (58).

Selective wireless power transfer

Abstract: Exemplary embodiments are directed to selective wireless power transfer. A method may include transferring wireless power to at least one electronic device while varying at least one parameter of the wireless power transfer according to a wireless power transfer scenario.

Multi-camera system using folded optics

Abstract: Described herein are methods and devices that employ a plurality of image sensors to capture a target image of a scene. As described, positioning at least one reflective or refractive surface near the plurality of image sensors enables the sensors to capture together an image of wider field of view and longer focal length than any sensor could capture individually by using the reflective or refractive surface to guide a portion of the image scene to each sensor. The different portions of the scene captured by the sensors may overlap, and may be aligned and cropped to generate the target image.

Highly Linear mm-Wave CMOS Power Amplifier

Abstract: A Ka-band highly linear power amplifier (PA) is implemented in 28-nm bulk CMOS technology. Using a deep class-AB PA topology with appropriate harmonic control circuit, highly linear and efficient PAs are designed at millimeter-wave band. This PA architecture provides a linear PA operation close to the saturated power. Also elaborated harmonic tuning and neutralization techniques are used to further improve the transistor gain and stability. A two-stack PA is designed for higher gain and output power than a common source (CS) PA. Additionally, average power tracking (APT) is applied to further reduce the power consumption at a low power operation and, hence, extend battery life. Both the PAs are tested with two different signals at 28.5 GHz; they are fully loaded long-term evolution (LTE) signal with 16-quadrature amplitude modulation (QAM), a 7.5-dB peak-to-average power ratio (PAPR), and a 20-MHz bandwidth (BW), and a wireless LAN (WLAN) signal with 64-QAM, a 10.8-dB PAPR, and an 80-MHz BW. The CS/two-stack PAs achieve power-added efficiency (PAE) of 27%/25%, error vector magnitude (EVM) of 5.17%/3.19%, and adjacent channel leakage ratio (ACLR $_{\mathrm{ E-UTRA}}$ ) of −33/−33 dBc, respectively, with an average output power of 11/14.6 dBm for the LTE signal. For the WLAN signal, the CS/2-stack PAs achieve the PAE of 16.5%/17.3%, and an EVM of 4.27%/4.21%, respectively, at an average output power of 6.8/11 dBm.

Tweet2Vec: Character-Based Distributed Representations for Social Media

Abstract: Text from social media provides a set of challenges that can cause traditional NLP approaches to fail. Informal language, spelling errors, abbreviations, and special characters are all commonplace in these posts, leading to a prohibitively large vocabulary size for word-level approaches. We propose a character composition model, tweet2vec, which finds vector-space representations of whole tweets by learning complex, non-local dependencies in character sequences. The proposed model outperforms a word-level baseline at predicting user-annotated hashtags associated with the posts, doing significantly better when the input contains many out-of-vocabulary words or unusual character sequences. Our tweet2vec encoder is publicly available.