Nokia

About: Nokia is a company organization based out in Espoo, Finland. It is known for research contribution in the topics: Signal & Mobile station. The organization has 16625 authors who have published 28347 publications receiving 695725 citations. The organization is also known as: Nokia Oyj & Oy Nokia Ab.

Mining large-scale smartphone data for personality studies

Abstract: In this paper, we investigate the relationship between automatically extracted behavioral characteristics derived from rich smartphone data and self-reported Big-Five personality traits (extraversion, agreeableness, conscientiousness, emotional stability and openness to experience). Our data stem from smartphones of 117 Nokia N95 smartphone users, collected over a continuous period of 17 months in Switzerland. From the analysis, we show that several aggregated features obtained from smartphone usage data can be indicators of the Big-Five traits. Next, we describe a machine learning method to detect the personality trait of a user based on smartphone usage. Finally, we study the benefits of using gender-specific models for this task. Apart from a psychological viewpoint, this study facilitates further research on the automated classification and usage of personality traits for personalizing services on smartphones.

Electronic device cover with embedded radio frequency (rf) transponder and methods of using same

Abstract: A changeable cover (100) for an electronic device and method of using same in a payment system is provided. The cover (100) has a transponder (110) responsive to interrogation by an electric field. The cover provides an electronic identification number and other information in response to the interrogation signal. Also provided is a system for making payments, comprising at least one mobile station (4) which has an associated cover (100) for providing local data transfer. The system also comprises at least one point of sale terminal or the like, which has a second transceiver for providing data transfer.

Apparatus and method for facilitating contact selection in communication devices

Abstract: An apparatus and method for facilitating user selection of communication recipients via communication devices. Communication attributes of previous communication events involving the communication device are monitored. Selection criteria are established based on the monitored communication attributes. One or more adaptive recipient history lists are maintained, which correspond to contacts that are affiliated with the communication events satisfying the selection criteria. At least one of the adaptive recipient history lists are presented to the user via the communication device, thereby allowing the user to select the intended recipient via more relevant, focused short lists of likely recipients.

Collaborative Cyclostationary Spectrum Sensing for Cognitive Radio Systems

Abstract: This paper proposes an energy efficient collaborative cyclostationary spectrum sensing approach for cognitive radio systems. An existing statistical hypothesis test for the presence of cyclostationarity is extended to multiple cyclic frequencies and its asymptotic distributions are established. Collaborative test statistics are proposed for the fusion of local test statistics of the secondary users, and a censoring technique in which only informative test statistics are transmitted to the fusion center (FC) during the collaborative detection is further proposed for improving energy efficiency in mobile applications. Moreover, a technique for numerical approximation of the asymptotic distribution of the censored FC test statistic is proposed. The proposed tests are nonparametric in the sense that no assumptions on data or noise distributions are required. In addition, the tests allow dichotomizing between the desired signal and interference. Simulation experiments are provided that show the benefits of the proposed cyclostationary approach compared to energy detection, the importance of collaboration among spatially displaced secondary users for overcoming shadowing and fading effects, as well as the reliable performance of the proposed algorithms even in very low signal-to-noise ratio (SNR) regimes and under strict communication rate constraints for collaboration overhead.

Recognizing human motion with multiple acceleration sensors

Abstract: In this paper experiments with acceleration sensors are described for human activity recognition of a wearable device user. The use of principal component analysis and independent component analysis with a wavelet transform is tested for feature generation. Recognition of human activity is examined with a multilayer perceptron classifier. Best classification results for recognition of different human motion were 83-90%, and they were achieved by utilizing independent component analysis and principal component analysis. The difference between these methods turned out to be negligible.