Abstract-The correct general form of relativistic transformation equations for the three-vector force is derived without using fourvectors, via the relativistic Newton's second law. The four-vector approach to the problem is also presented. The derivations extend or rectify previous derivations.
This work investigates the use of class-level difficulty factors in multi-label classification problems for the first time. Four class-level difficulty factors are proposed: frequency, visual variation, semantic abstraction, and class co-occurrence. Once computed for a given multi-label classification dataset, these difficulty factors are shown to have several potential applications including the prediction of class-level performance across datasets and the improvement of predictive performance through difficulty weighted optimisation. Significant improvements to mAP and AUC performance are observed for two challenging multi-label datasets (WWW Crowd and Visual Genome) with the inclusion of difficulty weighted optimisation. The proposed technique does not require any additional computational complexity during training or inference and can be extended over time with inclusion of other class-level difficulty factors.
Recently, we experience the increasing prevalence of wearable cameras, some of which feature Wireless Local Area Network (WLAN) connectivity, and the abundance of mobile devices equipped with on-board camera and WLAN modules. Motivated by this fact, this work presents an indoor localization system that leverages both imagery and WLAN data for enabling and supporting a wide variety of envisaged location-aware applications ranging from ambient and assisted living to indoor mobile gaming and retail analytics. The proposed solution integrates two complementary localization approaches, i.e., one based on WLAN and another one based on image location-dependent data, using a fusion engine. Two fusion strategies are developed and investigated to meet different requirements in terms of accuracy, run time, and power consumption. The one is a light-weight threshold-based approach that combines the location outputs of two localization algorithms, namely a WLAN-based algorithm that processes signal strength readings from the surrounding wireless infrastructure using an extended Naive Bayes approach and an image-based algorithm that follows a novel approach based on hierarchical vocabulary tree of SURF (Speeded Up Robust Features) descriptors. The second fusion strategy employs a particle filter algorithm that operates directly on the WLAN and image readings and also includes prior position estimation information in the localization process. Extensive experimental results using real-life data from an indoor office environment indicate that the proposed fusion strategies perform well and are competitive against standalone WLAN and imaged-based algorithms, as well as alternative fusion localization solutions.
In this paper we address the automatic identification of indoor locations using a combination of WLAN and image sensing. Our motivation is the increasing prevalence of wearable cameras, some of which can also capture WLAN data. We propose to use image-based and WLAN-based localisation individually and then fuse the results to obtain better performance overall. We demonstrate the effectiveness of our fusion algorithm for localisation to within a 8.9m2 room on very challenging data both for WLAN and image-based algorithms. We envisage the potential usefulness of our approach in a range of ambient assisted living applications.
Van Siclen (1988 Am. J. Phys. 56 1142) reported a curious property of a dielectric sphere in the field of an external point charge: the field outside the sphere generated by the combination of the original charge exterior and the Kelvin image charge interior to the sphere is independent of the permittivity of the sphere. In this paper, we simplify and correct the original derivation and give a detailed analysis of the sources of the field. We also present various checks for the theory, providing instructive exercises for advanced undergraduates.
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