SenseLens: An Efficient Social Signal Conditioning System for True Event Detection

This article narrows the gap between physical sensing systems that measure physical signals and social sensing systems that measure information signals by (i) defining a novel algorithm for extracting information signals (building on results from text embedding) and (ii) showing that it increases the accuracy of truth discovery—the separation of true information from false/manipulated one. The work is applied in the context of separating true and false facts on social media, such as Twitter and Reddit, where users post predominantly short microblogs. The new algorithm decides how to aggregate the signal across words in the microblog for purposes of clustering the miscroblogs in the latent information signal space, where it is easier to separate true and false posts. Although previous literature extensively studied the problem of short text embedding/representation, this article improves previous work in three important respects: (1) Our work constitutes unsupervised truth discovery, requiring no labeled input or prior training. (2) We propose a new distance metric for efficient short text similarity estimation, we call Semantic Subset Matching , that improves our ability to meaningfully cluster microblog posts in the latent information signal space. (3) We introduce an iterative framework that jointly improves miscroblog clustering and truth discovery. The evaluation shows that the approach improves the accuracy of truth-discovery by 6.3%, 2.5%, and 3.8% (constituting a 38.9%, 14.2%, and 18.7% reduction in error, respectively) in three real Twitter data traces.

Action of Finite Group Presentations on Signal Space

The use of finite group presentations in signal processing has not been exploit in the current literature. Based on the existing signal processing algorithms (not necessarily group theoretic approach), various signal processing transforms have unique decomposition capabilities, that is, different types of signal has different transformation combination. This paper aimed at studying representation of finite groups via their actions on Signal space and to use more than one transformation to process a signal within the context of group theory. The objective is achieved by using group generators as actions on Signal space which produced output signal for every corresponding input signal. It is proved that the subgroup presentations act on signal space by conjugation. Hence, a different approach to signal processing using group of transformations and presentations is established.

Optical Wireless Communications Using Signal Space Diversity with Spatial Modulation

A signal space diversity (SSD) scheme was proposed to be incorporated with spatial modulation (SM) in an intensity-modulation/direct-detection-based multiple-input-single-output (MISO) indoor optical wireless communication (OWC) system to improve bit-error-rate (BER) performance and system throughput. SSD was realized via signal constellation rotation and diversity interleaving using different channel gains to improve the BER. With SM incorporated, the MISO-OWC system throughput increased. Theoretical BER expressions of the SSD scheme were established for the first time by investigating the distance of neighboring constellation symbols upon maximum-likelihood detection. Such BER expressions were further verified by numerical results. The results showed that, except for the slightly-lower-accuracy performance brought by comparable distances of neighboring constellation symbols in cases of low signal-to-noise ratios, these BER expressions were accurate in most scenarios. Moreover, theoretical investigations of channel gain distributions were performed at different signal constellation rotation angles to show the capability of the SSD scheme to improve the BER. The results showed that a significantly improved BER by two orders of magnitude could be achieved using a reasonably high channel-gain ratio and a larger constellation rotation angle. The SSD-SM scheme provides a promising option to achieve transmitter diversity with an enhanced throughput in high-speed indoor OWC systems.

Analytical Blind Beamforming for a Multi-Antenna UAV Base-Station Receiver in Millimeter-Wave Bands

Over the last decade, unmanned aerial vehicles (UAVs) with antenna arrays have usually been employed for the enhancement of wireless communication in millimeter-wave bands. They are commonly used as aerial base stations and relay platforms in order to serve multiple users. Many beamforming methods for improving communication quality based on channel estimation have been proposed. However, these methods can be resource-intensive due to the complexity of channel estimation in practice. Thus, in this paper, we formulate an MIMO blind beamforming problem at the receivers for UAV-assisted communications in which channel estimation is omitted in order to save communication resources. We introduce one analytical method, which is called the analytical constant modulus algorithm (ACMA), in order to perform blind beamforming at the UAV base station; this relies only on data received by the antenna. The feature of the constant modulus (CM) is employed to restrict the target user signals. Algebraic operations, such as singular value decomposition (SVD), are applied to separate the user signal space from other interferences. The number of users in the region served by the UAV can be detected by exploring information in the measured data. We seek solutions that are expressible as one Kronecker product structure in the signal space; then, the beamformers that correspond to each user can be successfully estimated. The simulation results show that, by using this analytically derived blind method, the system can achieve good signal recovery accuracy, a reasonable system sum rate, and acceptable complexity.

Convergent acoustic community structure in South Asian dry and wet grassland birds

Although the study of bird acoustic communities has great potential in long-term monitoring and conservation, their assembly and dynamics remain poorly understood. Grassland habitats in South Asia comprise distinct biomes with unique avifauna, presenting an opportunity to address how community-level patterns in acoustic signal space arise. Similarity in signal space of different grassland bird assemblages may result from phylogenetic similarity, or because different bird groups partition the acoustic resource, resulting in convergent distributions in signal space. Here, we quantify the composition, signal space and phylogenetic diversity of bird acoustic communities from dry semiarid grasslands of Northwest India and wet floodplain grasslands of Northeast India, two major South Asian grassland biomes. We find that acoustic communities occupying these distinct biomes exhibit convergent, overdispersed distributions in signal space. However, dry grasslands exhibit higher phylogenetic diversity, and the two communities are not phylogenetically similar. The Sylvioidea encompasses half the species in the wet grassland acoustic community, with an expanded signal space compared to the dry grasslands. We therefore hypothesize that different clades colonizing grasslands partition the acoustic resource, resulting in convergent community structure across biomes. Many of these birds are threatened, and acoustic monitoring will support conservation measures in these imperiled, poorly-studied habitats.