Uncover the Power of Multipath : Detecting NLOS Drones Using Low-Cost WiFi Devices

In recent years, consumer UAV technology has seen considerable advances. Consumer UAVs have become an ideal vector for privacy invasions due to their affordability, size, maneuverability, and their ability to stream live high-quality video. There is considerable proliferation of drones in both civil and military domains. Hence it is critical to detect invading unmanned aerial vehicles (UAVs) or drones in a timely manner for both security and safeguarding privacy. Currently available solutions like active radar, video or acoustic sensors are very expensive (especially for individuals) and have considerable constraints (e.g., requiring visual line of sight). Recent research on drone detection with passive RF signals provides an opportunity for low-cost deployment of drone detectors on commodity wireless devices. The state of the arts in this direction mainly focus on detecting drones using line-of-sight (LOS) RF signals which are less noisy as compared to their non-LOS (NLOS) counterparts. To the best of our knowledge, there is no existing cost-effective solution for the general public to enable non-LOS(NLOS) detection for drone privacy invasion, which is the most common condition and it still remains an open challenge. This thesis research provides a low-cost UAV detection system for privacy invasion caused by customer drone. Our model supports NLOS detection with low-cost hardware under $50, and hence it is affordable for the general public to deploy in their house, apartments, and office. Our work utilizes inherent drone motions (i.e., body shifting and vibrations) as unique signatures for drone detection. Firstly, we validated the relationship between drone motions and RF signal under the NLOS condition using extensive experiments. This is motivated by the fact that under NLOS conditions slight changes to the position or motion of a drone could lead to dramatic change in multi-path components in received RF signals. The NLOS condition “amplifies the RF signatures introduced by drone motions. We designed a deep learning model to capture the complex features from NLOS RF signals. In particular, we designed and trained a long short-term memory (LSTM) neural network [15, 27], a generative model which can effectively extract features of inputs for NLOS drone detection. Moreover, without knowing the presence of drones, our system starts with classifying any detected RF signals into LOS signals and NLOS signals before the NLOS drone learner is used. Classification of LOS and NLOS signals is feasible because they exhibit different combined features such as strength, variance, and distribution due to their differences in multipath effects. We used the supervised support vector machine (S-SVM) [17] as the learning model, which is effective for binary classification. This design is validated via extensive experiments using commodity drones in resident areas with other Wi-Fi enabled mobile devices

Indian Museums in Community Identity and Development: A Critical Study

Museums are complexinstitutions of integration of cultures; tangibleand intangible traditions exhibit the man andnature relationship and promote education andresearch activities. And in elaborating manner,the Museums stand for the holistic presentationof various tribal and folk populations in thesystematic way of socio-cultural economic andtechnological aspects. It focuses not justspecifically on a particular community, orsubject; trend or theme; and tradition ortechnology, but always presents the unbiasedaspects of the past and present of the existed andexisting societies. And through its activities,museum plays a vital role in presenting theequality and dignity of all cultures in paralleldevelopmental approach for their preserving thecultural identity. Thus, museums have come upwith people’s aspirations and inspirations interms of promoting and safeguarding the variouscommunities on single platform

On equivalence of additive-combinatorial inequalities for Shannon entropy and differential entropy

Entropy inequalities are very important in information theory and they play a crucial role in various communication-theoretic problems, for example, in the study of the degrees of freedom of interference channels. In this thesis, we are concerned with the additive-combinatorial entropy inequalities which are motivated by their combinatorial counterparts: cardinality inequalities for subsets of abelian groups. As opposed to the existing approaches in the literature in the study of the discrete and continuous entropy inequalities, we consider a general framework of balanced linear entropy inequalities. In particular, we show a fundamental equivalence relationship between these classes of discrete and continuous entropy inequalities. In other words, we show that a balanced Shannon entropy inequality holds if and only if the corresponding differential entropy inequality holds. We also investigate these findings in a more general setting of connected abelian Lie groups and in the study of the sharp constants for entropy inequalities

Evolvulus alsinoides methanolic extract triggers apoptosis in HepG2 cells

Objective: The objective of the present study was to evaluate the cytotoxic potentials of Evolvulus alsinoides in human hepatoma HepG2 cells. Materials and Methods: HepG2 cells were treated with methanolic extract of E. alsinoides at 20, 40 and 80 µg/ml for 24 hr and cytotoxic effect was analyzed by MTT assay. The apoptosis rate was investigated by Hoechst 33342 and annexin V/propidium iodide staining. Mitochondrial membrane potential was evaluated by rhodamine staining. Also, the expression of catenin – β 1 protein was analyzed by western blotting. Results: E. alsinoides methanolic extract treatment caused significant cytotoxicity in HepG2 cells in a concentration-dependent manner.Dual staining assayconfirmed the presence of early and late apoptotic cells only in extract-treated groups. Plant extract treatment also caused nuclear fragmentation and chromatin condensation in HepG2 cells. Mitochondrial membrane potential also reduced upon E. alsinoides treatments.This treatment also modulated the catenin – β 1 protein expression. Conclusion:In this study, we demonstrated theproapoptotic potential E. alsinoides in HepG2 cells; thus, this plant may be beneficial in the treatment of liver cancer

CRISP: Curriculum based Sequential Neural Decoders for Polar Code Family

Polar codes are widely used state-of-the-art codes for reliable communication that have recently been included in the 5th generation wireless standards (5G). However, there remains room for the design of polar decoders that are both efficient and reliable in the short blocklength regime. Motivated by recent successes of data-driven channel decoders, we introduce a novel $\textbf{C}$ur$\textbf{RI}$culum based $\textbf{S}$equential neural decoder for $\textbf{P}$olar codes (CRISP). We design a principled curriculum, guided by information-theoretic insights, to train CRISP and show that it outperforms the successive-cancellation (SC) decoder and attains near-optimal reliability performance on the Polar(32,16) and Polar(64,22) codes. The choice of the proposed curriculum is critical in achieving the accuracy gains of CRISP, as we show by comparing against other curricula. More notably, CRISP can be readily extended to Polarization-Adjusted-Convolutional (PAC) codes, where existing SC decoders are significantly less reliable. To the best of our knowledge, CRISP constructs the first data-driven decoder for PAC codes and attains near-optimal performance on the PAC(32,16) code.Comment: 23 pages, 23 figures. ICML 202