arXiv subjects

Ahmed Shokry

Publications and source records attributed to Ahmed Shokry.

30 records · Page 2Linked to original sources

arXiv2022

Photometric and Spectroscopic Analysis of the SX Phe Star BL Cam

In the present paper, we report the photometric and spectroscopic observations obtained by the 1.88 m telescope at the Kottamia astronomical observatory of the pulsating star BL Cam. Fourier analysis of the light curves reveals that the fundamental mode has two harmonics. The O-C method is used to establish the period changes. So far, the analysis has been very successful in mapping the pulsation amplitude of the star across the instability strip. By using the formalism of Eddington and Plakidis (1929), we found significant results and strong indications of the evolutionary period change. A total of 55 new maximum light timings are reported. New values of (1/P) dP/dt are estimated using the O-C diagram based on all newly obtained times of maximum light combined with those taken from the literature, assuming the periods are decreasing and changing smoothly. To compute the effective temperature and surface gravity of the star, we performed model atmosphere analysis on its spectra. The physical parameters of the star are calculated and compared with the evolutionary models.

astro-ph.SR↗

arXiv2022

A Novel Framework for Brain Tumor Detection Based on Convolutional Variational Generative Models

Brain tumor detection can make the difference between life and death. Recently, deep learning-based brain tumor detection techniques have gained attention due to their higher performance. However, obtaining the expected performance of such deep learning-based systems requires large amounts of classified images to train the deep models. Obtaining such data is usually boring, time-consuming, and can easily be exposed to human mistakes which hinder the utilization of such deep learning approaches. This paper introduces a novel framework for brain tumor detection and classification. The basic idea is to generate a large synthetic MRI images dataset that reflects the typical pattern of the brain MRI images from a small class-unbalanced collected dataset. The resulted dataset is then used for training a deep model for detection and classification. Specifically, we employ two types of deep models. The first model is a generative model to capture the distribution of the important features in a set of small class-unbalanced brain MRI images. Then by using this distribution, the generative model can synthesize any number of brain MRI images for each class. Hence, the system can automatically convert a small unbalanced dataset to a larger balanced one. The second model is the classifier that is trained using the large balanced dataset to detect brain tumors in MRI images. The proposed framework acquires an overall detection accuracy of 96.88% which highlights the promise of the proposed framework as an accurate low-overhead brain tumor detection system.

eess.IV↗

arXiv2021

Autofocusing Optimal Search Algorithm for a Telescope System

Focus accuracy affects the quality of the astronomical observations. Auto-focusing is necessary for imaging systems designed for astronomical observations. The automatic focus system searches for the best focus position by using a proposed search algorithm. The search algorithm uses the image's focus levels as its objective function in the search process. This paper aims to study the performance of several search algorithms to select a suitable one. The proper search algorithm will be used to develop an automatic focus system for Kottamia Astronomical Observatory (KAO). The optimal search algorithm is selected by applying several search algorithms into five sequences of star-clusters observations. Then, their performance is evaluated based on two criteria, which are accuracy and number of steps. The experimental results show that the Binary search is the optimal search algorithm.

eess.IV↗

arXiv2021

The Effect of Ground Truth Accuracy on the Evaluation of Localization Systems

The ability to accurately evaluate the performance of location determination systems is crucial for many applications. Typically, the performance of such systems is obtained by comparing ground truth locations with estimated locations. However, these ground truth locations are usually obtained by clicking on a map or using other worldwide available technologies like GPS. This introduces ground truth errors that are due to the marking process, map distortions, or inherent GPS inaccuracy. In this paper, we present a theoretical framework for analyzing the effect of ground truth errors on the evaluation of localization systems. Based on that, we design two algorithms for computing the real algorithmic error from the validation error and marking/map ground truth errors, respectively. We further establish bounds on different performance metrics. Validation of our theoretical assumptions and analysis using real data collected in a typical environment shows the ability of our theoretical framework to correct the estimated error of a localization algorithm in the presence of ground truth errors. Specifically, our marking error algorithm matches the real error CDF within 4%, and our map error algorithm provides a more accurate estimate of the median/tail error by 150%/72% when the map is shifted by 6m.

eess.SP↗

arXiv2021

The Tale of Two Localization Technologies: Enabling Accurate Low-Overhead WiFi-based Localization for Low-end Phones

WiFi fingerprinting is one of the mainstream technologies for indoor localization. However, it requires an initial calibration phase during which the fingerprint database is built manually. This process is labour intensive and needs to be repeated with any change in the environment. While a number of systems have been introduced to reduce the calibration effort through RF propagation models or crowdsourcing, these still have some limitations. Other approaches use the recently developed iBeacon technology as an alternative to WiFi for indoor localization. However, these beacon-based solutions are limited to a small subset of high-end phones. In this paper, we present HybridLoc: an accurate low-overhead indoor localization system. The basic idea HybridLoc builds on is to leverage the sensors of high-end phones to enable localization of lower-end phones. Specifically, the WiFi fingerprint is crowdsourced by opportunistically collecting WiFi-scans labeled with location data obtained from BLE-enabled high-end smart phones. These scans are used to automatically construct the WiFi-fingerprint, that is used later to localize any lower-end cell phone with the ubiquitous WiFi technology. HybridLoc also has provisions for handling the inherent error in the estimated BLE locations used in constructing the fingerprint as well as to handle practical deployment issues including the noisy wireless environment, heterogeneous devices, among others. Evaluation of HybridLoc using Android phones shows that it can provide accurate localization in the same range as manual fingerprinting techniques under the same conditions. Moreover, the localization accuracy on low-end phones supporting only WiFi is comparable to that achieved with high-end phones supporting BLE. This accuracy is achieved with no training overhead, is robust to the different user devices, and is consistent under environment changes.

cs.CY↗

arXiv2021

DeepLoc: A Ubiquitous Accurate and Low-Overhead Outdoor Cellular Localization System

Recent years have witnessed fast growth in outdoor location-based services. While GPS is considered a ubiquitous localization system, it is not supported by low-end phones, requires direct line of sight to the satellites, and can drain the phone battery quickly. In this paper, we propose DeepLoc: a deep learning-based outdoor localization system that obtains GPS-like localization accuracy without its limitations. In particular, DeepLoc leverages the ubiquitous cellular signals received from the different cell towers heard by the mobile device as hints to localize it. To do that, crowd-sensed geo-tagged received signal strength information coming from different cell towers is used to train a deep model that is used to infer the user's position. As part of DeepLoc design, we introduce modules to address a number of practical challenges including scaling the data collection to large areas, handling the inherent noise in the cellular signal and geo-tagged data, as well as providing enough data that is required for deep learning models with low-overhead. We implemented DeepLoc on different Android devices. Evaluation results in realistic urban and rural environments show that DeepLoc can achieve a median localization accuracy within 18.8m in urban areas and within 15.7m in rural areas. This accuracy outperforms the state-of-the-art cellular-based systems by more than 470% and comes with 330% savings in power compared to the GPS. This highlights the promise of DeepLoc as a ubiquitous accurate and low-overhead localization system.

cs.LG↗

arXiv2021

Quantum Computing for Location Determination

Quantum computing provides a new way for approaching problem solving, enabling efficient solutions for problems that are hard on classical computers. It is based on leveraging how quantum particles behave. With researchers around the world showing quantum supremacy and the availability of cloud-based quantum computers with free accounts for researchers, quantum computing is becoming a reality. In this paper, we explore both the opportunities and challenges that quantum computing has for location determination research. Specifically, we introduce an example for the expected gain of using quantum algorithms by providing an efficient quantum implementation of the well-known RF fingerprinting algorithm and run it on an instance of the IBM Quantum Experience computer. The proposed quantum algorithm has a complexity that is exponentially better than its classical algorithm version, both in space and running time. We further discuss both software and hardware research challenges and opportunities that researchers can build on to explore this exciting new domain.

quant-ph↗

arXiv2020

DynamicSLAM: Leveraging Human Anchors for Ubiquitous Low-Overhead Indoor Localization

We present DynamicSLAM: an indoor localization technique that eliminates the need for the daunting calibration step. DynamicSLAM is a novel Simultaneous Localization And Mapping (SLAM) framework that iteratively acquires the feature map of the environment while simultaneously localizing users relative to this map. Specifically, we employ the phone inertial sensors to keep track of the user's path. To compensate for the error accumulation due to the low-cost inertial sensors, DynamicSLAM leverages unique points in the environment (anchors) as observations to reduce the estimated location error. DynamicSLAM introduces the novel concept of mobile human anchors that are based on the encounters with other users in the environment, significantly increasing the number and ubiquity of anchors and boosting localization accuracy. We present different encounter models and show how they are incorporated in a unified probabilistic framework to reduce the ambiguity in the user location. Furthermore, we present a theoretical proof for system convergence and the human anchors ability to reset the accumulated error. Evaluation of DynamicSLAM using different Android phones shows that it can provide a localization accuracy with a median of 1.1m. This accuracy outperforms the state-of-the-art techniques by 55%, highlighting DynamicSLAM promise for ubiquitous indoor localization.

cs.CY↗

arXiv2019

Breaking the dichotomy between typical and anomalous globular clusters: the case of NGC 3201

We recently discovered that NGC 3201 has characteristics that set it outside the current twofold classification scheme for Galactic globular clusters (GCs). Most GCs are mono-metallic and show light-element abundance variations (e.g., Na-O and C-N anti-correlations); but a minority of clusters also present variations in Fe correlating with s-process element and C+N+O abundances, and they possess multiple C-N sequences. These anomalous GCs also have a broad sub-giant branch (SGB) and follow the same mass-size relation as dwarf galaxies possibly evolving into GCs. We now revealed that NGC 3201 belongs to neither group. It has multiple C-N sequences, but no broad SGB, no strong evidence of a Fe-spread, and it does not follow the mass-size relation.

astro-ph.SR↗

arXiv2019

Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learning

Recently, deep learning-based positioning systems have gained attention due to their higher performance relative to traditional methods. However, obtaining the expected performance of deep learning-based systems requires large amounts of data to train model. Obtaining this data is usually a tedious process which hinders the utilization of such deep learning approaches. In this paper, we introduce a number of techniques for addressing the data collection problem for deep learning-based cellular localization systems. The basic idea is to generate synthetic data that reflects the typical pattern of the wireless data as observed from a small collected dataset. Evaluation of the proposed data augmentation techniques using different Android phones in a cellular localization case study shows that we can enhance the performance of the localization systems in both indoor and outdoor scenarios by 157% and 50.5%, respectively. This highlights the promise of the proposed techniques for enabling deep learning-based localization systems.

eess.SP↗

arXiv2019

Shortest Path Algorithms between Theory and Practice

Utilizing graph algorithms is a common activity in computer science. Algorithms that perform computations on large graphs are not always efficient. This work investigates the Single-Source Shortest Path (SSSP) problem, which is considered to be one of the most important and most studied graph problems. This thesis contains a review of the SSSP problem in both theory and practice. In addition, it discusses a new single-source shortest-path algorithm that achieves the same $O(n \cdot m)$ time bound as the traditional Bellman-Ford-Moore algorithm but outperforms it and other state-of-the-art algorithms in practice. The work is comprised of three parts. The first discusses some basic shortest-path and negative-cycle-detection algorithms in literature from the theoretical and practical point of view. The second contains a discussion of a new algorithm for the single-source shortest-path problem that outperforms most state-of-the-art algorithms for several well-known families of graphs. The main idea behind the proposed algorithm is to select the fewest most-effective vertices to scan. We also propose a discussion of correctness, termination, and the proof of the worst-case time bound of the proposed algorithm. This section also suggests two different implementations for the proposed algorithm, the first runs faster while the second performs a fewer number of operations. Finally, an extensive computational study of the different shortest paths algorithms is conducted. The results are proposed using a new evaluation metric for shortest-path algorithms. A discussion of outcomes, strengths, and weaknesses of the various shortest path algorithms are also included in this work.

cs.DS↗

arXiv2018

Galactic or extragalactic chemical tagging for NGC3201? Discovery of an anomalous CN-CH relation

(ABRIDGED) The origin of the globular cluster (GC) NGC3201 is under debate. Its retrograde orbit points to an extragalactic origin, but no further chemical evidence supports this idea. Light-element chemical abundances are useful to tag GCs and can be used to shed light on this discussion. We aim to derive CN and CH band strengths for red giant stars in NGC3201 and compare these with photometric indices and high-resolution spectroscopy and discuss in the context of GC chemical tagging. We found three groups in the CN-CH distribution. A main sequence (S1), a secondary less-populated sequence (S2), and a group of peculiar (pec) CN-weak and CH-weak stars, one of which was previously known. The three groups seem to have different C+N+O and/or s-process element abundances, to be confirmed by high-resolution spectroscopy. These are typical characteristics of anomalous GCs. The CN distribution of NGC 3201 is quadrimodal, which is more common in anomalous clusters. However, NGC3201 does not belong to the trend of anomalous GCs in the mass-size relation. Three scenarios are postulated here: (i) if the sequence pec-S1-S2 has increasing C+N+O and s-process element abundances, NGC3201 would be the first anomalous GC outside of the mass-size relation; (ii) if the abundances are almost constant, NGC3201 would be the first non-anomalous GC with multiple CN-CH anti-correlation groups; or (iii) it would be the first anomalous GC without variations in C+N+O and s-process element abundances. In all cases, the definition of anomalous clusters and the scenario in which they have an extragalactic origin must be revised.

astro-ph.GA↗