NICMOS Snapshot Survey of Damped Lyman Alpha Quasars

We image 19 quasars with 22 damped Lyman alpha (DLA) systems using the F160W filter and the Near-Infrared Camera and Multiobject Spectrograph aboard the Hubble Space Telescope, in both direct and coronagraphic modes. We reach 5 sigma detection limits of ~H=22 in the majority of our images. We compare our observations to the observed Lyman-break population of high-redshift galaxies, as well as Bruzual & Charlot evolutionary models of present-day galaxies redshifted to the distances of the absorption systems. We predict H magnitudes for our DLAs, assuming they are producing stars like an L* Lyman-break galaxy (LBG) at their redshift. Comparing these predictions to our sensitivity, we find that we should be able to detect a galaxy around 0.5-1.0 L* (LBG) for most of our observations. We find only one new possible candidate, that near LBQS0010-0012. This scarcity of candidates leads us to the conclusion that most DLA systems are not drawn from a normal LBG luminosity function nor a local galaxy luminosity function placed at these high redshifts.Comment: 31 pages, 8 figures, Accepted for Feb. 10 issue of Ap

An Electron-Tracking Compton Telescope for a Survey of the Deep Universe by MeV gamma-rays

Photon imaging for MeV gammas has serious difficulties due to huge backgrounds and unclearness in images, which are originated from incompleteness in determining the physical parameters of Compton scattering in detection, e.g., lack of the directional information of the recoil electrons. The recent major mission/instrument in the MeV band, Compton Gamma Ray Observatory/COMPTEL, which was Compton Camera (CC), detected mere $\sim30$ persistent sources. It is in stark contrast with $\sim$2000 sources in the GeV band. Here we report the performance of an Electron-Tracking Compton Camera (ETCC), and prove that it has a good potential to break through this stagnation in MeV gamma-ray astronomy. The ETCC provides all the parameters of Compton-scattering by measuring 3-D recoil electron tracks; then the Scatter Plane Deviation (SPD) lost in CCs is recovered. The energy loss rate (dE/dx), which CCs cannot measure, is also obtained, and is found to be indeed helpful to reduce the background under conditions similar to space. Accordingly the significance in gamma detection is improved severalfold. On the other hand, SPD is essential to determine the point-spread function (PSF) quantitatively. The SPD resolution is improved close to the theoretical limit for multiple scattering of recoil electrons. With such a well-determined PSF, we demonstrate for the first time that it is possible to provide reliable sensitivity in Compton imaging without utilizing an optimization algorithm. As such, this study highlights the fundamental weak-points of CCs. In contrast we demonstrate the possibility of ETCC reaching the sensitivity below $1\times10^{-12}$ erg cm$^{-2}$ s$^{-1}$ at 1 MeV.Comment: 19 pages, 12 figures, Accepted to the Astrophysical Journa

The Privacy Leakage of IP Camera Systems

For in-home security, intelligent operations like top individual recognition and minimizing losses due to home break-ins, emergencies, and fraud are keys to success. This application integrates the closed-circuit television (CCTV) camera and the deep learning algorithms used to process these images. Automated intrusion detection alerts, real-time fire alerts, smart checkout, and potentially fraudulent point of sale (POS) transactions are its main features. Dynamic intrusion with machine learning is a software program in which the price of certain products changes over time through an algorithm that considers a variety of pricing variables. The face locator is a part of the algorithm that locates and detects motion by using the image search function. The system collects all available product locations from the live videos from multiple cameras. This is a helpful feature for finding misplaced products and detecting POS user fraud. This intrusion detection system (IDS) records POS transaction details on the screen as an overlay on video images to reduce home break-ins. To improve the ease and speed of transaction searches, the faces of individuals are used to search for disputed cases. Smart Checkout System (SCS) utilizes a self-service kiosk where users can generate bills by showing products to the linked camera. SCS uses Google vision technology to identify products. Motion detector and queue detection will detect long queues at the checkout counter in real-time and open new lanes to speed up the transaction, improve the experience, and reduce the number of abandoned purchases. Face recognition premium and alerts can also be provided

Automated Defect Detection Tool For Sewer Pipelines

In sewer networks, the economic effects and costs that result from a pipeline break are rising sharply. In Qatar, majority of the sewer network pipelines were installed in the last 20 years and are currently in poor condition and constantly deteriorating. As a result, there is huge demand for inspection and rehabilitation of sewer pipelines. In addition to being inaccurate, current Practices of sewer pipelines inspection are time consuming and may not keep up with the deterioration rate of the pipelines. Consequently, this research aims to develop an automated tool to detect different defects such as cracks, deformation, settled deposits and joint displacement in sewer pipelines. The automated approach is dependent upon using image-processing techniques and several mathematical formulas to analyze output data from CCTV camera photos. Given that one inspection session can result in hundreds of CCTV Camera footage, introducing an automated tool would help yield faster results. Additionally, given the subjective nature of most defects, it will result in more systematic results since the current method rely heavily on the operator's experience. The automated tool was able to successfully detect cracks, displaced joints, ovality and settled deposits in pipelines using CCTV Camera inspection output footage. Using two different data sets, the constructed Matlab code could successfully differentiate between cracks and displaced joints with an overall crack detection success rate of 84% and an overall displaced joint detection rate of 94%. The code was also able to efficiently detect settled deposits in the pipelines with a detection rate of 90%. In addition, the automated ovality detection resulted in 100% compatibility with the manual circularity detection

The use of imaging systems to monitor shoreline dynamics

The development of imaging systems is nowadays established as one of the most powerful and reliable tools for monitoring beach morphodynamics. Two different techniques for shoreline detection are presented here and, in one case, applied to the study of beach width oscillations on a sandy beach (Pauanui Beach, New Zealand). Results indicate that images can provide datasets whose length and sample interval are accurate enough to resolve inter-annual and seasonal oscillations, and long-term trends. Similarly, imaging systems can be extremely useful in determining the statistics of rip current occurrence. Further improvements in accuracy and reliability are expected with the recent introduction of digital systems

The Reliability and Effectiveness of a Radar-Based Animal Detection System

This document contains data on the reliability and effectiveness of an animal detection system along U.S. Hwy 95 near Bonners Ferry, Idaho. The system uses a Doppler radar to detect large mammals (e.g., deer and elk) when they approach the highway. The system met most of the suggested minimum norms for reliability. The total time the warning signs were activated was at most 90 seconds per hour, and likely substantially less. Animal detection systems are designed to detect an approaching animal. After an animal has been detected, warning signs are activated which allow drivers to respond. Results showed that 58.1–67.9% of deer were detected sufficiently early for northbound drivers, and 70.4–85% of deer were detected sufficiently early for southbound drivers. The effect of the activated warning signs on vehicle speed was greatest when road conditions were challenging (e.g., freezing temperatures and snow- and ice-covered road surface) and when visibility was low (night). In summer, there was no measurable benefit of activated warning signs, at least not as far as vehicle speed is concerned. Depending on the conditions in autumn and winter, the activated warning signs resulted in a speed reduction of 0.69 to 4.43 miles per hour. The report includes practical recommendations for operation and maintenance of the system and suggestions for potential future research

Semantic analysis of field sports video using a petri-net of audio-visual concepts

The most common approach to automatic summarisation and highlight detection in sports video is to train an automatic classifier to detect semantic highlights based on occurrences of low-level features such as action replays, excited commentators or changes in a scoreboard. We propose an alternative approach based on the detection of perception concepts (PCs) and the construction of Petri-Nets which can be used for both semantic description and event detection within sports videos. Low-level algorithms for the detection of perception concepts using visual, aural and motion characteristics are proposed, and a series of Petri-Nets composed of perception concepts is formally defined to describe video content. We call this a Perception Concept Network-Petri Net (PCN-PN) model. Using PCN-PNs, personalized high-level semantic descriptions of video highlights can be facilitated and queries on high-level semantics can be achieved. A particular strength of this framework is that we can easily build semantic detectors based on PCN-PNs to search within sports videos and locate interesting events. Experimental results based on recorded sports video data across three types of sports games (soccer, basketball and rugby), and each from multiple broadcasters, are used to illustrate the potential of this framework