Compressive sensing using the modified entropy functional

Cataloged from PDF version of article.In most compressive sensing problems, 1 norm is used during the signal reconstruction process. In this article, a modified version of the entropy functional is proposed to approximate the 1 norm. The proposed modified version of the entropy functional is continuous, differentiable and convex. Therefore, it is possible to construct globally convergent iterative algorithms using Bregman’s row-action method for compressive sensing applications. Simulation examples with both 1D signals and images are presented. © 2013 Elsevier Inc. All rights reserved

Generating design-sensitive occupant-related schedules for building performance simulations

Despite the benefits of occupant behavior (OB) models in simulating the effect of design factors on OB, there are challenges associated with their use in the building simulation industry due to extensive time and computational requirements. To this end, we present a novel method to incorporate these models in building performance simulations (BPS) as design-sensitive schedules. Over 2,900 design alternatives of an office were generated by varying orientation, window to wall ratio (WWR), the optical characteristics of windows and blinds, as well as indoor surfaces’ reflectance. By using daylight simulations and stochastic OB modeling, unique light use schedules were generated for each design alternative. A decision tree was then developed to be used by building designers to select light use schedules based on design parameters. These findings are relevant for building energy codes as they provide an approach to incorporate design-sensitive operational schedules for use as BPS inputs by practitioners. These design-sensitive schedules are expected to be superior to default ones currently specified in codes and standards, which ignore the effect of design factors on OB, and ultimately on energy consumption

Projections Onto Convex Sets (POCS) Based Optimization by Lifting

Two new optimization techniques based on projections onto convex space (POCS) framework for solving convex and some non-convex optimization problems are presented. The dimension of the minimization problem is lifted by one and sets corresponding to the cost function are defined. If the cost function is a convex function in R^N the corresponding set is a convex set in R^(N+1). The iterative optimization approach starts with an arbitrary initial estimate in R^(N+1) and an orthogonal projection is performed onto one of the sets in a sequential manner at each step of the optimization problem. The method provides globally optimal solutions in total-variation, filtered variation, l1, and entropic cost functions. It is also experimentally observed that cost functions based on lp, p<1 can be handled by using the supporting hyperplane concept

Exploring occupants\u27 impact at different spatial scales

Buildings\u27 users have widely been accepted as a source of uncertainty in building energy performance predictions. However, it is not evident that the diversity of occupants\u27 presence and behavior at the building level is as important as at the room level. The questions are: How should occupants be modeled at different spatial scales? At the various scales of interest, how much difference does it make if: (1) industry standard assumptions or a dynamic occupant modeling approach is used in a simulation-based analysis, and (2) probabilistic or deterministic models are used for the dynamic modeling of occupants? This paper explores the reliability of building energy predictions and the ability to quantify uncertainty associated with occupant modeling at different scales. To this end, the impacts of occupancy and occupants\u27 use of lighting and window shades on the predicted building lighting energy performance at the room and building level are studied. The simulation results showed that the inter-occupant variation at larger scales is not as important as at the room level. At larger scales (about 100 offices), the rule-base model, custom schedule model, and stochastic lighting use model compared closely for predicting mean annual lighting energy use

Entropy-Functional-Based Online Adaptive Decision Fusion Framework with Application to Wildfire Detection in Video

Cataloged from PDF version of article.In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented

Wavelet based flickering flame detector using differential PIR sensors

Cataloged from PDF version of article.A Pyro-electric Infrared (PIR) sensor based flame detection system is proposed using a Markovian decision algorithm. A differential PIR sensor is only sensitive to sudden temperature variations within its viewing range and it produces a time-varying signal. The wavelet transform of the PIR sensor signal is used for feature extraction from sensor signal and wavelet parameters are fed to a set of Markov models corresponding to the flame flicker process of an uncontrolled fire, ordinary activity of human beings and other objects. The final decision is reached based on the model yielding the highest probability among others. Comparative results show that the system can be used for fire detection in large rooms. (C) 2012 Elsevier Ltd. All rights reserved

Video fire detection - Review

Cataloged from PDF version of article.This is a review article describing the recent developments in Video based Fire Detection (VFD). Video surveillance cameras and computer vision methods are widely used in many security applications. It is also possible to use security cameras and special purpose infrared surveillance cameras for fire detection. This requires intelligent video processing techniques for detection and analysis of uncontrolled fire behavior. VFD may help reduce the detection time compared to the currently available sensors in both indoors and outdoors because cameras can monitor “volumes” and do not have transport delay that the traditional “point” sensors suffer from. It is possible to cover an area of 100 km2 using a single pan-tiltzoom camera placed on a hilltop for wildfire detection. Another benefit of the VFD systems is that they can provide crucial information about the size and growth of the fire, direction of smoke propagation. © 2013 Elsevier Inc. All rights reserve

Beautiful Mirrors at the LHC

We explore the "Beautiful Mirrors" model, which aims to explain the measured value of $A^b_{FB}$, discrepant at the $2.9\sigma$ level. This scenario introduces vector-like quarks which mix with the bottom, subtly affecting its coupling to the $Z$. The spectrum of the new particles consists of two bottom-like quarks and a charge -4/3 quark, all of which have electroweak interactions with the third generation. We explore the phenomenology and discovery reach for these new particles at the LHC, exploring single mirror quark production modes whose rates are proportional to the same mixing parameters which resolve the $A_{FB}^b$ anomaly. We find that for mirror quark masses $\lesssim 500 GeV, a 14 TeV LHC with 300 {\rm fb}^{-1}$ is required to reasonably establish the scenario and extract the relevant mixing parameters.Comment: version to be published in JHE

Detailed Case Studies

Wireless body area networks (WBANs) are one of the key technologies that support the development of pervasive health monitoring (remote patient monitoring systems), which has attracted more attention in recent years. These WBAN applications requires stringent security requirements as they are concerned with human lives. In the recent scenario of the corona pandemic, where most of the healthcare providers are giving online services for treatment, DDoS attacks become the major threats over the internet. This chapter particularly focusses on detection of DDoS attack using machine learning algorithms over the healthcare environment. In the process of attack detection, the dataset is preprocessed. After preprocessing the dataset, the cleaned dataset is given to the popular classification algorithms in the area of machine learning namely, AdaBoost, J48, k-NN, JRip, Random Committee and Random Forest classifiers. Those algorithms are evaluated independently and the results are recorded. Results concluded that J48 outperform with accuracy of 99.98% with CICIDS dataset and random forest outperform with accuracy of 99.917, but it takes the longest model building time. Depending on the evaluation performance the appropriate classifier is selected for further DDoS detection at real-time