Optimization Models for Layout and Pipe Design for Storm Sewer Systems

Optimization models are developed for simultaneously determining the pipe layout and the pipe design for storm sewer systems. The pipe design process includes computation of commercial diameters, slopes, and crown elevations for the storm sewer pipes. The optimization models aim to minimize the total costs of the layout and the pipe design for most of system elements. The optimization models are formulated as a 0–1 Integer Nonlinear Programming problem and solved using the General Algebraic Modeling System without the use of heuristic models which were characteristic of all previous models for the simultaneous determine of the pipe layout and pipe design of sewer networks. The models are based upon two different optimization approaches: (1) considers one or more commercial diameters of pipe connecting two manholes and (2) considers only one commercial diameter in a pipe connecting two manholes. The commercial diameters, pipe slopes, crown elevations, and total costs of the storm sewer system were compared for the two approaches using an example that illustrates the savings in cost by allowing multiple pipe sizes. The two new optimization modeling approaches developed herein can simultaneously determine the minimum cost pipe design (commercial diameters, slopes, and crown elevations) and pipe layout of storm sewer systems and satisfy all design constraints.

     

Computationally efficient 2D beamspace matrix pencil method for direction of arrival estimation

In this paper, we propose a new 2-dimensional beamspace matrix pencil (2D BMP) method for direction of arrival (DOA) estimation of plane wave signals using a uniform rectangular array (URA). Based on some a priori information about DOA, the proposed method transforms the complex signal subspace in 2D matrix pencil (2D MP) method [Y. Hua, Estimating two-dimensional frequencies by matrix enhancement and matrix pencil, IEEE Trans. Signal Process. 40 (9) (1992) 2267–2280] into a real and reduced dimensional beamspace using the discrete Fourier transform (DFT) matrix transformation. Consequently, the computational complexity is reduced (several times) in comparison with 2D MP method. Computer simulations are provided to show that 2D BMP method gives comparable performance in terms of average mean square error of the estimated DOA with lesser floating point operations as compared to the existing (MP) methods.     

Fabrication and characterization of MnO2 based composite sheets for development of flexible energy storage electrodes

Development of cost efficient, flexible and light weight paper electrodes for high-tech applications is high in demand in era of modern disposable technology. In this study α-MnO₂ nanorods were fabricated through hydrothermal method by varying growth time and further combined with lignocelluloses fibers extracted from self growing plant, Monochoria Vaginalis. Crystal structure, morphology and thermal properties of MnO₂ nanorods were characterized by X. Ray Diffraction (XRD), Field Emission Scanning Electron Microscope (FESEM) and Thermogravimetric Analysis (TGA), respectively. FESEM image analysis revealed the highest aspect ratio of 48.016 for 4?h treated MnO₂ sample and high purity level was confirmed by XRD. MnO₂ sample with high aspect ratio, relatively pure and larger yield was selected for incorporation of lignocelluloses fibers to fabricate flexible, light-weight and environmentally safe LC/MnO₂ composite paper sheet. Furthermore, LC/MnO₂ composite sheet was employed as working electrode in 2?M sodium sulfate electrolyte for cyclic voltammetry measurements. Presented LC/MnO₂ composite sheet revealed specific capacitances 117, 59, 39, 25 and 23?F/g at scan rates of 5, 10, 20, 50 and 100?mV/s, respectively. Incorporation of LC fibers within MnO₂ nanorods as binders will open the possibilities to fabricate the flexible paper electrode for application in supercapacitors and batteries due to facile synthesis, light-weight and environmentally friendly aspects.     

Ontology based E-learning framework: A personalized,adaptive and context aware model

Multimedia Tools and Applications - Enhancing the degree of learner productivity, one of the major challenges in E-Learning systems, may be catered through effective personalization, adaptivity and...     

Convolutional neural network based early fire detection

The detection of manmade disasters particularly fire is valuable because it causes many damages in terms of human lives. Research on fire detection using wireless sensor network and video-based methods is a very hot research topic. However, the WSN based detection model need fire happens and a lot of smoke and fire for detection. Similarly, video-based models also have some drawbacks because conventional algorithms need feature vectors and high rule-based models for detection. In this paper, we proposed a fire detection method which is based on powerful machine learning and deep learning algorithms. We used both sensors data as well as images data for fire prevention. Our proposed model has three main deep neural networks i.e. a hybrid model which consists of Adaboost and many MLP neural networks, Adaboost-LBP model and finally convolutional neural network. We used Adaboost-MLP model to predict the fire. After the prediction, we proposed two neural networks i.e. Adaboost-LBP model and convolutional neural network for detection of fire using the videos and images taken from the cameras installed for the surveillance. Adaboost-LBP model is to generate the ROIs from the image where emergencies exist Our proposed model results are quite good, and the accuracy is almost 99%. The false alarming rate is very low and can be reduced more using further training.

     

Delay and link utilization aware routing protocol for wireless multimedia sensor networks

Wireless Multimedia Sensor Networks (WMSNs) consist of networks of interconnected devices involved in retrieving multimedia content, such as, video, audio, acoustic, and scalar data, from the environment. The goal of these networks is optimized delivery of multimedia content based on quality of service (QoS) parameters, such as delay, jitter and distortion. In multimedia communications each packet has strict playout deadlines, thus late arriving packets and lost packets are treated equally. It is a challenging task to guarantee soft delay deadlines along with energy minimization, in resource constrained, high data rate WMSNs. Conventional layered approach does not provide optimal solution for guaranteeing soft delay deadlines due to the large amount of overhead involved at each layer. Cross layer approach is fast gaining popularity, due to its ability to exploit the interdependence between different layers, to guarantee QoS constraints like latency, distortion, reliability, throughput and error rate. The paper presents a channel utilization and delay aware routing (CUDAR) protocol for WMSNs. This protocol is based on a cross-layer approach, which provides soft end-to-end delay guarantees along with efficient utilization of resources. Extensive simulation analysis of CUDAR shows that it provides better delay guarantees than existing protocols and consequently reduces jitter and distortion in WMSN communication.     

CDCSS: cluster-based distributed cooperative spectrum sensing model against primary user emulation (PUE) cyber attacks

In cognitive radio network, the secondary users (SUs) use the spectrum of primary users for communication which arises the security issues. The status of SUs as legitimate users is compulsory for the stability of the system. This paper addresses the issue of delay caused by a band-selection decision process that directly affects the security and performance. The model cluster-based distributed cooperative spectrum sensing is proposed. In this model, cluster heads (CHs) exchange control information with other CHs and ordinary nodes. This model significantly reduced the delay, sensing, convergence, routing, in band-selection process. This also reduces the energy consumption while sensing the spectrum which seriously leads to performance upgradation. The simulated results show the improved performance of cognitive radio networks in terms of delay, packet loss ratio and bandwidth usage as compared to cluster-based cooperative spectrum sensing model. The opportunity for primary user emulation attacker is minimized as the overall delay is reduced.     

Embedded passive components in advanced 3D chips and micro/nano electronic systems

Microsystem Technologies - Electronic devices are shrinking day by day, while the speed and reliability is increasing. At the same time, IC designs and micro/nano electronic systems are becoming...     

Enhanced halftone-based secure and improved visual cryptography scheme for colour/binary Images

Visual Cryptography (VC) is gaining attraction during the past few years to secure the visual information in the transmission network. It enables the visual data i.e. handwritten notes, photos, printed text, etc. to encrypt in such a way that their decryption can be done through the human visual framework. Hence, no computational assistance is required for the decryption of the secret images they can be seen through naked eye. In this paper, a novel enhanced halftoning-based VC scheme is proposed that works for both binary and color images. Fake share is generated by the combination of random black and white pixels. The proposed algorithm consists of 3 stages i.e., detection, encryption, and decryption. Halftoning, Encryption, (2, 2) visual cryptography and the novel idea of fake share, make it even more secure and improved. As a result, it facilitates the original restored image to the authentic user, however, the one who enters the wrong password gets the combination of fake share with any real share. Both colored and black images can be processed with minimal capacity using the proposed scheme.