Strength Characteristics of Class F Fly Ash Modified with Lime and Gypsum

This paper presents the shear strength characteristics of a low lime class F fly ash modified with lime alone or in combination with gypsum. Unconfined compression tests were conducted for both unsoaked and soaked specimens cured up to 90 days. Addition of a small percentage of gypsum (0.5 and 1.0%) along with lime (4–10%) enhanced the shear strength of modified fly ash within short curing periods (7 and 28 days). The gain in unsoaked unconfined compressive strength (qu) of the fly ash was 2,853 and 3,567% at 28 and 90 days curing, respectively, for addition of 10% lime along with 1% gypsum to the fly ash. The effect of 24?h soaking showed reduction of qu varying from 30 to 2% depending on mix proportions and curing period. Unconsolidated undrained triaxial tests with pore-pressure measurements were conducted for 7 and 28 days cured specimens. The cohesion of the Class F fly ash increased up to 3,150% with addition of 10% lime along with 1% gypsum to the fly ash and cured for 28 days. The modified fly ash shows the values of Skempton’s pore-pressure parameter, Af similar to that of over consolidated soils. The effects of lime content, gypsum content, and curing period on the shear strength parameters of the fly ash are highlighted herein. Empirical relationships are proposed to estimate the design parameters like deviatoric stress at failure, and cohesion of the modified fly ash. Thus, this modified fly ash with considerable shear strength may find potential use in civil engineering construction fields.     

A Single-Phase Boost Rectifier System for Wide Range of Load Variations

Converters operated in discontinuous-conduction-mode (DCM) and in continuous-conduction-mode (CCM) are suitable for lighter and higher loads, respectively. A new, constant switching frequency based single-phase rectifier system is proposed, which operates in DCM and in CCM for outputs less than and greater than 50% rated load, respectively, covering a wide range of load variation. The power circuit and the control circuit of the proposed rectifier are easily configurable for DCM and CCM operations. The measured load current is used to select the desired operating mode. The peak device current under DCM is limited to rated device current under CCM without using a device of higher current rating. The input current shaping under CCM and DCM are based on the comparison of measured input current with linear and nonlinear carriers, respectively. A load current feedforward scheme is presented to improve the system dynamic performance and also to ensure a smooth transition between the two operating modes. All the necessary control operations are performed without using multiplication, division and square-root operation. The proposed rectifier shows improved input current characteristics over the existing CCM converters for the above load range. This is validated on a 600-W rectifier prototype. Simulation and experimental results are presented     

Midwavelength Infrared Avalanche Photodiode Using InAs–GaSb Strain Layer Superlattice

InAs-GaSb strain layer superlattice p⁺-n^--n avalanche photodiodes (APDs) are fabricated using a newly introduced electron-beam aided zinc sulfide deposition. Temperature-dependent measurements were performed on 300 times 300 mum² mesa etched APDs. The effect of passivation was also studied on the diode characteristics and APD performances. Temperature-dependent gain strongly correlates with avalanche mechanism.     

Notice of Violation of IEEE Publication PrinciplesLDPC decoder: A cognitive radio perspective for next generation (XG) communication

With the rapid growth of multimedia communication systems during the last decade, there has been an increasing demand for improved technology for Error Correcting Code (ECO to enable the communication systems to have a reliable transmission over noisy channels. Low Density Parity Check (LDPC) codes are the best known ECC codes that can achieve data rates very close to the Shannon limit. In addition, superior error correction performance and parallelizable decoding algorithms have made LDPC codes a powerful competitor to turbo codes for reliable high speed communication applications. Recently, Cognitive Radio (CR) has been proposed as a promising technology to solve today's spectrum scarcity problem. CR promises to alleviate this spectrum shortage problem by dynamically accessing free spectrum resources. This implies that the radio has to work in multi-band, cope with various wireless channels and support various services such as voice, data and video. The basic requirement for CR is that it has a reconfigurable architecture to support multi-band and frequency adaptive operations. One of the ambitious design goals of future wireless systems, including 4G, IEEE 802.11n/802.16 standards, is to provide reliably very high data rate transmission in hostile environments: for example, around 100 Mb/s peak rate for downlink and around 30 Mb/s sum rate for uplink transmission with a low frame error rate (FER), typically less than 5 times10^-4. To ensure reliable nd error-free communication, there is a demand to consider implementing LDPC decoders in CR and frequency agile environments. In this article we discuss the design of adaptable as well as efficient LDPC decoders with low bit-error rate (BER) in low signal-to-noise ratio (SNR) channels for CR environments.     

Channel Assignment Strategies for Multiradio Wireless Mesh Networks: Issues and Solutions

Next-generation wireless mobile communications will be driven by converged networks that integrate disparate technologies and services. The wireless mesh network is envisaged to be one of the key components in the converged networks of the future, providing flexible high- bandwidth wireless backhaul over large geographical areas. While single radio mesh nodes operating on a single channel suffer from capacity constraints, equipping mesh routers with multiple radios using multiple nonoverlap- ping channels can significantly alleviate the capacity problem and increase the aggregate bandwidth available to the network. However, the assignment of channels to the radio interfaces poses significant challenges. The goal of channel assignment algorithms in multiradio mesh networks is to minimize interference while improving the aggregate network capacity and maintaining the connectivity of the network. In this article we examine the unique constraints of channel assignment in wireless mesh networks and identify the key factors governing assignment schemes, with particular reference to interference, traffic patterns, and multipath connectivity. After presenting a taxonomy of existing channel assignment algorithms for WMNs, we describe a new channel assignment scheme called MesTiC, which incorporates the mesh traffic pattern together with connectivity issues in order to minimize interference in multi- radio mesh networks.     

CONSENSUS-BASED ENSEMBLES OF SOFT CLUSTERINGS

The problem of obtaining a single “consensus” clustering solution from a multitude or ensemble of clusterings of a set of objects, has attracted much interest recently because of its numerous practical applications. While a wide variety of approaches including graph partitioning, maximum likelihood, genetic algorithms, and voting-merging have been proposed so far to solve this problem, virtually all of them work on hard partitionings, i.e., where an object is a member of exactly one cluster in any individual solution. However, many clustering algorithms such as fuzzy c-means naturally output soft partitionings of data, and forcibly hardening these partitions before applying a consensus method potentially involves loss of valuable information. In this article we propose several consensus algorithms that can be applied directly to soft clusterings. Experimental results over a variety of real-life datasets are also provided to show that using soft clusterings as input does offer significant advantages, especially when dealing with vertically partitioned data.     

CFNN-PSO: An Iterative Predictive Model for Generic Parametric Design of Machining Processes

ABSTRACT

Every production process consists of a large number of dependent and independent variables, which substantially influence the quality of the machined parts. Due to the large impact of process variabilities, it is difficult to design optimal models for the machining processes. Mathematical or numerical models for production processes are resource driven, which are not cost effective approaches in terms of computation and economical production. In this paper, a new artificial neural network (ANN) based predictive model is introduced, which exploits particle swarm optimization (PSO) algorithm to minimize the root mean square errors (RMSE) for the network training. This approach can effectively obtain an optimized predictive model that can calculate precise output responses for the production processes. In order to verify the proposed approach, two case studies are considered from literature and shown to produce significant improvements. Furthermore, the proposed model is validated on abrasive water jet machining (AWJM) with industrial garnet abrasives and optimal machining conditions have been obtained with optimized responses, which are substantially improved while compared with gray relational analysis (GRA).     

Change detection for moving object segmentation with robust background construction under Wronskian framework

Although background subtraction techniques have been used for several years in vision systems for moving object detection, many of them fail to provide good results in presence of noise, illumination variation, non-static background, etc. A basic requirement of background subtraction scheme is the construction of a stable background model and then comparing each incoming image frame with it so as to detect moving objects. The novelty of the proposed scheme is to construct a stable background model from a given video sequence dynamically. The constructed background model is compared with different image frames of the same sequence to detect moving objects. In the proposed scheme the background model is constructed by analyzing a sequence of linearly dependent past image frames in Wronskian framework. The Wronskian based change detection model is further used to detect the changes between the constructed background scene and the considered target frame. The proposed scheme is an integration of Gaussian averaging and Wronskian change detection model. Gaussian averaging uses different modes which arise over time to capture the underlying richness of background, and it is an approach for background building by considering temporal modes. Similarly, Wronskian change detection model uses a spatial region of support in this regard. The proposed scheme relies on spatio-temporal modes arising over time to build the appropriate background model by considering both spatial and temporal modes. The results obtained by the proposed model is found to provide accurate shape of moving objects. The effectiveness of the proposed scheme is verified by comparing the results with those of some of the existing state of the art background subtraction techniques on public benchmark databases. We found that the average F-measure is significantly improved by the proposed scheme from that of the state-of-the-art techniques.     

Impacts of reservoirs on groundwater and vegetation: A study based on remote sensing and GIS techniques

Suspended sediment is an important parameter for the monitoring of water quality, water movement, erosion, and deposition. Quantitative suspended sediment concentrations have been identified through the analysis of remotely sensed airborne multi-spectral video imagery. From a statistical analysis of the data, a single band ( 675-725 nm) was shown to be sufficient for determining suspended sediment concentrations within lakes of the Mackenzie Delta, N.W.T., Canada. A statistical analysis and derived linear regression equation were used to quantitatively determine suspended sediment concentrations for a number of lakes within the Mackenzie Delta. The results were compared with a qualitative interpretation of simultaneous colour photography. The multi-spectral video imagery proved to have superior suspended sediment concentration resolution characteristics.