Desorption kinetics of trichloroethylene and perchloroethylene from aged dense nonaqueous phase liquid-contaminated soil

Batch experiments were conducted to investigate the desorption kinetics of a contaminated soil collected from a field site at Hill Air Force Base (AFB), Utah. The site has been contaminated with many dense nonaqueous phase liquids (DNAPLs), primarily trichloroethylene (TCE) and perchloroethylene (PCE), for more than 60 years. Similar desorptive behaviour of both TCE and PCE was observed: a stage of fast release of the contaminant at the start followed by a very slow stage. Moreover, it is suggested that not all the contaminant in the solid phase may be released to the aqueous phase, apparently due to the age factor. Local variations were observed in the effective diffusivity and other sorption-related parameters across the site due to aquifer heterogeneity. Experimental results were then simulated using a numerical code based on the intraparticle diffusion model. Good agreement was found between both results.     

Software‐defined networking approach for enhanced evolved packet core network

The evolved packet core (EPC) network is the mobile network standardized by the 3rd Generation Partnership Project and represents the recent evolution of mobile networks providing high‐speed data rates and on‐demand connectivity services. Software‐defined networking (SDN) is recently gaining momentum in network research as a new generation networking technique. An SDN‐based EPC is expected to introduce gains to the EPC control plane architecture in terms of simplified, and perhaps even software‐based, vendor independent infrastructure nodes. In this paper, we propose a novel SDN‐based EPC architecture along with the protocol‐level detailed implementation and provide a mechanism for identifying information fields exchanged between SDN‐EPC entities that maintains correct functionality with minimal impact on the conventional design. Furthermore, we present the first comprehensive network performance evaluation for the SDN‐based EPC versus the conventional EPC and provide a comparative analysis of 2 networks performances identifying potential bottlenecks and performance issues. The evaluation focuses on 2 network control operations, namely, the S1‐handover and registration operations, taking into account several factors, and assessing performance metrics such as end‐to‐end delay (E2ED) for completion of the respective control operation, and EPC nodes utilization figures.     

A provable and secure mobile user authentication scheme for mobile cloud computing services

The mobile cloud computing (MCC) has enriched the quality of services that the clients access from remote cloud‐based servers. The growth in the number of wireless users for MCC has further augmented the requirement for a robust and efficient authenticated key agreement mechanism. Formerly, the users would access cloud services from various cloud‐based service providers and authenticate one another only after communicating with the trusted third party (TTP). This requirement for the clients to access the TTP during each mutual authentication session, in earlier schemes, contributes to the redundant latency overheads for the protocol. Recently, Tsai et al have presented a bilinear pairing based multi‐server authentication (MSA) protocol, to bypass the TTP, at least during mutual authentication. The scheme construction works fine, as far as the elimination of TTP involvement for authentication has been concerned. However, Tsai et al scheme has been found vulnerable to server spoofing attack and desynchronization attack, and lacks smart card‐based user verification, which renders the protocol inapt for practical implementation in different access networks. Hence, we have proposed an improved model designed with bilinear pairing operations, countering the identified threats as posed to Tsai scheme. Additionally, the proposed scheme is backed up by performance evaluation and formal security analysis.     

Side-Channel Information Characterisation Based on Cascade-Forward Back-Propagation Neural Network

Traditional cryptanalysis assumes that an adversary only has access to input and output pairs, but has no knowledge about internal states of the device. However, the advent of side-channel analysis showed that a cryptographic device can leak critical information. In this circumstance, Machine learning is known as a powerful and promising method of analysing of side-channel information. In this paper, an experimental investigation on a FPGA implementation of elliptic curve cryptography (ECC) was conducted to explore the efficiency of side-channel information characterisation based on machine learning techniques. In this work, machine learning is used in terms of principal component analysis (PCA) for the preprocessing stage and a Cascade-Forward Back-Propagation Neural Network (CFBP) as a multi-class classifier. The experimental results show that CFBP can be a promising approach in characterisation of side-channel information.     

Efficient Resource Management for Cloud-enabled Video Surveillance over Next Generation Network

The next generation video surveillance systems are expected to face challenges in providing computation support for an unprecedented amount of video streams from multiple video cameras in a timely and scalable fashion. Cloud computing offers huge computation resources for large-scale storage and processing on demand, which are deemed suitable for video surveillance tasks. Cloud also provides quality of service guaranteed hardware and software solutions with the virtual machine (VM) technology using a utility-like service costing model. In cloud-based video surveillance context, the resource requests to handle video surveillance tasks are translated in the form of VM resource requests, which in turn are mapped to VM resource allocation referring to physical server resources hosting the VMs. Due to the nature of video surveillance tasks, these requests are highly time-constrained, heterogeneous and dynamic in nature. Hence, it is very challenging to actually manage the cloud resources from the perspective of VM resource allocation given the stringent requirements of video surveillance tasks. This paper proposes a computation model to efficiently manage cloud resources for surveillance tasks allocation. The proposed model works on optimizing the trade-off between average service waiting time and long-term service cost, and shows that long-term service cost is inversely proportional to high and balanced utilization of cloud resources. Experiments show that our approach provides a near-optimal solution for cloud resource management when handling the heterogeneous and unpredictable video surveillance tasks dynamically over next generation network.     

A Novel Analytical Framework for Integrated Cross-Layer Study of Call-Level and Packet-Level QoS in Wireless Mobile Multimedia Networks

We present a novel integrated analytical framework for analyzing the quality-of-service (QoS) performance measures in a wireless mobile multimedia network. The framework integrates physical, radio link, and network layer parameters and protocols to analyze the call-level and packet-level performances. In the network layer, call admission control (CAC) is responsible for deciding whether an incoming call can be accepted or not so that the performances of the ongoing calls do not deteriorate below the acceptable level. Also, an adaptive channel allocation (ACA) scheme is used to maximize the utilization of the radio resources. In the data link layer, queue management and error control are used for non-real-time loss-sensitive traffic. In the physical layer, a finite state Markov channel (FSMC) is used to model channel fading, and adaptive modulation is used for rate adaptation according to channel quality. Various call-level and packet-level QoS measures for real-time, non-real-time, and best-effort traffic are obtained. The analytical results are validated by extensive simulations. Examples of the applications of the presented analytical framework are also provided     

Pad effects on material-removal rate in chemical-mechanical planarization

The role of a porous pad in controlling material-removal rate (MRR) during the chemical-mechanical planarization (CMP) process has been studied numerically. The numerical results are used to develop a phenomenological model that correlates the forces on each individual abrasive particle to the applied nominal pressure. The model provides a physical explanation for the experimentally observed domains of pressure-dependent MRR, where the pad deformation controls the load sharing between active-abrasive particles and direct pad-wafer contact. The predicted correlations between MRR and slurry characteristics, i.e., particle size and concentration, are in agreement with experimentally measured trends reported by Ouma¹ and Izumitani.²     

Autocorrelation model-based identification method for ARMA systems in noise

A novel method for parameter estimation of minimum-phase autoregressive moving average (ARMA) systems in noise is presented. The ARMA parameters are estimated using a damped sinusoidal model representation of the autocorrelation function of the noise-free ARMA signal. The AR parameters are obtained directly from the estimates of the damped sinusoidal model parameters with guaranteed stability. The MA parameters are estimated using a correlation matching technique. Simulation results show that the proposed method can estimate the ARMA parameters with better accuracy as compared to other reported methods, in particular for low SNRs.     

Estimating the parameters of a non-homogeneous Poisson-processmodel for software reliability

A stochastic model (G-O) for the software failure phenomenon based on a nonhomogeneous Poisson process (NHPP) was suggested by Goel and Okumoto (1979). This model has been widely used but some important work remains undone on estimating the parameters. The authors present a necessary and sufficient condition for the likelihood estimates to be finite, positive, and unique. A modification of the G-O model is suggested. The performance measures and parametric inferences of the new model are discussed. The results of the new model are applied to real software failure data and compared with G-O and Jelinski-Moranda models     

Dynamic rate adaptation based on multidimensional multicode DS-CDMA in cellular wireless networks

Dynamic rate adaptation for uplink data transmission in a cellular multidimensional multicode (MDMC) direct-sequence code-division multiple-access packet data network is modeled and analyzed. An analytical framework is developed to evaluate the performances of radio link level dynamic rate adaptation schemes under multipath fading and log-normal shadowing. The radio link level throughput under optimal dynamic rate adaptation (having exponential computational complexity) and different heuristic-based suboptimal rate adaptation schemes can be assessed under the presented analytical framework. The performance of MDMC signaling is compared with that of the single-code variable spreading factor (VSF) signaling. To this end, based on an equilibrium point analysis of the system in steady-state, a base station-assisted and mobile-controlled dynamic rate adaptation scheme is presented.