Generic and efficient framework for search trees on flash memory storage systems

Tree index structures are crucial components in data management systems. Existing tree index structure are designed with the implicit assumption that the underlying external memory storage is the conventional magnetic hard disk drives. This assumption is going to be invalid soon, as flash memory storage is increasingly adopted as the main storage media in mobile devices, digital cameras, embedded sensors, and notebooks. Though it is direct and simple to port existing tree index structures on the flash memory storage, that direct approach does not consider the unique characteristics of flash memory, i.e., slow write operations, and erase-before-update property, which would result in a sub optimal performance. In this paper, we introduce FAST (i.e., Flash-Aware Search Trees) as a generic framework for flash-aware tree index structures. FAST distinguishes itself from all previous attempts of flash memory indexing in two aspects: (1) FAST is a generic framework that can be applied to a wide class of data partitioning tree structures including R-tree and its variants, and (2) FAST achieves both efficiency and durability of read and write flash operations through memory flushing and crash recovery techniques. Extensive experimental results, based on an actual implementation of FAST inside the GiST index structure in PostgreSQL, show that FAST achieves better performance than its competitors.     

Observer-based adaptive fuzzy control for a class of nonlinear time-delay systems

An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control method based on the observed states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy logic system in Mamdani type is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.     

Spatial and seasonal variations and ecotoxicological significance of sediment trace metal concentrations in Kebir-Rhumel basin (Northeast of Algeria)

This study sought to assess sediment contamination by trace metals (cadmium, chromium, cobalt, copper, manganese, nickel, lead and zinc), to localize contaminated sites and to identify environmental risk for aquatic organisms in Wadis of Kebir Rhumel basin in the Northeast of Algeria. Water and surficial sediments (0-5 cm) were sampled in winter, spring, summer and autumn from 37 sites along permanent Wadis of the Kebir Rhumel basin. Sediment trace metal contents were measured by Flame Atomic Absorption Spectroscopy. Trace metals median concentrations in sediments followed a decreasing order: Mn > Zn > Pb > Cr > Cu > Ni > Co > Cd. Extreme values (dry weights) of the trace metals are as follows: 0.6-3.4 microg/g for Cd, 10-216 microg/g for Cr, 9-446 microg/g for Cu, 3-20 microg/g for Co, 105-576 microg/g for Mn, 10-46 microg/g for Ni, 11-167 microg/g for Pb, and 38-641 microg/g for Zn. According to world natural concentrations, all sediments collected were considered as contaminated by one or more elements. Comparing measured concentrations with American guidelines (Threshold Effect Level: TEL and Probable Effect Level: PEL) showed that biological effects could be occasionally observed for cadmium, chromium, lead and nickel levels but frequently observed for copper and zinc levels. Sediment quality was shown to be excellent for cobalt and manganese but medium to bad for cadmium, chromium, copper, lead, nickel and zinc regardless of sites.     

Multiple responses analysis and modeling of Fenton process for treatment of high strength landfill leachate

Landfill leachate is one of the most recalcitrant wastes for biotreatment and can be considered a potential source of contamination to surface and groundwater ecosystems. In the present study, Fenton oxidation was employed for degradation of stabilized landfill leachate. Response surface methodology was applied to analyze, model and optimize the process parameters, i.e. pH and reaction time as well as the initial concentrations of hydrogen peroxide and ferrous ion. Analysis of variance showed that good coefficients of determination were obtained (R2 > 0.99), thus ensuring satisfactory agreement of the second-order regression model with the experimental data. The results indicated that, pH and its quadratic effects were the main factors influencing Fenton oxidation. Furthermore, antagonistic effects between pH and other variables were observed. The optimum H2O2 concentration, Fe(II) concentration, pH and reaction time were 0.033 mol/L, 0.011 mol/L, 3 and 145 min, respectively, with 58.3% COD, 79.0% color and 82.1% iron removals.     

Adaptive fuzzy sliding mode power system stabilizer using Nussbaum gain

Power system stability is enhanced through a novel stabilizer developed around an adaptive fuzzy sliding mode approach which applies the Nussbaum gain to a nonlinear model of a single-machine infinite-bus （SMIB） and multi-machine power system stabilizer subjected to a three phase fault. The Nussbaum gain is used to avoid the positive sign constraint and the problem of controllability of the system. A comparative simulation study is presented to evaluate the achieved performance.     

Data Security Model for Cloud Computing

From the perspective of data security, which has always been an important aspect of quality of service, cloud computing focuses a new challenging security threats. Therefore, a data security model must solve the most challenges of cloud computing security. The proposed data security model provides a single default gateway as a platform. It used to secure sensitive user data across multiple public and private cloud applications, including Salesforce, Chatter, Gmail, and Amazon Web Services, without influencing functionality or performance. Default gateway platform encrypts sensitive data automatically in a real time before sending to the cloud storage without breaking cloud application. It did not effect on user functionality and visibility. If an unauthorized person gets data from cloud storage, he only sees encrypted data. If authorized person accesses successfully in his cloud, the data is decrypted in real time for your use. The default gateway platform must contain strong and fast encryption algorithm, file integrity, malware detection, firewall, tokenization and more. This paper interested about authentication, stronger and faster encryption algorithm, and file integrity.     

A deployment methodology of real-time industrial control applications in distributed controllers

This paper deals with the deployment of industrial control applications in distributed devices (considered as multi-tasking Programmable Logic Controllers (PLCs)). These applications are developed according to the component-based standard IEC 61499. In this standard, a function block is an event triggered component and the application is a network of distributed blocks on several devices. To deploy these blocks in feasible OS tasks of the execution support, we propose a hybrid scheduling approach allowing in particular to check according to specifications the temporal behavior of the application in devices and also on the communication network. This approach is based on static and dynamic policies. The static policy allows to construct OS tasks from function blocks, whereas the dynamic policy allows their scheduling at run-time.     

A cooperative recurrent neural network for solving L(1) estimation problems with general linear constraints

The constrained L(1) estimation is an attractive alternative to both the unconstrained L(1) estimation and the least square estimation. In this letter, we propose a cooperative recurrent neural network (CRNN) for solving L(1) estimation problems with general linear constraints. The proposed CRNN model combines four individual neural network models automatically and is suitable for parallel implementation. As a special case, the proposed CRNN includes two existing neural networks for solving unconstrained and constrained L(1) estimation problems, respectively. Unlike existing neural networks, with penalty parameters, for solving the constrained L(1) estimation problem, the proposed CRNN is guaranteed to converge globally to the exact optimal solution without any additional condition. Compared with conventional numerical algorithms, the proposed CRNN has a low computational complexity and can deal with the L(1) estimation problem with degeneracy. Several applied examples show that the proposed CRNN can obtain more accurate estimates than several existing algorithms.     

Remote flood monitoring system based on plastic optical fibres and wireless motes

This paper summarises an effort in the development of a remote flood monitoring system based on plastic optical fibre (POF) sensors and a wireless mote network. The wireless mote, comprising of a network of MICA2DOT™ units, was used as a platform to monitor and record the signal from the POF sensors and transmit this information to a base station wirelessly. A prototype of the integrated wireless POF sensor unit has been constructed, rendering it possible to deploy the autonomous unit remotely at multiple monitoring points as required. A flood monitoring simulation was carried out in a 24 m × 10 m × 0.9 m wave basin where four of these wireless optical fibre mote sensors were used to detect the rising water level in the basin. The novelty of the work lies in the successful integration of the wireless platform to a POF-based liquid level sensor and the subsequent demonstration of the prototype of the system for the purposes of flood monitoring applications.The sensing principle of the POF sensor developed here is well-known and is based on the loss of total internal reflection of the optical signal as the sensor probe comes in contact with the liquid. Compared to optical fibre-based sensors reported previously in the literature, the probe profile used in this study differs in terms of its simplicity in design, while exhibiting an excellent signal intensity loss ratio without the need for additional attachments to the probe such as optical prisms. The tests carried out showed that the POF sensor is capable of detecting a variety of fluids. Exhibiting good signal stability, the sensor also detects the liquid level reliably when the liquid rises or falls to the predetermined level. The responsiveness of the optical fibre sensor was evaluated by simulating different rates at which the liquid rises by immersing the sensor tip into the liquid and vice-versa at various speeds ranging from 1 mm/min to 500 mm/min.