Impact of power-supply noise on timing in high-frequency microprocessors

This paper analyzes the impact of power-supply noise on the performance of high-frequency microprocessors. First, delay models that take this noise into account are proposed for device-dominated and interconnect-dominated timing paths. For typical circuits, it is shown that the peak of the noise is largely irrelevant and that the average supply voltage during switching is more important. It is then argued that global differential noise can potentially have a greater timing impact than common-mode noise. Finally, realistic values for the model parameters are measured on a 2.53-GHz Pentium4 microprocessor using a 130-nm technology. These values imply that the power-supply noise present on the system board reduces clock frequency by 6.7%. The model suggests that the frequency penalty associated with this power-supply noise will steadily increase and reach 7.6% for the 90-nm technology generation.     

A general enthalpy method for modeling solidification processes

In the present work, a general implicit source-based enthalpy method is presented for the analysis of solidification systems. The proposed approach is both robust and efficient. The performance of the method is illustrated by application to a number of problems taken from recent metallurgical literature.     

Analysis of Caching and Replication Strategies for Web Applications

Developers often use replication and caching mechanisms to enhance Web application performance. The authors present a qualitative and quantitative analysis of state-of-the art replication and caching techniques used to host Web applications. Their analysis shows that selecting the best mechanism depends heavily on data workload and requires a careful review of the application's characteristics. They also propose a technique for Web practitioners to compare different mechanisms' performance on their own     

Modification of glassy carbon electrode with multi-walled carbon nanotubes and iron(III)-porphyrin film: Application to chlorate, bromate and iodate detection

In this study, multi-wall carbon nanotubes (MWCTs) is evaluated as a transducer, stabilizer and immobilization matrix for the construction of amperometric sensor based on iron-porphyrin. 5,10,15,20-Tetraphenyl-21H,23H-porphine iron(III) chloride (Fe(III)P) adsorbed on MWCNTs immobilized on the surface of glassy carbon electrode. Cyclic voltammograms of the Fe(III)P-incorporated-MWCNTs indicate a pair of well-defined and nearly reversible redox couple with surface confined characteristics at wide pH range (2-12). The surface coverage (Γ) and charge transfer rate constant (k_s) of Fe(III)P immobilized on MWCNTs were 7.68 × 10⁻⁹ mol cm⁻² and 1.8 s⁻¹, respectively, indicating high loading ability of MWCNTs for Fe(III)P and great facilitation of the electron transfer between Fe(III)P and carbon nanotubes immobilized on the electrode surface. Modified electrodes exhibit excellent electrocatalytic activity toward reduction of ClO₃⁻, IO₃⁻ and BrO₃⁻ in acidic solutions. The catalytic rate constants for catalytic reduction of bromate, chlorate and iodate were 6.8 × 10³, 7.4 × 10³ and 4.8 × 10² M⁻¹ s⁻¹, respectively. The hydrodynamic amperometry of rotating-modified electrode at constant potential versus reference electrode was used for detection of bromate, chlorate and iodate. The detection limit, linear calibration range and sensitivity for chlorate, bromate and iodate detections were 0.5 μM, 2 μM to 1 mM, 8.4 nA/μM, 0.6 μM, 2 μM to 0.15 mM, 11 nA/μM, and 2.5 μM, 10 μM to 4 mM and 1.5 nA/μM, respectively. Excellent electrochemical reversibility of the redox couple, good reproducibility, high stability, low detection limit, long life time, fast amperometric response time, wide linear concentration range, technical simplicity and possibility of rapid preparation are great advantages of this sensor. The obtained results show promising practical application of the Fe(III)P-MWCNTs-modified electrode as an amperometric sensor for chlorate, iodate and bromate detections.     

TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data

Maintaining a fluid and safe traffic is a major challenge for human societies because of its social and economic impacts. Various technologies have considerably paved the way for the elimination of traffic problems and have been able to effectively detect drivers’ violations. However, the high volume of the real-time data collected from surveillance cameras and traffic sensors along with the data obtained from individuals have made the use of traditional methods ineffective. Therefore, using Hadoop for processing large-scale structured and unstructured data as well as multimedia data can be of great help. In this paper, the TVD-MRDL system based on the MapReduce techniques and deep learning was employed to discover effective solutions. The Distributed Deep Learning System was implemented to analyze traffic big data and to detect driver violations in Hadoop. The results indicated that more accurate monitoring automatically creates the power of deterrence and behavior change in drivers and it prevents drivers from committing unusual behaviors in society. So, if the offending driver is identified quickly after committing the violation and is punished with the appropriate punishment and dealt with decisively and without negligence, we will surely see a decrease in violations at the community level. Also, the efficiency of the TVD-MRDL performance increased by more than 75% as the number of data nodes increased.

     

Energy and Trust Management Framework for MANET using Clustering Algorithm

In General, Mobile Ad-Hoc Network (MANET) has limited energy resources, and it cannot recharge itself. This research goal focuses on building a power management scheme that saves energy in the MANET. Due to power instability, there is a chance that cluster heads fail and function incorrectly in cluster-based routing. As a result, instability occurs with the cluster heads while collecting data and communicating with others effectively. This work focuses on detecting the unstable cluster heads, which are replaced by other nodes implementing the envisaged self-configurable cluster mechanism. A self-configurable cluster mechanism with a k-means protocol approach is proposed to designate cluster heads effectively. The proposed k-means procedure is based on periodic irregular cluster head rotations or altering the number of clusters. We also propose a trust management mechanism in this research to detect and avoid MANET vulnerabilities. Because of the continuously changing topology and limited resources (power, bandwidth, computing), the trust management algorithm should only use local data. Consequently, compared to traditional protocols, the proposed approach with the k-means procedure and its experimental results show lower power usage and provide an optimal system for trust management.

     

A computational method of forecasting based on high-order fuzzy time series

This paper presents a computational method of forecasting based on high-order fuzzy time series. The developed computational method provides a better approach to overcome the drawback of existing high-order fuzzy time series models. Its simplicity lies with the use of differences in consecutive values of various orders as forecasting parameter and a w-step fuzzy predictor in place of complicated computations of fuzzy logical relations. The objective of the present study is to examine the suitability of various high-order fuzzy time series models in forecasting. The general suitability of the developed method has been tested by implementing it in the forecasting of student enrollments of the University of Alabama and in the forecasting of crop (Lahi) production, a case of high uncertainty in time series data. The results obtained have been compared in terms of average error of forecast to show superiority of the proposed model.     

Extraction of S‐parameters from time domain reflectometry and transmission measurements using rational functions

The extraction of the broad band S‐parameter response from transient reflection and transmission measurements is discussed. The proposed method uses the generalized pencil‐of‐function method, recursive deconvolution, and calibration structures to obtain a response using rational functions. A low loss printed circuit board plane and a lossy thin film plane are characterized to capture the two‐port S‐parameters. The results are compared with network analyzer measurements. The effects of the measurement parameters such as the sampling interval, time window, and number of averages are shown. The effect of time jitter on the extracted frequency response is quantified through repeated measurements. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 74–85, 2003.     

Improvement of tea leaves fermentation with Aspergillus spp. pectinase

The pectinase enzymes isolated from Aspergillus spp., A. indicus, A. flavus and A. niveus were used for fermentation of tea leaves. The enzymes were purified and characterized. The effect of both crude enzyme preparation and purified pectinase enzymes on the improvement of tea leaf fermentation were determined in terms of theaflavin, thearubigin, high polymerized substances, total liquor colour, dry matter content and total soluble solids of the tea produced. The crude enzyme preparations obtained from ethanol precipitation were found to be more effective in improving tea leaf fermentation than the purified pectinase enzymes.