Uplink Scheduling in CDMA Packet-Data Systems

Uplink scheduling in wireless systems is gaining importance due to arising uplink intensive data services (ftp, image uploads etc.), which could be hampered by the currently in-built asymmetry in favor of the downlink. In this work, we propose and study algorithms for efficient uplink packet-data scheduling in a CDMA cell. The algorithms attempt to maximize system throughput under transmit power limitations on the mobiles assuming instantaneous knowledge of user queues and channels. However no channel statistics or traffic characterization is necessary. Apart from increasing throughput, the algorithms also improve fairness of service among users, hence reducing chances of buffer overflows for poorly located users. The major observation arising from our analysis is that it is advantageous on the uplink to schedule “strong” users one-at-a-time, and “weak” users in larger groups. This contrasts with the downlink where one-at-a-time transmission for all users has shown to be the preferred mode in much previous work. Based on the optimal schedules, we propose less complex and more practical approximate methods, both of which offer significant performance improvement compared to one-at-a-time transmission, and the widely acclaimed Proportional Fair (PF) algorithm, in simulations. When queue content cannot be fed back, we propose a simple modification of PF, Uplink PF (UPF), that offers similar improvement. Hereafter, we refer to users with low recieved power at the base even when transmitting at peak transmit power as “weak” users, and the strongly recieved users at the base as “strong” users. Krishnan Kumaran is currently a member of the Complex Systems Modeling section in the Corporate Strategic Research of ExxonMobil Corp., Clinton, NJ. Formerly, he was a Member of Technical Staff in the Mathematics of Networks and Systems Research Department at Bell Labs in Murray Hill, NJ, where his research interests were in modeling, analysis and simulation of design, resource management and scheduling issues in telecommunication networks. Lijun Qian is an assistant professor in the Department of Electrical Engineering at Prairie View A&M University. He received his B.S. from Tsinghua University in Beijing, M.S. from Technion-Israel Institute of Technology, and Ph.D. from WINLAB, Rutgers University, all in electrical engineering. Before joining PVAMU, he was a researcher at Networks and Systems Research Department of Bell Labs in Murray Hill, NJ. His major research interests are in wireless communications and networking technologies, especially in radio resource management, protocol design, TCP/RLP optimization and MPLS traffic engineering.     

Synthesis and characterization of acidic properties of Al-SBA-15 materials with varying Si/Al ratios

Al-SBA-15 of varying Si/Al ratios in the range 11.4–78.4 was synthesized using tri-block copolymer P123. The calcined materials were examined by XRD, pore size distribution, surface area, ²⁷Al NMR spectroscopy. The acidity and acid strength distribution were studied using microcalorimetric adsorption of NH₃. The acidic properties were also examined by cumene cracking reaction as a function of Si/Al ratios. Systematic variation of acidity and activity was observed as a function of Si/Al ratio. The initial heats of NH₃ adsorption correlated well with activity indicate that acid sites with ΔH > 100 kJ/mole is responsible for cumene cracking activity. Linear correlations were obtained with total acidity and cumene cracking activities. The tetrahedral aluminum was found to be responsible for the observed acidities and catalytic activities.     

PID controller tuning for a multivariable glass furnace process by genetic algorithm

Standard genetic algorithms (SGAs) are investigated to optimise discrete-time proportional-integral-derivative (PID) controller parameters, by three tuning approaches, for a multivariable glass furnace process with loop interaction. Initially, standard genetic algorithms (SGAs) are used to identify control oriented models of the plant which are subsequently used for controller optimisation. An individual tuning approach without loop interaction is considered first to categorise the genetic operators, cost functions and improve searching boundaries to attain the desired performance criteria. The second tuning approach considers controller parameters optimisation with loop interaction and individual cost functions. While, the third tuning approach utilises a modified cost function which includes the total effect of both controlled variables, glass temperature and excess oxygen. This modified cost function is shown to exhibit improved control robustness and disturbance rejection under loop interaction.     

Network Lifetime Enhancement of Wireless Sensor Networks Using EFRP Protocol

This paper presents a novel transceiver architecture for in-band full duplex radio. A transceiver for full duplex radio requires a self-interference (SI) canceler to remove the SI occurring from the transmitter to the receiver, and a full duplex transceiver generally has two SI cancelers: one at the analog RF stage and the other at the baseband stage. The output from the SI canceler at the RF stage includes much residual SI, and it decreases the number of bits allocated to the analog baseband signal at the analog-to-digital converter. A 1-tap analog baseband SI canceler that uses a replica signal including only the direct path component of the residual SI has been presented for preventing degradation. However, the architecture cannot remove the SI well due to the high Ricial K-factor. To address the problem, the presented architecture has an SI canceler at the analog baseband stage, and this canceler employs a replica signal that is output from a digital-to-analog converter. Because the replica signal is generated in the digital domain, the architecture can generate a multipath replica signal, and improved performance can be expected. Numerical and theoretical analyses are shown to validate the effectiveness of the presented architecture.

     

Organometallic L-Alanine Cadmium Iodide Crystals for Optical Device Fabrication

Single crystals of L-alanine cadmium iodide (LACI) were grown by the slow evaporation technique at room temperature. A single-crystal X-ray diffraction (SXRD) model was used to evaluate the crystal structure of the as-grown LACI crystal. The energy dispersive X-ray (EDX) analysis and ultraviolet-visible-near infrared (UV-vis-NIR) transmittance studies were carried out, and the results reveal the presence of elements in the title compound. From the transmittance data, the optical bandgap as a function of photon energy was estimated, and the different optical constants were calculated. A fluorescence study was performed for the LACI crystal. Thermogravimetric and differential thermal analyses have also been studied to investigate the thermal property of the LACI crystal. The efficiency of the second harmonic generation (SHG) of the title crystal was investigated. The magnetic and electrical properties were estimated by the vibrating sample magnetometer (VSM) analysis and impedance study, respectively.     

Experimental study on conventional solar still integrated with inclined solar still under different water depth

This study primarily focuses on comparative experimental analysis on standalone conventional solar still (CSS), inclined solar still (ISS), and integrated conventional and inclined solar still (CSS‐ISS) for different parameters that affect the freshwater yield. For enhancing the freshwater yield only a few studies are available on still‐still integration. The present novel study provides a greater improvement in improving the freshwater yield by integrating ISS with CSS. This experimental work mainly concentrates on the importance of water depth (d _w) and mass flow rate of water ( m _w) in the solar still. Water depth inside the conventional still varied from 0.02 to 0.06 m whereas, water is constantly flown with a mass flow rate of 8.33 kg/hour in an ISS with baffles. The experimental result shows that the accumulated freshwater yield from CSS‐ISS, ISS, and CSS were 6.2, 5.04, and 4.24 kg, respectively. CSS‐ISS and ISS produced 46.23% and 18.87% higher productivity than the CSS. From the experimental investigation, it is also identified that the water temperature is significantly improved by 20% using integration as compared with CSS without integration under the same water depth of d _w = 0.02 m. The overall improvement in yield was higher in the case of CSS‐ISS. The deviations between experimental and theoretical values of yield from the conventional and modified solar still were in the range of ±7%.     

Ipomea carnea-based silver nanoparticle synthesis for antibacterial activity against selected human pathogens

The biosynthesis of silver nanoparticles (AgNps) has a wide range of applications, and here we develop a rapid synthesis using the leaf extract of Ipomea carnea. We demonstrated that 100?mL of a 1?mM silver nitrate solution was reduced to AgNps by 500?µL of I. carnea extract in 5?min and that one or more of the chemical constituents present in the extract acted as the reducing agent. Surface plasmon resonance peaks were observed from 410 to 440?nm for AgNps synthesised using the plant extract, and the peaks showed a characteristic blue shift with variation of pH from 2 to 8. Particle size analysis revealed the size of the AgNps to be from 30 to 130?nm, which was also confirmed by dynamic light scattering, atomic force microscopy and transmission electron microscopy. Additionally, the antibacterial effects of the AgNps were evaluated against selected human pathogens such as Staphylococcus aureus, Bacillus cereus, Bacillus subtilis, Klebsiella pneumoniae, Aeromonas hydrophila, Salmonella typhi, Proteus vulgaris and Pseudomonas aeruginosa. Finally, the AgNps were impregnated with a cellulose acetate membrane to form an antimycobacterial membrane. Antimycobacterial activity against a non-pathogenic Mycobacterium smegmatis showed that the AgNp-embedded membrane system has a zone of inhibition of 14?mm.     

Long-term Sector-wise Electrical Energy Forecasting Using Artificial Neural Network and Biogeography-based Optimization

This article presents a hybrid model involving artificial neural networks and biogeography-based optimization for long-term forecasting of India's sector-wise electrical energy demand. It involves socio-economic indicators, such as population and per capita gross domestic product, and uses two artificial neural networks, which are trained through a biogeography-based optimization algorithm with a goal of perfect mapping of the input–output data in the non-linear space through obtaining the global best weight parameters. The biogeography-based optimization based training of the artificial neural network improves the forecasting accuracy and avoids trapping in local optima besides enhancing the convergence to the lowest mean squared error at the minimum number of iterations than existing approaches. The model requires an input and the year of the forecast and predicts the sector-wise energy demand. Forecasts up to the year 2025 are compared with those of the regression model, the artificial neural network model trained by back-propagation, and the artificial neural network model trained by harmony search algorithm to exhibit its effectiveness.