A Call-Admission Control (CAC) Algorithm for Providing Guaranteed QoS in Cellular Networks

Future broadband wireless access systems are expected to integrate various classes of mobile terminals (MTs), each class with a different type of quality of service (QoS) requirement. When the load on a wireless network is high, the guarantee of QoS for each class of MTs is a challenging task. This study considers two classes of MTs—profiled MTs and nonprofiled or regular MTs. It is assumed that profiled users require a guaranteed QoS. The measure of QoS is the probability of forced termination of a call that was allowed to access the network. Two previous handoff prioritization schemes—(i) prerequest scheme and (ii) guard channel scheme—decrease handoff failure (and hence forced termination). In this work, we compare and contrast both the schemes through extensive simulation and we find that neither guard channel nor channel prerequest scheme can guarantee a desired level of QoS for the profiled MTs. We then propose a novel call-admission control (CAC) algorithm that can maintain any desired level of QoS, while the successful call completion rate is very high. In the proposed algorithm, the new call arrival rate is estimated continuously, and when the estimated arrival rate is higher than a predetermined level, some new calls are blocked irrespective of the availability of channels. The objective of this new call preblocking is to maintain a cell's observed new call arrival rate at no more than the predetermined rate. We show that the proposed method can guarantee any desired level of QoS for profiled users.     

Microhardness study of (1-x-y)(B2O3)-x(Li2O)-y(MCI2), (M=Cd, Zn) glasses

Results of microhardness measurements on (1-x-y)(B₂O₃)-x(Li₂O)-y(MCI₂), (M=Cd, Zn) glasses, in the applied load range 25–500 g, are presented. The microhardness was found to decrease with increase in load up to 50 g, then it increased and finally attained a practically constant value with increase in load. The effects of composition of the glasses on microhardness are discussed.     

Call admission and handoff control in multi-tier cellular networks: algorithms and analysis

In this paper, we investigate a call-admission and handoff-control framework for multi-tier cellular networks. We first propose and compare Call-Admission Control (CAC) algorithms based on the cell-dwelling time, by studying their impact on the handoff-call dropping and new-call blocking probabilities and the channel partitioning between the two tiers. Our results show that a simple, cell-dwelling-time-insensitive algorithm performs better under various mixes of user mobilities and call types. Moreover, there is an optimal channel partition of the overall spectrum between the tiers which minimizes the dropping and blocking probabilities for the two different CAC algorithms studied in this paper. Once the call is admitted into the network, we propose and compare various handoff- queuing strategies to reduce the call dropping probability. We show that implementing a queuing framework in one of the tiers (especially the upper, i.e., macrocellular, tier), results in a significant reduction in the dropping probability.     

Enhanced Mechanical Energy Harvesting in Triboelectric Nanogenerator by the Reinforcement of Polypyrrole-Decorated rGO Sheets in PDMS

Energy harvesting triboelectric nanogenerators (TENGs) to scavenge unused mechanical energy have received significant attention in this decade. Herein, the development of reduced graphene oxide (rGO):polypyrrole (PPy) hybrid-modified polydimethylsiloxane (PDMS) as TENG for various device applications is reported. The bulk of PDMS is altered by different fillers such as rGO, PPy, and the binary hybrids of rGO and PPy with varying weight ratios. Among various PDMS composites, 1 wt% of 1:8 rGO:PPy–PDMS composite exhibits higher TENG responses than other PDMS composite. The superior TENG performances of 1 wt% 1:8 rGO:PPy–PDMS composite are attributed to the formation of intensified negative charges inside the PDMS matrix. This charge intensification in the composite is due to various mechanisms, including the charge trapping ability of rGO:PPy filler, microcapacitor formation by introducing hybrid filler in the system with proper conducting networks, and the electron-donating nature of PPy conducting polymer. A 3D stacked device proposed using 1 wt% 1:8 rGO:PPy–PDMS composite delivered a short-circuit current of 16 μA and an open-circuit potential of 60 V by simple palm pressing. Also, the ability of the stacked device for charging/powering portable devices and light-emitting diodes is demonstrated.     

Internet based software system for surface texture and form analysis

With the emergence of a global market and the recent trend of outsourcing, it has become increasingly necessary for companies to have remote access to data for process diagnostics and quality control. This paper presents an Internet based surface texture and form analysis system that will allow a user at one geographic location to upload surface profiles into a database that another user can later analyze. This system has all the features found on commercial instruments and also some special analysis tools such as wavelet analysis, valley removal algorithms etc. The capabilities include surface texture analysis with database support, straightness, flatness, roundness and cylindricity analysis.     

Optical properties of (1-x-y)B2O3-xLi2O-yMCI2 (M=Cd, Zn) glasses

Optical absorption and transmission spectra of (1-x-y)B₂O₃-xLi₂O-yMCI₂ (M=Cd, Zn) glasses of varying compositions were recorded in the UV-visible region. From the spectra, various optical parameters such as optical energy gap (E _opt), refractive index (n), optical dielectric constant (ɛ′), width of the tail of localized states in the forbidden gap (ΔE), ratio of carrier concentration to the effective mass (N/m*) and the constantB were evaluated. The effects of composition of glasses on these parameters are discussed.     

Design and development of smart electricity meter – the path towards sustainable globe

Reducing energy demand in the domestic segment is an important problem worldwide. This study focused on the awareness of residents on energy conservation and on the potential of reducing energy demand through energy-saving activities. The following observations were noted down during the experiment of smart meter in 10 houses of Mumbai city. (1) Its installation led to a reduction in power consumption, (2) a change in energy-saving behaviours of the household members such as the reduction of standby power and a better control of appliance operation, and (3) energy-conservation awareness affected not only the power consumption of the appliances explicitly shown on the display monitor but also other household appliances. (4) Development of sustainable environmental system.     

A convolutional neural network (CNN)-based direct method to detect stiction in control valves

Control valves are considered important capital assets in any process industry. A properly maintained control valve can have a significant impact on how well the process is controlled as well as the overall cost of the plant. However, control valves can suffer from poor control performance due to valve non-linearities. One of the main reasons for non-linearity is control valve stiction. Stiction not only causes oscillations in the process variables but also shortens the life of the control valve, resulting in an economic loss for the process. In a process plant, a control engineer generally analyzes the time series plot of process value (PV), set point (SP), and controller output (OP) data and identifies stiction based on the typical shape pattern of PV/SP/OP plot. In this study, the same shape pattern methodology is adapted to identify stiction using convolutional neural network (CNN) technique. A one-dimensional convolution neural network (Conv1D) algorithm is developed, which works directly on PV/SP/OP time series data for stiction detection. The proposed CNN algorithm is tested on both simulated and industrial control loop data. The suggested method provides promising results with a combined stiction prediction accuracy of 92% (92.2% in predicting non-sticky and 91.53% in predicting sticky loops) for the industrial loops data studied.