A modified PSO algorithm with dynamic parameters for solving complex engineering design problem

This paper proposed a new approach of particle swarm optimization (PSO). The proposed modified PSO algorithm is equipped with some specially designed mechanisms of adaptively updating algorithm parameters to preserve the diversity of the swarm and to keep the balance between exploration and exploitation searches. All these mechanisms help the algorithm to avoid the premature convergence and to strengthen its robustness. Experiments are conducted on different complicated, unimodal and multimodal test functions, as well as a typical engineering inverse problem, the TEAM Workshop problem 22. The numerical results illustrate that the proposed PSO shows better performance as compared to other well developed evolutionary algorithms.     

Computed SAR and thermal elevation in a 0.25-mm 2-D model of the human eye and head in response to an implanted retinal stimulator - part II: results

This is the second of a series of two papers on the thermal increase in the human eye and head in response to an implanted retinal stimulator. This paper provides specific absorption rates induced in the human head by the extraocular unit and the temperature increases associated with induced electromagnetic fields and power dissipation of the implanted microchip. Results are provided for different assumptions about choroid blood flow. It is shown that computed results associated with the power dissipation of the implanted microchip, corresponding to temperature increases of approximately 0.6/spl deg/C in the midvitreous of the eye and 0.2/spl deg/C in the retina, closely parallel in-vivo experimental results in animals.     

An arbitrary waveform stimulus circuit for visual prostheses using a low-area multibias DAC

Attempts are underway to construct a retinal prosthesis to recover limited vision for blind patients with retinitis pigmentosa using implantable electronic devices. These microchips provide electrical stimulation to damaged retinal tissues using an array of stimulus circuits. This paper describes improvements to conventional circuit designs with significantly decreased implementation area and the ability to support arbitrary stimulus waveforms where an array of such stimulus circuits is required. This yields greater spatial resolution in stimulation owing to more stimulus circuits per chip area. Also introduced are digital-to-analog converter gain prescalar and dc-offset circuits which tune the stimulus circuits to an optimally effective range due to variation in retinal degradation. The prototype chip was fabricated by MOSIS in 1.2-/spl mu/m CMOS technology.     

Shared Cache Based on Content Addressable Memory in a Multi-Core Architecture

Modern shared-memory multi-core processors typically have shared Level 2 (L2) or Level 3 (L3) caches. Cache bottlenecks and replacement strategies are the main problems of such architectures, where multiple cores try to access the shared cache simultaneously. The main problem in improving memory performance is the shared cache architecture and cache replacement. This paper documents the implementation of a Dual-Port Content Addressable Memory (DPCAM) and a modified Near-Far Access Replacement Algorithm (NFRA), which was previously proposed as a shared L2 cache layer in a multi-core processor. Standard Performance Evaluation Corporation (SPEC) Central Processing Unit (CPU) 2006 benchmark workloads are used to evaluate the benefit of the shared L2 cache layer. Results show improved performance of the multicore processor’s DPCAM and NFRA algorithms, corresponding to a higher number of concurrent accesses to shared memory. The new architecture significantly increases system throughput and records performance improvements of up to 8.7% on various types of SPEC 2006 benchmarks. The miss rate is also improved by about 13%, with some exceptions in the sphinx3 and bzip2 benchmarks. These results could open a new window for solving the long-standing problems with shared cache in multi-core processors.     

Authentication of Vehicles and Road Side Units in Intelligent Transportation System

Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents, economically damaging traffic jams, hijacking, motivating to wrong routes, and financial losses for businesses and governments. Smart and autonomous vehicles are connected wirelessly, which are more attracted for attackers due to the open nature of wireless communication. One of the problems is the rogue attack, in which the attacker pretends to be a legitimate user or access point by utilizing fake identity. To figure out the problem of a rogue attack, we propose a reinforcement learning algorithm to identify rogue nodes by exploiting the channel state information of the communication link. We consider the communication link between vehicle-tovehicle, and vehicle-to-infrastructure. We evaluate the performance of our proposed technique by measuring the rogue attack probability, false alarm rate (FAR), misdetection rate (MDR), and utility function of a receiver based on the test threshold values of reinforcement learning algorithm. The results show that the FAR and MDR are decreased significantly by selecting an appropriate threshold value in order to improve the receiver’s utility.     

A Compact Rhombus Shaped Antenna with Extended Stubs for Ultra-Wideband Applications

Ultra-wideband (UWB) is highly preferred for short distance communication. As a result of this significance, this project targets the design of a compact UWB antennas. This paper describes a printed UWB rhombus-shaped antenna with a partial ground plane. To achieve wideband response, two stubs and a notch are incorporated at both sides of the rhombus design and ground plane respectively. To excite the antenna, a simple microstrip feed line is employed. The suggested antenna is built on a 1.6 mm thick FR4 substrate. The proposed design is very compact with overall electrical size of 0.18λ × 0.25λ (14 × 18 mm²). The rhombus shaped antenna covers frequency ranging from 3.5 to 11 GHz with 7.5 GHz impedance bandwidth. The proposed design simulated and measured bandwidths are 83.33% and 80%, respectively. Radiation pattern in terms of E-field and H-field are discussed at 4, 5.5 and 10 GHz respectively. The proposed design has 65% radiation efficiency and 1.5 dBi peak gain. The proposed design is simulated in CST (Computer Simulation Technology) simulator and the simulated design is fabricated for the measured results. The simulated and measured findings are in close resemblance. The obtained results confirm the application of the proposed design for the ultra-wide band applications.     

A-Eye: Automating the role of the third umpire in the game of cricket

Cricket is a sport that involves two teams, say Team A and Team B, and two field umpires. Initially, Team A bats to score some runs, while Team B balls. Then, Team A balls and Team B bats to overcome the score of Team A. If this happens, then Team B wins. Otherwise, Team A wins. The balling team can dismiss a batsman from scoring through a Run-Out, i.e., the batsman fails to enter an area before three stumps are dislodged in that area. In this case, a third umpire makes the ‘Run-Out/Not-Out’ decision through video technology. This process can consume around one minute which disrupts the pace of the game. In this paper, we propose and validate a novel technology called A-Eye, which automates the role of the third umpire. By applying A-Eye to a set of autonomously-filmed Run-Out videos, we show that it is efficient as compared to the third umpire, and almost as accurate. Also, it can be used to estimate a rating for the field umpires. These results have been recognized internationally, and have convinced our local cricket council to employ A-Eye within a professional cricket tournament.     

Application of neural networks in space‐mapping optimization of microwave filters

In this paper, a design methodology combining coupling matrix representation of filters, neural models and space‐mapping techniques is presented for further enhancement of optimization efficency of microwave filters. Neural models are developed for both initial dimension generation and design parameter sensitivity analysis. Combining neural models of filter substructures with space‐mapping optimization, the total number of EM simulations of the complete filter structure is significantly reduced. The improvement in efficiency over conventional method is demonstrated using simulation and measurement results of both end‐coupled and side‐coupled waveguide dual‐mode pseudo‐elliptic filters. The total CPU times for design and optimization are reduced by 50% to 70 %.© 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.     

A Novel Machine Learning–Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

Machine learning is a technique for analyzing data that aids the construction of mathematical models. Because of the growth of the Internet of Things (IoT) and wearable sensor devices, gesture interfaces are becoming a more natural and expedient human-machine interaction method. This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns. The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition. Potential applications of hand gesture recognition research span from online gaming to surgical robotics. The location of the hands, the alignment of the fingers, and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures. Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition. Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition. In this scenario, it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling. When a user performs the same dynamic gesture, the hand shapes and speeds of each user, as well as those often generated by the same user, vary. A machine-learning-based Gesture Recognition Framework (ML-GRF) for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation. We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions, and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes. The findings from the simulation support the accuracy, precision, gesture recognition, sensitivity, and efficiency rates. The Machine Learning-based Gesture Recognition Framework (ML-GRF) had an accuracy rate of 98.97%, a precision rate of 97.65%, a gesture recognition rate of 98.04%, a sensitivity rate of 96.99%, and an efficiency rate of 95.12%.     

Efficient route optimization scheme for nested-NEMO

Network mobility (NEMO) basic support protocol maintains the connectivity when mobile router (MR) of a mobile network changes its point of attachment to the Internet by establishing a bi-directional tunnel between the MR and the home agent (HA). A packet from a correspondent node (CN) traverses through the tunnel to reach the mobile network. Nesting occurs in NEMO when a MR's new attachment point is in another mobile network that has also moved away from its home link. The level of tunneling increases as the level of nesting increases. Multiple levels of tunneling in nested NEMO adds multiple legs to a non-optimized routing path that the IP packets have to traverse in order to reach the final destination. As per our study, an efficient route optimization technique in NEMO, particularly in nested NEMO, is still a research challenge. In this paper, we propose an efficient route optimization scheme for nested NEMO. We use two care-of Addresses for each MR, as well as two types of entries, such as fixed and visiting, in the routing table in each MR. Our route optimization scheme removes the tunnels completely from the nested NEMO in a single step using only one binding update message irrespective of the number of levels in the nest. Our route optimization scheme also works for non-nested NEMO.