Real Time Surge Voltage Control using Smart IoT
Efficient Transmission of FECG Signal using MIMO – OFDM
Deep Convolutional Auto-Encoders for Emotion Recognition from Facial Expressions
A Survey on Network Admission Control Solution in 6LoWPAN using Cryptographic Mechanism
A Comprehensive Review of Visible Light Communication (VLC)
Cognitive Radio Simulator for Mobile Networks: Design and Implementation
Reduced End-To-End Delay for Manets using SHSP-EA3ACK Algorithm
Light Fidelity Design for Audio Transmission Using Light Dependent Resistor
Dynamic Digital Parking System
Performance Analysis of Multi User Transmit Antenna Selection Systems over TWDP Fading Channels
Comparison of Wavelet Transforms For Denoising And Analysis Of PCG Signal
Video Shot Boundary Detection – Comparison of Color Histogram and Gist Method
Curvelets with New Quantizer for Image Compression
Comparison of Hybrid Diversity Systems Over Rayleigh Fading Channel
Design of Close Loop Dual-Band BPF Using CascadedOpen Loop Triangular Ring Resonator Loaded With Open Stubs
The Indian Telecom Sector is expected to generate revenue of ` 378,000 crores (2010 to 2016)[1],putting it on a growth trajectory. Of this revenue, the Indian handsets industry will be anywhere between ` 30,000 to ` 35,000cr which is 10-12% of the total telecom revenue[2]. The Indian Telecom Sector remains a significant contributor to GDP and employs close to 10Million people (3 million directly and 7 million indirectly)[3]. Over the next decade, this sector is expected to double the employment of people and hence make it one of the most lucrative industries to aspire for a successful career. Indian telecom employment will be on a continuous rise. However, the Indian education system doesn't provide for a telecom led vocational skill and hence the need for students to start early for a career in telecom. The DGET (Director General of Employment and Training) has already released basis operating standards for skill development initiatives targeting Telecom Sector[4] and is being furthered by TSSC (Telecom Sector Skills Council). This paper will provide insights about the elements of telecom ecosystem that in turn opens up multiple career opportunities and the capabilities required for a career in individual telecom ecosystem element.
Multicast is communication between a single sender and multiple receivers on a network. Multicast routing in Mobile Ad hoc Networks (MANETs) poses several challenges due to inherent characteristics of the network such as node mobility, reliability, scarce resources, etc. There are many protocols that can help multicast data transmission. Agent based multicast routing is one of the ways to achieve multicast routing in an efficient manner. There are lot of concerns and challenges in adhoc environment in multicast routing like tree based or mesh based, mobility manner, and traffic in the adhoc environment. An Agent can be hardware or software program and it can be used to get routing information, source and destination nodes information. The desirable character is to reduce flooding of packet, minimum end to end delay and maintain reasonable packet delivery ratio. Three agent based protocols are taken from different environment and studied by their working nature of their agents. The protocols are ASSM, MAMR and ABMRS. Analysis the natures and limitations of these protocols helps to understand agents in an adhoc network and identify the base for agent based multicast transmissions.
Today, many businesses such as banks, insurance companies, and other service providers realize the importance of Customer Relationship Management (CRM) and its potential to help them acquire new customers retain existing ones and maximize their lifetime value. Artificial Intelligence adapts characteristics of human problem-solving skills and then applies them as algorithms easily comprehended by computer systems. Such systems are routinely and widely used today by banks, hospitals, corporations, militaries and homes around the world. Data mining gives an opportunity, uses a variety of data analysis and modeling methods to specific trends and relationships in data detection. This helps to understand what a customer wants and anticipate what they will do. In this paper, the authors examine the application of k-means clustering and classification multilayer perception of artificial neural network on CRM in the case of EFT of POS service of the Bank Muscat. The results demonstrate the final dataset consists of 110000 records in which different clustering models at k values of 6, 5, and 4 with different seed values has been used as an input for the multilayer perception neural network model and evaluated against their performances. Thus, the cluster model at k value of 6 with default seed value has shown a better performance by using Weka-3-7-2 tool.
The authors have analyzed a Bio-medical system for normal and abnormal heart sound identification based using Discrete Wavelet Transform (DWT) which is very useful in diagnosis of heart diseases. Due to the presence of sampling frequency components, the wavelets have a different decomposition level and therefore for better performance for a particular heart sound, DWT (Daubechies family) is applied up to 10 levels to extract the features for the individual heart signal. One dimensional feature extraction is obtained by evaluating the search parameters such as maximum energy, maximum variance, maximum entropy, and the analysis using these parameters provide best wavelet for determining suitable features of phonocardiaogram (PCG) signals.
Illumination variation is a big problem in face detection which usually requires a costly compensation prior to classification. To avoid this problem we are proposing a method for face detection irrespective of illumination variations. In this context the contribution of the work is twofold. First we introduce illumination invariant Local Structure Features for face detection. For an efficient computation we propose a Modified Census Transform which enhances the original work of Zabih and Wood [10]. Secondly we introduce an efficient face detection classifier for rapid detection to render high performance face detection rate. The Classifier structure is much simpler because we use only single stage classifier than multi-stage approaches, while having similar capabilities. The combination of illumination invariant features together with a simple classifier leads to a realtime processing[12]. Detection results are presented on two commonlyused databases namely BioID set of 1526images and Yale face data base set of 15 people with 11 images for each .We are achieving detection rates of about 99.76% with a very low false positive rate of 0.18%In this paper, we are also proposing a novel hardware architecture of face-detection engine for mobile applications. Here MCT (Modified Census Transform) and Adaboost learning technique as basic algorithms of face-detection engine. The face-detection chip is developed by verifying and implementing through FPGA and ASIC. The developed ASIC chip has advantage in real-time processing, low power consumption, high performance and low cost. So we expect this chip can be easily used in mobile applications.
