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A DWDM System consisting of transmitter, optical span and receiver is designed. The line coding technique NRZ (Non Return To Zero) has been used as the modulation schemes. The variation in the four wave mixing is analyzed with respect to the designed DWDM (Dense Wavelength Division Multiplexing) system. The DWDM is designed and simulated on Optisystem and their BER (Bit Error Rate) is calculated. The DWDM system is implemented in a FTTH (Fiber To The Home) system and simulated on Optisystem and their BER is calculated.

IoT technologies is improving life quality by enhancing several real-life smart applications. IoT includes large number of devices generating huge amount of data which needs large computation. Anomaly detection and security is the major concern in the IoT domain. This survey paper provides an overview of anomaly detection using machine learning and deep learning methods in IoT applications. Machine learning and deep learning are powerful tools for analyzing normal and abnormal behavior of IoT components and devices. In this paper we outline key issues in research and challenges using deep anomaly detection techniques for resource constrained devices in real-world problems of IoT. Fog computing move the computation at the device or the edge to solve some of the issues of network security and delay in computation.

Recently, Machine Learning methods have shown great improvement while analyzing the biomedical data. Raman Spectroscopy (RS), a non-invasive technique, and widely used in screening to diagnose the oral cancer. In order to spot cancer in a smarter and faster way, we have employed Meta-Learning (ML) techniques to learn such as Bagging and Boosting on RS data. Further, we employed normal and tumor tissue class classification by Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Adaptive Boosting (AdaBoost) classifiers. The present study aims at examining the RS data with total 110 samples, including 57 tumor and 53 normal ones. To evaluate the performance, we have used the training samples to optimize, and testing samples to generalize the model parameters. The results show that the AdaBoost classifier with Bagging techniques showed the significant changes in accuracy.

Presently, WSN (Wireless Sensor Network) is the most standard administrations utilized in business and mechanical applications, due to its specialized advancement in a processor, correspondence, and low-control utilization of implanted registering gadgets. As of late a proficient plan of a Wireless Sensor Network has turned into a main zone of research. A Sensor is a gadget that reacts and recognizes some sort of contribution from both the physical or ecological conditions, for example, weight, heat, light, and so on. The output of the sensor nodes is commonly used as an electrical flag that is transmitted to a controller for further handling. Challenging task in WSN configuration is the means by which to spare node energy while keeping up the required network behavior. Any WSN can just satisfy its central goal as long as it is considered alive (till the sensor nodes are alive or battery control exhaustion) however not after that. Once any of the sensor nodes is declared dead, it will not involve in further data transmission process.In this paper a novel approach has been discussed to reduce the replication of information shared by the sensor nodes and to increase the reliability of end to end data transmission.

Automation of logistics in pickup and delivery in hospital shows a great helping hand to the active workers. The application needs to allocate jobs to robots. The objective of the application is to produce an efficient system with the minimum total movement distance of all robots distance. In this paper, we propose two schemes, deep Hungarian (d-Hungarian) and deep Voronoi (d-Voronoi) job scheduling schemes. The d-Hungarian scheme is designed for one type of environment where the location of jobs is known prior whereas prior knowledge of jobs is not required in the d-Voronoi scheme. Simulation results show that our schemes efficiently reduce the traveled distance.

With the advances in 3D technology, digital photogrammetry and computer vision, solving the correspondence problem accurately and efficiently has gained popularity. Locating the position of corresponding pixel in target image for given pixel in reference image is referred as Correspondence problem in stereo-image matching. A number of stereo image matching approaches are available at present, but there has been trade-off between density of match, speed and accuracy. Techniques that are capable of producing dense disparity maps are prone to higher computational complexity thereby, requires longer time, however techniques that are fast are not capable of producing dense disparity maps. On the basis of density of disparity maps generated, we can broadly classify image matching methods as area-based image matching or feature-based image matching.[1] An alternative approach has been proposed in this paper in order to match pixels accurately using a mathematical approach referred as Polynomial Fitting which requires identification of minimum of 6 control points in the locality or neighborhood. If the number of control points for a given region or locality is less than 6, than window size increases automatically to accommodate more control points. Window continues to grow in size until number of control points in the region becomes 6 or exceeds 6. When the number of control points are greater than 6, all possible combination of 6 points are used to calculate and the combination that gives least Sum of Squared Error is used for further calculation. A statistical algorithm referred as Random Sample Consensus is applied in order to select 6 control points for calculation. The algorithm randomly selects any 6 pair of control points for calculation. We can divide this algorithm into two segments-1)Determination of control points 2) Determination of match points. The percentage of accurately matched pixels in stereo pairs was found to be 93.48% in one of the test data set, which was very high compared to standard Normalized Cross Correlation based approach (77.89%).

Mobile banking services have become one of the most important applications on the Internet, being provided by most of the banks all over the world. The end-user can manage the accounts or make some payments without being forced to go to the physical bank office. That's why security concerns regarding authentication have to be taken into the account and the bank should provide various and combined methods for login and payment, in order to increase the confidence in their services for mobile banking. This research paper will introduce some concepts about these two fields: Mobile banking and fingerprint authentication process. During our researches, we developed a Java based Mobile application to simulate access to Mobile Banking for login and payment options. We also perform sample test for this application and as a result, we found it is very secure and 100% successful and user-friendly.

Next generation health and fitness wearable devices offer the opportunities for improving personalized healthcare. To detect and predict health problems, wearable devices with limited power resource need to transmit sensing data uninterruptedly that could be a huge challenge. Many exist researches use various ways to reduce the energy consumption but, to the best of our knowledge, no one employs physical activities integrated with data transmission technique to save the power. In this paper, we have developed an Adaptive Energy-efficient Data transmission (AED) scheme, which can detect critical events such as myocardial infarction and, at the same time, minimizes data transmission from the devices. Simulation results show that AED reduces the number of transmission by 71.35% for continuous data transmission and 30.33% for batch data transmission.

Wi-Fi Aware is a new technique which enables devices to communicate with each other in the same vicinity without the help of access points. In this approach, we leverage Wi-Fi Aware technique and take field trials on the power efficiency of Wi-Fi Aware communications. Based on the experiment results, we conclude that the Wi-Fi Aware devices are suggested to transmit data with the help of proximity nodes according to their link speed so as to save energy.

The next-generation enterprise Xeon® processor [1] consists of 10 Westmere 32nm cores [2] and a shared inclusive L3 cache (LLC) integrated on a monolithic die, with link-based I/Os. This paper focuses on the innovations and circuit optimizations over the predecessor [3] targeting idle power reduction, robust high-speed I/O links, and performance per watt improvements. The processor is implemented in 32nm CMOS using high-κ metal gate transistors and nine copper interconnect layers [4].