Ramrao Adik Institute of Technology

About: Ramrao Adik Institute of Technology is a based out in . It is known for research contribution in the topics: Control theory & Feature extraction. The organization has 794 authors who have published 590 publications receiving 2298 citations.

Cognitive radio

Abstract: The radio frequency spectrum is a scarce natural resource and its efficient use is of the utmost importance. The spectrum bands are usually licensed to certain services, such as mobile, fixed, broadcast, and satellite, to avoid harmful interference between different networks to affect users. Most spectrum bands are allocated to certain services but worldwide spectrum occupancy measurements show that only portions of the spectrum band are fully used. Moreover, there are large temporal and spatial variations in the spectrum occupancy. In the development of future wireless systems the spectrum utilization functionalities will play a key role due to the scarcity of unallocated spectrum. Moreover, the trend in wireless communication systems is going from fully centralized systems into the direction of self organizing systems where individual nodes can instantaneously establish ad hoc networks whose structure is changing over time. Cognitive radios, with the capabilities to sense the operating environment, learn and adapt in real time according to environment creating a form of mesh network, are seen as a promising technology. Cognitive radio is a new technology that allows spectrum to be dynamically shared between users.

A review of PID control, tuning methods and applications

Abstract: This article provides a study of modern and classical approaches used for PID tuning and its applications in various domains. Most of the control systems that are implemented to date with the use of PID control because of its simple structure, ease of implementation, and active research in tuning the PID for a long time. The techniques reviewed in the paper are in the order from classical to modern optimization rules used for the PID tuning. This paper attempts to address the literature review of PID control in an era of control system and bio-medical applications. The development of classical PID to the integration of intelligent control to it, has been surveyed by consideration of various application domains. The primary purpose of this document is to provide a detailed point of information for the people to understand the command of PID in different application areas.

Security threats in the application layer in IOT applications

Abstract: The Internet of things aspires to connect anyone with anything at any point of time at any place. Internet of Thing is generally made up of three-layer architecture. Namely Perception, Network and Application layers. A lot of security principles should be enabled at each layer for proper and efficient working of these applications. This paper represents the overview of Security principles, Security Threats and Security challenges at the application layer and its countermeasures to overcome those challenges. The Application layer plays an important role in all of the Internet of Thing applications. The most widely used application layer protocol is MQTT. The security threats for Application Layer Protocol MQTT is particularly selected and evaluated. Comparison is done between different Application layer protocols and security measures for those protocols. Due to the lack of common standards for IoT protocols, a lot of issues are considered while choosing the particular protocol.

White Blood Cells Image Classification Using Deep Learning with Canonical Correlation Analysis

Abstract: White Blood Cells play an important role in observing the health condition of an individual. The opinion related to blood disease involves the identification and characterization of a patient's blood sample. Recent approaches employ Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and merging of CNN and RNN models to enrich the understanding of image content. From beginning to end, training of big data in medical image analysis has encouraged us to discover prominent features from sample images. A single cell patch extraction from blood sample techniques for blood cell classification has resulted in the good performance rate. However, these approaches are unable to address the issues of multiple cells overlap. To address this problem, the Canonical Correlation Analysis (CCA) method is used in this paper. CCA method views the effects of overlapping nuclei where multiple nuclei patches are extracted, learned and trained at a time. Due to overlapping of blood cell images, the classification time is reduced, the dimension of input images gets compressed and the network converges faster with more accurate weight parameters. Experimental results evaluated using publicly available database show that the proposed CNN and RNN merging model with canonical correlation analysis determines higher accuracy compared to other state-of-the-art blood cell classification techniques.