Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Support vector machine & Transconductance. The organization has 581 authors who have published 1045 publications receiving 8345 citations.

Enhancing localization accuracy of collaborative cognitive radio users by internal noise mitigation

Abstract: Location information of mobile primary users is one of the essential requirements for an underlay cognitive radio user to utilize the licensed spectrum efficiently. The performance of various location-based applications such as global navigation satellite system, device to device communication in dense urban 5G network also depends on the localization accuracy. In this paper, a collaborative localization scheme based on received signal strength has been proposed. The weighted centroid localization algorithm has been applied in the proposed network scenario to compute location coordinates of the mobile primary user. Since the channel noise effects are random and unavoidable, this paper has focused on the mitigation of the internal noise by designing a suitable reconfigurable FIR filter after the demodulator stage of a cognitive radio receiver circuit to improve precision of signal measurement during primary user localization. The localization error rate has come down to (1.3–1.62) % after internal noise mitigation. The enhancement in the localization accuracy improves the overall spectrum utilization efficiency and reduces the miss detection and false detection probabilities in the proposed underlay network.

Spatially aware recommendations using K-d trees

Abstract: Traditional Recommender Systems focus on recommending the most relevant items to users without considering any contextual features, such as time or location. In this work we propose a Recommendation Algorithm that takes user's location into account while recommending. We focus on exploring the concept of spatial autocorrelation, i.e., similar values cluster together on a map, by using some statistical measures. This work uses a k-d tree based space partitioning technique to tessellate the users' space with respect to location. Recommendations for the users are generated by combining their location and the preference statistics of other users that share the location. Our algorithm uses Collaborative Filtering, which is one of the widely used techniques for recommendation, by computing user-user or item-item similarities from the data. Since the Recommendation Algorithm is applied to each partition separately, we avoid the quadratic complexity typically associated with collaborative filtering. Our technique attempts to reduce the running time while ensuring that the quality of recommendations do not degrade. We have tested the algorithm on the MovieLens dataset. Experiments conducted indicate that our method is effective while reducing the running time.

A Low-Cost Pathological Gait Detection System in Multi-Kinect Environment

Abstract: Traditional vision-based systems used for automatic gait pathology detection, associate high-cost. However, with the advent of Microsoft Kinect sensor, researchers tried to model some low-cost gait assessment systems; but they suffer from the device-specific generic constraints. This study attempted to mitigate those pitfalls by introducing a noble multi-Kinect setup for automated gait diagnosis. Ten healthy participants were recruited to simulate pathological gait. Extracted salient features were classified using supervised learning, leading to an overall accuracy of 93%, which outperformed state-of-the-art.

Comparative analysis of real time resource access control protocols using UML 2.0

Abstract: The behaviors of real time software systems do not depend only on the values of input and output signals, but also on the times of their occurrences. Real time systems (RTS) interact with their environments using time constrained input/output signals. The complexity of Real Time Systems is continually increasing which makes their design very challenging. In RTS, the scheduling of tasks with hard deadlines has been an important area of research. Unified Modeling Language (UML), the standard visual object-oriented modeling language, is suitable to deal with this complexity. The main objective of this paper is to compare two protocols using UML 2.0 models. The shortcomings of the existing Priority Inheritance protocol are represented using one UML model. Further, the Stack Based Priority Ceiling protocol is used to overcome this difficulty using an improved model.

Green Energy Based Low Cost Smart Indoor Air Quality Monitoring and Purifying System

Abstract: This work deals with cost-effective Internet of Things (IoT)-based portable air quality and purifying system for a limited confined space, viz inside a room/car. Indoor air quality has become a major concern especially in India; it needs to be elevated because of its potential risk to human health. As a result, we are trying to develop an easy-to-adopt technique for detecting the pollutants in air around us and control the impurities as much as possible by using a portable filtering system. It is specially designed for indoor applications only and the main pollutants/hazards lie with dust particles, carbon monoxide(CO), nitrogen dioxide, sulfur dioxide, secondhand smoke, LPG, etc. In our system, we accommodate a group of sensors those can detect the above-mentioned pollutants. The detection or the monitoring system is directly connected with the sensors outputs and have a local display along with a remote monitoring and control system enabled by IoT. The controlling unit is consisted of series connected HEPA filter and activated charcoal filter along with ATmega328P microcontroller. To swift the process of filtering, a BLDCM fan is used to suck the air from surroundings at high RPM. The whole system is powered by a 12 V–10 W solar power.