Suchergebnisse

Innovations and applicability of time series data mining techniques have significantly increased the researchers' interest in the problem of time series classification. Several algorithms have been proposed for this purpose categorized under shapelet, interval, motif, and whole series-based techniques. Among this, the bag-of-words technique, an extensive application of the text mining approach, performs well due to its simplicity and effectiveness. To extend the efficiency of the bag-of-words technique, this paper proposes a discriminate supervised weighted scheme to identify the characteristic and representative pattern of a class for efficient classification. This paper uses a modified weighted matrix that discriminates the representative and non-representative pattern which enables the interpretability in classification. Experimentation has been carried out to compare the performance of the proposed technique with state-of-the-art techniques in terms of accuracy and statistical significance.

<p class="Abstract">Beam wandering is the main cause for major power loss in laser communication. To analyse this prerequisite at our environment, a 155 Mbps data transmission experimental setup is built with necessary optoelectronic components for the link range of 0.5 km at an altitude of 15.25 m. A neuro-controller is developed inside the FPGA and used to stabilise the received beam at the centre of detector plane. The Q-factor and bit error rate variation profiles are calculated using the signal statistics obtained from the eye-diagram. The performance improvements on the laser communication system due to the incorporation of beam wandering mitigation control are investigated and discussed in terms of various communication quality assessment key parameters.</p><p class="Abstract"> </p>

Rooftop farming in urban places is gaining more popularity which increases the cultivation of organic vegetables on the rooftop of houses and buildings with the minimal utilization of water. But rooftop farming is more vulnerable to pest infestation which reduces the quality of plants. Urban residents are novices in farming, and they are unaware of the pest attacks. Various researchers have proposed pest identification systems using image processing techniques and machine learning algorithms specific to particular disease which shows less accuracy on generaliztion and not user-friendly. To provide user-friendly pest identification system, this paper proposes a mobile based pest identification system using the concept of pre-trained convolutional neural network model – AlexNet. Experimental results have been analyzed with various rooftop pests using different kernel sizes and layers of convolutional neural network. In addition, the best evaluated pre-trained model has been converted to a mobile application using REST API for the recommendation of pesticide to the novice user.