Survey Paper on Online Software Performance Prediction

Now a days Performance is very important non-functional requirement for almost all software system. In survey study we are going to learn how performance prediction is possible before the development of that particular software. For this task we have to implement one analytical model which is going to be used for evaluating the performance of software with some specific parameter like response time, throughput etc

Cytological studies of an intergeneric hybrid of Cajanus cajan (Linn.) Millsp. and Atylosia lineata, W. and A.

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H2020 STRIKE3: Standardization of Interference Threat Monitoring and Receiver Testing - Significant Achievements and Impact

The H2020 project STRIKE3 contributes enormously for lifting EU industry and institutions to the premier position in the global market for GNSS interference monitoring, detection, reporting, receiver standardization, applica-tions and services. This has been achieved over the last three years through the deployment and operation of an international GNSS interference monitoring network to capture the scale and state of the problem, and through work with international GNSS partners to develop, nego-tiate, promote and implement standards for GNSS threat reporting and GNSS receiver testing. The achievements of STRIKE3 are based on the following cornerstones: i. STRIKE3 global interference monitoring network, ii. A draft interference reporting standard, iii. A draft receiver testing standard against interference, and iv. Internation-al knowledge sharing and awareness building against interference among key GNSS stakeholders across pub-lic and private sectors. All these aspects will be present-ed herein with greater details

Sensors and AI Techniques for Situational Awareness in Autonomous Ships : A Review

Autonomous ships are expected to improve the level of safety and efficiency in future maritime navigation. Such vessels need perception for two purposes: to perform autonomous situational awareness and to monitor the integrity of the sensor system itself. In order to meet these needs, the perception system must fuse data from novel and traditional perception sensors using Artificial Intelligence (AI) techniques. This article overviews the recognized operational requirements that are imposed on regular and autonomous seafaring vessels, and then proceeds to consider suitable sensors and relevant AI techniques for an operational sensor system. The integration of four sensors families is considered: sensors for precise absolute positioning (Global Navigation Satellite System (GNSS) receivers and Inertial Measurement Unit (IMU)), visual sensors (monocular and stereo cameras), audio sensors (microphones), and sensors for remote-sensing (RADAR and LiDAR). Additionally, sources of auxiliary data, such as Automatic Identification System (AIS) and external data archives are discussed. The perception tasks are related to well-defined problems, such as situational abnormality detection, vessel classification, and localization, that are solvable using AI techniques. Machine learning methods, such as deep learning and Gaussian processes, are identified to be especially relevant for these problems. The different sensors and AI techniques are characterized keeping in view the operational requirements, and some example state-of-the-art options are compared based on accuracy, complexity, required resources, compatibility and adaptability to maritime environment, and especially towards practical realization of autonomous systems