In this study, a Long Range (LoRa) based bidirectional secured communication link for controlling and monitoring the robots from a remote location is proposed. The security features and structure of the LoRa is build upon the standard protocol called LoRa wide area networks (LoRaWAN). To take advantage of these features, LoRa devices/end modules need to be connected to a network server through a LoRa gateway. However, for certain military scenarios like war zones, terrorist attack sites, disaster sites etc., the availability of standard network cannot be guaranteed, and therefore, the security features available with LoRaWAN protocol cannot be guaranteed. To overcome this critical limitation, a LoRa based secured device for establishing a bidirectional communication link between the base station and the robots without relying on any network is proposed. To secure the exchanged data, a cryptographic protocol is developed, and confidentiality, authenticity and integrity of the transmitted data between the base station and the robot are ensured. The protocol is then implemented with a pair of LoRa devices and its functionality is tested from a remote location by controlling and monitoring a Pioneer-P3Dx robot from a distance of 1.2 miles. The test shows that the proposed cryptographic protocol can securely control and track the robot from a remote location.
Depression is a disorder characterized by misery and gloominess felt over a period of time. Some symptoms of depression overlap with somatic illnesses implying considerable difficulty in diagnosing it. This paper contributes to its diagnosis through the application of data mining, namely classification, to predict patients who will most likely develop depression or are currently suffering from depression. Synthetic data is used for this study. To acquire the results, the popular suite of machine learning software, WEKA, is used.
No abstract
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Depression is a disorder characterized by misery and gloominess felt over a period of time. Some symptoms of depression overlap with somatic illnesses implying considerable difficulty in diagnosing it. This paper contributes to its diagnosis through the application of data mining, namely classification, to predict patients who will most likely develop depression or are currently suffering from depression. Synthetic data is used for this study. To acquire the results, the popular suite of machine learning software, WEKA, is used.
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