Brain computer interface (BCI) controlled assistive robotic systems have been developed with increasing success with the aim to rehabilitation of patients after brain injury [1] to increase independence and quality of life. While such systems may use surgically implanted invasive sensors, non-invasive alternatives can be better suited due to ease of use, reduced cost, improvements in accuracy and reliability with the advancement of the technology and practicality of use. The consumer grade BCI devices often capable of integrating multiple types of signals, including Electroencephalogram (EEG) and Electromyogram (EMG) signals, as well as basic motion based signals such as gyroscopic data. This paper summarises the development of a BCI controlled robotic system using a non-invasive BCI headset "Muse" [2] and an open source robotic arm, U-Arm [2], to accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EMG and gyroscopic sensor readings to control the arm, which could perform a number of different tasks such as picking/placing objects or assist users in eating. Preliminary work was carried out to capture event driven EEG signals as alternative inputs to the controller. To avoid risks of injury while the device is being used in clinical settings, appropriate measures were incorporated into the software control of the arm. The project was a success in collaboration between the University and the East Kent Hospitals Neuro-Rehabilitation Unit, with the long term goal of testing the system in a clinical environment and up-scaling the system to larger robotic effectors.

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Abstract. Secure messaging applications have been used for the purposes of major crime, creating the need for forensic research into the area. This paper forensically analyses two secure messaging applications, Wickr and Telegram, to recover artefacts from and then to compare them to reveal the differences between the applications. The artefacts were created on Android platforms by using the secure features of the applications, such as ephemeral messaging, the channel function and encrypted conversations. The results of the experiments documented in this paper give insight into the organisation of the data structures by both Wickr and Telegram, as well as the exploration of mobile digital forensics techniques to recover artefacts removed by the ephemeral functions.

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Abstract-Recent and sudden rise in the popularity of drones or UAVs (Unmanned Air Vehicles) can be attributed to the reduction in weight of electronic components and the relative ease by which the drones can be operated. Their potential applications range from simple leisure and recreational purposes to photography, transport, surveying, security, the list goes on. With this demand and subsequent availability, there has also been a rise in drones used in crimes. This creates a need for forensic analysis into these devices, which often use custom electronic flight systems for which appropriate forensic tools have not been developed. This paper covers the use and development of open source tools to aid forensic analyses of two popular drones -the DJI Phantom 3 Professional and AR Drone 2 with the aim of reconstructing the actions taken by these drones, identification of owners or operators, and extraction of data from associated mobile devices. While different UAV systems can vary in their operations owing to their capabilities, some generic methods will be used in analyses and extractions of the data and then results will be compared between models.