This study presents a novel spatial audio coding (SAC) technique, called analysis by synthesis SAC (AbS-SAC), with a capability of minimising signal distortion introduced during the encoding processes. The reverse one-to-two (R-OTT), a module applied in the MPEG Surround to down-mix two channels as a single channel, is first configured as a closed-loop system. This closed-loop module offers a capability to reduce the quantisation errors of the spatial parameters, leading to an improved quality of the synthesised audio signals. Moreover, a sub-optimal AbS optimisation, based on the closed-loop R-OTT module, is proposed. This algorithm addresses a problem of practicality in implementing an optimal AbS optimisation while it is still capable of improving further the quality of the reconstructed audio signals. In terms of algorithm complexity, the proposed sub-optimal algorithm provides scalability. The results of objective and subjective tests are presented. It is shown that significant improvement of the objective performance, when compared to the conventional open-loop approach, is achieved. On the other hand, subjective test show that the proposed technique achieves higher subjective difference grade scores than the tested advanced audio coding multichannel.

Privacy-preserving applications allow users to perform on-line daily actions without leaking sensitive information. The privacy-preserving scalar product is one of the critical algorithms in many private applications. The state-of-the-art privacy-preserving scalar product schemes use either computationally intensive homomorphic (public-key) encryption techniques such as Paillier encryption to achieve strong security (i.e., 128−bit) or random masking technique to achieve high efficiency for low security. In this paper, lattice structures have been exploited to develop an efficient privacy-preserving system. The proposed scheme is not only efficient in computation as compared to the state-of-the-art but also provides high degree of security against quantum attacks. Rigorous security and privacy analyses of the proposed scheme have been provided along with a concrete set of parameters to achieve 128−bit and 256 − bit security. Performance analysis shows that the scheme is at least five orders faster than the Paillier schemes and at least twice as faster than the existing randomisation technique at 128−bit security. Also the proposed scheme requires six-time fewer data compared to Paillier and randomisation based schemes for communications.

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For secure mobile wireless networks whose topologies are changed dynamically in insecure environments, mobile users need to keep in contact with each other for the purpose of user authentications. For instance, the network formed by a group of soldiers equipped with wireless devices in a battlefield. Maintaining a high connectivity is crucial in such networks in order to authenticate scattered individuals and to be able to communicate with each other. To establish connections, different mobile ad hoc network routing protocols have been developed. However, much research has shown that these protocols are incapable of maintaining high connectivity when the node density is lower in the network. This paper proposes a mechanism to enhance the node connectivity, which is specifically effective for mobile ad hoc networks with lower node densities. It selects some nodes with larger transmission power as strategic nodes to assist in establishing connections with remote nodes, which are unable to connect with otherwise. The strategic nodes have the ability to connect with each other. Whenever a remote mobile node has a request to connect to another remote mobile node, the strategic nodes function as normal mobile nodes and may forward the connection requests to the desired remote destination node. The mechanism is simulated in different scenarios with various node densities, and the results show that the node connectivity is generally enhanced with the benefit of lower node density network, gaining significant improvement.

Personal robots are set to assist humans in their daily tasks. Assisted living is one of the major applications of personal assistive robots, where the robots will support health and wellbeing of the humans in need, especially elderly and disabled. Indoor environments are extremely challenging from a robot perception and navigation point of view, because of the ever-changing decorations, internal organizations and clutter. Furthermore, human-robot-interaction in personal assistive robots demands intuitive and human-like intelligence and interactions. Above challenges are aggravated by stringent and often tacit requirements surrounding personal privacy that may be invaded by continuous monitoring through sensors. Towards addressing the above problems, in this paper we present an architecture for "Ambient Intelligence" for indoor intelligent mobility by leveraging IoTs within a framework of Scalable Multi-layered Context Mapping Framework. Our objective is to utilize sensors in home settings in the least invasive manner for the robot to learn about its dynamic surroundings and interact in a human-like manner. The paper takes a semi-survey approach to presenting and illustrating preliminary results from our in-house built fully autonomous electric quadbike.