The Internet of Things (IoT) envisions billions of sensors deployed around us and connected to the Internet, where the mobile crowd sensing technologies are widely used to collect data in different contexts of the IoT paradigm. Due to the popularity of Big Data technologies, processing and storing large volumes of data has become easier than ever. However, large scale data management tasks still require significant amounts of resources that can be expensive regardless of whether they are purchased or rented (e.g. pay-as-you-go infrastructure). Further, not everyone is interested in such large scale data collection and analysis. More importantly, not everyone has the financial and computational resources to deal with such large volumes of data. Therefore, a timely need exists for a cloud-integrated mobile crowd sensing platform that is capable of capturing sensors data, on-demand, based on conditions enforced by the data consumers. In this paper, we propose a context-aware, specifically, location and activity-aware mobile sensing platform called C-MOSDEN (Context-aware Mobile Sensor Data ENgine) for the IoT domain. We evaluated the proposed platform using three real-world scenarios that highlight the importance of selective sensing. The computational effectiveness and efficiency of the proposed platform are investigated and is used to highlight the advantages of context-aware selective sensing.

Mitigating the effects of reverberation is a significant challenge for real-world spatial soundfield reproduction, but the necessity of a large number of reproduction channels increases the complexity and presents several challenges to existing listening room compensation techniques. In this paper, we present an adaptive room compensation method to overcome the effects of reverberation within a region, using a model description of the reverberant soundfield. We propose the reverberant channel estimation and compensation be carried out in a single step using completely decoupled adaptive filters; thus, reducing the complexity of the overall process. We compare the soundfield reproduction performance with existing adaptive and nonadaptive room compensation methods through several simulation examples. The performance of the proposed method is comparable to existing techniques, and achieves a normalized wideband region reproduction error of 1% at a signal-to-noise ratio of 50 dB, within a 1 m radius region of interest using 60 loudspeakers and 55 microphones at frequencies below 1 kHz. Robust behavior of the room compensator is demonstrated down to direct-to-reverberant-path power ratios of -5 dB. Overall, the results suggest that the proposed method can diagonalize the room compensation system, leading to a more robust and parallel implementation for spatial soundfield reproduction.

Abstract-Determining the best partitioning structure of a Coding Tree Unit (CTU) is one of the most time consuming operations in HEVC encoding. Specifically, it is the evaluation of the quadtree hierarchy using the Rate-Distortion (RD) optimization that has the most significant impact on the encoding time, especially in the cases of High Definition (HD) and Ultra High Definition (UHD) videos. In order to expedite the encoding for low delay applications, this paper proposes a Coding Unit (CU) size selection and encoding algorithm for inter-prediction in the HEVC. To this end, it describes (i) two CU classification models based on Inter N×N mode motion features and RD cost thresholds to predict the CU split decision, (ii) an online training scheme for dynamic content adaptation, (iii) a motion vector reuse mechanism to expedite the motion estimation process, and finally introduces (iv) a computational complexity to coding efficiency trade-off process to enable flexible control of the algorithm. The experimental results reveal that the proposed algorithm achieves a consistent average encoding time performance ranging from 55% -58% and 57% -61% with average Bjøntegaard Delta Bit Rate (BDBR) increases of 1.93% -2.26% and 2.14% -2.33% compared to the HEVC 16.0 reference software for the low delay P and low delay B configurations, respectively, across a wide range of content types and bit rates.

The spectral localization cues contained in the head-related transfer function are known to play a contributory role in the sound source localization abilities of humans. However, existing localization techniques are unable to fully exploit this diversity to accurately localize a sound source. The availability of just two measured signals complicates matters further, and results in front to back confusions and poor performance distinguishing between the source locations in a vertical plane. This study evaluates the performance of a source location estimator that retains the frequency domain diversity of the head-related transfer function. First, a method for extracting the directional information in the subbands of a broadband signal is described, and a composite estimator based on signal subspace decomposition is introduced. The localization performance is experimentally evaluated for single and multiple source scenarios in the horizontal and vertical planes. The proposed estimator's ability to successfully localize a sound source and resolve the ambiguities in the vertical plane is demonstrated, and the impact of the source location, knowledge of the source and the effect of reverberation is discussed.