A Randomized Kernel-Based Secret Image Sharing Scheme

This paper proposes a ($k,n$)-threshold secret image sharing scheme that offers flexibility in terms of meeting contrasting demands such as information security and storage efficiency with the help of a randomized kernel (binary matrix) operation. A secret image is split into $n$ shares such that any $k$ or more shares ($k\leq n$) can be used to reconstruct the image. Each share has a size less than or at most equal to the size of the secret image. Security and share sizes are solely determined by the kernel of the scheme. The kernel operation is optimized in terms of the security and computational requirements. The storage overhead of the kernel can further be made independent of its size by efficiently storing it as a sparse matrix. Moreover, the scheme is free from any kind of single point of failure (SPOF).Comment: Accepted in IEEE International Workshop on Information Forensics and Security (WIFS) 201

Validation of multi-channel scanning microwave radiometer on-board Oceansat-I

Sea surface temperature (SST), sea surface wind speed (WS) and columnar water vapour (WV) derived from Multi-frequency Scanning Microwave Radiometer (MSMR) sensor on-board IRS-P4 (Oceansat-I) were validated against the in situ measurements from ship, moored buoy (MB), drifting buoy (DB) and autonomous weather station (AWS). About 1400 satellite in situ match-ups were used for the validation of SST and WS, while only 60 match-ups were available for the validation of WV. Therefore specific humidity, Q a was used as a proxy for validating WV. The drifting buoy SSTs showed good correlation with the satellite values (r = 0.84). The correlation of MB SSTs was better during night when the WS varied between 0 and 10 m/s. During the day, correlation peaked for higher wind speeds (> 10 m/s). MB (r > 0.80) was relatively better than AWS (r � 0.70) and ship (r < 0.50) for validating satellite-derived WS. Daytime winds exhibited better correlation with satellite values when measured from ocean platforms (MB and ship), but the winds measured from land-based platforms (AWS) were closer to satellite values during night-time. Q a values consistently showed higher correlation with satellite values during night-time. The low root mean square deviation (RMSD) of DB SST (1.17°C) and MB WS (1.52 m s -1) is within the achievable accuracy of the microwave sensor when validated with data collected over the tropical Indian Ocean. The RMSD of Q a (1.81 g kg -1), however, falls much beyond the attainable accuracy of the microwave sensor

Towards a Simplified Perceptual Quality Metric for Watermarking Applications

International audienceThis work is motivated by the limitations of statistical quality metrics to assess the quality of images distorted in distinct frequency range. Common quality metrics, which basically have been designed and tested for various kind of global distortions, such as image coding may not be efficient for watermarking applications, where the distortions might be restricted in a very narrow portion of the frequency spectrum. We hereby want to propose an objective quality metric which performances do not depend on the distortion frequency range, but we nevertheless want to provide a simplified objective quality metric in opposition to the complex HVS based quality metrics recently made available. The proposed algorithm is generic (not designed for a particular distortion), and exploits the contrast sensitivity function (CSF) along with an adapted Minkowski error pooling. The results show a high correlation between the proposed objective metric and the mean opinion score (MOS). A comparison with relevant existing objective quality metrics is provided

Propulsion/airframe interference for ducted propfan engines with ground effect

The advanced propfan propulsion systems design of the next-generation subsonic transport aircraft has been of interest to many airline companies in the past several years. This is due to the studies which indicate that an efficient ducted propfan engine technology offers a significant reduction in aircraft fuel consumption. However, because of the geometric complexity of the configuration, one challenge is the integration of the ducted propfan engine with the airframe so that aerodynamic interference effects frequently encountered near the nacelle can be minimized, or perhaps, optimized. To understand this interaction phenomenon better, it is desirable to have a reliable and efficient computational tool that can predict propeller effects on the flowfield around complex configurations

Advanced Scaffolds for Dental Pulp and Periodontal Regeneration

No current therapy promotes root canal disinfection and regeneration of the pulp-dentin complex in cases of pulp necrosis. Antibiotic pastes used to eradicate canal infection negatively affect stem cell survival. Three-dimensional easy-to-fit antibiotic-eluting nanofibers, combined with injectable scaffolds, enriched or not with stem cells and/or growth factors, may increase the likelihood of achieving predictable dental pulp regeneration. Periodontitis is an aggressive disease that impairs the integrity of tooth-supporting structures and may lead to tooth loss. The latest advances in membrane biomodification to endow needed functionalities and technologies to engineer patient-specific membranes/constructs to amplify periodontal regeneration are presented

Sound Event Detection by Exploring Audio Sequence Modelling

Everyday sounds in real-world environments are a powerful source of information by which humans can interact with their environments. Humans can infer what is happening around them by listening to everyday sounds. At the same time, it is a challenging task for a computer algorithm in a smart device to automatically recognise, understand, and interpret everyday sounds. Sound event detection (SED) is the process of transcribing an audio recording into sound event tags with onset and offset time values. This involves classification and segmentation of sound events in the given audio recording. SED has numerous applications in everyday life which include security and surveillance, automation, healthcare monitoring, multimedia information retrieval, and assisted living technologies. SED is to everyday sounds what automatic speech recognition (ASR) is to speech and automatic music transcription (AMT) is to music. The fundamental questions in designing a sound recognition system are, which portion of a sound event should the system analyse, and what proportion of a sound event should the system process in order to claim a confident detection of that particular sound event. While the classification of sound events has improved a lot in recent years, it is considered that the temporal-segmentation of sound events has not improved in the same extent. The aim of this thesis is to propose and develop methods to improve the segmentation and classification of everyday sound events in SED models. In particular, this thesis explores the segmentation of sound events by investigating audio sequence encoding-based and audio sequence modelling-based methods, in an effort to improve the overall sound event detection performance. In the first phase of this thesis, efforts are put towards improving sound event detection by explicitly conditioning the audio sequence representations of an SED model using sound activity detection (SAD) and onset detection. To achieve this, we propose multi-task learning-based SED models in which SAD and onset detection are used as auxiliary tasks for the SED task. The next part of this thesis explores self-attention-based audio sequence modelling, which aggregates audio representations based on temporal relations within and between sound events, scored on the basis of the similarity of sound event portions in audio event sequences. We propose SED models that include memory-controlled, adaptive, dynamic, and source separation-induced self-attention variants, with the aim to improve overall sound recognition