Multiple Description Coding Using Data Hiding and Regions of Interest for Broadcasting Applications

We propose an innovative scheme for multiple description coding (MDC) with regions of interest (ROI) support to be adopted in high-quality television. The scheme proposes to split the stream into two separate descriptors and to preserve the quality of the region of interest, even in case one descriptor is completely lost. The residual part of the frame (the background) is instead modeled through a checkerboard pattern, alternating the strength of the quantization. The decoder is provided with the necessary side-information to reconstruct the frame properly, namely, the ROI parameters and location, via a suitable data hiding procedure. Using data hiding, reconstruction parameters are embedded in the transform coefficients, thus allowing an improvement in PSNR of the single descriptions at the cost of a negligible overhead. To demonstrate its effectiveness, the algorithm has been implemented in two different scenarios, using the reference H.264/AVC codec and an MJPEG framework to evaluate the performance in absence of motion-compensated frames on 720p video sequences

Low-frequency gravitational-wave science with eLISA/NGO

We review the expected science performance of the New Gravitational-Wave Observatory (NGO, a.k.a. eLISA), a mission under study by the European Space Agency for launch in the early 2020s. eLISA will survey the low-frequency gravitational-wave sky (from 0.1 mHz to 1 Hz), detecting and characterizing a broad variety of systems and events throughout the Universe, including the coalescences of massive black holes brought together by galaxy mergers; the inspirals of stellar-mass black holes and compact stars into central galactic black holes; several millions of ultracompact binaries, both detached and mass transferring, in the Galaxy; and possibly unforeseen sources such as the relic gravitational-wave radiation from the early Universe. eLISA's high signal-to-noise measurements will provide new insight into the structure and history of the Universe, and they will test general relativity in its strong-field dynamical regime.Comment: 20 pages, 8 figures, proceedings of the 9th Amaldi Conference on Gravitational Waves. Final journal version. For a longer exposition of the eLISA science case, see http://arxiv.org/abs/1201.362

Physical-Layer Security: Wide-band Communications & Role of Known Interference

Data security is of such paramount importance that security measures have been implemented across all layers of a communication network. One layer at which security has not been fully developed and studied is the physical layer, the lowest layer of the protocol stack. Towards establishing fundamental limits of secure communications at the physical layer, we address in this dissertation two main problems. First, we study secure communication in the wide-band regime, and second we study the role of known interference in secure communication. The concept of channel capacity per unit cost was introduced by Verdu´ in 1990 to study the limits of cost-efficient wide-band communication. It was shown that orthogonal signaling can achieve the channel capacity per unit cost of memoryless stationary channels with a zero-cost input letter. The first part of this dissertation introduces the concept of secrecy capacity per unit cost to study cost-efficient wide- band secrecy communication. For degraded memoryless stationary wiretap channels, it is shown that an orthogonal coding scheme with randomized pulse position and constant pulse shape achieves the secrecy capacity per unit cost with a zero-cost input letter. For general memoryless stationary wiretap channels, the performance of orthogonal codes is studied, and the benefit of further randomizing the pulse shape is demonstrated via a simple example. Furthermore, the problem of secure communication in a MIMO setting is considered, and a single-letter expression for the secrecy capacity per unit cost is obtained for the MIMO wiretap channel. Recently there has been a lot of success in using the deterministic approach to provide approximate characterization of Gaussian network capacity. The second part of this dissertation takes a deterministic view and revisits the problem of wiretap channel with side information. A precise characterization of the secrecy capacity is obtained for a linear deterministic model, which naturally suggests a coding scheme which we show to achieve the secrecy capacity of the degraded Gaussian model (dubbed as “secret writing on dirty paper”) to within half a bit. The success of this approach allowed its application to the problem of “secret key agreement via dirty paper coding”, where also a suggested coding scheme achieves the secret-key capacity to within half a bit

Harnessing function from form: towards bio-inspired artificial intelligence in neuronal substrates

Despite the recent success of deep learning, the mammalian brain is still unrivaled when it comes to interpreting complex, high-dimensional data streams like visual, auditory and somatosensory stimuli. However, the underlying computational principles allowing the brain to deal with unreliable, high-dimensional and often incomplete data while having a power consumption on the order of a few watt are still mostly unknown. In this work, we investigate how specific functionalities emerge from simple structures observed in the mammalian cortex, and how these might be utilized in non-von Neumann devices like “neuromorphic hardware”. Firstly, we show that an ensemble of deterministic, spiking neural networks can be shaped by a simple, local learning rule to perform sampling-based Bayesian inference. This suggests a coding scheme where spikes (or “action potentials”) represent samples of a posterior distribution, constrained by sensory input, without the need for any source of stochasticity. Secondly, we introduce a top-down framework where neuronal and synaptic dynamics are derived using a least action principle and gradient-based minimization. Combined, neurosynaptic dynamics approximate real-time error backpropagation, mappable to mechanistic components of cortical networks, whose dynamics can again be described within the proposed framework. The presented models narrow the gap between well-defined, functional algorithms and their biophysical implementation, improving our understanding of the computational principles the brain might employ. Furthermore, such models are naturally translated to hardware mimicking the vastly parallel neural structure of the brain, promising a strongly accelerated and energy-efficient implementation of powerful learning and inference algorithms, which we demonstrate for the physical model system “BrainScaleS–1”