La thèse porte sur le déploiement d'un réseau de capteurs pour le diagnostic énergétique d'un bâtiment universitaire. Elle s'inscrit dans la thématique Bâtiment intelligent et durable de l'Université de Limoges. Il s'agit au cours de cette thèse, dans un premier temps, d'optimiser l'architecture d'un réseau de capteurs Zigbee ainsi que les méthodes d'interrogation de ces capteurs, pour minimiser la consommation énergétique des nœuds du réseau. On s'appuiera notamment sur des concepts de "compressive sensing" pour augmenter la durée de vie des nœuds autonomes qui pourra être éventuellement renforcée par des organes de récupération d'énergie. / This thesis deals with the roll out of Wireless Sensor Network for the energetic monitoring of an existing building of the University. This work wil be incorporated in the framework of the smart building program of the University of Limoges. The work aims to optimize the architecture of a Zigbee network as well as data collection methods to minimize the energy consumption of the network's nodes. Methods based on the compressive sensing concepts will be investigated to reduce the number of nodes and to extend the lifetime of the nodes. Those methods will eventually be complemented with energy harvesting techniques.

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<p>'Image volumes' refer to realizations of images in other dimensions such as time, spectrum, and focus. Recent advances in scientific, medical, and consumer applications demand improvements in image volume capture. Though image volume acquisition continues to advance, it maintains the same sampling mechanisms that have been used for decades; every voxel must be scanned and is presumed independent of its neighbors. Under these conditions, improving performance comes at the cost of increased system complexity, data rates, and power consumption. </p><p>This dissertation explores systems and methods capable of efficiently improving sensitivity and performance for image volume cameras, and specifically proposes several sampling strategies that utilize temporal coding to improve imaging system performance and enhance our awareness for a variety of dynamic applications. </p><p>Video cameras and camcorders sample the video volume (x,y,t) at fixed intervals to gain understanding of the volume's temporal evolution. Conventionally, one must reduce the spatial resolution to increase the framerate of such cameras. Using temporal coding via physical translation of an optical element known as a coded aperture, the compressive temporal imaging (CACTI) camera emonstrates a method which which to embed the temporal dimension of the video volume into spatial (x,y) measurements, thereby greatly improving temporal resolution with minimal loss of spatial resolution. This technique, which is among a family of compressive sampling strategies developed at Duke University, temporally codes the exposure readout functions at the pixel level.</p><p>Since video cameras nominally integrate the remaining image volume dimensions (e.g. spectrum and focus) at capture time, spectral (x,y,t,\lambda) and focal (x,y,t,z) image volumes are traditionally captured via sequential changes to the spectral and focal state of the system, respectively. The CACTI camera's ability to embed video volumes into images leads to exploration of other information within that video; namely, focal and spectral information. The next part of the thesis demonstrates derivative works of CACTI: compressive extended depth of field and compressive spectral-temporal imaging. These works successfully show the technique's extension of temporal coding to improve sensing performance in these other dimensions.</p><p>Geometrical optics-related tradeoffs, such as the classic challenges of wide-field-of-view and high resolution photography, have motivated the development of mulitscale camera arrays. The advent of such designs less than a decade ago heralds a new era of research- and engineering-related challenges. One significant challenge is that of managing the focal volume (x,y,z) over wide fields of view and resolutions. The fourth chapter shows advances on focus and image quality assessment for a class of multiscale gigapixel cameras developed at Duke.</p><p>Along the same line of work, we have explored methods for dynamic and adaptive addressing of focus via point spread function engineering. We demonstrate another form of temporal coding in the form of physical translation of the image plane from its nominal focal position. We demonstrate this technique's capability to generate arbitrary point spread functions.</p> / Dissertation

We have previously introduced a high throughput multiplexing computational spectral imaging device. The device measures scalar projections of pseudo-arbitrary spectral filters at each spatial pixel. This paper discusses simulation and initial experimental progress in performing computational spectral unmixing by taking advantage of the natural sparsity commonly found in the fractional abundances. The simulation results show a lower unmixing error compared to traditional spectral imaging devices. Initial experimental results demonstrate the ability to directly perform spectral unmixing with less error than multiplexing alone.

In this thesis, we investigate the application of compressive sensing and sparse signal processing techniques to image compression and inpainting problems. Considering that many signals are sparse in certain transformation domain, a natural question to ask is: can an image be represented by as few coefficients as possible? In this thesis, we propose a new model for image compression/decompression based on sparse representation. We suggest constructing an overcomplete dictionary by combining two compression matrices, the discrete cosine transform (DCT) matrix and Hadamard-Walsh transform (HWT) matrix, instead of using only one transformation matrix that has been used by the common compression techniques such as JPEG and JPEG2000. We analyze the Structural Similarity Index (SSIM) versus the number of coefficients, measured by the Normalized Sparse Coefficient Rate (NSCR) for our approach. We observe that using the same NSCR, SSIM for images compressed using the proposed approach is between 4%-17% higher than when using JPEG. Several algorithms have been used for sparse coding. Based on experimental results, Orthogonal Matching Pursuit (OMP) is proved to be the most efficient algorithm in terms of computational time and the quality of the decompressed image. In addition, based on compressive sensing techniques, we propose an image inpainting approach, which could be used to fill missing pixels and reconstruct damaged images. In this approach, we use the Gradient Projection for Sparse Reconstruction (GPSR) algorithm and wavelet transformation with Daubechies filters to reconstruct the damaged images based on the information available in the original image. Experimental results show that our approach outperforms existing image inpainting techniques in terms of computational time with reasonably good image reconstruction performance.

With the rising of new paradigms in wireless communications such as Internet of things (IoT), current static frequency allocation policy faces a primary challenge of spectrum scarcity, and thus encourages the IoT devices to have cognitive capabilities to access the underutilised spectrum in the temporal and spatial dimensions. Wideband spectrum sensing is one of the key functions to enable dynamic spectrum access, but entails a major implementation challenge in terms of sampling rate and computation cost since the sampling rate of analog-to-digital converters (ADCs) should be higher than twice of the spectrum bandwidth based on the Nyquist-Shannon sampling theorem. By exploiting the sparse nature of wideband spectrum, sub-Nyquist sampling and sparse signal recovery have shown potential capabilities in handling these problems, which are directly related to compressive sensing (CS) from the viewpoint of its origin. To invoke sub-Nyquist wideband spectrum sensing in IoT, blind signal acquisition with low-complexity sparse recovery is desirable on compact IoT devices. Moreover, with cooperation among distributed IoT devices, the complexity of sampling and reconstruc- tion can be further reduced with performance guarantee. Specifically, an adaptively- regularized iterative reweighted least squares (AR-IRLS) reconstruction algorithm is proposed to speed up the convergence of reconstruction with less number of iterations. Furthermore, a low-complexity compressive spectrum sensing algorithm is proposed to reduce computation complexity in each iteration of IRLS-based reconstruction algorithm, from cubic time to linear time. Besides, to transfer computation burden from the IoT devices to the core network, a joint iterative reweighted sparse recovery scheme with geo-location database is proposed to adopt the occupied channel information from geo- location database to reduce the complexity in the signal reconstruction. Since numerous IoT devices access or release the spectrum randomly, the sparsity levels of wideband spec-trum signals are varying and unknown. A blind CS-based sensing algorithm is proposed to enable the local secondary users (SUs) to adaptively adjust the sensing time or sam- pling rate without knowledge of spectral sparsity. Apart from the signal reconstruction at the back-end, a distributed sub-Nyquist sensing scheme is proposed by utilizing the surrounding IoT devices to jointly sample the spectrum based on the multi-coset sam- pling theory, in which only the minimum number of low-rate ADCs on the IoT devices are required to form coset samplers without the prior knowledge of the number of occu- pied channels and signal-to-noise ratios. The models of the proposed algorithms are derived and verified by numerical analyses and tested on both real-world and simulated TV white space signals.

Two efficient models in two-dimensional signal processing are proposed in the thesis. The first model deals with large scale spectral compressive sensing in continuous domain, which aims to recover a 2D spectrally sparse signal from partially observed time samples. The signal is assumed to be a superposition of s complex sinusoids. We propose a semidefinite program for the 2D signal recovery problem. Our model is able to handle large scale 2D signals of size 500*500, whereas traditional approaches only handle signals of size around 20*20. The second model deals with the problem of single image reflection suppression. Removing the undesired reflection from images taken through glass is of great importance in computer vision. It serves as a means to enhance the image quality for aesthetic purposes as well as to preprocess images in machine learning and pattern recognition applications. We propose a convex model to suppress the reflection from a single input image. Our model implies a partial differential equation with gradient thresholding, which is solved efficiently using Discrete Cosine Transform. Extensive experiments on synthetic and real-world images demonstrate that our approach achieves desirable reflection suppression results and dramatically reduces the execution time compared to the state of the art.

Compressive sensing (CS) is a new sampling theory which allows reconstructing signals using sub-Nyquist measurements. It states that a signal can be recovered exactly from randomly undersampled data points if the signal exhibits sparsity in some transform domain (wavelet, Fourier, etc). Instead of measuring it uniformly in a local scheme, signal is correlated with a series of sensing waveforms. These waveforms are so called sensing matrix or measurement matrix. Every measurement is a linear combination of randomly picked signal components. By applying a nonlinear convex optimization algorithm, the original can be recovered. Therefore, signal acquisition and compression are realized simultaneously and the amount of information to be processed is considerably reduced. Due to its unique sensing and reconstruction mechanism, CS creates a new situation in signal acquisition hardware design as well as software development, to handle the increasing pressure on imaging sensors for sensing modalities beyond visible (ultraviolet, infrared, terahertz etc.) and algorithms to accommodate demands for higher-dimensional datasets (hyperspectral or video data cubes). The combination of CS with traditional optical imaging extends the capabilities and also improves the performance of existing equipments and systems. Our research work is focused on the direct application of compressive sensing for imaging in both 2D and 3D cases, such as infrared imaging, hyperspectral imaging and sum frequency generation microscopy. Data acquisition and compression are combined into one step. The computational complexity is passed to the receiving end, which always contains sufficient computer processing power. The sensing stage requirement is pushed to the simplest and cheapest level. In short, simple optical engine structure, robust measuring method and high speed acquisition make compressive sensing-based imaging system a strong competitor to the traditional one. These applications have and will benefit our lives in a deeper and wider way.

Wide-band spectrum sensing is an approach for finding spectrum holes within a wideband signal with less complexity/delay than the conventional approaches. In this thesis, we propose four different algorithms for detecting the holes in a wide-band spectrum and finding the sparsity level of compressive signals. The first algorithm estimates the spectrum in an efficient manner and uses this estimation to find the holes. The second algorithm detects the spectrum holes by reconstructing channel energies instead of reconstructing the spectrum itself. In this method, the signal is fed into a number of filters. The energies of the filter outputs are used as the compressed measurement to reconstruct the signal energy. The third algorithm employs two information theoretic algorithms to find the sparsity level of a compressive signal and the last algorithm employs belief propagation for detecting the sparsity level.

Wide-band spectrum sensing is an approach for finding spectrum holes within a wideband signal with less complexity/delay than the conventional approaches. In this thesis, we propose four different algorithms for detecting the holes in a wide-band spectrum and finding the sparsity level of compressive signals. The first algorithm estimates the spectrum in an efficient manner and uses this estimation to find the holes. The second algorithm detects the spectrum holes by reconstructing channel energies instead of reconstructing the spectrum itself. In this method, the signal is fed into a number of filters. The energies of the filter outputs are used as the compressed measurement to reconstruct the signal energy. The third algorithm employs two information theoretic algorithms to find the sparsity level of a compressive signal and the last algorithm employs belief propagation for detecting the sparsity level.