Learning to Invert: Signal Recovery via Deep Convolutional Networks

The promise of compressive sensing (CS) has been offset by two significant challenges. First, real-world data is not exactly sparse in a fixed basis. Second, current high-performance recovery algorithms are slow to converge, which limits CS to either non-real-time applications or scenarios where massive back-end computing is available. In this paper, we attack both of these challenges head-on by developing a new signal recovery framework we call {\em DeepInverse} that learns the inverse transformation from measurement vectors to signals using a {\em deep convolutional network}. When trained on a set of representative images, the network learns both a representation for the signals (addressing challenge one) and an inverse map approximating a greedy or convex recovery algorithm (addressing challenge two). Our experiments indicate that the DeepInverse network closely approximates the solution produced by state-of-the-art CS recovery algorithms yet is hundreds of times faster in run time. The tradeoff for the ultrafast run time is a computationally intensive, off-line training procedure typical to deep networks. However, the training needs to be completed only once, which makes the approach attractive for a host of sparse recovery problems.Comment: Accepted at The 42nd IEEE International Conference on Acoustics, Speech and Signal Processin

DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks

In this paper we develop a novel computational sensing framework for sensing and recovering structured signals. When trained on a set of representative signals, our framework learns to take undersampled measurements and recover signals from them using a deep convolutional neural network. In other words, it learns a transformation from the original signals to a near-optimal number of undersampled measurements and the inverse transformation from measurements to signals. This is in contrast to traditional compressive sensing (CS) systems that use random linear measurements and convex optimization or iterative algorithms for signal recovery. We compare our new framework with $\ell_1$-minimization from the phase transition point of view and demonstrate that it outperforms $\ell_1$-minimization in the regions of phase transition plot where $\ell_1$-minimization cannot recover the exact solution. In addition, we experimentally demonstrate how learning measurements enhances the overall recovery performance, speeds up training of recovery framework, and leads to having fewer parameters to learn

A novel approach towards a lubricant-free deep drawing process via macro-structured tools

In today’s industry, the sustainable use of raw materials and the development of new green technology in mass production, with the aim of saving resources, energy and production costs, is a significant challenge. Deep drawing as a widely used industrial sheet metal forming process for the production of automotive parts belongs to one of the most energy-efficient production techniques. However, one disadvantage of deep drawing regarding the realisation of green technology is the use of lubricants in this process. Therefore, a novel approach for modifying the conventional deep drawing process to achieve a lubricant-free deep drawing process is introduced within this thesis. In order to decrease the amount of frictional force for a given friction coefficient, the integral of the contact pressure over the contact area has to be reduced. To achieve that, the flange area of the tool is macro-structured, which has only line contacts. To avoid the wrinkling, the geometrical moment of inertia of the sheet should be increased by the alternating bending mechanism of the material in the flange area through immersing the blankholder slightly into the drawing die

Band-edge Bilayer Plasmonic Nanostructure for Surface Enhanced Raman Spectroscopy

Spectroscopic analysis of large biomolecules is critical in a number of applications, including medical diagnostics and label-free biosensing. Recently, it has been shown that Raman spectroscopy of proteins can be used to diagnose some diseases, including a few types of cancer. These experiments have however been performed using traditional Raman spectroscopy and the development of the Surface enhanced Raman spectroscopy (SERS) assays suitable for large biomolecules could lead to a substantial decrease in the amount of specimen necessary for these experiments. We present a new method to achieve high local field enhancement in surface enhanced Raman spectroscopy through the simultaneous adjustment of the lattice plasmons and localized surface plasmon polaritons, in a periodic bilayer nanoantenna array resulting in a high enhancement factor over the sensing area, with relatively high uniformity. The proposed plasmonic nanostructure is comprised of two interacting nanoantenna layers, providing a sharp band-edge lattice plasmon mode and a wide-band localized surface plasmon for the separate enhancement of the pump and emitted Raman signals. We demonstrate the application of the proposed nanostructure for the spectral analysis of large biomolecules by binding a protein (streptavidin) selectively on the hot-spots between the two stacked layers, using a low concentration solution (100 nM) and we successfully acquire its SERS spectrum

A Deep Learning Approach to Structured Signal Recovery

In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse representations, and computationally complex convex/greedy algorithms, we introduce a deep learning framework that supports both linear and mildly nonlinear measurements, that learns a structured representation from training data, and that efficiently computes a signal estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an unsupervised feature learner. SDA enables us to capture statistical dependencies between the different elements of certain signals and improve signal recovery performance as compared to the CS approach

Microfluidics for Advanced Drug Delivery Systems.

Considerable efforts have been devoted towards developing effective drug delivery methods. Microfluidic systems, with their capability for precise handling and transport of small liquid quantities, have emerged as a promising platform for designing advanced drug delivery systems. Thus, microfluidic systems have been increasingly used for fabrication of drug carriers or direct drug delivery to a targeted tissue. In this review, the recent advances in these areas are critically reviewed and the shortcomings and opportunities are discussed. In addition, we highlight the efforts towards developing smart drug delivery platforms with integrated sensing and drug delivery components

Oil and state in the political economy of Iran, 1942-1979

In Iran, the regime of Mohammad Reza Shah deeply influenced the country's economic, political, and social life. Everything either began or ended with the state. The state was the most important institution in the country and enjoyed a significant monopoly in political and economic decision-making, trade, etc. It enjoyed autonomy from social pressures and pursued its own policies, whilst controlling political groups. Therefore, to analyse the function and behaviour of the state and the economic development of the country, I have used a political economy approach. To this end, a model has been constructed to describe the political structure of the Iranian state and to map the country’s political and economic development. It has been argued that: 1. The political structure of the regime was authoritarian-bureaucratic, based on the three pillars of the state bureaucracy, the security machinery and the armed forces, and the network of court patronage. A parallel to this structure can be seen in the Safavid dynasty whose pillars of power were the same.2 The decision-making process and exercise of power was manipulated by, and depended upon, the Shah and the ruling elite. This manipulation depended substantially on the degree of state autonomy from social and economic groups.3. The state acted as the engine of economic growth and the main source of capital accumulation. Thus, the political system became the main economic decision-making body who dictated economic policies for more than the market. In spite of this direction, the state never questioned the essence of private capital accumulation.4. Oil revenues, both by increasing the magnitude of resources at the disposal of the state and by easing structural constraints, substantially increased the capacity of the state to intervene in economy and society to pursue its own policies, and5. Oil revenues provided a new kind of economy, built on rent and heavily reliant on the export of a single raw material, the production of which required little contact with the rest of the economy. It brought spectacular growth, yet at the same time engendered dependency on volatile markets. In the long run oil also created new international interdependencies as the state relied on foreign markets for capital, labour, and goods. Furthermore, Iran had no independent technological capacity and had to import semi finished goods to meet its industrial needs. Therefore, a process of 'dependent development' was shaped during the 1960s and 1970s