Academic literature on the topic 'Compressed sensing, compressive camera identification'

Nowadays, user identification plays a more and more important role for authorized machine access and remote personal data usage. For reasons of privacy and convenience, biometrics-based user identification, such as iris, fingerprint, and face ID, has become mainstream methods in our daily lives. However, most of the biometric methods can be easily imitated or artificially cracked. New types of biometrics, such as electrocardiography (ECG), are based on physiological signals rather than traditional biological traits. Recently, compressive sensing (CS) technology that combines both sampling and compression has been widely applied to reduce the power of data acquisition and transmission. However, prior CS-based frameworks suffer from high reconstruction overhead and cannot directly align compressed ECG signals. In this paper, in order to solve the above two problems, we propose a compressed alignment-aided compressive analysis (CA-CA) algorithm for ECG-based biometric user identification. With CA-CA, it can avoid reconstruction and extract information directly from CS-based compressed ECG signals to reduce overall complexity and power. Besides, CA-CA can also align the compressed ECG signals in the eigenspace-domain, which can further enhance the precision of identifications and reduce the total training time. The experimental result shows that our proposed algorithm has a 94.16% accuracy based on a public database of 22 people.

We develop a novel compressive coded rotating mirror (CCRM) camera to capture events at high frame rates in passive mode with a compact instrument design at a fraction of the cost compared to other high-speed imaging cameras. Operation of the CCRM camera is based on amplitude optical encoding (grey scale) and a continuous frame sweep across a low-cost detector using a motorized rotating mirror system which can achieve single pixel shift between adjacent frames. Amplitude encoding and continuous frame overlapping enable the CCRM camera to achieve a high number of captured frames and high temporal resolution without making sacrifices in the spatial resolution. Two sets of dynamic scenes have been captured at up to a 120 Kfps frame rate in both monochrome and colored scales in the experimental demonstrations. The obtained heavily compressed data from the experiment are reconstructed using the optimization algorithm under the compressive sensing (CS) paradigm and the highest sequence depth of 1400 captured frames in a single exposure has been achieved with the highest compression ratio of 368 compared to other CS-based high-speed imaging technologies. Under similar conditions the CCRM camera is 700× faster than conventional rotating mirror based imaging devices and could reach a frame rate of up to 20 Gfps.

The Compressed Sensing (CS) camera can compress images in real time without consuming computing resources. Applying CS theory in the Laser Communication (LC) system can minimize the assumed transmission bandwidth (normally from a satellite to a ground station) and minimize the storage costs of beacon light-spot images; this can save more than ten times the typical bandwidth or storage space. However, the CS compressive process affects the light-spot tracking and key parameters in the images. In this study, we quantitatively explored the feasibility of the CS technique to capture light-spots in LC systems. We redesigned the measurement matrix to adapt to the requirement of light-tracking. We established a succinct structured deep network, the Compressed Sensing Denoising Center Net (CSD-Center Net) for denoising tracking computation from compressed image information. A series of simulations was made to test the performance of information preservation in beacon light spot image storage. With the consideration of CS ratio and application scenarios, coupled with CSD-Center Net and standard centroid, CS can achieve the tracking function well. The information preserved in compressed information correlates with the CS ratio; higher CS ratio can preserve more details. In fact, when the data rate is up than 10%, the accuracy could meet the requirements what we need in most application scenarios.

The majority of digital images are stored in compressed form. Generally, image classification using convolution neural network (CNN) is done in uncompressed form rather than compressed one. Training the CNN in the compressed domain eliminates the requirement for decompression process and results in improved efficiency, minimal storage, and lesser cost. Compressive sensing (CS) is one of the effective and efficient method for signal acquisition and recovery and CNN training on CS measurements makes the entire process compact. The most popular sensing phenomenon used in CS is based on image acquisition using single pixel camera (SPC) which has complex design implementation and usually a matrix simulation is used to represent the SPC process in numerical demonstration. The CS measurements using this phenomenon are visually different from the image and to add this in the training set of the compressed learning framework, there is a need for an inverse SPC process that is to be applied all through the training and testing dataset image samples. In this paper we proposed a simple sensing phenomenon which can be implemented using the image output of a standard digital camera by retaining few pixels and forcing the rest of the pixels to zero and this reduced set of pixels is assumed as CS measurements. This process is modeled by a binary mask application on the image and the resultant image still subjectively legible for human vision and can be used directly in the training dataset. This sensing mask has very few active pixels at arbitrary locations and there is a lot of scope to heuristically learn the sensing mask suitable for the dataset. Only very few attempts had been made to learn the sensing matrix and the sole effect of this learning process on the improvement of CNN model accuracy is not reported. We proposed to have an ablation approach to study how this sensing matrix learning improves the accuracy of the basic CNN architecture. We applied CS for two class image dataset by applying a Primitive Walsh Hadamard (PWH) binary mask function and performed the classification experiment using a basic CNN. By retaining arbitrary amount of pixel in the training and testing dataset we applied CNN on the compressed measurements to perform image classification and studied and reported the model performance in terms of training and validation accuracies by varying the amount of pixels retained. A novel Genetic Algorithm-based compressive learning (GACL) method is proposed to learn the PWH mask to optimize the model training accuracy by using two different crossover techniques. In the experiment conducted for the case of compression ratio (CR) 90% by retaining only 10% of the pixels in every images both in training and testing dataset that represent two classes, the training accuracy is improved from 67% to 85% by using diagonal crossover in offspring creation of GACL. The robustness of the method is examined by applying GACL for user defined multiclass dataset and achieved better CNN model accuracies. This work will bring out the strength of sensing matrix learning which can be integrated with advanced training models to minimize the amount of information that is to be sent to central servers and will be suitable for a typical IoT frame work.

According to the theory of compressive sensing, a single-pixel imaging system was built in our laboratory, and imaging scenes are successfully reconstructed by single-pixel imaging, but the quality of reconstructed images in traditional methods cannot meet the demands of further engineering applications. In order to improve the imaging accuracy of our single-pixel camera, some optimization methods of key technologies in compressive sensing are proposed in this paper. First, in terms of sparse signal decomposition, based on traditional discrete wavelet transform and the characteristics of coefficients distribution in wavelet domain, a constraint condition of the exponential decay is proposed and a corresponding constraint matrix is designed to optimize the original wavelet decomposition basis. Second, for the construction of deterministic binary sensing matrices in the single-pixel camera, on the basis of a Gram matrix, a new algorithm model and a new method of initializing a compressed sensing measurement matrix are proposed to optimize the traditional binary sensing matrices via mutual coherence minimization. The gradient projection-based algorithm is used to solve the new mathematical model and train deterministic binary sensing measurement matrices with better performance. Third, the proposed optimization methods are applied to our single-pixel imaging system for optimizing the existing imaging methods. Compared with the conventional methods of single-pixel imaging, the accuracy of image reconstruction and the quality of single-pixel imaging have been significantly improved by our methods. The superior performance of our proposed methods has been fully tested and the effectiveness has also been demonstrated by numerical simulation experiments and practical imaging experiments.

Imaging with the use of a single pixel camera and based on compressed sensing (CS) is a new and promising technology. The use of CS allows reconstruction of images in various spectrum ranges depending on the spectrum sensibility of the used detector. During the study image reconstruction was performed in the LWIR range based on a thermogram from a simulated single pixel camera. For needs of reconstruction CS was used. A case analysis showed that the CS method may be used for construction of infrared-based observation single pixel cameras. This solution may also be applied in measuring cameras. Yet the execution of a measurement of radiation temperature requires calibration of results obtained by CS reconstruction. In the study a calibration method of the infrared observation camera was proposed and studies were carried out of the impact exerted by the number of measurements made on the quality of reconstruction. Reconstructed thermograms were compared with reference images of infrared radiation. It has been ascertained that the reduction of the reconstruction error is not directly in proportion to the number of collected samples being collected. Based on a review of individual cases it has been ascertained that apart from the number of collected samples, an important factor that affects the reconstruction fidelity is the structure of the image as such. It has been proven that estimation of the error for reconstructed thermograms may not be based solely on the quantity of executed measurements.

Single-pixel imaging via compressed sensing can reconstruct high-quality images from a few linear random measurements of an object known a priori to be sparse or compressive, by using a point/bucket detector without spatial resolution. Nevertheless, random measurements still have blindness, limiting the sampling ratios and leading to a harsh trade-off between the acquisition time and the spatial resolution. Here, we present a new compressive imaging approach by using a strategy we call cake-cutting, which can optimally reorder the deterministic Hadamard basis. The proposed method is capable of recovering images of large pixel-size with dramatically reduced sampling ratios, realizing super sub-Nyquist sampling and significantly decreasing the acquisition time. Furthermore, such kind of sorting strategy can be easily combined with the structured characteristic of the Hadamard matrix to accelerate the computational process and to simultaneously reduce the memory consumption of the matrix storage. With the help of differential modulation/measurement technology, we demonstrate this method with a single-photon single-pixel camera under the ulta-weak light condition and retrieve clear images through partially obscuring scenes. Thus, this method complements the present single-pixel imaging approaches and can be applied to many fields.

In recent years, user identification has become crucial for authorized machine access. Electrocardiography (ECG) is a new and rising biometrics signature. Rather than traditional biological traits, ECG cannot be easily imitated. In the long-term monitoring system, the wireless wearable ECG biomedical sensor nodes are resource-limited. Recently, compressive sensing (CS) technology is extensively applied to reduce the power of data transmission and acquisition. The prior CS-based reconstruction process aims at improving energy efficiency with different schemes, and they focus on the performance of reconstruction only. Therefore, we present a sparse coding-based classifier, trained by task-driven dictionary learning (TDDL), to realize low-complexity user identification in compressed-domain directly. TDDL is one of the dictionary learning and designed for classification tasks. It co-optimizes the dictionary and classifier weighting simultaneously, which gives better accuracy. In this article, we are proposing a TDDL-based compression learning algorithm for ECG biometric user identification as this directly identifies user identity (ID) without undergoing reconstruction process and conventional classifier. It can extract necessary information from the compressed-ECG signal directly to save the system power and computational complexity. The algorithm has 2%–10% accuracy improvements compared with state-of-the-art algorithms and maintains low complexity at the same time. Our proposed TDDL-CL will be the better choice in the long-term wearable ECG biometric devices.

The successful recovery of the plaintext in the simplified diffractive-imaging-based encryption (S-DIBE) scheme needs to record one intact axial intensity map as the ciphertext. By aid of compressive sensing, we propose here a new image encryption approach, referred to as compressed DIBE (C-DIBE), which allows further compression of the intensity map. The plaintext is sampled before being sent to DIBE. Afterwards, the intensity map recorded by the CCD camera is also processed by such sampling operation to generate the ciphertext. For decryption, we first obtain the sparse plaintext using the proposed phase retrieval algorithm, and then reobtain the primary plaintext from it via compressive sensing. Numerical results show that a proper proportion of the intensity map (e.g. 50%) is enough to totally recover a grayscale image. We achieve multiple-image encryption by space multiplexing without enlarging the size of the ciphertext. The robustness of C-DIBE against brute-force attack evidently outperforms S-DIBE due to the extended key space. Numerical simulation has been presented to confirm the proposal.

The cooperation for big data applications through the cognitive radio innovation requires wideband spectrum sensing. Conversely, it is expensive to employ long haul wideband detecting and is particularly troublesome within the sight of vulnerability. For example, more noise, obstruction, anomalies, as well as channel blurring. In this article, we project the planning of successive compacted range detecting which together endeavors compressive sensing (CS) and consecutive occasional identification procedures to accomplish increasingly exact and convenient wideband detecting. Rather than summoning CS to recreate the signal in every period, our projected plan executes in reverse assembled packed information consecutive likelihood proportion test (in reverse GCD-SPRT) utilizing compacted information tests in successive identification, while CS recuperation is just sought after when required. This technique altogether diminishes the compressed sensing recuperation overhead, and on different exploits successive location to increase the detecting excellence. Moreover, we project an inside and out detecting plan to quicken detecting basic leadership when an adjustment in channel position is suspicious, (b) a square scanty CS remaking calculation to abuse the square sparsityfeatures of wide range, and (c) a lot of plans to meld results from the recuperated range signs to additionally improve the general detecting exactness. Broad execution assessment results demonstrate that the projected plans can altogether outflank peer conspires below adequately low SNR properties.

Photos captured in the shortwave infrared (SWIR) spectrum are interesting in military applications because they are independent of what time of day the pic- ture is captured because the sun, moon, stars and night glow illuminate the earth with short-wave infrared radiation constantly. A major problem with today’s SWIR cameras is that they are very expensive to produce and hence not broadly available either within the military or to civilians. Using a relatively new tech- nology called compressive sensing (CS), enables a new type of camera with only a single pixel sensor in the sensor (a SPC). This new type of camera only needs a fraction of measurements relative to the number of pixels to be reconstructed and reduces the cost of a short-wave infrared camera with a factor of 20. The camera uses a micromirror array (DMD) to select which mirrors (pixels) to be measured in the scene, thus creating an underdetermined linear equation system that can be solved using the techniques described in CS to reconstruct the im- age. Given the new technology, it is in the Swedish Defence Research Agency (FOI) interest to evaluate the potential of a single pixel camera. With a SPC ar- chitecture developed by FOI, the goal of this thesis was to develop methods for sampling, reconstructing images and evaluating their quality. This thesis shows that structured random matrices and fast transforms have to be used to enable high resolution images and speed up the process of reconstructing images signifi- cantly. The evaluation of the images could be done with standard measurements associated with camera evaluation and showed that the camera can reproduce high resolution images with relative high image quality in daylight.

A reliable design of a composite structure needs to consider the failure of the composites. Hashin failure criterion is one of the most popular phenomenological models in engineering practice due to its simplicity of application. Although remarkable success has been achieved from the Hashin failure criterion, it does not always fit the experimental results very well. Over the past few years, a few experimental failure data have been collected. It would be of interest to leverage the existing data to improve the prediction of failure criteria. In this paper, we proposed to apply a framework that combines sparse regression with compressed sensing to discover failure criteria from data. Following the phenomenological failure models, we divided the failure of composites into tensile and compressive fiber modes, tensile and compressive matrix modes. Two examples were studied with the proposed framework. The first example was presented to demonstrate the capability of the framework. The data was generated by the Hashin failure criterion and added various magnitudes of noise. The proposed framework was implemented to discover the failure criterion from the noised data. For the second example, the proposed method was used to discover failure criteria from the experimental data which are collected from the first world wide failure exercise (WWFE I). Both examples show that the proposed method can discover the failure criteria accurately.