Suppressing zero order diffraction beam is important to improve the performance of pixelated phase-only spatial light modulator. This issue also impacts the performance of the Texas Instruments Phase Light Modulator (PLM). However, PLM has a unique loading sequence which includes a short fraction of time that the mirrors return to flat resulting in only zero-order diffraction (ZOD) pattern to be generated, followed by the intended computer-generated hologram (CGH) with the ZOD at a less intensity. In this paper, we will show the captured images of ZOD and CGH using a highspeed camera with high dynamic range. We employed a sequence subtraction approach to suppress ZOD using data collected from Texas Instruments 0.67” PLM EVM. This process can be used in conjunction with other ZOD suppression techniques. The experimental results are presented in the paper.
Phase light modulator has been used in many underwater applications, such as turbulence mitigation, or laser beam shaping, to improve imaging and communication in the underwater environment. Liquid crystal on silicon (LCOS) or Liquid Crystal Display (LCD)-based phase light modulators are used in these applications. In recent years, Texas Instruments Digital Light Processing division developed a piston-mode Phase Light Modulator MEMS device (TI-PLM). One of the benefits of the device is its capability of supporting a high frame rate (i.e., 5.7kframes/sec). In this paper, we evaluated this TI-PLM device on an optical benchtop. While the main focus was the image quality of the Computer-Generated Hologram (CGH), the results also shed some light on this device’s capability for other PLM applications.
We develop a remote hyperspectral (HS) imaging work flow that relays spectral and spatial information of a scene via a minimal amount of encoded samples along with a robust data reconstruction scheme. To fully exploit the redundant and multidimensional structure of HS images, we adopt the canonical polyadic (CP) decomposition of multiway tensors. This approach represents our HS cube in a compressive manner while being naturally suitable for the linear mixing model, commonly used by practitioners to analyze the spectral content of each pixel. Under this low CP rank model we achieve frugal HS sensing by attenuating and encoding the incoming spectrum, thereby faithfully capturing the information with few measurements relative to its ambient dimensions. To further reduce the complexity of HS data, we apply image segmentation techniques to our encoded observations. By clustering the pixels into groups of endmembers with similar structure, we obtain a set of simplified data cubes each well approximated by a low CP rank tensor. To decode the measurements, we apply CP alternating least squares to each set of clustered pixels and combine the outputs to obtain our final HS image. We present several numerical experiments on synthetic and real HS data with various levels of input noise. We demonstrate that the approach outperforms state of the art methods, achieving noise attenuation while reducing the amount of collected data by a factor of 1/14.
Notification services are widely used nowadays in many scenarios. For instance, they are useful if a patient needs care or an IT incident needs support. There lacks of a simple, affordable and efficient notification system that can deliver important messages across various channels to a certain number of people. Therefore, we developed a serverless 1-click notification system using the Amazon Web Services (AWS) IoT platform. The system allows a user to notify the designated personnel(s) with a simple click using configurable IoT buttons via Wi-Fi. An AWS cloud server receives the request sent by the buttons, processes the request and sends the notification such as emails, text messages or phone calls to the designated service members based on the preprogramed procedures and database. The system is user friendly and easy to implement since no hardware installation is required. The serverless system and the pay-as-you-go policy make the system very cost effective. The system is reliable as it is supported by the AWS. In this article we will describe a few key cloud functions and services that we adopted in the notification system and discuss two potential use cases in classroom technical support and home-based long-term care settings. We validated the system functionality at Texas Christian University's patient care simulation center.
In many space-borne surveillance missions, hyperspectral imaging (HSI) sensors are essential to enhance the ability to analyze and classify oceanic and terrestrial parameters and objects/areas of interest. A significant technical challenge is that the amount of raw data acquired by these sensors will begin to exceed the data transmission bandwidths between the spacecraft and the ground station using classical approaches such as imaging onto a detector array. To address such an issue, the compressive line sensing (CLS) imaging concept, originally developed for energy-efficient active laser imaging, is adopted in the design of a hyperspectral imaging sensor. CLS HSI imaging is achieved using a digital micromirror device (DMD) spatial light modulator. A DMD generates a series of 2D binary sensing patterns from a codebook that can be used to encode cross-track spatial-spectral slices in a push-broom type imaging device. In this paper, the development of a testbed using the TI DLP NIRscan™ Nano Evaluation Module to investigate the CLS HSI concept is presented. Initial test results are discussed.
Compressive Line Sensing (CLS) imaging system is a compressive sensing (CS) based imaging system with the goal of developing a compact and resource efficient imaging system for the degraded visual environment. In the CLS system, each line segment is sensed independently; however, the correlation among the adjacent lines (sources) is exploited via the joint sparsity in the distributed compressing sensing model during signal reconstruction. Several different CLS prototypes have been developed. This paper discusses the development of a pulsed CLS system. Initial experimental results using this system in a turbid water environment are presented.
The compressive line sensing imaging system adopts distributed compressive sensing (CS) to acquire data and reconstruct images. Dynamic CS uses Bayesian inference to capture the correlated nature of the adjacent lines. An image reconstruction technique that incorporates dynamic CS in the distributed CS framework was developed to improve the quality of reconstructed images. The effectiveness of the technique was validated using experimental data acquired in an underwater imaging test facility. Results that demonstrate contrast and resolution improvements will be presented. The improved efficiency is desirable for unmanned aerial vehicles conducting long-duration missions.
The Compressive Line Sensing (CLS) active imaging system has been demonstrated to be effective in scattering mediums, such as turbid coastal water through simulations and test tank experiments. Since turbulence is encountered in many atmospheric and underwater surveillance applications, a new CLS imaging prototype was developed to investigate the effectiveness of the CLS concept in a turbulence environment. Compared with earlier optical bench top prototype, the new system is significantly more robust and compact. A series of experiments were conducted at the Naval Research Lab's optical turbulence test facility with the imaging path subjected to various turbulence intensities. In addition to validating the system design, we obtained some unexpected exciting results – in the strong turbulence environment, the time-averaged measurements using the new CLS imaging prototype improved both SNR and resolution of the reconstructed images. We will discuss the implications of the new findings, the challenges of acquiring data through strong turbulence environment, and future enhancements.
The Compressive Line Sensing (CLS) active imaging system has been demonstrated to be effective in scattering mediums, such as coastal turbid water, fog and mist, through simulations and test tank experiments. The CLS prototype hardware consists of a CW laser, a DMD, a photomultiplier tube, and a data acquisition instrument. CLS employs whiskbroom imaging formation that is compatible with traditional survey platforms. The sensing model adopts the distributed compressive sensing theoretical framework that exploits both intra-signal sparsity and highly correlated nature of adjacent areas in a natural scene. During sensing operation, the laser illuminates the spatial light modulator DMD to generate a series of 1D binary sensing pattern from a codebook to “encode” current target line segment. A single element detector PMT acquires target reflections as encoder output. The target can then be recovered using the encoder output and a predicted on-target codebook that reflects the environmental interference of original codebook entries. In this work, we investigated the effectiveness of the CLS imaging system in a turbulence environment. Turbulence poses challenges in many atmospheric and underwater surveillance applications. A series of experiments were conducted in the Naval Research Lab’s optical turbulence test facility with the imaging path subjected to various turbulence intensities. The total-variation minimization sparsifying basis was used in imaging reconstruction. The preliminary experimental results showed that the current imaging system was able to recover target information under various turbulence strengths. The challenges of acquiring data through strong turbulence environment and future enhancements of the system will be discussed.
In recent years, a compressive sensing based underwater imaging system has been under investigation: the Compressive Line Sensing (CLS) imaging system. In the CLS system, each line segment is sensed independently; with regard to signal reconstruction, the correlation among the adjacent lines is exploited via the joint sparsity in the distributed compressive sensing model. Interestingly, the dynamic compressive sensing signal model is also capable of exploiting the correlated nature of the adjacent lines through a Bayesian framework. This paper proposes a new CLS reconstruction technique through the integration of these different models, and includes an evaluation of the proposed technique using the experiment dataset obtained from an underwater imaging test setup.
The compressive line sensing (CLS) active imaging system was proposed and validated through a series of test-tank experiments. As an energy-efficient alternative to the traditional line-scan serial image, the CLS system will be highly beneficial for long-duration surveillance missions using unmanned, power-constrained platforms such as unmanned aerial or underwater vehicles. In this paper, the application of an active spatial light modulator (SLM), the individually addressable laser diode array, in a CLS imaging system is investigated. In the CLS context, active SLM technology can be advantageous over passive SLMs such as the digital micro-mirror device. Initial experimental results are discussed.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.