Hybrid nanomaterials have received voluminous interest due to the combination of unique properties of organic and inorganic component in one material. In this class, magnetic polymer nanocomposites are of particular interest because of the combination of excellent magnetic properties, stability, and good biocompatibility. Organic–inorganic magnetic nanocomposites can be prepared by in situ, ex situ, microwave reflux, co-precipitation, melt blending, and ceramic–glass processing and plasma polymerization techniques. These nanocomposites have been exploited for in vivo imaging, as superparamagnetic or negative contrast agents, drug carriers, heavy metal adsorbents, and magnetically recoverable photocatalysts for degradation of organic pollutants. This review article is mainly focused on fabrication of magnetic polymer nanocomposites and their applications. Different types of magnetic nanoparticles, methods of their synthesis, properties, and applications have also been reviewed briefly. The review also provides detailed insight into various types of magnetic nanocomposites and their synthesis. Diverse applications of magnetic nanocomposites including environmental and biomedical uses have been discussed.

Magnetic polymer nanocomposites for environmental and biomedical applications

A digital micromirror device (DMD) is a product of micromechanics. The DMD employs numerous micromirrors as the actuating components to switch small portions of light on and off. During the past few decades, such devices have been widely applied in digital light processing technology. The expanding range of applications makes the DMD increasingly important in various research aspects. Recent advances demonstrate that the DMD is potentially better than the traditional liquid crystal spatial light modulator in speed, spectrum sensitivity, and polarization modulation. These characteristics have been verified in a series of recently reported experiments. This review summarizes the related theory, experimental techniques, and applications for wavefront shaping with DMDs in both statically shaping various spatial modes and dynamically compensating for wavefront distortion caused by the scattering medium.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/andp.201500111

Tailoring light with a digital micromirror device

The diverse vision systems found in nature can provide interesting design inspiration for imaging devices, ranging from optical subcomponents to digital cameras and visual prostheses, with more desirable optical characteristics compared to conventional imagers. The advantages of natural vision systems include high visual acuity, wide field of view, wavelength-free imaging, improved aberration correction and depth of field, and high motion sensitivity. Recent advances in soft materials, ultrathin electronics, and deformable optoelectronics have facilitated the realization of novel processes and device designs that mimic biological vision systems. This review highlights recent progress and continued efforts in the research and development of bioinspired artificial eyes. At first, the configuration of two representative eyes found in nature: a single-chambered eye and a compound eye, is explained. Then, advances in bioinspired optic components and image sensors are discussed in terms of materials, optical/mechanical designs, and integration schemes. Subsequently, novel visual prostheses as representative application examples of bioinspired artificial eyes are described.

Bioinspired Artificial Eyes: Optic Components, Digital Cameras, and Visual Prostheses

The natural compound eye has received much attention in recent years due to its remarkable properties, such as its large field of view (FOV), compact structure, and high sensitivity to moving objects. Many studies have been devoted to mimicking the imaging system of the natural compound eye. The paper gives a review of state-of-the-art artificial compound eye imaging systems. Firstly, we introduce the imaging principle of three types of natural compound eye. Then, we divide current artificial compound eye imaging systems into four categories according to the difference of structural composition. Readers can easily grasp methods to build an artificial compound eye imaging system from the perspective of structural composition. Moreover, we compare the imaging performance of state-of-the-art artificial compound eye imaging systems, which provides a reference for readers to design system parameters of an artificial compound eye imaging system. Next, we present the applications of the artificial compound eye imaging system including imaging with a large FOV, imaging with high resolution, object distance detection, medical imaging, egomotion estimation, and navigation. Finally, an outlook of the artificial compound eye imaging system is highlighted.

https://iopscience.iop.org/article/10.1088/1748-3190/aaffb5/pdf

Review of state-of-the-art artificial compound eye imaging systems.

An artificial compound eye system is the bionic system of natural compound eyes with much wider field-of-view, better capacity to detect moving objects and higher sensitivity to light intensity than ordinary single-aperture eyes. In recent years, renewed attention has been paid to the artificial compound eyes, due to their better characteristics inheriting from insect compound eyes than ordinary optical imaging systems. This paper provides a comprehensive survey of the state-of-the-art work on artificial compound eyes. This review starts from natural compound eyes to artificial compound eyes including their system design, theoretical development and applications. The survey of artificial compound eyes is developed in terms of two main types: planar and curved artificial compound eyes. Finally, the most promising future research developments are highlighted.

Artificial compound eye: a survey of the state-of-the-art

In this paper, we will analyze the performance limit for a multiple sensor system (MSS) based on compressive sensing. In our MSS, all of the sensors measure signals from a common source. There exists the redundancy in the measured signal because the measured signal comes from the common source. To reduce communication costs, this redundancy must be removed. For this purpose, we use compressive sensing at each sensor to obtain compressed measurements. After all of the sensors obtain compressed measurements, they transmit them to a central unit. A decoder at the central unit receives all of the transmitted signals and attempts to jointly estimate the correct support set, which is the set of indices corresponding to the locations of the non-zero coefficients of the measured signals. In order to analyze our MSS, we present a jointly typical decoder inspired by recent work [4]. We first obtain the upper bound probability that the jointly typical decoder fails to estimate the correct support set. Next, we prove that as the number of sensors increases, the compressed measurements per sensor (per-sensor measurements) can be reduced to sparsity, which is the number of non-zero coefficients in the measured signal. We present the sufficient number of sensors required with the increase in the noise variance.

Study on performance behavior of compressive sensing measurements for multiple sensor system

The input numerical aperture (NA) of multimode fiber (MMF) can be effectively increased by placing turbid media at the input end of the MMF. This provides the potential for high-resolution imaging through the MMF. While the input NA is increased, the number of propagation modes in the MMF and hence the output NA remains the same. This makes the image reconstruction process underdetermined and may limit the quality of the image reconstruction. In this paper, we aim to improve the signal to noise ratio (SNR) of the image reconstruction in imaging through MMF. We notice that turbid media placed in the input of the MMF transforms the incoming waves into a better format for information transmission and information extraction. We call this transformation as holistic random (HR) encoding of turbid media. By exploiting the HR encoding, we make a considerable improvement on the SNR of the image reconstruction. For efficient utilization of the HR encoding, we employ sparse representation (SR), a relatively new signal reconstruction framework when it is provided with a HR encoded signal. This study shows for the first time to our knowledge the benefit of utilizing the HR encoding of turbid media for recovery in the optically underdetermined systems where the output NA of it is smaller than the input NA for imaging through MMF.

Holistic random encoding for imaging through multimode fibers

Sphere decoding (SD) is a promising detection strategy for multiple-input multiple-output (MIMO) systems because it can achieve maximum-likelihood (ML) detection performance with a reasonable complexity. The standard and most SD algorithms operate on real valued systems. These real valued SDs (RV-SDs) are known to be applicable to MIMO communication systems with only rectangular QAM signal constellations. In addition to this restriction on applicable constellations, RV-SDs are not suitable for VLSI implementations. Complex valued SD (CV-SD) is a good SD candidate for its flexibility on the choice of constellations and its efficiency in VLSI implementations. But, the low complexity CV-SD algorithm for general two dimensional (2D) constellations is not available, especially with the one that attains the ML performance. In this paper, we present a low complexity CV-SD algorithm, referred to as Circular Sphere Decoding (CSD) which is applicable to arbitrary 2D constellations. CSD provides a new constraint test. This constraint test is carefully designed so that the element-wise dependency is removed in the metric computation for the test. As a result, the constraint test becomes simple to perform without restriction on its constellation structure. By additionally employing this simple test as a prescreening test, CSD reduces the complexity of the CV-SD search. We show that the complexity reduction is significant while its ML performance is not compromised. We also provide a powerful tool to estimate the pruning capacity of any particular search tree. Using this tool, we propose the Predict-And-Change (PAC) strategy which leads to a further considerable complexity reduction in CSD.

Hwanchol Jang

Papers

Study on performance behavior of compressive sensing measurements for multiple sensor system

Holistic random encoding for imaging through multimode fibers

Complex Valued Sphere Decoding with Element-wise Selective Prescreening for General Two-dimensional Signal Constellations.