Graphene’s efficiency in providing plasmonics media for controlling light at nanoscale gives the necessary response at picosecond time ranges. A high-mobility grapheme monolayer is utilized to activate plasmons is one of the most promising to produce infrared radiation. The suggested methods are introduced in order to control the plasmonic media in metal-based plasmonics for nano-scale light manipulating. The electronic and optical properties of elemental metals are difficult to modify, especially by external means.
THz Identification is a developing technology. Sensing in the THz range potentially gives opportunity for short range
radar sensing because THz waves can better penetrate through obscured atmosphere, such as fog, than visible light. The
lower scattering of THz as opposed to the visible light results also in significantly better imaging than in IR spectrum. A
much higher contrast can be achieved in medical trans-illumination applications than with X-rays or visible light. The
same THz radiation qualities produce better tomographical images from hard surfaces, e.g. ceramics. This effect comes
from the delay in time of reflected THz pulses detection. For special or commercial applications alike, the industrial
quality control of defects is facilitated with a lower cost. The effectiveness of THz wave measurements is increased with computational methods. One of them is Bayes modeling. Examples of this kind of mathematical modeling are considered.
Proc. SPIE. 9476, Automatic Target Recognition XXV
KEYWORDS: Visual process modeling, Detection and tracking algorithms, Data modeling, Sensors, Digital filtering, Control systems, Target recognition, Dynamical systems, Filtering (signal processing), Data fusion
The present approach combines data fusion from several sensor types to enhance the overall detection and classification performance. The fusion of different sensors is implemented at data and feature levels that results in enhanced target identification by the means of spatial spectral analysis.
Proc. SPIE. 9476, Automatic Target Recognition XXV
KEYWORDS: Detection and tracking algorithms, Data modeling, Graphene, Sensors, Digital filtering, Electronic filtering, Algorithm development, Systems modeling, Filtering (signal processing), Data fusion
Recent development of a new 2D material graphene necessitates sample characterization (in particular localization and distribution of defects). The presence of defects is unavoidable, however, it is possible to determine and predict defect distribution in graphene samples prior to the actual device making. A ramification algorithm is used for the above purpose.
If we assume that a natural image can be modeled as a succession of a multilevel system, we can develop an optimal
routine of a matrix superposition. Each matrix separates the fundamental elements by a set of optimal criteria. The
matrix superposition is then characterized by a tree-based principle which is applied adaptively. We will also
demonstrate how the missing data constrains may be overcome by collecting additional measurements.
Recently, substantial efforts have been made to find an alternative approach to the Shannon sampling theorem with a method that can deal with large data sets, something for which the Shannon theorem is not easily applicable. If applied, the above approach would have to surmount difficult computational problems resulting from large data. In order to deal with the large data sets, we avoid a universal image acquisition and use wavelet matrices based on tree structures. The proposed approach allows a calculation reduction that yields a better control over the compressed image quality. The suggested technique also advocates a selective approach over the non-adaptive, random functions favored by the Shannon sampling theorem.
A recursive algorithm based on hidden Markov models is used to build a model of the identification target. The end
result of the recursive matching is an optimal scene-to-model transformation, along with a recognition degree of
suitability value between the scene and the model. The hierarchical structure of the model allows a maximization of the
target identification probability.
An algorithm is proposed for nonlinear and non-stationary processes concerning ATR. The general approach is to
decompose a complex task into multiple domains in space and time based on predictability of the object modification
dynamics. The model is composed of multiple modules, each of which consists of a state prediction model and
correctional multivariate system. Prediction error function is used to weigh the outputs of multiple hierarchical levels.
This paper presents an approach related to automated recognition of small features of movable targets including fast
moving objects such as airplanes, etc. Small features recognition is a challenging problem in both fields: pattern
recognition of particular configurations and of complexes comprising a number of configurations. Specific target
details, although well characterized by their features are often arranged in an elaborated way which makes the
recognition task very difficult and welcomes new ideas (approaches). On the other hand, the variety of small characters
(features) is intrinsically linked to the technology development of the identified targets and is unavoidable. Due to the
complexity of possible technological designs, the feature representation is one of the key issues in optical pattern
recognition. A flexible hierarchical prediction modeling is proposed with application examples.
Terahertz imaging proves advantageous for metamaterials characterization since the interaction of THz radiation with
the metamaterials produces clear patterns of the material. Characteristic "finger prints" of the crystal structure help
locating defects, dislocations, contamination, etc. TDS-THz spectroscopy is one of the tools to control metamaterials
design and manufacturing. A computational technique is suggested that provides a reliable way of calculation of the
metamaterials structure parameters, spotting defects. Based on missing data analysis, the applied signal processing
facilitates a better quality image while compensating for partially absent information. Results are provided.
Proc. SPIE. 8391, Automatic Target Recognition XXII
KEYWORDS: Detection and tracking algorithms, Data modeling, Image segmentation, Image processing, Data processing, Signal processing, Target recognition, Automatic target recognition, Process modeling, Expectation maximization algorithms
Time series modeling is proposed for identification of targets whose images are not clearly seen. The model building
takes into account air turbulence, precipitation, fog, smoke and other factors obscuring and distorting the image. The
complex of library data (of images, etc.) serving as a basis for identification provides the deterministic part of the
identification process, while the partial image features, distorted parts, irrelevant pieces and absence of particular
features comprise the stochastic part of the target identification. The missing data approach is elaborated that helps the
prediction process for the image creation or reconstruction. The results are provided.
Active imaging (AI) is necessary for measuring parameters of the objects that do not give out or reflect a specific type
of radiation. AI systems offer a number of advantages over passive imaging systems that operate at visible through nearinfrared
wavelengths and usually rely on solar illumination. The reliability and precision of the target identification
depends on how the signal received from a sensor is processed. Often, obstacles or the imperfection of the sensors and
processing electronics cause loss of some of the information. The technique of processes with missing data is suggested
as part of time series prediction and analysis. Thus, the image may be reconstructed even if the necessary data is
partially absent in the input signal. The suggested method reduces the false alarm rate of the target identification.
Results are provided.
Proc. SPIE. 8023, Terahertz Physics, Devices, and Systems V: Advance Applications in Industry and Defense
KEYWORDS: Mathematical modeling, Data modeling, Data processing, Smoothing, Terahertz radiation, System identification, Signal detection, Material characterization, Time series analysis, Process modeling
Characterization of materials with THz waves is not trivial at the moment. Expensive and often bulky equipment,
usually laboratory (rather than portable) set-up realization, possible water content, low depth of penetration, etc. are
some typical problems. As a result, the desired characteristics of the material are not reliable and often difficult to
obtain. In this situation, computational methods may be helpful in alleviating the above difficulties. In the long run,
sophisticated mathematical model building may make THz characterization devices more suitable for field applications,
implementing portable THz devices and set-ups as well as minimizing the measurement error without repetitive
measurements. The computational methods based on time series analysis are described and results provided.
The characterization of anhydrous and hydrated forms of materials is of great importance to science and industry. Water
content poses difficulties for successful identification of the material structure by THz radiation. However, biological
tissues and hydrated forms of nonorganic substances still may be investigated by THz radiation. This paper outlines the
range of possibilities of the above characterization, as well as provides analysis of the physical mechanism that allows
or prevents penetration of THz waves through the substance. THz-TDS is used to measure the parameters of the
characterization of anhydrous and hydrated forms of organic and nonorganic samples. Mathematical methods (such as
prediction models of time-series analysis) are used to help identifying the absorption coefficient and other parameters of
interest. The discovered dependencies allow designing techniques for material identification/characterization (e.g. of
drugs, explosives, etc. that may have water content). The results are provided.
The reliability of the data analysis from sensors is the main factor for making the right decisions for target
recognition. Obviously, the reliability depends on the quality of the sensors and processing electronics. However, the
cases where the target is clearly determined are not numerous. More often, we receive partial and distorted images from
the sensors employed. Thus, we have a task of determining the correct initial image that was distorted by various factors
before and after it was detected by the sensors. The proposed approach is an adaptive intelligent system that uses
algorithms, renewable data base as well as the possibility of changing the detection system's parameters and modes of
operation depending on the signal received from the identified objects.
Recently evolved THz technology opens up more possibilities for identification and characterization of different
semiconductor crystal-based compounds. Since the THz waveform is essentially a direct manifestation of the crystal
domain structure, the multicycle THz generation methods allow measuring of geometrical parameters of semiconductor
internal structures as well as of dislocations and other structural defects. The above is useful for both characterization
and identification of semiconductor materials. Further, methods of THz characterization of II-VI, III-V as well as tinary
compounds are discussed. Computational techniques are suggested allowing the noise level reduction for the
Described is the principal of THz sensing and its implementation based on a newly discovered
possibility in a sub visible range to penetrate through various materials and to be absorbed by them to a various
extent. The advantage of the proposed solution stems from a greater degree of mobility of the sensor and its
ability to distinguish between different materials - the feature not attainable by the X-Ray apparatus. The
identification may be also more "diplomatic" since it does not involve "seeing through". Presented is the
description of the identification devices as well as the results of the measurements of the object of interest, and
the prospects of a further development of the proposed principle/device.
Nanoimprint is an emerging lithographic technology that promises high-throughput pattering of nanostructures. Based on the mechanical embossing principle, nanoimprint technique can achieve pattern resolutions beyond the limitations set by the light diffraction or beam scattering in other conventional techniques. The difficulty arises with the exact 900 setting of the mould above the wafer. Proposed is the method of achieving this perpendicularity by the means of the crystallographic properties of Si or GaAs and the matrix made of the above-mentioned materials.
In the recent years, multifarious devices, systems and applications working in the THz frequency domain have been brought to life. Many of them are meant for security and military purposes, such as non-invasive detection of explosives, weapons, biological and chemical agents, etc. The problem, however, is not only with the detection and ever-increasing accuracy of measurements but often with understanding of what is seen; discriminating between the objects and the materials they are made of. It seems especially important to create an automatic or semi-automatic system and thus release the operator from constant watching. The proposed solution is an adaptive intelligent system based on usage of THz waves as the probing signal by means of mathematical statistics. Time series analysis is one of the forms that is employed in this research. The adaptivity of the system to various objects under investigation is based on the data base installed as well as on the possibility of changing of the detection system's parameters and modes of operation depending on the signal received from the identified objects. In other words, the suggested method allows for the detection system to switch from, say, the metal object mode to the pharmaceutical one and so forth.
The necessity to control certain areas from outside intrusion or, vice versa, preventing subjects/objects (e.g. prisoners) from leaving a controlled area has brought to life numerous designs of surveillance systems for the above-mentioned tasks. Fibers, laser beams, microwaves, etc have been used for decades to provide an alarm signal, should anyone or anything cross a light, radio beam or break a fiber. However, it is difficult to distinguish a stray animal from a human being, or even a snow ball from the first two using the conventional surveillance designs. False alarms render practically useless the above means, especially for field applications. It is possible, nonetheless, to set up an automatic system that discriminates objects/subjects crossing the control line/perimeter - a statistical approach which includes time series analysis is proposed as a solution for the problem.
Optical coherence tomography (OCT) is a method of high-resolution imaging originally developed for the transparent tissue of the eye. Recently, the technology has been advanced to such an extent that imaging of nontransparent tissues has become feasible as well. However, new challenges have surfaced: one of them- detection of the weak signal with high intensity background noise. The common approach of using Lock-in amplifiers (as well as some other techniques proposed) is not sufficiently effective or not effective at all. A
solution to this problem has risen in the form of a resonant amplifier when the frequency of the response is known. The principle of such an amplifier and its application are discussed below.