In automatic target recognition systems based on the use of inverse synthetic aperture radar (ISAR) images, it is essential to obtain unbiased and accurate scaled two-dimensional target images in the range-cross range domain. To accomplish this, the modulus of the target effective rotation vector, which is generally unknown for noncooperative targets, must be estimated. This letter proposes an efficient method for estimating the cross-range scaling factor and significantly improving cross-range resolution based on the second-order local polynomial Fourier transform. The estimation requires solving a series of one-dimensional optimizations of a kurtosis objective. Simulations show the proposed approach to be effective and able to accurately estimate the scaling factor in the presence of noise.
Machine vision has long been an important topic of study ever since electronic image sensors were developed. One of the main problems in machine vision is achieving a reliable estimate of velocity for objects of interest within the sensor's visual field. Traditionally, estimating velocity based on full correlations has been impractical due to hefty computational requirements. As a result, most vision chips have adopted simplified models, such as the Reichardt correlator. With advances in digital microelectronics and mixed signal techniques, there appears to be an opportunity for the development of a velocity estimation chip which performs full correlation-based velocity estimates. This paper presents an algorithmic investigation into the feasibility of such a scheme. In our proposed approach, the image information is encoded using a non-linear, over-sampled single-bit representation. Correlation computations are performed on this over-sampled signal, with the reduced precision of the single-bit representation providing a trade-off against the increased sampling rate. The quality of the achievable velocity estimates are evaluated against correlators operating on conventional image sensors, using rotated natural panoramic image sequences as input. Preliminary results suggest the proposed scheme to provide a reasonable estimations of velocity, with the potential advantage of requiring very simple logical circuits.
Terahertz radiation or T-rays, show promise in quality control of food products. As T-rays are inherently sensitive to water, they are very suitable for moisture detection. This proves to be a valuable asset in detecting the moisture content of dried food, a critical area for some products. As T-rays are transparent to plastics, food
additives can also be probed through the packaging, providing checks against a manufacturer's claims, such as the presence of certain substances in foods.
KEYWORDS: Terahertz radiation, Signal to noise ratio, Wavelets, Spectroscopy, Statistical modeling, System identification, Absorption, Signal processing, Signal attenuation, Classification systems
This work compares classification results of lactose, mandelic acid and dl-mandelic acid, obtained on the basis of their
respective THz transients. The performance of three different pre-processing algorithms applied to the time-domain
signatures obtained using a THz-transient spectrometer are contrasted by evaluating the classifier performance. A range
of amplitudes of zero-mean white Gaussian noise are used to artificially degrade the signal-to-noise ratio of the time-domain
signatures to generate the data sets that are presented to the classifier for both learning and validation purposes.
This gradual degradation of interferograms by increasing the noise level is equivalent to performing measurements
assuming a reduced integration time. Three signal processing algorithms were adopted for the evaluation of the complex
insertion loss function of the samples under study; a) standard evaluation by ratioing the sample with the background
spectra, b) a subspace identification algorithm and c) a novel wavelet-packet identification procedure. Within class and
between class dispersion metrics are adopted for the three data sets. A discrimination metric evaluates how well the three
classes can be distinguished within the frequency range 0.1 - 1.0 THz using the above algorithms.
Terahertz (THz) radiation has many far reaching applications - of specific interest is that many non-metallic
and non-polar substances are transparent in the THz frequency range. This provides many practical uses
for security purposes, where it is possible to detect and determine various substances that may be hidden or
undetectable via conventional methods such as X-rays. In addition to this property, terahertz radiation can
either be used in reflection or transmission modes.
This paper will look into the use of transmission techniques to detect various substances using a terahertz
system. Common materials used in bags and suitcases such as nylon, polycarbonate (PC), and polyethylene
(PE) are tested for transparency. These materials then sandwich various illicit substances, and are scanned
by the terahertz system to obtain spectral data, simulating the probing of a suitcase. The sample materials
are then subtracted from the obtained data, which is then compared with previously obtained data of known
substances, and an examination of features in the sample is carried out to determine if a particular substance
is present in the sample.
This study investigates binary and multiple classes of classification via support vector machines (SVMs). A couple of groups of two dimensional features are extracted via frequency orientation components, which result in the effective classification of Terahertz (T-ray) pulses for discrimination of RNA data and various powder samples. For each classification task, a pair of extracted feature vectors from the terahertz signals corresponding to each class is viewed as two coordinates and plotted in the same coordinate system. The current classification method extracts specific features from the Fourier spectrum, without applying an extra feature extractor. This method shows that SVMs can employ conventional feature extraction methods for a T-ray classification task. Moreover, we discuss the challenges faced by this method. A pairwise classification method is applied for the multi-class classification of powder samples. Plots of learning vectors assist in understanding the classification task, which exhibit improved clustering, clear learning margins, and least support vectors. This paper highlights the ability to use a small number of features (2D features) for classification via analyzing the frequency spectrum, which greatly reduces the computation complexity in achieving the preferred classification performance.
Terahertz transmission through freshly excised biological tissue is limited by the tissue's high water content.
Tissue fixation methods that remove water, such as fixation in Formalin, destroy the structural information
of proteins hence are not suitable for THz applications. Dehydration is one possible method for revealing the
tissue's underlying molecular structure and components. In this study, we measured the THz responses over time
of dehydrating fresh, necrotic and lyophilized rat tissue. Our results show that as expected, THz absorption
increases dramatically with drying and tissue freshness can be maintained through lyophilization. Dehydrated
biological tissue with retained molecular structure can be useful for future laser-based THz wave molecular
analysis.
Low density parity check decoders use computation nodes with multioperand adders on their critical path. This
paper describes the design of estimating multioperand adders to reduce the latency, power and area of these
nodes. The new estimating adders occasionally produce inaccurate results. The effect of these errors and the
subsequent trade-off between latency and decoder frame error rate is examined. For the decoder investigated it
is found that the estimating adders do not degrade the frame error rate.
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