Volume Holographic Hyperspectral Imaging

A volume hologram has two degenerate Bragg-phase-matching dimensions and provides the capability of volume holographic imaging. We demonstrate two volume holographic imaging architectures and investigate their imaging resolution, aberration, and sensitivity. The first architecture uses the hologram directly as an objective imaging element where strong aberration is observed and confirmed by simulation. The second architecture uses an imaging lens and a transmission geometry hologram to achieve linear two-dimensional optical sectioning and imaging of a four-dimensional (spatial plus spectral dimensions) object hyperspace. Multiplexed holograms can achieve simultaneously three-dimensional imaging of an object without a scanning mechanism

Improved processing of microarray data using image reconstruction techniques

Spotted cDNA microarray data analysis suffers from various problems such as noise from a variety of sources, missing data, inconsistency, and, of course, the presence of outliers. This paper introduces a new method that dramatically reduces the noise when processing the original image data. The proposed approach recreates the microarray slide image, as it would have been with all the genes removed. By subtracting this background recreation from the original, the gene ratios can be calculated with more precision and less influence from outliers and other artifacts that would normally make the analysis of this data more difficult. The new technique is also beneficial, as it does not rely on the accurate fitting of a region to each gene, with its only requirement being an approximate coordinate. In experiments conducted, the new method was tested against one of the mainstream methods of processing spotted microarray images. Our method is shown to produce much less variation in gene measurements. This evidence is supported by clustering results that show a marked improvement in accuracy

Low-complexity RLS algorithms using dichotomous coordinate descent iterations

In this paper, we derive low-complexity recursive least squares (RLS) adaptive filtering algorithms. We express the RLS problem in terms of auxiliary normal equations with respect to increments of the filter weights and apply this approach to the exponentially weighted and sliding window cases to derive new RLS techniques. For solving the auxiliary equations, line search methods are used. We first consider conjugate gradient iterations with a complexity of O(N-2) operations per sample; N being the number of the filter weights. To reduce the complexity and make the algorithms more suitable for finite precision implementation, we propose a new dichotomous coordinate descent (DCD) algorithm and apply it to the auxiliary equations. This results in a transversal RLS adaptive filter with complexity as low as 3N multiplications per sample, which is only slightly higher than the complexity of the least mean squares (LMS) algorithm (2N multiplications). Simulations are used to compare the performance of the proposed algorithms against the classical RLS and known advanced adaptive algorithms. Fixed-point FPGA implementation of the proposed DCD-based RLS algorithm is also discussed and results of such implementation are presented

Theory of the perceived motion direction of equal-spatial-frequency plaid stimuli.

At an early stage, 3 different systems independently extract visual motion information from visual inputs. At later stages, these systems combine their outputs. Here, we consider a much studied (>650 publications) class of visual stimuli, plaids, which are combinations of 2 sine waves. Currently, there is no quantitative theory that can account for the perceived motion of plaids. We consider only perceived plaid direction, not speed, and obtain a large set of data exploring the various dimensions in which same-spatial-frequency plaids differ. We find that only 2 of the 3 motion systems are active in plaid processing, and that plaids with temporal frequencies 10 Hz or greater typically stimulate only the first-order motion system, which combines the plaid components by vector summation: Each plaid component is represented by a contrast-strength vector whose length is contrast-squared times a factor representing the relative effectiveness of that component's temporal frequency. The third-order system, which becomes primary at low temporal frequencies, also represents a plaid as 2 vectors that sum according to their contrast strength: a pure plaid in which both components have equal contrast and a residual sine wave. Second-order motion is irrelevant for these plaids. These principles enable a contrast-strength-vector summation theory for the responses of the first-order and third-order motion systems. With zero parameters estimated from the data, the theory captures the essence of the full range of the plaid data and supports the counterintuitive hypothesis that motion direction is processed independently of speed at early stages of visual processing. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

Autonomous dexterous end-effectors for space robotics

The development of a knowledge-based controller is summarized for the Belgrade/USC robot hand, a five-fingered end effector, designed for maximum autonomy. The biological principles of the hand and its architecture are presented. The conceptual and software aspects of the grasp selection system are discussed, including both the effects of the geometry of the target object and the task to be performed. Some current research issues are presented