The standard Findlay Clay Analysis cannot be applied to diode side-pumped Nd:YAG lasers because both the pump wavelength as well as the gain change with the diode current and the cooling water temperature. We have developed a modified method which is based on the variation of the cooling water temperature to determine the lowest threshold for each output coupler. We have applied this technique to a 300 Watt class side Nd:YAG rod laser. The resulting Findlay Clay plot exhibits very good linearity and the measured gain and loss were confirmed by comparing measured output power at different cooling water temperatures with theoretical values provided by a Rigrod power model.
The goal of this study is to demonstrate how a DPSS laser beam's quality parameters can be simultaneously
optimized through pump current tuning. Two DPSS lasers of the same make and model were used where the laser
diode pump current was first varied to ascertain the lowest RMS noise region. The lowest noise was found to be
0.13% in this region and the best M2 value of 1.0 and highest laser output power were simultaneously attained at the
same current point. The laser manufacturer reported a M2 value of 1.3 and RMS noise value of .14% for these
lasers. This study therefore demonstrates that pump current tuning a DPSS laser can simultaneously optimize RMS
Noise, Power and M2 values. Future studies will strive to broaden the scope of the beam quality parameters impacted
by current tuning.
The goal of this study is to demonstrate how a planar ensemble of Gaussian beams (TEM00) can be
modeled to create flattop beams for laser applications. We employed the Gaussian Beam Propagation
Method (GBPM) in the ASAP (BRO, Tucson AZ) computing and visualization environment in this
endeavor. Our study revealed that ensemble individual beam's widths must be set at 1.6 mm, half-angle
divergence cannot exceed .5 degrees. In addition, individual laser positions in the ensemble must be such
that all ensemble Gaussian beams reach the target/detector in phase. Such beam manipulations are critical
for many applications as they strive to customize industry de facto Gaussian laser beams into flattops. In
addition, our study has demonstrated that beam flatness is achievable without the aid of dissipative optical
An ad-hoc group of has "self-assembled" in the Silicon Valley for the purpose of generating the wonder and excitement of science in young people, via optics. A systematic approach to science outreach is presented, and sample projects will be described, such as the Science NanoGrant project.
The goal of this study is to test the feasiblity of directly using molecular descriptor data to generate clusters of similar molecules. We have developed an approach that utilizes the "most orthogonal" molecular descriptor variables as a basis set for clustering. In this study we have specifically utilized normal skin tissue and melanoma cancer data derived via Fourier transform infrared (FTIR) spectroscopy to generate these clusters, but the approach presented should be applicable to any other molecular descriptor or response data. Using the three most orthogonal FTIR frequencies as a basis set for cluster analysis, normal skin and melanoma tumors' clusters were resolved and localized in the three-dimensional variable/frequency space. Such clusters can be used to rapidly identify molecules with similar structures, and biological activity given their physico-chemical descriptors or molecular response data. This study also points out possible fallacies when inspecting clusters and how they can be avoided.
The goal of this study is to test the feasibility of using noise factor/eigenvector bands as general clinical analytical tools for diagnoses. We developed a new technique, Noise Band Factor Cluster Analysis (NBFCA), to diagnose benign tumors via their Fourier transform IR fiber optic evanescent wave spectral data for the first time. The middle IR region of human normal skin tissue and benign and melanoma tumors, were analyzed using this new diagnostic technique. Our results are not in full-agreement with pathological classifications hence there is a possibility that our approaches could complement or improve these traditional classification schemes. Moreover, the use of NBFCA make it much easier to delineate class boundaries hence this method provides results with much higher certainty.
Chemical Factor Analysis (CFA) algorithms were applied to transform complex Fourier transform infrared fiberoptical evanescent wave (FTIR-FEW) normal and malignant skin tissue spectra into factor spaces for analysis and classification. The factor space approach classified melanoma beyond prior pathological classifications related to specific biochemical alterations to health states in cluster diagrams allowing diagnosis with more biochemical specificity, resolving biochemical component spectra and employing health state eigenvector angular configurations as disease state sensors. This study demonstrated a wealth of new information from in vivo FTIR-FEW spectral tissue data, without extensive a priori information or clinically invasive procedures. In particular, we employed a variety of methods used in CFA to select the rank of spectroscopic data sets of normal benign and cancerous skin tissue. We used the Malinowski indicator function (IND), significance level and F-Tests to rank our data matrices. Normal skin tissue, melanoma and benign tumors were modeled by four, two and seven principal abstract factors, respectively. We also showed that the spectrum of the first eigenvalue was equivalent to the mean spectrum. The graphical depiction of angular disparities between the first abstract factors can be adopted as a new way to characterize and diagnose melanoma cancer.
Fourier transform infrared fiberoptic evanescent wave (FTIR- FEW) spectra, in the middle infrared (MIR) region, of human normal skin tissue, and melanoma and benign tumors were analyzed using chemical factor analysis (CFA). The first step in CFA is to determine the rank of the factor space and in this study several model validation techniques were employed. In particular we compare results obtained from Complete Cross Validation (CCV), Binary Cross Validation (BCV), Fisher variance ratios (F-Tests), Malinowiski indicator function (IND) and significance level (%SL). All methods' results were in agreement expect for F-Tests which differed with the other methods for normal skin tissue and melanoma tumors' rank. Using the two highest ranking eigenvectors for normal skin tissue as a basis set for cluster analysis, normal skin and melanoma tumors' clusters were well separated and localized in the two dimensional factor space. The projection of benign tumors' spectral points in this factor space revealed that some of the benign tumors had already regressed into either cancerous and other abnormal skin states. Furthermore the angular disparity between normal skin tissue and melanoma tumors' eigenvectors was successfully used to discriminate between the two skin states.
Fiberoptical evanescent wave Fourier transform infrared (FEW- FTIR) spectroscopy has been applied in the middle infrared (MIR) wavelength range (3 to 20 micrometer) to the in vivo diagnostics of normal skin tissue, acupuncture points as well as precancerous and cancerous conditions. The FTIR-FEW technique, using nontoxic unclad fibers, is suitable for noninvasive, sensitive investigations of skin tissue for various dermatological studies of skin caner, aging, laser treatment, cosmetics, skin allergies, etc. This method is direct, nondestructive, and fast (seconds). Our optical fibers are nonhygroscopic, flexible, and characterized by extremely low losses. In this study, we have noninvasively investigated more than 300 cases of normal skin, acupuncture points, precancerous and cancerous tissue in the range of 1400 to 1800 cm-1. The results of our analysis of skin and other tissue are discussed in terms of structural and mathematical similarities and differences on a molecular level. In addition, we have also performed cluster analysis, using principal component scores, to confirm pathological classifications and to discriminate between genders. We have found good agreement with prior pathological classifications for normal skin tissue and melanoma tumors and normal females were distinctly separate from males.
For characterization of skin cancer, an artificial neural network method has been developed to diagnose normal tissue, benign tumor and melanoma. The pattern recognition is based on a three-layer neural network fuzzy learning system. In this study, the input neuron data set is the Fourier transform IR spectrum obtained by a new fiberoptic evanescent wave Fourier transform IR spectroscopy method in the range of 1480 to 1850 cm-1. Ten input features are extracted from the absorbency values in this region. A single hidden layer of neural nodes with sigmoids activation functions clusters the feature space into small subclasses and the output nodes are separated in different nonconvex classes to permit nonlinear discrimination of disease states. The output is classified as three classes: normal tissue, benign tumor and melanoma. The results obtained from the neural network pattern recognition are shown to be consistent with traditional medical diagnosis. Input features have also been extracted from the absorbency spectra using chemical factor analysis. These abstract features or factors are also used in the classification.
A new Fourier transform infrared fiberoptic evanescent wave (FTIR-FEW) spectroscopy method has been developed for tissue diagnostics in the middle infrared (MIR) wavelength range (3 to 20 micrometers). Specific novel fiberoptical chemical and biological sensors have been studied and used for spectroscopic diagnostic purposes. These nontoxic and nonhygroscopic fiber sensors are characterized by (1) low optical losses (0.05 to 0.2 dB/m at about 10 micrometer) and (2) high flexibility. Our new fiber optical devices can be utilized with standard commercially available Fourier transform spectrometers including attenuated total reflection (ATR) techniques. They are in particular ideally suited for noninvasive, fast, direct, sensitive investigations of in vivo and ex vivo medical diagnostics applications. Here we present data on IR spectra of skin tissue in vivo for various cases of melanoma and nevus in the range of 1480 - 1800 cm-1. The interpretation of the spectra of healthy and different stages of tumor and cancer skin tissue clearly indicates that this technique can be used for precancer and cancer diagnostics. This technique can be designed for real-time and on-line computer modeling and analysis of tissue changes.
SC972: Basic Laser Technology: Fundamentals and Performance Specifications
If you are uncomfortable working with lasers as "black boxes" and would like to have a basic understanding of their inner workings, this introductory course will be of benefit to you. The workshop will cover the basic principles common to the operation of any laser/laser system. Next, we will discuss laser components and their functionality. Components covered will include laser pumps/energy sources, mirrors, active media, nonlinear crystals, and Q-switches. The properties of laser beams will be described in terms of some of their common performance specifications such as longitudinal modes and monochromaticity, transverse electromagnetic (TEM) modes and focusability, continuous wave (CW) power, peak power and power stability. Laser slope and wall-plug efficiencies will also be discussed.
This workshop will provide attendees with a basic understanding of laser beam performance specifications. Topics to be covered include Beam Pointing Stability, Polarization Ratio, RMS Noise, Peak-to-Peak Noise, Pulse Duration and Duty Cycle, Peak Power, Average Power, Pulse Repetition Rate, and M2. These specifications constitute the critical parameters that determine whether or not a laser, or laser system, will do the intended job.