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Ever wondered how understanding human behavior can make you smarter, faster, and harder to kill? Buckle up as we navigate through this thrilling conversation with Brian Marren and Greg Williams, the brains behind the 'Left of Greg' podcast. \Our chat extends beyond the military and law enforcement, touching on concepts that are applicable to everyday life. How can you use frequencies and de-escalation to better understand your behavior and those around you? How can the notion of 'duty to intervene' apply to you? We also discuss their business,  Arcadia Cognerati, aimed at facilitating learning about human behavior, pattern recognition, and analysis. Don't miss out on this captivating exploration of the human mind and behavior.Support the showMake sure to check out Jason on IG @drjasonpiccolo

Ever wondered how understanding human behavior can make you smarter, faster, and harder to kill? Buckle up as we navigate through this thrilling conversation with Brian Marren and Greg Williams, the brains behind the 'Left of Greg' podcast. \Our chat extends beyond the military and law enforcement, touching on concepts that are applicable to everyday life. How can you use frequencies and de-escalation to better understand your behavior and those around you? How can the notion of 'duty to intervene' apply to you? We also discuss their business,  Arcadia Cognerati, aimed at facilitating learning about human behavior, pattern recognition, and analysis. Don't miss out on this captivating exploration of the human mind and behavior.Support the showMake sure to check out Jason on IG @drjasonpiccolo

Ever wondered how understanding human behavior can make you smarter, faster, and harder to kill? Buckle up as we navigate through this thrilling conversation with Brian Marren and Greg Williams, the brains behind the 'Left of Greg' podcast. \Our chat extends beyond the military and law enforcement, touching on concepts that are applicable to everyday life. How can you use frequencies and de-escalation to better understand your behavior and those around you? How can the notion of 'duty to intervene' apply to you? We also discuss their business,  Arcadia Cognerati, aimed at facilitating learning about human behavior, pattern recognition, and analysis. Don't miss out on this captivating exploration of the human mind and behavior.Support the showMake sure to check out Jason on IG @drjasonpiccolo

In this paper we borrow concepts from Information Theory and Statistical Mechanics to perform a pattern recognition procedure on a set of x-ray hazelnut images. We identify two relevant statistical scales, whose ratio affects the performance of a machine learning algorithm based on statistical observables, and discuss the dependence of such scales on the image resolution. Finally, by averaging the performance of a Support Vector Machines algorithm over a set of training samples, we numerically verify the predicted onset of an optimal scale of resolution, at which the pattern recognition is favoured.

Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models. This distinction is based on the insight that brain state features, that are found to be relevant in an experimental paradigm, carry a different meaning in encoding- than in decoding models. In this paper, we argue that this distinction is not sufficient: Relevant features in encoding- and decoding models carry a different meaning depending on whether they represent causal- or anti-causal relations. We provide a theoretical justification for this argument and conclude that causal inference is essential for interpretation in neuroimaging.

This is a recorded reading of the article "Criminalizing Pattern Recognition" written by Gregory Hood and posted on AmRen.com on October 31, 2023. The article being read: https://www.amren.com/commentary/2023/10/criminalizing-pattern-recognition/ Links to the AmRen and other things referenced and pictured (in order): AmRen "GR" Archive: https://www.amren.com/tag/great-replacement/ Is American Renaissance on Google’s Blacklist?: https://www.amren.com/news/2020/06/is-american-renaissance-on-googles-blacklist/ How It Got This Bad: https://www.amren.com/features/2023/09/how-it-got-this-bad/ ^^^Video of an article narration of that article: https://youtu.be/MN2It0PaVow More of Gregory Hood's Articles: https://www.amren.com/author/jartaylor/ For more narrations like this, subscribe to this Substack (audio only): https://renaissancereadings.substack.com/?sd=pf Subscribe to AmRen's Rumble Channel: https://rumble.com/user/AmRen Subscribe to AmRen's Bitchute Channel: https://www.bitchute.com/channel/5Q4sa6rObtGx/ Subscribe to AmRen's Odysee Channel: https://odysee.com/@AmericanRenaissance:7 AmRen's Website: https://www.amren.com/ Jared Taylor's Gab: https://gab.com/jartaylor AmRen's Gab: https://gab.com/AmRenaissance AmRen's Telegram: https://t.me/amrenofficial Gregory Hood's AmRen Articles: https://www.amren.com/author/gregoryhood/ Subscribe to 'Fan of AmRen Podcasts' on YouTube for Jared Taylor/Gregory Hood/AmRen podcasts on YouTube: https://www.youtube.com/channel/UC32g0Qmze7LO7iR4MglqyaQ Subscribe to 'AmRen Article Narrations' on YouTube (which for now serves as a backup channel): https://www.youtube.com/channel/UCE9ViBAummkc-awushKWsYw Subscribe to 'AmRen Article Narrations' on Rumble, a nearly-perfect censorship-free platform: https://rumble.com/c/AmRenArticleNarrations Subscribe to 'AmRen Conference Fan' on Rumble for American Renaissance conference speeches from 2019 and earlier (which are not already uploaded on AmRen's Rumble channel): https://rumble.com/c/AmRenConferenceFan Subscribe to 'Jared Taylor Podcast Fan' on Rumble for Jared Taylor's great weekly podcast: https://rumble.com/c/JaredTaylorPodcastFan Subscribe to 'United Right Clips, Podcasts & Speeches' on Rumble for clips, podcasts, and speeches from a variety of right-wing/dissident-right people: https://rumble.com/c/UnitedRight Need something new to read? Here's a great list of book recommendations: *!!Check the Community Tab for the full book list (this list had to be shrunk down to fit in the description)!!* *As an Amazon Associate, I earn from qualifying purchases.* *Select New Books from 2020-2023* -'War on Whites' by Ed Brodow: https://amzn.to/46ohMTD (Amazon) -'The Origins of Woke' by Richard Hanania: https://amzn.to/3tBzV1R (Amazon) -'Untenable: The True Story of White Ethnic Flight from America's Cities' by Jack Cashill: https://amzn.to/3S9mvV7 (Amazon) *Jared Taylor's Books* -'If We Do Nothing' by Jared Taylor: https://store.amren.com/product/if-we-do-nothing/ (AmRen) -'White Identity: Racial Consciousness in the 21st Century' by Jared Taylor: https://store.amren.com/product/white-identity/ (AmRen) *Race* -'Social Justice Fallacies' by Thomas Sowell: https://amzn.to/3tw6Vsd (Amazon) -'The Diversity Delusion' by Heather Mac Donald: https://amzn.to/46UDeQ8 (Amazon) *Culture/Politics* -'Conservatism Inc.: The Battle for the American Right' by James Kirkpatrick: https://amzn.to/46FDlzm (Amazon) -'Domestic Extremist: A Practical Guide to Winning the Culture War' by Peachy Keenan: https://amzn.to/3txpcpc (Amazon) -'Irreversible Damage: The Transgender Craze Seducing Our Daughters' by Abigail Shrier: https://amzn.to/49d9hg7 (Amazon) *Immigration* -'Alien Nation' by Peter Brimelow: https://amzn.to/3M4OLV1 (Amazon) -'We Wanted Workers' by George Borjas: https://amzn.to/3QyBLtp (Amazon) *Philosophy/History* -'Always with Honor' by Pytor Wrangel: https://amzn.to/46MuTOL (Amazon) -'Catholic and Identitarian' by Julien Langella: https://amzn.to/472JyoD (Amazon) -'Churchill, Hitler, and 'The Unnecessary War': How Britain Lost Its Empire and the West Lost the World' by Pat Buchanan: https://amzn.to/3Qxu8Dv (Amazon) -'Leviathan and Its Enemies' by Sam Francis: https://amzn.to/3Q2eNZX (Amazon) -'Reflections on the Revolution in France' by Edmund Burke: https://amzn.to/3FkP1eM (Amazon) -'The Storm of Steel' by Ernst Jünger: https://amzn.to/3SjmRZ9 (Amazon) *Intelligence/Genetics* -'Breeding the Human Herd: Eugenics, Dysgenics and the Future of the Species' by Edward Dutton: https://amzn.to/45BCu17 (Amazon) -'Race Differences in Intelligence' by Richard Lynn: https://amzn.to/3MaH93d (Amazon) *American Indians* -'Not Stolen: The Truth About European Colonialism in the New World' by Jeff Fynn-Paul: https://amzn.to/3ZZJ547 (Amazon) *Southern/Confederate* -'The Honorable Cause: A Free South: Twelve Southern Essays' by Padraig Martin: https://amzn.to/3Q1K9QD (Amazon) *As an Amazon Associate, I earn from qualifying purchases.*

The selectivity of fluorescence spectroscopy is exploited for the characterization of marine algae. Two-dimensional, digital images of in vivo fluorescence intensity versus excitation and emission wavelengths, called excitation-emission matrices (EEMs), are used as spectral fingerprints for marine phytoplankton populations. Fourier-transform-based pattern recognition is described along with its inherent strengths and weaknesses for the analysis of natural populations. The EEMs of unknown algae are compared to a library of standard EEMs representing 23 algal species and 6 classes with better than 80% accuracy. The EEMs acquired under different physiological conditions are used in determining pattern recognition reliability. The potential for fingerprinting mixed populations and oceanographic regions is also discussed. Originator supplied keywords include: Fluorescence analysis; Marine analysis; Fourier data analysis; Pattern recognition; Chlorophyll fluorescence; Spectral matching.

50 p. 28 cm

50 p. 28 cm

50 p. 28 cm

50 p. 28 cm

Wavelet transform (WT) has recently drawn the attention of the researchers due to its potential in electromyography (EMG) recognition system. However, the optimal mother wavelet selection remains a challenge to the application of WT in EMG signal processing. This paper presents a detail study for different mother wavelet function in discrete wavelet transform (DWT) and continuous wavelet transform (CWT). Additionally, the performance of different mother wavelet in DWT and CWT at different decomposition level and scale are also investigated. The mean absolute value (MAV) and wavelength (WL) features are extracted from each CWT and reconstructed DWT wavelet coefficient. A popular machine learning method, support vector machine (SVM) is employed to classify the different types of hand movements. The results showed that the most suitable mother wavelet in CWT are Mexican hat and Symlet 6 at scale 16 and 32, respectively. On the other hand, Symlet 4 and Daubechies 4 at the second decomposition level are found to be the optimal wavelet in DWT. From the analysis, we deduced that Symlet 4 at the second decomposition level in DWT is the most suitable mother wavelet for accurate classification of EMG signals of different hand movements. 

line-001: various artists - pattern recognition part one style: ebm, dark electro, idm, ambient quality: mp3, 320 kbps, 44.1 kHz size: 118 mb release date: 20 april 2007

A three-step approach has been developed for detecting and classifying the foraging calls of the blue whale, Balaenoptera musculus, and fin whale, Balaenoptera physalus, in passive acoustic recordings. This approach includes a pattern recognition algorithm to reduce the effects of ambient noise and to detect the foraging calls. The detected calls are then classified as blue whale D-calls or fin whale 40-Hz calls using a machine learning technique, a logistic regression classifier. These algorithms have been trained and evaluated using the Detection, Classification, Localization, and Density Estimation (DCLDE) annotated passive acoustic data, which were recorded off the Central and Southern California coast from 2009 to 2013. By using the cross-validation method and DCLDE scoring tool, this research shows high out-of-sample performance for these algorithms, namely 96% recall with 92% precision for pattern recognition and 96% accuracy for the logistic regression classifier. The result was published by the Institute of Electrical and Electronics Engineers (2016). The advantages of this automated approach over traditional manual methods are reproducibility, known performance, cost-efficiency, and automation. This approach has the potential to conquer the challenges of detecting and classifying the foraging calls, including the analysis of large acoustic data sets and real-time acoustic data processing.

One potential approach to the quantitative acquisition of discriminatory information that can isolate a single structural state is pattern recognition. The pattern recognition characterizations of micromechanical and morphological materials states via analytical quantiative ultrasonics are outlined. The concepts, terminology, and techniques of statistical pattern recognition are reviewed. Feature extraction and classification and states of the structure can be determined via a program of ultrasonic data generation.

One potential approach to the quantitative acquisition of discriminatory information that can isolate a single structural state is pattern recognition. The pattern recognition characterizations of micromechanical and morphological materials states via analytical quantiative ultrasonics are outlined. The concepts, terminology, and techniques of statistical pattern recognition are reviewed. Feature extraction and classification and states of the structure can be determined via a program of ultrasonic data generation.

One potential approach to the quantitative acquisition of discriminatory information that can isolate a single structural state is pattern recognition. The pattern recognition characterizations of micromechanical and morphological materials states via analytical quantiative ultrasonics are outlined. The concepts, terminology, and techniques of statistical pattern recognition are reviewed. Feature extraction and classification and states of the structure can be determined via a program of ultrasonic data generation.

One potential approach to the quantitative acquisition of discriminatory information that can isolate a single structural state is pattern recognition. The pattern recognition characterizations of micromechanical and morphological materials states via analytical quantiative ultrasonics are outlined. The concepts, terminology, and techniques of statistical pattern recognition are reviewed. Feature extraction and classification and states of the structure can be determined via a program of ultrasonic data generation.

We propose a method based on finite mixture models for classifying a set of observations into number of different categories. In order to demonstrate the method, we show how the component densities for the mixture model can be derived by using the maximum entropy method in conjunction with conservation of Pythagorean means. Several examples of distributions belonging to the Pythagorean family are derived. A discussion on estimation of model parameters and the number of categories is also given.

Mr. Lee Recognition Of The Pattern 3 tracks of dark ambient & experimental drones tracktitles come from the book Immersion Into Noise (Joseph Nechvatal) 1. The Spectral Repetitions Of His Mental-machinic Procedures 2. Curved-space Is Approximately Euclidean Over Very Small Regions 3. An Information-loaded Nuclear Weapon Had Already Exploded, Showering Us With Bits Of Radioactive-like Informational Sound Bytes Each track of exactly eight minutes was made with four different handcrafted soundsources, no fx added afterwards, just fading in/out of each other and some eq. It took me about 4 hours to create this thing, making it the fastest finished album I ever made!! By doing this I finally have a slight notion of what anatta means... to be continued!

The pattern recognition code ARTHUR was tested on a limited cratering data base to examine its utility in studying complex continuous data. The code was found to be a convenient data analysis package but possessed no continuous data capability that is not already available to any researcher. (Author)

A new strapped-down system for on-board, real-time spacecraft attitude determination is discussed. The electro-optical system is capable of sub-ten-arc second precision with no moving parts. The light-sensitive element is an array-type Charged Coupled Device (CCD) having about 2 x 10 to the 5th power silicon pixels. Parallel, high speed analog circuits scan the pixels (row by row) to locate and A/D convert only those pixel response values (about 100 to 200 per scan) about a preset analog threshold. Angular rate measurements from conventional rate gyros are used to estimate motion continuously. Three intermittently communicating microcomputers operate in parallel to perform the functions: (i) star image centroid determination, (ii) star pattern identification and discrete attitude estimation (subsets of measured stars are identified as specific cataloged stars), (iii) optimal Kalman attitude motion estimation/integration. The system is designed to be self-calibrating with provision for routine updating of interlock angles, gyro bias parameters, and other system calibration parameters. For redundancy and improved precision, two optical ports are employed. This interim report documents Phase I of a three phase effort to research, develop, and laboratory test the basic concepts of this new system. Included in Phase I is definition, formulation, and test of the basic algorithms, including preliminary implementations and results from a laboratory microcomputer system. (Author)

Pattern Recognition Letters is an Active peer-reviewed academic Journal covering theoretical, methodological and empirical studies of image processing and pattern recognition. It was established in 1982 and is published 16 times a year by Elsevier. It is an official publication of the International Association for Pattern Recognition. The journal provides a platform for researchers, computer scientists, engineers, and practitioners to publish original research articles, review papers, and case studies that contribute to the understanding and development of pattern recognition techniques and address theoretical foundations, algorithmic innovations, experimental evaluations, and practical applications of pattern recognition. Coverage includes machine learning, computer vision, data mining, artificial intelligence, statistical pattern recognition, and pattern recognition in multimedia data. The journal encourages submissions that propose novel algorithms, methodologies, and frameworks for pattern analysis and classification as well as studies that demonstrate the practical use of pattern recognition techniques in areas such as image processing, biometrics, document analysis, speech recognition, and bioinformatics. The Internet Archive Collection contains microfilm published between 1992 and 1996 Pattern Recognition Letters can be found online at: www.sciencedirect.com/journal/pattern-recognition-letters Wikipedia: wikipedia.org/wiki/Pattern_Recognition_Letters The ISSN is: 0167-8655

Pattern Recognition Letters is an Active peer-reviewed academic Journal covering theoretical, methodological and empirical studies of image processing and pattern recognition. It was established in 1982 and is published 16 times a year by Elsevier. It is an official publication of the International Association for Pattern Recognition. The journal provides a platform for researchers, computer scientists, engineers, and practitioners to publish original research articles, review papers, and case studies that contribute to the understanding and development of pattern recognition techniques and address theoretical foundations, algorithmic innovations, experimental evaluations, and practical applications of pattern recognition. Coverage includes machine learning, computer vision, data mining, artificial intelligence, statistical pattern recognition, and pattern recognition in multimedia data. The journal encourages submissions that propose novel algorithms, methodologies, and frameworks for pattern analysis and classification as well as studies that demonstrate the practical use of pattern recognition techniques in areas such as image processing, biometrics, document analysis, speech recognition, and bioinformatics. The Internet Archive Collection contains microfilm published between 1992 and 1996 Pattern Recognition Letters can be found online at: www.sciencedirect.com/journal/pattern-recognition-letters Wikipedia: wikipedia.org/wiki/Pattern_Recognition_Letters The ISSN is: 0167-8655

LBP-like variants are used for texture recognition from an image. Several algorithms have been published through- out the years to improve the texture. In this paper, an extended version of the Local Directional Ternary Pattern (ELDTP) is proposed where the LDP and LTP descriptors are combined and extended to achieve using enhanced adjacent neighbor for better and more accurate textures. Moreover, we maintain color information, therefore, the proposed EALDTP works for color images rather than traditional LBP variants. To testify the performance, we have done several experiments on two different texture datasets KTH-TIPs and KTH-TIPS-2b.

(Information science and statistics) Christopher M. Bishop - Pattern Recognition and Machine Learning-Springer (2006)

Myoelectric pattern recognition (MPR) is used to detect user’s intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (kNN).

Myoelectric pattern recognition (MPR) is used to detect user’s intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (kNN).

Myoelectric pattern recognition (MPR) is used to detect user’s intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (kNN).

Philip Morris Records; abstract; bibliography; chart; computer printout; graph; report; extr, extra

If V(R) is the vertex set of a symmetric cycle R in the tope graph of a simple oriented matroid M, then for any tope T of M there exists a unique inclusion-minimal subset Q(T;R) of V(R) such that T is the sum of the topes of Q(T;R). If |Q(T;R)|>3, then the decomposition Q(T;R) of the tope T with respect to the symmetric cycle R satisfies certain Dehn-Sommerville type relations.

The primary goal of this research is to study a programmable joint- transform correlator (JTC) using liquid crystal television (LCTV) panels for adaptive realtime pattern recognition applications. The technique can improve the pattern recognition and identification technology that is of interest to the U.S. Army. The technique we studied is a real-time programmable electro-optical architecture. There are several reasons for selecting the optical technique over their digital and electronic counterparts, as follows: Optical technique is capable of handling a large space-bandwidth image; optical technique is capable of performing parallel operations; optics can perform massive interconnections; optical transformation can be operated at high speed, etc. By using the LCTV, the pattern under observation can be correlated with a large number of recallable image memories. In addition, the LCTV technique is rather simple and economical to operate. The LCTV-optical correlator, in principle, can be designed into a compact portable form for insitu application. Brief outlines of the major research findings and publications are provided in this report.... Joint transform correlator, Liquid crystal television, Correlator, Optical pattern recognition.

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This monograph deals with mathematical constructions that are foundational in such an important area of data mining as pattern recognition. By using combinatorial and graph theoretic techniques, a closer look is taken at infeasible systems of linear inequalities, whose generalized solutions act as building blocks of geometric decision rules for pattern recognition. Infeasible systems of linear inequalities prove to be a key object in pattern recognition problems described in geometric terms thanks to the committee method. Such infeasible systems of inequalities represent an important special subclass of infeasible systems of constraints with a monotonicity property – systems whose multi-indices of feasible subsystems form abstract simplicial complexes (independence systems), which are fundamental objects of combinatorial topology. The methods of data mining and machine learning discussed in this monograph form the foundation of technologies like big data and deep learning, which play a growing role in many areas of human-technology interaction and help to find solutions, better solutions and excellent solutions.

Pattern Recognition 1991: Volume 24 , Issue CONTENTS. Digitized from IA1646616-06 . Previous issue: sim_pattern-recognition_1990_23_12_0 . Next issue: sim_pattern-recognition_1991_24_1_0 .

This research focuses on three iconic Indonesian batik patterns-Kawung, Mega Mendung, and Parang-due to their cultural significance and recognition. Kawung symbolizes harmony, Mega Mendung represents power, and Parang signifies protection and spiritual power. Using the YOLOv5 deep learning model, the study aimed to accurately identify these patterns. Results showed mean average precision (mAP) scores of 77% for Kawung, 80% for Parang, and an impressive 99% for Mega Mendung. The highest precision results were 91% for Kawung, 88% for Parang, and 77% for Mega Mendung. These findings highlight the potential of pattern recognition in preserving cultural heritage. Understanding these designs contributes to the appreciation of Indonesia s culture. The research suggests applications in cultural studies, digital archiving, and the textile industry, ensuring the legacy of these patterns endures.

Spatial light modulators (SLMs) are being used in correlation-based optical pattern recognition systems to implement the Fourier domain filters. Currently available SLMs have certain limitations with respect to the realizability of these filters. Therefore, it is necessary to incorporate the SLM constraints in the design of the filters. The design of a SLM-constrained minimum average correlation energy (SLM-MACE) filter using the simulated annealing-based optimization technique was investigated. The SLM-MACE filter was synthesized for three different types of constraints. The performance of the filter was evaluated in terms of its recognition (discrimination) capabilities using computer simulations. The correlation plane characteristics of the SLM-MACE filter were found to be reasonably good. The SLM-MACE filter yielded far better results than the analytical MACE filter implemented on practical SLMs using the constrained magnitude technique. Further, the filter performance was evaluated in the presence of noise in the input test images. This work demonstrated the need to include the SLM constraints in the filter design. Finally, a method is suggested to reduce the computation time required for the synthesis of the SLM-MACE filter.

The goal of the work is to develop pattern-recognition and signal-processing method that will use Army-supplied human cardiovascular and psychomotor data to provide indices of responsivity to challenge. Time Series and point-process techniques will form the basis of the approach, and the assumptions that underlie the methods will be examined and tested. The detection of infrequent and brief events, and the ellucidation of their relationships, if any, to the indices, will be carried out.

Typical multispectral/hyperspectral image (MSI/HSI) data analysis focuses on single pixel-at-a-time analysis and pattern matching to known or trained spectral signatures. Nearest neighbor and object feature classification techniques are not fully exploited through traditional MSI/HSI approaches. Inputs from multiple sensors of varying formats, spectral resolution, and spatial resolution are only beginning to be fused together.

Feature extraction is important step to extract the useful and valuable information from the electromyography (EMG) signal. However, the process of feature extraction requires prior knowledge and expertise. In this paper, a featureless EMG pattern recognition technique is proposed to tackle the feature extraction problem. Initially, spectrogram is employed to transform the raw EMG signal into time-frequency representation (TFR). The TFRs or spectrogram images are then directly fed into the convolutional neural network (CNN) for classification. Two CNN models are proposed to learn the features automatically from the spectrogram images without the need of manual feature extraction. The proposed CNN models are evaluated using the EMG data acquired from the publicly access NinaPro database. Our results show that CNN classifier can offer the best mean classification accuracy of 88.04% for the recognition of the hand and wrist movements. 

A method for real-time on-board spacecraft attitude determination is presented. The method is suitable for processing CCD or CID stellar camera and rate gyro output to determine orientation to better than five arc seconds. The system is self-calibrating; uncertain parameters such as interlock angles and gyro biases can be included in the estimation algorithms. The system is implemented in a microcomputer system which conclusively establishes that the system is compatible with on-board computation constraints. (Author)

A method for real-time on-board spacecraft attitude determination is presented. The method is suitable for processing CCD or CID stellar camera and rate gyro output to determine orientation to better than five arc seconds. The system is self-calibrating; uncertain parameters such as interlock angles and gyro biases can be included in the estimation algorithms. The system is implemented in a microcomputer system which conclusively establishes that the system is compatible with on-board computation constraints. (Author)

Effort on Phase I tasks continued and good progress was made during this The hardware design of the CNAPS/PC board was completed and five (5) prototype boards were fabricated. Preliminary debug of the boards has been successfully completed. Very good progress was made on the software port and debug of the CNAPS-C compiler and the source-level debugger. Progress was made, but not as much as planned, on the software port and debug of the OEMlib control software to the CNAPS/PC hardware and the DOS/Windows environment. At the time of this report, the project has expenditures and commitments totaling 66% of the funds allocated for Phase I of the contract. Pattern Recognition Image Processing

The primary goal of this research is to study a programmable joint- transform correlator (JTC) using liquid crystal television (LCTV) panels for adaptive real-time pattern recognition applications. The technique can improve the pattern recognition and identification technology that is of interest to the U.S. Army. The technique we studied is a real-time programmable electro-optical architecture. There are several reasons for selecting the optical technique over their digital and electronic counterparts, as follows: Optical technique is capable of handling a large space-bandwidth image; optical technique is capable of performing parallel operations; optics can perform massive interconnections; optical transformation can be operated at high speed, etc. By using the LCTV, the pattern under observation can be correlated with a large number of recallable image memories. In addition, the LCTV technique is rather simple and economical to operate. The LCTV-optical correlator, in principle, can be designed into a compact portable form for insitu application. Brief outlines of the major research findings and publications are provided in this report.

The primary goal of this research is to study a programmable joint- transform correlator (JTC) using liquid crystal television (LCTV) panels for adaptive real-time pattern recognition applications. The technique can improve the pattern recognition and identification technology that is of interest to the U.S. Army. The technique we studied is a real-time programmable electro-optical architecture. There are several reasons for selecting the optical technique over their digital and electronic counterparts, as follows: Optical technique is capable of handling a large space-bandwidth image; optical technique is capable of performing parallel operations; optics can perform massive interconnections; optical transformation can be operated at high speed, etc. By using the LCTV, the pattern under observation can be correlated with a large number of recallable image memories. In addition, the LCTV technique is rather simple and economical to operate. The LCTV-optical correlator, in principle, can be designed into a compact portable form for insitu application. Brief outlines of the major research findings and publications are provided in this report.

PASM, a Large-scale multimicroprocessor system being designed at Purdue University for image processing and pattern recognition, is described. This system can be dynamically reconfigured to operate as one or more independent SIMD and/or MIMD machines. PASM consists of a Parallel Computation Unit, which contains N processors, N memories, and an interconnection network; Q Micro Controllers, each of which controls N/Q processors; N/Q parallel secondary storage devices; a distributed Memory Management System; and a System Control Unit, to coordinate the other system components. Possible values for N and Q are 1024 and 16, respectively. The control schemes, interprocessor communications, and memory management in PASM are explored. Examples of how PASM can be used to perform image processing tasks are given. (Author)

Semiconductor materials synthesized as oriented nanocrystals or assembled into nanowires and quantum dot solids of a single or multiple materials have technological potential in device fabrication. Our research over the past several years has demonstrated the application of peptides and biological templates to control growth and assemble nanocrystals into potential technologically important materials. The research covered by this grant had three key stages: Selection of Peptides that interacted specifically with a certain material; Using peptides to bind and/or grow nanoparticles or quantum dots; and finally using specific peptide moieties to pattern materials, including heterostructured materials. M13 bacteriophage library screening was used to isolate peptide fusions capable of not only material specific recognition, but directed crystal phase nucleation of some if not all of the semiconductor and magnetic materials studied, including ZnS, CdS, PbS, Co, CoPt, FePt, Fe2O3, Fe3O4, GaAs, InP, Si and carbon. Virus-semiconductor films were synthesized using the recognition viruses isolated. Genetic engineering encoding the specific peptides was also used to create a viral template to nucleate nanocrystals in an oriented fashion, forming wire-like structures, thus achieving patterning of materials using biological precursors. The nanocrystalline viral nanowires were further transformed to single crystalline nanowires through a heat treatment process.

Semiconductor materials synthesized as oriented nanocrystals or assembled into nanowires and quantum dot solids of a single or multiple materials have technological potential in device fabrication. Our research over the past several years has demonstrated the application of peptides and biological templates to control growth and assemble nanocrystals into potential technologically important materials. The research covered by this grant had three key stages: Selection of Peptides that interacted specifically with a certain material; Using peptides to bind and/or grow nanoparticles or quantum dots; and finally using specific peptide moieties to pattern materials, including heterostructured materials. M13 bacteriophage library screening was used to isolate peptide fusions capable of not only material specific recognition, but directed crystal phase nucleation of some if not all of the semiconductor and magnetic materials studied, including ZnS, CdS, PbS, Co, CoPt, FePt, Fe2O3, Fe3O4, GaAs, InP, Si and carbon. Virus-semiconductor films were synthesized using the recognition viruses isolated. Genetic engineering encoding the specific peptides was also used to create a viral template to nucleate nanocrystals in an oriented fashion, forming wire-like structures, thus achieving patterning of materials using biological precursors. The nanocrystalline viral nanowires were further transformed to single crystalline nanowires through a heat treatment process.

Semiconductor materials synthesized as oriented nanocrystals or assembled into nanowires and quantum dot solids of a single or multiple materials have technological potential in device fabrication. Our research over the past several years has demonstrated the application of peptides and biological templates to control growth and assemble nanocrystals into potential technologically important materials. The research covered by this grant had three key stages: Selection of Peptides that interacted specifically with a certain material; Using peptides to bind and/or grow nanoparticles or quantum dots; and finally using specific peptide moieties to pattern materials, including heterostructured materials. M13 bacteriophage library screening was used to isolate peptide fusions capable of not only material specific recognition, but directed crystal phase nucleation of some if not all of the semiconductor and magnetic materials studied, including ZnS, CdS, PbS, Co, CoPt, FePt, Fe2O3, Fe3O4, GaAs, InP, Si and carbon. Virus-semiconductor films were synthesized using the recognition viruses isolated. Genetic engineering encoding the specific peptides was also used to create a viral template to nucleate nanocrystals in an oriented fashion, forming wire-like structures, thus achieving patterning of materials using biological precursors. The nanocrystalline viral nanowires were further transformed to single crystalline nanowires through a heat treatment process.

Myoelectric control system (MCS) had been applied to hand exoskeleton to improve the human-machine interaction. The current MCS enables the exoskeleton to move all fingers concurrently for opening and closing hand and does not consider robustness issue caused by the condition not considered in the training stage. This study addressed a new MCS employing novel myoelectric pattern recognition (M-PR) to handle more movements. Furthermore, a rejection-based radial-basis function extreme learning machine (RBF-ELM) was proposed to tackle the movements that are not included in the training stage. The results of the offline experiments showed the RBF-ELM with rejection mechanism (RBF-ELM-R) outperformed RBF-ELM without rejection mechanism and other well-known classifiers. In the online experiments, using 10-trained classes, the M-PR achieved an accuracy of 89.73% and 89.22% using RBF-ELM-R and RBF-ELM, respectively. In the experiment with 5-trained classes and 5-untrained classes, the M-PR accuracy was 80.22% and 59.64% using RBF-ELM-R and RBF-ELM, respectively