Relevant bibliographies by topics / ENHANCEMNT ALGORITHM

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'ENHANCEMNT ALGORITHM'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "ENHANCEMNT ALGORITHM"

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Park, Gyuseok, Woohyeong Cho, Kyu-Sung Kim, and Sangmin Lee. "Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises." Applied Sciences 10, no. 17 (September 2, 2020): 6077. http://dx.doi.org/10.3390/app10176077.

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Hearing aids are small electronic devices designed to improve hearing for persons with impaired hearing, using sophisticated audio signal processing algorithms and technologies. In general, the speech enhancement algorithms in hearing aids remove the environmental noise and enhance speech while still giving consideration to hearing characteristics and the environmental surroundings. In this study, a speech enhancement algorithm was proposed to improve speech quality in a hearing aid environment by applying noise reduction algorithms with deep neural network learning based on noise classification. In order to evaluate the speech enhancement in an actual hearing aid environment, ten types of noise were self-recorded and classified using convolutional neural networks. In addition, noise reduction for speech enhancement in the hearing aid were applied by deep neural networks based on the noise classification. As a result, the speech quality based on the speech enhancements removed using the deep neural networks—and associated environmental noise classification—exhibited a significant improvement over that of the conventional hearing aid algorithm. The improved speech quality was also evaluated by objective measure through the perceptual evaluation of speech quality score, the short-time objective intelligibility score, the overall quality composite measure, and the log likelihood ratio score.
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Mu, Qi, Xinyue Wang, Yanyan Wei, and Zhanli Li. "Low and non-uniform illumination color image enhancement using weighted guided image filtering." Computational Visual Media 7, no. 4 (July 23, 2021): 529–46. http://dx.doi.org/10.1007/s41095-021-0232-x.

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AbstractIn the state of the art, grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumination. As these methods are applied to each RGB channel independently, imbalanced inter-channel enhancements (color distortion) can often be observed in the resulting images. On the other hand, images with non-uniform illumination enhanced by the retinex algorithm are prone to artifacts such as local blurring, halos, and over-enhancement. To address these problems, an improved RGB color image enhancement method is proposed for images captured under non-uniform illumination or in poor visibility, based on weighted guided image filtering (WGIF). Unlike the conventional retinex algorithm and its variants, WGIF uses a surround function instead of a Gaussian filter to estimate the illumination component; it avoids local blurring and halo artifacts due to its anisotropy and adaptive local regularization. To limit color distortion, RGB images are first converted to HSI (hue, saturation, intensity) color space, where only the intensity channel is enhanced, before being converted back to RGB space by a linear color restoration algorithm. Experimental results show that the proposed method is effective for both RGB color and grayscale images captured under low exposure and non-uniform illumination, with better visual quality and objective evaluation scores than from comparator algorithms. It is also efficient due to use of a linear color restoration algorithm.
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H K, Ravikiran, H. S. Mohana, Pooja P, Nayana C S, Dhruva D B, and Shreenidhi MA. "VQ-Codebook Enhancement using HGAPSO Algorithm." International Journal of Research Publication and Reviews 4, no. 3 (March 2023): 808–12. http://dx.doi.org/10.55248/gengpi.2023.32168.

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Sivakumar, Ramah, and Dr J.G.R. Sathiaseelan. "An enhanced constraint based technique for frequent itemset mining in transactional databases." International Journal of Engineering & Technology 7, no. 2.22 (April 20, 2018): 45. http://dx.doi.org/10.14419/ijet.v7i2.22.11807.

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Mining frequent patterns is one of the wide area of research in recent times as it has numerous social applications. Variety of frequent patterns finds usage in diverse applications and the research to mine those in an optimized way is an important aspect under consideration. So far, many algorithms had been proposed for mining frequent itemsets and each has their own pros and cons. The basic algorithms used in the process are Apriori, Fpgrowth and Eclat. Many enhancements of these algorithms are ongoing process in recent times. In this paper, an enhanced Varied Support Frequent Itemset (VSFIM) algorithm is proposed which is an enhancement of FPGrowth algorithm. Unique minimum support for each item in the transaction is provided and then mining is done in the proposed approach. The performance of the proposed algorithm is tested with existing algorithms. It is found that VSFIM outperformed the existing algorithms in both processing time and space utilization.
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Lian, Jian, Yan Zhang, and Cheng Jiang Li. "An Efficient K-Shortest Paths Based Routing Algorithm." Advanced Materials Research 532-533 (June 2012): 1775–79. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1775.

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We present an efficient K-shortest paths routing algorithm for computer networks. This Algorithm is based on enhancements to currently used link-state routing algorithms such as OSPF and IS-IS, which are only focusing on finding the shortest path route by adopting Dijkstra algorithm. Its desire effect to achieve is through the use of K-shortest paths algorighm, which has been implemented successfully in some fileds like traffic engineering. The correctness of this Algorithm is discussed at the same time as long as the comparison with OSPF.
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WANG Gaiyun, 王改云, 郭智超 GUO Zhichao, 路皓翔 LU Haoxiang, 陆家卓 LU Jiazhuo, and 张琦 ZHANG Qi. "融合遗传算法的多域值分块低照度图像增强算法." ACTA PHOTONICA SINICA 51, no. 4 (2022): 0410007. http://dx.doi.org/10.3788/gzxb20225104.0410007.

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Pozdeev, Alexandr A., Nataliia A. Obukhova, and Alexandr A. Motyko. "Algorithms for Real-Time Endoscopy Image Processing Pipeline in Clinical Decision Support Systems." International Journal of Embedded and Real-Time Communication Systems 10, no. 4 (October 2019): 39–59. http://dx.doi.org/10.4018/ijertcs.2019100103.

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A set of algorithms, taking in account endoscopic image features and computational cost for real-time realization is proposed. A noise reduction algorithm is based on determining the level of detail in an image fragment. For fragments with a different level of detail, different noise reduction filters are used. The enhancement algorithm is based on nonlinear contrast enhancement which highlights the contrast of vessels relative to the background without significant noise stressing, which is one of the main disadvantages of nonlinear enhancement algorithms. The custom color correction algorithm takes into account user preferences and provides a mean error less than 0.5% for each color coordinate. The “mosaic” synthesis algorithm gets panoramic images of low detail images with a mean stitching error less than 0.75 pix. The software realization of algorithms allows processing 4K endoscopic video with a speed of about 30 fps.
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Zhang, Su Ling. "Experimental Study of Human Fingerprint Image Recognition Analysis." Advanced Materials Research 971-973 (June 2014): 1616–19. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1616.

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respectively cited the fingerprint image preprocessing for image segmentation , demand pattern, image enhancement and binarization of several algorithms , and each algorithm were compared. Image segmentation algorithm studied in this paper , image enhancement algorithms, can be very good to complete the project requirements. Because each method has its advantages and disadvantages , and therefore use different methods to get different results after image processing .
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Wang, Yaming, Jiajun Wang, Yuanmei Wang, and Yude Dong. "Enhancement of Eyeround Images Based on an Improved Fuzzy Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 6 (December 20, 1999): 441–45. http://dx.doi.org/10.20965/jaciii.1999.p0441.

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Eye ground images are complex, with many details and uncertainties. Conventional enhancement algorithms do not enhance these images suitably of inferior processing. S. K. Pal proposed a fuzzy enhancement algorithm with advantages, but these were compromised by slow processing and information loss. We propose a fuzzy enhancement algorithm for eyeground images introducing mapping and implementing the algorithm through table searches, significantly improving image quality and processing speed.
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M, Reshma, and Priestly B. Shan. "Oretinex-DI: Pre-Processing Algorithms for Melanoma Image Enhancement." Biomedical and Pharmacology Journal 11, no. 3 (July 30, 2018): 1381–87. http://dx.doi.org/10.13005/bpj/1501.

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In Medical imaging, the dermoscopic images analysis is quite useful for the skin cancer detection. The automatic computer assisted diagnostic systems (CADS) require dermoscopic image enhancement for human perception and analysis. The traditional image enhancements methods lack the synchronization among contrast perception between human and the digital images. This paper proposes an optimized-Retinex (ORetinex) image enhancement algorithm to remove light effects, which is quite suitable for the dermoscopic image for clinical analysis for Melanoma. The value of global contrast factor (GCF) and contrast per pixel (CPP) is computed and compared with the traditional methods of image enhancements including contrast enhancement, CLAHE,Adaptive histogram equalization, Bilinear filtering and the proportion of GCF and CPP is found quite optimal as compare to these traditional methods.
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Dissertations / Theses on the topic "ENHANCEMNT ALGORITHM"

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Andrianakis, Ioannis. "Bayesian algorithms for speech enhancement." Thesis, University of Southampton, 2007. https://eprints.soton.ac.uk/66244/.

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The portability of modern voice processing devices allows them to be used in environments where background noise conditions can be adverse. Background noise can deteriorate the quality of speech transmitted through such devices, but speech enhancement algorithms can ameliorate this degradation to some extent. The development of speech enhancement algorithms that improve the quality of noisy speech is the aim of this thesis, which consists of three main parts. In the first part, we propose a framework of algorithms that estimate the clean speech Short Time Fourier Transform (STFT) coefficients. The algorithms are derived from the Bayesian theory of estimation and can be grouped according to i) the STFT representation they estimate ii) the estimator they apply and iii) the speech prior density they assume. Apart from the introduction of algorithms that surpass the performance of similar algorithms that exist in the literature, the compilation of the above framework offers insight on the effect and relative importance of the different components of the algorithms (e.g. prior, estimator) to the quality of the enhanced speech. In the second part of this thesis, we develop methods for the estimation of the power of time varying noise. The main outcome is a method that exploits some similarities between the distribution of the noisy speech spectral amplitude coefficients within a single frequency bin, and the corresponding distribution of the corrupting noise. The above similarities allow the extraction of samples that are more likely to correspond to noise, from a window of past spectral amplitude observations. The extracted samples are then used to produce an estimate of the noise power. In the final part of this thesis, we are concerned with the incorporation of the time and frequency dependencies of speech signals in our estimation model. The theoretical framework on which the modelling is based is provided by Markov Random Fields (MRF’s). Initially, we develop a MAP estimator of speech based on the Gaussian MRF prior. In the following, we introduce the Chi MRF, which is employed in the development of an improved speech estimator. Finally, the performance of fixed and adaptive schemes for the estimation of the MRF parameters is investigated.
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Imanguliyev, Azar. "Enhancements for the Bees Algorithm." Thesis, Cardiff University, 2013. http://orca.cf.ac.uk/56503/.

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This work introduces new enhancements to the Bees Algorithm in order to improve its overall performance. These enhancements are early neighbourhood search process, efficiency based recruitment for neighbourhood search process, hybrid strategy involving tabu search, new escape mechanism to escape locals with similar fitness values and autonomy to minimise interaction between search process and the user. The proposed enhancements were applied alone or in pair to develop improved versions of the Bees Algorithm. Three Enhanced Bees Algorithms were introduced: the Early Neighbourhood Search and Efficiency Based recruitment Bees Algorithm (ENSEBRBA), the Hybrid Tabu Bees Algorithm (TBA) and the Autonomous Bees Algorithm (ABA). The ENSEBRBA with an empowered initialisation stage and extra recruitment for neighbourhood search is introduced to improve performance of the Bees Algorithms on high dimensional problems. The TBA is proposed as a new version of the Bees Algorithm which utilises the memory lists to memorise less productive patches. Moreover, the local escape strategy was also implemented to this algorithm. Proposed modifications increased the productivity of the Bees Algorithm by decreasing number of evaluations needed to converge to the global optimum. iii The ABA is developed to provide independency to the Bees Algorithm, thus it is able to self tune its control parameters in a sub-optimal manner. All enhanced Algorithms were tested on continuous type benchmark functions and additionally, statistical analysis was carried out. Observed experimental results proved that proposed enhancements improved the Bees Algorithm’s performance.
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Gagnon, Luc. "A speech enhancement algorithm based upon resonator filterbanks." Thesis, University of Ottawa (Canada), 1991. http://hdl.handle.net/10393/7767.

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Weith-Glushko, Seth A. "Quantitative analysis of infrared contrast enhancement algorithms /." Online version of thesis, 2007. http://hdl.handle.net/1850/4208.

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Beattie, Robert Scott. "Side scan sonar image formation, restoration and modelling." Thesis, Robert Gordon University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318551.

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Arif, Annatoma Arif. "BLURRED FINGERPRINT IMAGE ENHANCEMENT: ALGORITHM ANALYSIS AND PERFORMANCE EVALUATION." Miami University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=miami1473428137332997.

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Hines, Glenn Derrick. "Real -time Retinex image enhancement: Algorithm and architecture optimizations." W&M ScholarWorks, 2006. https://scholarworks.wm.edu/etd/1539623490.

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The field of digital image processing encompasses the study of algorithms applied to two-dimensional digital images, such as photographs, or three-dimensional signals, such as digital video. Digital image processing algorithms are generally divided into several distinct branches including image analysis, synthesis, segmentation, compression, restoration, and enhancement. One particular image enhancement algorithm that is rapidly gaining widespread acceptance as a near optimal solution for providing good visual representations of scenes is the Retinex.;The Retinex algorithm performs a non-linear transform that improves the brightness, contrast and sharpness of an image. It simultaneously provides dynamic range compression, color constancy, and color rendition. It has been successfully applied to still imagery---captured from a wide variety of sources including medical radiometry, forensic investigations, and consumer photography. Many potential users require a real-time implementation of the algorithm. However, prior to this research effort, no real-time version of the algorithm had ever been achieved.;In this dissertation, we research and provide solutions to the issues associated with performing real-time Retinex image enhancement. We design, develop, test, and evaluate the algorithm and architecture optimizations that we developed to enable the implementation of the real-time Retinex specifically targeting specialized, embedded digital signal processors (DSPs). This includes optimization and mapping of the algorithm to different DSPs, and configuration of these architectures to support real-time processing.;First, we developed and implemented the single-scale monochrome Retinex on a Texas Instruments TMS320C6711 floating-point DSP and attained 21 frames per second (fps) performance. This design was then transferred to the faster TMS320C6713 floating-point DSP and ran at 28 fps. Then we modified our design for the fixed-point TMS320DM642 DSP and achieved an execution rate of 70 fps. Finally, we migrated this design to the fixed-point TMS320C6416 DSP. After making several additional optimizations and exploiting the enhanced architecture of the TMS320C6416, we achieved 108 fps and 20 fps performance for the single-scale, monochrome Retinex and three-scale, color Retinex, respectively. We also applied a version of our real-time Retinex in an Enhanced Vision System. This provides a general basis for using the algorithm in other applications.
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Melnik, Sergey. "Generic model management : concepts and algorithms /." Berlin [u.a.] : Springer, 2004. http://www.loc.gov/catdir/enhancements/fy0813/2004104636-d.html.

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Teillaud, Monique. "Towards dynamic randomized algorithms in computational geometry /." Berlin [u.a.] : Springer, 1993. http://www.loc.gov/catdir/enhancements/fy0815/93023628-d.html.

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Gorokhovskiy, Konstantin. "Enhancement of demosaicking algorithms for digital still cameras." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/35680.

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Demosaicking is the interpolation of missed colour samples in a colour filter array (CFA). The term demosaicking has its roots in the word "mosaic" which, in turn explains the structure of a colour filter array typically used in a digital camera. The detectors (cells) of blue, red and green colours or their combinations are spread regularly (mosaicked) on the electronic sensor chip (CMOS, CCD or other technology). The resulting mosaic of colour samples is passed through an interpolation procedure to determine the intensities of colours that are not sampled by the array. The pattern of the mosaic is important as most interpolation methods make use of a priori knowledge of the configuration for a more precise image restoration. The most popular is currently the Bayer CFA. It has twice as many green detectors than blue or red, however there are alternative sensors which are based on cyan, magenta, yellow and green colours.
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Books on the topic "ENHANCEMNT ALGORITHM"

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Mo, Y. An investigation and enhancement of genetic algorithms. Manchester: UMIST, 1994.

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Li, Yuying. An affine scaling algorithm for minimizing total variation in image enhancement. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1994.

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R, Dougherty Edward, ed. Enhancement and restoration of digital documents: Statistical design of nonlinear algorithms. Bellingham, Wash., USA: SPIE Optical Engineering Press, 1997.

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Partridge, William J. Image enhancement software for underwater recovery operations - user's manual. Monterey, Calif: Naval Postgraduate School, 1989.

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Ciulla, Carlo. Improved signal and image interpolation in biomedical applications: The case of magnetic resonance imaging (MRI). Hershey PA: Medical Information Science Reference, 2009.

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A, Whitmore Stephen, Dryden Flight Research Facility, and AIAA Aerospace Sciences Meeting (29th : 1991 : Reno, Nevada), eds. Preliminary results from an airdata enhancement algorithm with application to high-angle-of-attack flights. Edwards, Calif: National Aeronautics and Space Administration, Ames Resarch Center, Dryden Flight Research Facility, 1991.

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Sandy, Napel, Yan Chye H, and United States. National Aeronautics and Space Administration., eds. Serial scanning and registration of high resolution quantitative computed tomography volume scans for the determination of local bone density changes: Final report. [Washington, DC: National Aeronautics and Space Administration, 1996.

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David, Lane, and United States. National Aeronautics and Space Administration., eds. Enhanced line integral convolution with flow feature detection: NAS technical report NAS-96-007. [Washington, DC: National Aeronautics and Space Administration, 1996.

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Castillo, Oscar, Patricia Melin, and Janusz Kacprzyk, eds. Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35445-9.

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S, Dulikravich Djordje, and United States. National Aeronautics and Space Administration., eds. Reliability enhancement of Navier-Stokes codes through convergence acceleration: Final report. [Washington, DC: National Aeronautics and Space Administration, 1995.

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Book chapters on the topic "ENHANCEMNT ALGORITHM"

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Kovalevsky, Vladimir. "Contrast Enhancement." In Modern Algorithms for Image Processing, 43–64. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4237-7_3.

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Paxton, John, and John Evans. "Two Genetic Algorithm Enhancements." In Intelligent Systems Third Golden West International Conference, 451–56. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-7108-3_46.

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Kunche, Prajna, and K. V. V. S. Reddy. "Speech Enhancement Based on Bat Algorithm (BA)." In Metaheuristic Applications to Speech Enhancement, 91–110. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31683-3_8.

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Gerstel, Ori, Shay Kutten, Rachel Matichin, and David Peleg. "Hotlink Enhancement Algorithms for Web Directories." In Algorithms and Computation, 68–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24587-2_9.

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Kunche, Prajna, and K. V. V. S. Reddy. "Speech Enhancement Approach Based on Gravitational Search Algorithm (GSA)." In Metaheuristic Applications to Speech Enhancement, 61–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31683-3_6.

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Verma, Prem Kumari, and Nagendra Pratap Singh. "Retinal Image Enhancement Using Hybrid Approach." In Algorithms for Intelligent Systems, 515–24. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9650-3_40.

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Vamsidhar, A., T. Surya Kavitha, and G. Ramesh Babu. "Image Enhancement Using Chicken Swarm Optimization." In Algorithms for Intelligent Systems, 555–65. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6893-7_49.

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Mustafi, Abhijit, and P. K. Mahanti. "An Optimal Algorithm for Contrast Enhancement of Dark Images Using Genetic Algorithms." In Computer and Information Science 2009, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01209-9_1.

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Kim, Tae-Chan, Chang-Won Huh, Meejoung Kim, Bong-Young Chung, and Soo-Won Kim. "Real-Time Advanced Contrast Enhancement Algorithm." In Computer and Information Sciences - ISCIS 2003, 691–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39737-3_86.

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Yin, Xuefei, Yanming Zhu, and Jiankun Hu. "A Robust Contactless Fingerprint Enhancement Algorithm." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 127–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90775-8_11.

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Conference papers on the topic "ENHANCEMNT ALGORITHM"

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Can Kuran, Emre, Umut Kuran, and Mehmet Bilal Er. "Sub-Image Histogram Equalization using Coot Optimization Algorithm for Segmentation and Parameter Selection." In 9th International Conference on Artificial Intelligence and Applications (AIAPP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120903.

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Contrast enhancement is very important in terms of assessing images in an objective way. Contrast enhancement is also significant for various algorithms including supervised and unsupervised algorithms for accurate classification of samples. Some contrast enhancement algorithms solve this problem by addressing the low contrast issue. Mean and variance based sub-image histogram equalization (MVSIHE) algorithm is one of these contrast enhancements methods proposed in the literature. It has different parameters which need to be tuned in order to achieve optimum results. With this motivation, in this study, we employed one of the most recent optimization algorithms, namely, coot optimization algorithm (COA) for selecting appropriate parameters for the MVSIHE algorithm. Blind/referenceless image spatial quality evaluator (BRISQUE) and natural image quality evaluator (NIQE) metrics are used for evaluating fitness of the coot swarm population. The results show that the proposed method can be used in the field of biomedical image processing.
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SEO, JUNWON, EUISEOK JEONG, and JAMES P. WACKER. "UAS INSPECTION IMAGE ENHANCEMENT COUPLED WITH DENOISE ALGORITHM BASED ON DEEP NEURAL NETWORK." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36333.

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Unmanned Aerial System (UAS) technologies integrated with image processing algorithms are considered timely and useful for bridge inspections because of improved accessibility, recording ability, and cost-efficiency compared to the conventional inspection approach. The image processing algorithms can improve the ability of the UAS-aided bridge inspections in efficiently identifying and quantifying deterioration. This study was aimed to inspect an in-service single-span precast concrete bridge on a rural roadway in South Dakota using UAS technologies coupled with a Deep Neural Network (DNN) denoise algorithm. During the inspections, Phantom 4 and DJI Matrice 210 UASs recorded several videos for different bridge elements (e.g., girders and decking), and a total of 21,784 inspection images were extracted from the videos with a duration of more than 14 minutes. Deteriorations specific to the bridge elements such as spalling and rust were characterized by performing the DNN-aided image processing algorithm with the extracted inspection images. The DNN allowed for computation and analysis between input and output image data to reduce the noises on the images. Besides, a grayscale image enhancement algorithm was considered to improve the visibility of images by optimizing image contrast settings. With the visibility-improved images, detailed quantification on the detected deterioration per bridge element was carried out using a pixel-based measurement tool. Based upon the study’s results, it was revealed that the UAS technologies with the DNN denoise algorithm were able to successfully characterize and quantify visible deteriorations to the certain bridge elements using pixel-based tools.
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Zhao, Kai, Jingen Ni, and Xiaoping Chen. "Improved nonnegative adaptive filtering algorithms." In 2016 IEEE International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE, 2016. http://dx.doi.org/10.1109/iwaenc.2016.7602966.

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Schwartz, Boaz, Sharon Gannot, and Emanuel A. P. Habets. "LPC-based speech dereverberation using Kalman-EM algorithm." In 2014 14th International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE, 2014. http://dx.doi.org/10.1109/iwaenc.2014.6953329.

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Shi, Juan, Jingen Ni, and Xiaoping Chen. "Variable step-size diffusion proportionate affine projection algorithm." In 2016 IEEE International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE, 2016. http://dx.doi.org/10.1109/iwaenc.2016.7602940.

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Kang, Hong-Goo, Michael Graczyk, and Jan Skoglund. "On pre-filtering strategies for the GCC-PHAT algorithm." In 2016 IEEE International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE, 2016. http://dx.doi.org/10.1109/iwaenc.2016.7602964.

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Konieczka, Adam, Julian Balcerek, Agata Chmielewska, and Adam Dabrowski. "Approach to local contrast enhancement." In 2015 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). IEEE, 2015. http://dx.doi.org/10.1109/spa.2015.7365106.

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Zhang, Junqing, Liming Shi, Mads G. Christensen, Wen Zhang, Lijun Zhang, and Jingdong Chen. "Robust Acoustic Contrast Control with Positive Semidefinite Constraint Using Iterative POTDC Algorithm." In 2022 International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE, 2022. http://dx.doi.org/10.1109/iwaenc53105.2022.9914730.

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Flocon-Cholet, Joachim, Julien Faure, Alexandre Guerin, and Pascal Scalart. "A robust howling detection algorithm based on a statistical approach." In 2014 14th International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE, 2014. http://dx.doi.org/10.1109/iwaenc.2014.6953339.

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Reju, V. G., Rajan S. Rashobh, Anh H. T. Nguyen, and Andy W. H. Khong. "An Efficient Multi-Source DOA Estimation Algorithm for Underdetermined System." In 2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE, 2018. http://dx.doi.org/10.1109/iwaenc.2018.8521370.

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Reports on the topic "ENHANCEMNT ALGORITHM"

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Kuperman, Gilbert G., and Dorit Shaya. Subjective Assessment of SAR Imagery Enhancement Algorithms. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada341722.

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Harvey, Scott D., and Bob W. Wright. Safety Report for Raman Spectroscopy: Safety Evaluation and Search Algorithm Enhancement. Office of Scientific and Technical Information (OSTI), July 2002. http://dx.doi.org/10.2172/15010225.

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Linn, J. Privacy enhancement for Internet electronic mail: Part III - algorithms, modes, and identifiers. RFC Editor, August 1989. http://dx.doi.org/10.17487/rfc1115.

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Balenson, D. Privacy Enhancement for Internet Electronic Mail: Part III: Algorithms, Modes, and Identifiers. RFC Editor, February 1993. http://dx.doi.org/10.17487/rfc1423.

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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform lighting conditions. In the second part of the framework, the context-based segmentation is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. Context based segmentation makes use of a cascade of pre-trained classifiers to search for regions that are not threats. The context based segmentation algorithm accelerates threat identification and improves object detection rates. The last phase of the framework is an efficient object detection model. Efficient object detection �follows a three-stage approach which includes extraction of the local phase in the image and the use of local phase characteristics to locate machinery threats. The local phase is an image feature extraction technique which partially removes the lighting variance and preserves the edge information of the object. Multiple orientations of the same object are matched and the correct orientation is selected using feature matching by histogram of local phase in a multi-scale framework. The classifier outputs locations of threats to pipeline.�The advanced automatic image analysis system is intended to be capable of detecting construction equipment along the ROW of pipelines with a very high degree of accuracy in comparison with manual threat identification by a human analyst. �
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Zhang, Yongping, Wen Cheng, and Xudong Jia. Enhancement of Multimodal Traffic Safety in High-Quality Transit Areas. Mineta Transportation Institute, February 2021. http://dx.doi.org/10.31979/mti.2021.1920.

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Numerous extant studies are dedicated to enhancing the safety of active transportation modes, but very few studies are devoted to safety analysis surrounding transit stations, which serve as an important modal interface for pedestrians and bicyclists. This study bridges the gap by developing joint models based on the multivariate conditionally autoregressive (MCAR) priors with a distance-oriented neighboring weight matrix. For this purpose, transit-station-centered data in Los Angeles County were used for model development. Feature selection relying on both random forest and correlation analyses was employed, which leads to different covariate inputs to each of the two jointed models, resulting in increased model flexibility. Utilizing an Integrated Nested Laplace Approximation (INLA) algorithm and various evaluation criteria, the results demonstrate that models with a correlation effect between pedestrians and bicyclists perform much better than the models without such an effect. The joint models also aid in identifying significant covariates contributing to the safety of each of the two active transportation modes. The research results can furnish transportation professionals with additional insights to create safer access to transit and thus promote active transportation.
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Mniszewski, Susan, Stan Moore, Sam Reeve, Stuart Slattery, Damien Lebrun-Grandie, Shane Fogerty, and Steve Plimpton. Algorithmic and GPU enhancements for molecular dynamics in Cabana and LAMMPS. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1856126.

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Sinclair, Samantha, and Sandra LeGrand. Reproducibility assessment and uncertainty quantification in subjective dust source mapping. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41523.

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Accurate dust-source characterizations are critical for effectively modeling dust storms. A previous study developed an approach to manually map dust plume-head point sources in a geographic information system (GIS) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery processed through dust-enhancement algorithms. With this technique, the location of a dust source is digitized and recorded if an analyst observes an unobscured plume head in the imagery. Because airborne dust must be sufficiently elevated for overland dust-enhancement algorithms to work, this technique may include up to 10 km in digitized dust-source location error due to downwind advection. However, the potential for error in this method due to analyst subjectivity has never been formally quantified. In this study, we evaluate a version of the methodology adapted to better enable reproducibility assessments amongst multiple analysts to determine the role of analyst subjectivity on recorded dust source location error. Four analysts individually mapped dust plumes in Southwest Asia and Northwest Africa using five years of MODIS imagery collected from 15 May to 31 August. A plume-source location is considered reproducible if the maximum distance between the analyst point-source markers for a single plume is ≤10 km. Results suggest analyst marker placement is reproducible; however, additional analyst subjectivity-induced error (7 km determined in this study) should be considered to fully characterize locational uncertainty. Additionally, most of the identified plume heads (> 90%) were not marked by all participating analysts, which indicates dust source maps generated using this technique may differ substantially between users.
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Sinclair, Samantha, and Sandra LeGrand. Reproducibility assessment and uncertainty quantification in subjective dust source mapping. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41542.

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Accurate dust-source characterizations are critical for effectively modeling dust storms. A previous study developed an approach to manually map dust plume-head point sources in a geographic information system (GIS) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery processed through dust-enhancement algorithms. With this technique, the location of a dust source is digitized and recorded if an analyst observes an unobscured plume head in the imagery. Because airborne dust must be sufficiently elevated for overland dust-enhancement algorithms to work, this technique may include up to 10 km in digitized dust-source location error due to downwind advection. However, the potential for error in this method due to analyst subjectivity has never been formally quantified. In this study, we evaluate a version of the methodology adapted to better enable reproducibility assessments amongst multiple analysts to determine the role of analyst subjectivity on recorded dust source location error. Four analysts individually mapped dust plumes in Southwest Asia and Northwest Africa using five years of MODIS imagery collected from 15 May to 31 August. A plume-source location is considered reproducible if the maximum distance between the analyst point-source markers for a single plume is ≤10 km. Results suggest analyst marker placement is reproducible; however, additional analyst subjectivity-induced error (7 km determined in this study) should be considered to fully characterize locational uncertainty. Additionally, most of the identified plume heads (> 90%) were not marked by all participating analysts, which indicates dust source maps generated using this technique may differ substantially between users.
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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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