The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch

Recent and forthcoming advances in instrumentation, and giant new surveys, are creating astronomical data sets that are not amenable to the methods of analysis familiar to astronomers. Traditional methods are often inadequate not merely because of the size in bytes of the data sets, but also because of the complexity of modern data sets. Mathematical limitations of familiar algorithms and techniques in dealing with such data sets create a critical need for new paradigms for the representation, analysis and scientific visualization (as opposed to illustrative visualization) of heterogeneous, multiresolution data across application domains. Some of the problems presented by the new data sets have been addressed by other disciplines such as applied mathematics, statistics and machine learning and have been utilized by other sciences such as space-based geosciences. Unfortunately, valuable results pertaining to these problems are mostly to be found only in publications outside of astronomy. Here we offer brief overviews of a number of concepts, techniques and developments, some "old" and some new. These are generally unknown to most of the astronomical community, but are vital to the analysis and visualization of complex datasets and images. In order for astronomers to take advantage of the richness and complexity of the new era of data, and to be able to identify, adopt, and apply new solutions, the astronomical community needs a certain degree of awareness and understanding of the new concepts. One of the goals of this paper is to help bridge the gap between applied mathematics, artificial intelligence and computer science on the one side and astronomy on the other.Comment: 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in Astronomy, special issue "Robotic Astronomy

Near real-time early cancer detection using a graphics processing unit

Automatically detecting early cancer using medical images is challenging, yet very crucial to help save millions of lives in the early stages of cancer. In this work, we improved a method that was originally developed by Yamaguchi et al. from the Saga University in Saga Japan. The original method would first decompose the endoscopic image into four color elements: red, green, blue and luminance (RGBL). Next each component is again decomposed to non-overlapping blocks of smaller images. Each smaller image undergoes two phases of DWT(s) and finally the Fractal Dimension (FD) is calculated per smaller image and abnormal regions are detectable. Our proposed method not only used GPU technology to speed up processing, this method also applied edge enhancement via Gaussian Fuzzy Edge Enhancement. After edge enhancement, multiple thresholds (or tuning variables) were identified and adjusted to reduce computational requirements, decrease false positives and increase the accuracy of detecting early cancer. Most lesions where a physician had manually indicated that could be an area of concern were detected quickly, less than four seconds, which is roughly 25x quicker than the existing work. The false positive rate was reduced but still needs improvement. In the future, a Support Vector Machine (SVM) would be an ideal solutions to reduce the false positive rate while also aiding in increasing detection and SVM technology has been implemented on the GPU. Once a technology, like a SVM, is implemented with better results, video processing will be the nearing the final step to \u27Near Real Time Automatic Detection of Early Esophageal Cancer from an Endoscopic Image\u27 --Leaf iv

A multimodal deep learning framework using local feature representations for face recognition

YesThe most recent face recognition systems are mainly dependent on feature representations obtained using either local handcrafted-descriptors, such as local binary patterns (LBP), or use a deep learning approach, such as deep belief network (DBN). However, the former usually suffers from the wide variations in face images, while the latter usually discards the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the DBN is proposed to address the face recognition problem in unconstrained conditions. Firstly, a novel multimodal local feature extraction approach based on merging the advantages of the Curvelet transform with Fractal dimension is proposed and termed the Curvelet–Fractal approach. The main motivation of this approach is that theCurvelet transform, a newanisotropic and multidirectional transform, can efficiently represent themain structure of the face (e.g., edges and curves), while the Fractal dimension is one of the most powerful texture descriptors for face images. Secondly, a novel framework is proposed, termed the multimodal deep face recognition (MDFR)framework, to add feature representations by training aDBNon top of the local feature representations instead of the pixel intensity representations. We demonstrate that representations acquired by the proposed MDFR framework are complementary to those acquired by the Curvelet–Fractal approach. Finally, the performance of the proposed approaches has been evaluated by conducting a number of extensive experiments on four large-scale face datasets: the SDUMLA-HMT, FERET, CAS-PEAL-R1, and LFW databases. The results obtained from the proposed approaches outperform other state-of-the-art of approaches (e.g., LBP, DBN, WPCA) by achieving new state-of-the-art results on all the employed datasets

JSCC-Cast: A Joint Source Channel Coding Video Encoding and Transmission System with Limited Digital Metadata

[Abstract] This work considers the design and practical implementation of JSCC-Cast, a comprehensive analog video encoding and transmission system requiring a reduced amount of digital metadata. Suitable applications for JSCC-Cast are multicast transmissions over time-varying channels and Internet of Things wireless connectivity of end devices having severe constraints on their computational capabilities. The proposed system exhibits a similar image quality compared to existing analog and hybrid encoding alternatives such as Softcast. Its design is based on the use of linear transforms that exploit the spatial and temporal redundancy and the analog encoding of the transformed coefficients with different protection levels depending on their relevance. JSCC-Cast is compared to Softcast, which is considered the benchmark for analog and hybrid video coding, and with an all-digital H.265-based encoder. The results show that, depending on the scenario and considering image quality metrics such as the structural similarity index measure, the peak signal-to-noise ratio, and the perceived quality of the video, JSCC-Cast exhibits a performance close to that of Softcast but with less metadata and not requiring a feedback channel in order to track channel variations. Moreover, in some circumstances, the JSCC-Cast obtains a perceived quality for the frames comparable to those displayed by the digital one.This work has been funded by the Xunta de Galicia (by grant ED431C 2020/15 and grant ED431G 2019/01 to support the Centro de Investigación de Galicia “CITIC”), the Agencia Estatal de Investigación of Spain (by grants RED2018-102668-T and PID2019-104958RB-C42), and ERDF funds of the EU (FEDER Galicia 2014–2020 and AEI/FEDER Programs, UE)Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED431G 2019/0

Characterisation of the Physical Chemical Processes Using the Fractal and Harmonic Analysis

Existuje mnoho různých způsobů jak analyzovat disperzní systémy a fyzikálně chemické processy ke kterým v takových systémech dochází. Tato práce byla zaměřena na charakterizaci těchto procesů pomocí metod harmonické fraktální analýzy. Obrazová data sledovaných systémů byly analyzovány pomocí waveletové analýzy. V průběhu práce byly navrženy různé optimalizace samotné analýzy, převážně zaměřené na odstranění manuálních operací během analýzy a tyto optimalizace byly také inkorporovány do softérového vybavení pro Harmonickou Fraktální Analýzu HarFA, který je vyvíjen na Fakultě chemické, VUT Brno.There are many different ways to characterize the dispersed systems and processes occuring in such systems. This work focuses on use of Fractal properties of such systems to describe the physical and chemical processes occuring in such systems. The Fractal properties are calculated from the image data of the systems under the observation using the Wavelet analysis. Since the Harmonic Fractal Analysis can be relatively easily automated, the work also focuses on algorithmisation of the analysis and the removal all manual steps from the process. The automation have been performed by incorporating all the findings into the software for Harmonic Fractal Analysis HarFA developed at the Faculty of Chemistry, BUT.

Analysis of Image Compression Methods Based On Transform and Fractal Coding

Image compression is process to remove the redundant information from the image so that only essential information can be stored to reduce the storage size, transmission bandwidth and transmission time. The essential information is extracted by various transforms techniques such that it can be reconstructed without losing quality and information of the image. In this thesis work comparative analysis of image compression is done by four transform method, which are Discrete Cosine Transform (DCT), Discrete Wavelet Transform( DWT) & Hybrid (DCT+DWT) Transform and fractal coding. MATLAB programs were written for each of the above method and concluded based on the results obtained that hybrid DWT-DCT algorithm performs much better than the standalone JPEG-based DCT, DWT algorithms in terms of peak signal to noise ratio (PSNR), as well as visual perception at higher compression ratio. The popular JPEG standard is widely used in digital cameras and web ¨Cbased image delivery. The wavelet transform, which is part of the new JPEG 2000 standard, claims to minimize some of the visually distracting artifacts that can appear in JPEG images. For one thing, it uses much larger blocks- selectable, but typically1024 x 1024 pixels ¨C for compression, rather than the 8 X 8 pixel blocks used in the original JPEG method, which often produced visible boundaries. Fractal compression has also shown promise and claims to be able to enlarge images by inserting ¨Drealistic¡¬ detail beyond the resolution limit of the original. Each method is discussed in the thesis

Compression Of 2-Tone Manuscript For Multimedia Application [QA76.9.D33 B171 2008 f rb].

Malaysia seperti negara lain kaya dengan dokumen lama berlandaskan unsur sejarah dan kebudayaan yang jarang ditemui. Malaysia like any other country has old and rare documents that depict its history and culture

Mengenal pasti tahap pengetahuan pelajar tahun akhir Ijazah Sarjana Muda Kejuruteraan di KUiTTHO dalam bidang keusahawanan dari aspek pengurusan modal

Malaysia ialah sebuah negara membangun di dunia. Dalam proses pembangunan ini, hasrat negara untuk melahirkan bakal usahawan beijaya tidak boleh dipandang ringan. Oleh itu, pengetahuan dalam bidang keusahawanan perlu diberi perhatian dengan sewajarnya; antara aspek utama dalam keusahawanan ialah modal. Pengurusan modal yang tidak cekap menjadi punca utama kegagalan usahawan. Menyedari hakikat ini, kajian berkaitan Pengurusan Modal dijalankan ke atas 100 orang pelajar Tahun Akhir Kejuruteraan di KUiTTHO. Sampel ini dipilih kerana pelajar-pelajar ini akan menempuhi alam pekeijaan di mana mereka boleh memilih keusahawanan sebagai satu keijaya. Walau pun mereka bukanlah pelajar dari jurusan perniagaan, namun mereka mempunyai kemahiran dalam mereka cipta produk yang boleh dikomersialkan. Hasil dapatan kajian membuktikan bahawa pelajar-pelajar ini berminat dalam bidang keusahawanan namun masih kurang pengetahuan tentang pengurusan modal terutamanya dalam menentukan modal permulaan, pengurusan modal keija dan caracara menentukan pembiayaan kewangan menggunakan kaedah jualan harian. Oleh itu, satu garis panduan Pengurusan Modal dibina untuk memberi pendedahan kepada mereka

Complex Bases, Number Systems and Their Application to Fractal-Wavelet Image Coding

This thesis explores new approaches to the analysis of functions by combining tools from the fields of complex bases, number systems, iterated function systems (IFS) and wavelet multiresolution analyses (MRA). The foundation of this work is grounded in the identification of a link between two-dimensional non-separable Haar wavelets and complex bases. The theory of complex bases and this link are generalized to higher dimensional number systems. Tilings generated by number systems are typically fractal in nature. This often yields asymmetry in the wavelet trees of functions during wavelet decomposition. To acknowledge this situation, a class of extensions of functions is developed. These are shown to be consistent with the Mallat algorithm. A formal definition of local IFS on wavelet trees (LIFSW) is constructed for MRA associated with number systems, along with an application to the inverse problem. From these investigations, a series of algorithms emerge, namely the Mallat algorithm using addressing in number systems, an algorithm for extending functions and a method for constructing LIFSW operators in higher dimensions. Applications to image coding are given and ideas for further study are also proposed. Background material is included to assist readers less familiar with the varied topics considered. In addition, an appendix provides a more detailed exposition of the fundamentals of IFS theory