Renyi’s entropy based multilevel thresholding using a novel meta-heuristics algorithm

Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively

Fireworks explosion boosted Harris Hawks optimization for numerical optimization: Case of classifying the severity of COVID-19

Harris Hawks optimization (HHO) is a swarm optimization approach capable of handling a broad range of optimization problems. HHO, on the other hand, is commonly plagued by inadequate exploitation and a sluggish rate of convergence for certain numerical optimization. This study combines the fireworks algorithm's explosion search mechanism into HHO and proposes a framework for fireworks explosion-based HHo to address this issue (FWHHO). More specifically, the proposed FWHHO structure is comprised of two search phases: harris hawk search and fireworks explosion search. A search for fireworks explosion is done to identify locations where superior hawk solutions may be developed. On the CEC2014 benchmark functions, the FWHHO approach outperforms the most advanced algorithms currently available. Moreover, the new FWHHO framework is compared to four existing HHO and fireworks algorithms, and the experimental results suggest that FWHHO significantly outperforms existing HHO and fireworks algorithms. Finally, the proposed FWHHO is employed to evolve a kernel extreme learning machine for diagnosing COVID-19 utilizing biochemical indices. The statistical results suggest that the proposed FWHHO can discriminate and classify the severity of COVID-19, implying that it may be a computer-aided approach capable of providing adequate early warning for COVID-19 therapy and diagnosis

Swarm intelligence algorithms adaptation for various search spaces

U današnje vrijeme postoji mnogo algoritama inteligencije rojeva koji se uspiješno koriste za rešavanje raznih teških problema optimizacije. Zajednicki elementi svih ovih algoritama su operator za lokalnu pretragu (eksploataciju) oko prona enih obecavajucih rješenja i operator globalne pretrage (eksploracije) koji pomaže u bijegu iz lokalnih optimuma. Algoritmi inteligencije rojeva obicno se inicijalno testiraju na neogranicenim, ogranicenim ili visoko-dimenzionalnim skupovima standardnih test funkcija. Nadalje, mogu se poboljšati, prilagoditi, izmijeniti, hibridizirati, kombinirati s lokalnom pretragom. Konacna svrha je korištenje takve metaheuristike za optimizaciju problema iz stvarnog svijeta. Domeni rješenja odnosno prostori pretrage prakticnih teških problema optimizacije mogu biti razliciti. Rješenja mogu biti vektori iz skupa realnih brojeva, cijelih brojeva ali mogu biti i kompleksnije strukture. Algoritmi inteligencije rojeva moraju se prilagoditi za razlicite prostore pretrage što može biti jednostavno podešavanje parametera algoritma ili prilagodba za cjelobrojna rješenja jednostavnim zaokruživanjem dobivenih realnih rješenja ali za pojedine prostore pretrage potrebnao je skoro kompletno prepravljanja algoritma ukljucujuci i operatore ekploatacije i ekploracije zadržavajuci samo proces vo enja odnosno inteligenciju roja. U disertaciji je predstavljeno nekoliko algoritama inteligencije rojeva i njihova prilagodba za razlicite prostore pretrage i primjena na prakticne probleme. Ova disertacija ima za cilj analizirati i prilagoditi, u zavisnosti od funkcije cilja i prostora rješenja, algoritme inteligencije rojeva. Predmet disertacije ukljucuje sveobuhvatan pregled postojecih implementacija algoritama inteligencije rojeva. Disertacija tako er obuhvaca komparativnu analizu, prikaz slabosti i snaga jednih algoritama u odnosu na druge zajedno s istraživanjem prilagodbi algoritama inteligencije rojeva za razlicite prostore pretrage i njihova primjena na prakticne problem. Razmatrani su problemi sa realnim rješenjima kao što su optimizacija stroja potpornih vektora, grupiranje podataka, sa cijelobrojnim rješenjima kao što je slucaj problema segmentacije digitalnih slika i za probleme gdje su rješenja posebne strukture kao što su problemi planiranja putanje robota i triangulacije minimalne težine. Modificirani i prilago eni algoritmi inteligencije rojeva za razlicite prostore pretrage i primjenih na prakticne probleme testirani su na standardnim skupovima test podataka i uspore eni s drugim suvremenim metodama za rješavanje promatranih problema iz literature. Pokazane su uspješne prilagodbe algoritama inteligencije rojeva za razne prostore pretrage. Ovako prilago eni algoritmi su u svim slucajevima postigli bolje rezultate u usporedbi sa metodama iz literature, što dovodi do zakljucka da je moguce prilagoditi algoritme inteligencije rojeva za razne prostore pretrage ukljucujuci i kompleksne strukture i postici bolje rezultate u usporedbi sa metodama iz literature

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Unapređenje procesiranja medicinskih digitalnih slika pomocu algoritama inteligencije rojeva

Medicina je jedna od nauka gde je omogucen znacajan napredak pojavom digitalnih slika i obrade digitalnih slika. Racunarska obrada digitalnih medicinskih slika može drasticno ubrzati proces dijagnostike pri tome otkrivajuci i najsitnije promene na tkivima koje nisu vidljive ljudskom oku. Obrada medicinskih slika ukljucuje slike generisane razlicitim izvorima kao što su rendgen, ultrazvuk, magnetna rezonanca, i snimljene razlicitim urđajima kao što su skeneri, mikroskopske slike, endoskopske kapsule i drugi. Stalni napredak u medicinskoj tehnologiji snimanja doveo je do slika visokih rezolucija, trodimenzionalnih anatomskih i fizioloških slika. Sa druge strane, ovi napreci doveli su do novih problema i izazova u procesiranju medicinskih slika. Mnogi od ovih problema predstavljaju teške optimizacione probleme za cije se rešavanje u poslednje dve decenije uspešno koriste algoritmi inspirisani prirodom, posebno algoritmi inteligencije rojeva. Da bi se ovi algoritmi primenili na probleme optimizacije u obradi medicinskih digitalnih slika, neohodno je da se posebno prilagode konkretnom problemu. Ova tema predstavlja aktivnu oblast naucnog istraživanja što se može zakljuciti na osnovu velikog broja naucnih i strucnih radova, knjiga, casopisa i konferencija koji su joj posveceni. U ovoj tezi predstavljeno je nekoliko algoritma inteligencije rojeva i njihova primena na razlicite optimizacione probleme obrade medicinskih digitalnih slika. Konkretno, algoritam slepog miša, algoritam vatrometa i algoritam svica korišceni su za registraciju slika retine, segmentaciju MRI slika mozga, detekciju krvarenja na slikama endoskopske kapsule, kompresiju slika, detekciju leukemije na mikroskopskim slikama i detekciju emfisema na CT slikama pluca. Svaki od razmatranih problema je specifican i za njihovo rešavanje prilagođeni su algoritmi inteligencije rojeva. Modifikovani i prilagođeni algoritmi inteligencije rojeva za primenu u obradi medicinskih digitalnih slika testirani su na standardnim skupovima test slika prikupljenim za razmatrane probleme. Poređenjem predloženih metoda unapređenja obrade medicinskih digitalnih slika pomocu algoritama inteligencije rojeva sa drugim savremenim algoritmima iz literature, pokazano je da su dobijeni bolji rezultati, što dovodi do zakljucka da je moguce pronaci bolje metode i tehnike za rešavanje problema optimizacije koji se pojavljuju prilikom analize i obrade medicinskih digitalnih slika prilagođavanjem i primenom algoritama inteligencije rojeva

Improving Deep Representation Learning with Complex and Multimodal Data.

Representation learning has emerged as a way to learn meaningful representation from data and made a breakthrough in many applications including visual object recognition, speech recognition, and text understanding. However, learning representation from complex high-dimensional sensory data is challenging since there exist many irrelevant factors of variation (e.g., data transformation, random noise). On the other hand, to build an end-to-end prediction system for structured output variables, one needs to incorporate probabilistic inference to properly model a mapping from single input to possible configurations of output variables. This thesis addresses limitations of current representation learning in two parts. The first part discusses efficient learning algorithms of invariant representation based on restricted Boltzmann machines (RBMs). Pointing out the difficulty of learning, we develop an efficient initialization method for sparse and convolutional RBMs. On top of that, we develop variants of RBM that learn representations invariant to data transformations such as translation, rotation, or scale variation by pooling the filter responses of input data after a transformation, or to irrelevant patterns such as random or structured noise, by jointly performing feature selection and feature learning. We demonstrate improved performance on visual object recognition and weakly supervised foreground object segmentation. The second part discusses conditional graphical models and learning frameworks for structured output variables using deep generative models as prior. For example, we combine the best properties of the CRF and the RBM to enforce both local and global (e.g., object shape) consistencies for visual object segmentation. Furthermore, we develop a deep conditional generative model of structured output variables, which is an end-to-end system trainable by backpropagation. We demonstrate the importance of global prior and probabilistic inference for visual object segmentation. Second, we develop a novel multimodal learning framework by casting the problem into structured output representation learning problems, where the output is one data modality to be predicted from the other modalities, and vice versa. We explain as to how our method could be more effective than maximum likelihood learning and demonstrate the state-of-the-art performance on visual-text and visual-only recognition tasks.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113549/1/kihyuks_1.pd

On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

Lossless and low-cost integer-based lifting wavelet transform

Discrete wavelet transform (DWT) is a powerful tool for analyzing real-time signals, including aperiodic, irregular, noisy, and transient data, because of its capability to explore signals in both the frequency- and time-domain in different resolutions. For this reason, they are used extensively in a wide number of applications in image and signal processing. Despite the wide usage, the implementation of the wavelet transform is usually lossy or computationally complex, and it requires expensive hardware. However, in many applications, such as medical diagnosis, reversible data-hiding, and critical satellite data, lossless implementation of the wavelet transform is desirable. It is also important to have more hardware-friendly implementations due to its recent inclusion in signal processing modules in system-on-chips (SoCs). To address the need, this research work provides a generalized implementation of a wavelet transform using an integer-based lifting method to produce lossless and low-cost architecture while maintaining the performance close to the original wavelets. In order to achieve a general implementation method for all orthogonal and biorthogonal wavelets, the Daubechies wavelet family has been utilized at first since it is one of the most widely used wavelets and based on a systematic method of construction of compact support orthogonal wavelets. Though the first two phases of this work are for Daubechies wavelets, they can be generalized in order to apply to other wavelets as well. Subsequently, some techniques used in the primary works have been adopted and the critical issues for achieving general lossless implementation have solved to propose a general lossless method. The research work presented here can be divided into several phases. In the first phase, low-cost architectures of the Daubechies-4 (D4) and Daubechies-6 (D6) wavelets have been derived by applying the integer-polynomial mapping. A lifting architecture has been used which reduces the cost by a half compared to the conventional convolution-based approach. The application of integer-polynomial mapping (IPM) of the polynomial filter coefficient with a floating-point value further decreases the complexity and reduces the loss in signal reconstruction. Also, the “resource sharing” between lifting steps results in a further reduction in implementation costs and near-lossless data reconstruction. In the second phase, a completely lossless or error-free architecture has been proposed for the Daubechies-8 (D8) wavelet. Several lifting variants have been derived for the same wavelet, the integer mapping has been applied, and the best variant is determined in terms of performance, using entropy and transform coding gain. Then a theory has been derived regarding the impact of scaling steps on the transform coding gain (GT). The approach results in the lowest cost lossless architecture of the D8 in the literature, to the best of our knowledge. The proposed approach may be applied to other orthogonal wavelets, including biorthogonal ones to achieve higher performance. In the final phase, a general algorithm has been proposed to implement the original filter coefficients expressed by a polyphase matrix into a more efficient lifting structure. This is done by using modified factorization, so that the factorized polyphase matrix does not include the lossy scaling step like the conventional lifting method. This general technique has been applied on some widely used orthogonal and biorthogonal wavelets and its advantages have been discussed. Since the discrete wavelet transform is used in a vast number of applications, the proposed algorithms can be utilized in those cases to achieve lossless, low-cost, and hardware-friendly architectures

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures