Realization of multi-input/multi-output switched linear systems from Markov parameters

This paper presents a four-stage algorithm for the realization of multi-input/multi-output (MIMO) switched linear systems (SLSs) from Markov parameters. In the first stage, a linear time-varying (LTV) realization that is topologically equivalent to the true SLS is derived from the Markov parameters assuming that the submodels have a common MacMillan degree and a mild condition on their dwell times holds. In the second stage, zero sets of LTV Hankel matrices where the realized system has a linear time-invariant (LTI) pulse response matching that of the original SLS are exploited to extract the submodels, up to arbitrary similarity transformations, by a clustering algorithm using a statistics that is invariant to similarity transformations. Recovery is shown to be complete if the dwell times are sufficiently long and some mild identifiability conditions are met. In the third stage, the switching sequence is estimated by three schemes. The first scheme is based on forward/backward corrections and works on the short segments. The second scheme matches Markov parameter estimates to the true parameters for LTV systems and works on the medium-to-long segments. The third scheme also matches Markov parameters, but for LTI systems only and works on the very short segments. In the fourth stage, the submodels estimated in Stage~2 are brought to a common basis by applying a novel basis transformation method which is necessary before performing output predictions to given inputs. A numerical example illustrates the properties of the realization algorithm. A key role in this algorithm is played by time-dependent switching sequences that partition the state-space according to time, unlike many other works in the literature in which partitioning is state and/or input dependent

Basis transform in switched linear system state-space models from input-output data

This paper tackles the basis selection issue in the context of state-space hybrid system identification from input-output data. It is often the case that an identification scheme responsible for state-space switched linear system (SLS) estimation from input-output data operates on local levels. Such individually identified local estimates reside in distinct state bases, which call for the need to perform some basis correction mechanism that facilitates their coherent patching for the ultimate goal of performing output predictions for predefined input test signals. We derive necessary and sufficient conditions on the submodel set, the switching sequence, and the dwell times that guarantee the presented approach's success. Such conditions turn out to be relatively mild, which contributes to the application potential of the devised algorithm. We also provide a linkage between this work and the existing literature by providing several insightful remarks that highlight the discussed method's favorability. We supplement the theoretical findings by an elaborative numerical simulation that puts our methodology into action

Hierarchical segmentation, object detection and classification in remotely sensed images

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent Univiversity, 2007.Thesis (Master's) -- Bilkent University, 2007.Includes bibliographical references leaves 68-76Automatic content extraction and classification of remotely sensed images have become highly desired goals by the advances in satellite technology and computing power. The usual choice for the level of processing image data has been pixelbased analysis. However, spatial information is an important element to interpret the land cover because pixels alone do not give much information about image content. Automatic segmentation of high-resolution remote sensing imagery is an important problem in remote sensing applications because the resulting segmentations can provide valuable spatial and structural information that are complementary to pixel-based spectral information in classification. In this thesis, we first present a method that combines structural information extracted by morphological processing with spectral information summarized using principal components analysis to produce precise segmentations that are also robust to noise. First, principal components are computed from hyper-spectral data to obtain representative bands. Then, candidate regions are extracted by applying connected components analysis to the pixels selected according to their morphological profiles computed using opening and closing by reconstruction with increasing structuring element sizes. Next, these regions are represented using a tree, and the most meaningful ones are selected by optimizing a measure that consists of two factors: spectral homogeneity, which is calculated in terms of variances of spectral features, and neighborhood connectivity, which is calculated using sizes of connected components. Experiments on three data sets show that the method is able to detect structures in the image which are more precise and more meaningful than the structures detected by another approach that does not make strong use of neighborhood and spectral information.Then, we introduce an unsupervised method that combines both spectral and structural information for automatic object detection. First, a segmentation hierarchy is constructed and candidate segments for object detection are selected by the proposed segmentation method. Given the observation that different structures appear more clearly in different principal components, we present an algorithm that is based on probabilistic Latent Semantic Analysis (PLSA) for grouping the candidate segments belonging to multiple segmentations and multiple principal components. The segments are modeled using their spectral content and the PLSA algorithm builds object models by learning the objectconditional probability distributions. Labeling of a segment is done by computing the similarity of its spectral distribution to the distribution of object models using Kullback-Leibler divergence. Experiments on three data sets show that our method is able to automatically detect, group, and label segments belonging to the same object classes. Finally, we present an approach for classification of remotely sensed imagery using spatial information extracted from multi-scale segmentations. Different structuring element size ranges are used to obtain multiple representations of an image at different scales to capture different details inherently found in different structures. Then, pixels at each scale are grouped into contiguous regions using the proposed segmentation method. The resulting regions are modeled using the statistical summaries of their spectral properties. These models are used to cluster the regions by the proposed grouping method, and the cluster memberships assigned to each region at multiple scales are used to classify the corresponding pixels into land cover/land use categories. Final classification is done using decision tree classifiers. Experiments with three ground truth data sets show the effectiveness of the proposed approach over traditional techniques that do not make strong use of region-based spatial information.Akçay, Hüseyin GökhanM.S

Automated bird counting with deep learning for regional bird distribution mapping

A challenging problem in the field of avian ecology is deriving information on bird population movement trends. This necessitates the regular counting of birds which is usually not an easily-achievable task. A promising attempt towards solving the bird counting problem in a more consistent and fast way is to predict the number of birds in different regions from their photos. For this purpose, we exploit the ability of computers to learn from past data through deep learning which has been a leading sub-field of AI for image understanding. Our data source is a collection of on-ground photos taken during our long run of birding activity. We employ several state-of-the-art generic object-detection algorithms to learn to detect birds, each being a member of one of the 38 identified species, in natural scenes. The experiments revealed that computer-aided counting outperformed the manual counting with respect to both accuracy and time. As a real-world application of image-based bird counting, we prepared the spatial bird order distribution and species diversity maps of Turkey by utilizing the geographic information system (GIS) technology. Our results suggested that deep learning can assist humans in bird monitoring activities and increase citizen scientists’ participation in large-scale bird surveys.No sponso

Effects of chromosomal translocations on sperm count in azoospermic and oligospermic cases

Purpose: A number of mechanisms have been proposed for the effect of chromosomal translocations on spermatogenesis and sperm maturation. However, there are still numerous ambiguous issues regarding these two processes. The aim of this study is to evaluate the effect of chromosome break areas on sperm count in the light of the literature. Material and Methods: The study was conducted on the data of 16 male patients with reciprocal or Robertsonian translocation among 152 patients who were admitted to Adana Numune Training and Research Hospital and Kanuni Sultan Suleyman Training and Research Hospital Genetic Diagnosis Centers between 2013 and 2016 due to azoospermia and oligospermia. Results: 11 of these patients had reciprocal and five patients had Robertsonian translocations. All the patients with Robertsonian translocations were detected with azoospermia. Of the patients with reciprocal translocation, five of them were azoospermic and six of them were severe oligospermic. Conclusion: A total of 21 chromosomal breakpoints were identified in the 11 patients with reciprocal translocations. These chromosomal breakpoints may contribute to the clarification of ambiguous issues related to spermatogenesis and sperm maturation. The results also showed the importance of genetic counselling in patients with translocations

Effective Mass Dirac-Morse Problem with any kappa-value

The Dirac-Morse problem are investigated within the framework of an approximation to the term proportional to $1/r^2$ in the view of the position-dependent mass formalism. The energy eigenvalues and corresponding wave functions are obtained by using the parametric generalization of the Nikiforov-Uvarov method for any $\kappa$-value. It is also studied the approximate energy eigenvalues, and corresponding wave functions in the case of the constant-mass for pseudospin, and spin cases, respectively.Comment: 12 page

Production and characterization of Al2024/SiC Composites with high SiC reinforcement

Bu çalışmada, ağırlıkça %10, %20 ve %40 SiC takviyesi içeren Al2024/SiC parçacıklarının mekanik öğütme (MÖ) yöntemiyle üretimi ve üretilen bu parçacıkların sıcak presleme (SP) yöntemiyle sıkıştırılması gerçekleştirilmiştir. Yapı içerisinde değişen ağırlık oranlarında (%10, 20 ve 40) SiC toz takviyesinin morfoloji, mikro yapı, element analizi, sertlik ve yoğunluk özellikleri üzerindeki etkileri araştırılmıştır. Deneysel çalışmalar sonucunda MÖ işlemi ile elde edilen homojen parçacıklar sayesinde mikro yapıda homojen bir element dağılımı gözlemlenmiştir. Ancak, yapıda ağırlıkça artan SiC toz miktarının porozite oluşumuna neden olduğu belirlenmiştir. Ağırlıkça %10, %20 ve %40 SiC içeriğine sahip kompozit numunelerin % bağıl yoğunluk değerleri sırasıyla %99.060, %98.301 ve %95.252 olarak ölçülmüştür. Yapıdaki SiC tanecik takviyesinin ağırlığının artmasıyla sertlik değerlerinde çok yüksek bir artış gözlenmiştir. Ağırlıkça %10, %20 ve %40 SiC toz içeren Al2024/SiC kompozit malzemeler için sertlik değerleri sırasıyla 177.23 HV(0.5), 250.617 HV(0.5) ve 316.67 HV(0.5) olarak ölçülmüştür.In this study, the production of Al2024/SiC particles containing 10%, 20% and 40% by weight SiC reinforcement by mechanical milling (MM) method and the compaction of these produced particles by hot pressing (SP) method were carried out. The effects of SiC particle reinforcement in varying weight ratios (%10, 20 and 40) in the structure on morphology, microstructure, elemental analysis, hardness and density properties were investigated. As a result of the experimental studies, a homogeneous elemental distribution was observed in the microstructure thanks to the homogeneous particles obtained by the MM process. However, it was determined that the amount of SiC particles increasing in weight in the structure caused the formation of porosity. The % relative density values of the composite samples with 10, 20 and 40 wt% SiC contents were measured as 99.060%, 98.301% and 95.252%, respectively. A very high increase in hardness values was observed with the increase in weight of SiC particle reinforcement in the structure. Hardness values were measured as 177.23 HV(0.5), 250.617 HV(0.5) and 316.67 HV(0.5) for Al2024/SiC composite materials containing 10, 20 and 40 wt% SiC particles, respectively

Dijagnostička točnost sastojaka mlijeka kod dijagnostike gravidnosti u krava srednje i kasne laktacije

The aims of this study were to establish a cut-off point by evaluating the usability of the somatic cell count (SCC) and milk components (fat, fat-free dry matter (FFDM), protein, lactose, freezing point, electrical conductivity and pH) to observe the pregnancy status, and to determine the practical usage of these parameters as diagnostic biomarker of pregnancy status. In the present study, primiparous Holstein cows (n=133) were included in the mid and late lactation. Milk samples were collected in sterile tubes for SCC and milk components analysis. In each lactation period, SCC, milk yield and milk component parameters were analysed by Student\u27s t test according to pregnancy status. Receiver operating characteristic curves were used to determine the predictive threshold using SCC and milk component parameters to discriminate between pregnant and non-pregnant cows. SCC levels were similar for all cows in the mid and late-lactation. In the mid lactation, FFDM, protein, lactose and electrical conductivity were higher and milk yield, fat, freezing point and pH were lower in pregnant cows (p<0.05). In the late lactation, FFDM, protein, lactose and electrical conductivity were significantly higher and milk yield, fat and pH were significantly lower in pregnant cows (p<0.05). Furthermore, fat, FFDM, protein, lactose, freezing point, electrical conductivity, and pH were the best predictors for pregnancy diagnosis in mid-lactating cows with the AUC values of 0.840, 0.768, 0.780, 0.772, 0.693, 0.792, and 0.901 respectively. Furthermore, fat, FFDM, protein, lactose, electrical conductivity, and pH could be useful diagnostic tools for pregnancy determination in late lactating cows with the AUC values of 0.869, 0.684, 0.661, 0.689, 0.756, and 0.841 respectively. In conclusion, the milk components could be used as rapid, easily accessible, and inexpensive markers for the evaluation of the diagnosis of pregnancy status in primiparous Holstein cows.Ciljevi ove studije bili su utvrditi granične vrijednosti procjenom mogućnosti upotrebe broja somatskih stanica (SCC) i pojedinih fizikalno-kemijskih parametara mlijeka (udio masti, bezmasne suhe tvari (FFDM), proteina, laktoze, točke zamrzavanja, električne vodljivosti i pH) za promatranje statusa gravidnosti kao i u svrhu korištenja navedenih parametara kao bioloških markera u dijagnozi statusa gravidnosti. U ovu su studiju bile uključene prvotelke holstein pasmine (n=133) u srednjoj i kasnoj laktaciji. Uzorci mlijeka sakupljani su u sterilne epruvete za analizu SCC i fizikalno-kemijskih parametara. U svakom razdoblju laktacije, SCC, prinos mlijeka i fizikalno-kemijski parametri analizirani su Student t-testom u odnosu na status gravidnosti. Za određivanje prediktivnog praga korištene su krivulje odnosa specifičnosti i osjetljivosti klasifikatora (ROC), korištenjem SCC i fizikalno-kemijskih parametara mlijeka za razlikovanje gravidnih i negravidnih krava. Za sve krave u srednjoj i kasnoj laktaciji razine SCC bile su slične. Sredinom laktacije, FFDM, udjeli proteina i laktoze te električna vodljivost bili su viši, a prinos mlijeka, udio masti, točka ledišta i pH bili su niži u gravidnih krava (p<0,05). U kasnoj laktaciji, FFDM, udjeli proteina i laktoze te električna vodljivost bili su značajno viši, a prinos mlijeka, udio masti i pH bili su značajno niži u gravidnih krava (p<0,05). Udjeli masti, proteina i laktoze, FFDM, točka ledišta, električna vodljivost i pH bili su najbolji prediktori za dijagnozu gravidnosti kod krava u srednjoj laktaciji s vrijednostima površina ispod ROC krivulje (AUC) 0,840, 0,768, 0,780, 0,772, 0,693, 0,792 i 0,901. Udjeli masti, proteina i laktoze, FFDM, električna vodljivost i pH mogu biti korisni dijagnostički alati za određivanje gravidnosti kod krava u kasnoj laktaciji s AUC vrijednostima 0,869, 0,684, 0,661, 0,689, 0,756 i 0,841. Zaključno, komponente mlijeka mogu se koristiti kao brzi, lako dostupni i jeftini markeri za procjenu dijagnoze statusa gravidnosti kod prvotelki holstein krava

Immediate recovery of the left atrial and left ventricular diastolic function after transcatheter aortic valve implantation: A transesophageal echocardiography study

Background: Chronic increased afterload due to severe aortic stenosis (AS) results in com­pensatory concentric left ventricular (LV) hypertrophy and LV dysfunction. These in turn cause remodeling of the left heart. The aim of this study was to investigate the acute effect of transcatheter aortic valve implantation (TAVI) on left atrial (LA) mechanics and LV diastolic function. Methods: The study consisted of a total of 35 consecutive patients (mean age was 77.7 ± 5.0 years, 25 female) undergoing TAVI. All TAVI procedures have been performed under the transesophageal echocardiography (TEE) guidance. Before and 24 h after TAVI, all patients underwent transthoracic echocardiography (TTE) and mitral inflow velocities with pulsed-wave (PW) Doppler including early filling wave (E), late diastolic filling wave (A), and E/A ratio were obtained. LV diastolic function was also explored by pulsed tissue Doppler imaging (TDI). Early (E’) and late (A’) diastolic annular velocities, E’/A’ ratio and E/E’ ratio were obtained. In addition, during the procedure before and minutes after the valve implantation, the left atrial appendage-peak antegrade flow velocity (LAA-PAFV) was measured and recorded with TEE. Results: Compared with baseline, the mean mitral E, septal E’ and E’/A’ ratio increased significantly after TAVI. In addition, the LAA-PAFV increased significantly within minutes of TAVI (32.45 ± 10.7 cm/s vs. 47.6 ± 12.6 cm/s, p &lt; 0.001). Conclusions: TAVI improves LV diastolic function and LA performance immediately

Impact of transcatheter aortic valve implantation in patients with reduced ejection fraction

Background: Aortic stenosis increases with age. According to guidelines, left ventricular systolic dysfunction is an indication for aortic valve replacement, even in asymptomatic patients. There is no clear data on the application of transcatheter aortic valve implantation (TAVI), which is a method showing continuous improvement in recent years, in patients with reduced ejection fraction (REF) having a poor prognosis for surgical aortic valve replacement. We therefore aimed to investigate the effect of TAVI on left ventricular ejection fraction (LVEF) and also its efficacy and safety in patients with REF. Methods and results: The study included 104 patients who underwent transfemoral TAVI in our clinic. The patients were divided into two groups: LVEF ≤ 45% (REF group, n = 28) and LVEF &gt; 45% (preserved ejection fraction [PEF] group, n = 76). Follow-up measure­ments were performed at baseline, discharge, 1st, 6th and 12th months. No statistical difference was found between the groups with respect to complications and mortality rates. A statistically significant difference was detected in LVEF after TAVI, either in all patients (53.9 ± 14.6, 57.0 ± 11.4, 59.4 ± 8.4, 60.4 ± 6.8, 63.2 ± 3.9, respectively, at baseline, discharge, 1st, 6th and 12th months, p &lt; 0.001) or in the groups separately. A statistically significant increase in LVEF (p &lt; 0.001) was determined at discharge, 1st, 6th and 12th months, whereas LVEF increased in all follow-ups of the PEF group, however this elevation reached a statistical significance only at the 1st month (p = 0.04). Conclusions: Our study has shown the positive effect of TAVI on LVEF and its effective and safe applicability in patients with REF.