Surface differentiation by parametric modeling of infrared intensity scans

We differentiate surfaces with different properties with simple low-cost IR emitters and detectors in a location-invariant manner. The intensity readings obtained with such sensors are highly dependent on the location and properties of the surface, which complicates the differentiation and localization process. Our approach, which models IR intensity scans parametrically, can distinguish different surfaces independent of their positions. Once the surface type is identified, its position (r, θ) can also be estimated. The method is verified experimentally with wood; Styrofoam packaging material; white painted matte wall; white and black cloth; and white, brown, and violet paper. A correct differentiation rate of 100% is achieved for six surfaces, and the surfaces are localized within absolute range and azimuth errors of 0.2 cm and 1.1 deg, respectively. The differentiation rate decreases to 86% for seven surfaces and to 73% for eight surfaces. The method demonstrated shows that simple IR sensors, when coupled with appropriate signal processing, can be used to recognize different types of surfaces in a location-invariant manner. © 2005 Society of Photo-Optical Instrumentation Engineers

Surface differentiation and localization by parametric modeling of infrared intensity scans

In this study, surfaces with different properties are differentiated with simple low-cost infrared (IR) emitters and detectors in a location-invariant manner. The intensity readings obtained from such sensors are highly dependent on the location and properties of the surface, which complicates the differentiation and localization process. Our approach, which models IR intensity scans parametrically, can distinguish different surfaces independent of their positions. The method is verified experimentally with wood, Styrofoam packaging material, white painted wall, white and black cloth, and white, brown, and violet paper. A correct differentiation rate of 100% is achieved for six surfaces and the surfaces are localized within absolute range and azimuth errors of 0.2 cm and 1.1°, respectively. The differentiation rate decreases to 86% for seven surfaces and to 73% for eight surfaces. The method demonstrated shows that simple IR sensors, when coupled with appropriate processing, can be used to differentiate different types of surfaces in a location-invariant manner. © 2005 IEEE

Position-invariant surface recognition and localization using infrared sensors

Low-cost infrared emitters and detectors are used for the recognition of surfaces with different properties in a location-invariant manner. The intensity readings obtained with such devices are highly dependent on the location and properties of the surface in a way that cannot be represented in a simple manner, complicating the recognition and localization process. We propose the use of angular intensity scans and present an algorithm to process them. This approach can distinguish different surfaces independently of their positions. Once the surface is identified, its position can also be estimated. The method is verified experimentally with the surfaces aluminum, white painted wall, brown kraft paper, and polystyrene foam packaging material. A correct differentiation rate of 87% is achieved, and the surfaces are localized within absolute range and azimuth errors of 1.2 cm and 1.0 deg, respectively. The method demonstrated shows that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than they are commonly employed for. © 2003 Society of Photo-Optical Instrumentation Engineers

Target classification with simple infrared sensors using artificial neural networks

This study investigates the use of low-cost infrared (IR) sensors for the determination of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders using artificial neural networks (ANNs). The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way which cannot be represented by a simple analytical relationship, therefore complicating the localization and classification process. We propose the use of angular intensity scans and feature vectors obtained by modeling of angular intensity scans and present two different neural network based approaches in order to classify the geometry and/or the surface type of the targets. In the first case, where planes, 90° corners, and 90° edges covered with aluminum, white cloth, and Styrofoam packaging material are differentiated, an average correct classification rate of 78% of both geometry and surface over all target types is achieved. In the second case, where planes, 90° edges, and cylinders covered with different surface materials are differentiated, an average correct classification rate of 99.5% is achieved. The method demonstrated shows that ANNs can be used to extract substantially more information than IR sensors are commonly employed for. © 2008 IEEE

Rule-based target differentiation and position estimation based on infrared intensity measurements

This study investigates the use of low-cost infrared sensors in the differentiation and localization of target primitives commonly encountered in indoor environments, such as planes, corners, edges, and cylinders. The intensity readings from such sensors are highly dependent on target location and properties in a way that cannot be represented in a simple manner, making the differentiation and localization difficult. We propose the use of angular intensity scans from two infrared sensors and present a rule-based algorithm to process them. The method can achieve position-invariant target differentiation without relying on the absolute return signal intensities of the infrared sensors. The method is verified experimentally. Planes, 90-deg corners, 90-deg edges, and cylinders are differentiated with correct rates of 90%, 100%, 82.5%, and 92.5%, respectively. Targets are localized with average absolute range and azimuth errors of 0.55 cm and 1.03 deg. The demonstration shows that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than they are commonly employed for

Differentiation and localization of target primitives using infrared sensors

This study investigates the use of low-cost infrared sensors in the differentiation and localization of commonly encountered target primitives in indoor environments, such as planes, corners, edges, and cylinders. The intensity readings from such sensors are highly dependent on target location and properties in a way which cannot be represented in a simple manner, making the differentiation and localization process difficult. In this paper, we propose the use of angular intensity scans and present an algorithm to process them. This approach can determine the target type independent of its position. Once the target type is identified, its position can also be estimated. The method is verified experimentally. An average correct classification rate of 97% over all target types is achieved and targets are localized within absolute range and azimuth errors of 0.8 cm and 1.6°, respectively. The proposed method should facilitate the use of infrared sensors in mobile robot applications for differentiation and localization beyond their common usage as simple proximity sensors for object detection and collision avoidance

Simultaneous extraction of geometry and surface properties of targets using simple infrared sensors

We investigate the use of low-cost infrared (IR) sensors for the simultaneous extraction of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, and edges. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and recognition process. We propose the use of angular intensity scans and present an algorithm to process them to determine the geometry and the surface type of the target and estimate its position. The method is verified experimentally with planes, 90-deg corners, and 90-deg edges covered with aluminum, white cloth, and Styrofoam packaging material. An average correct classification rate of 80% of both geometry and surface over all target types is achieved and targets are localized within absolute range and azimuth errors of 1.5 cm and 1.1 deg, respectively. Taken separately, the geometry and surface type of targets can be correctly classified with rates of 99 and 81%, respectively, which shows that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor. The method demonstrated shows that simple IR sensors, when coupled with appropriate processing, can be used to extract substantially more information than that for which such devices are commonly employed. © 2004 Society of Photo-Optical Instrumentation Engineers

Microwave-assisted synthesis, characterizations, antimicrobial activities, and DFT studies on some pyridine derived Schiff base

This study reports a joint experimental, theoretical and microbiological investigation on the (E)-N,N-dimethyl-4-((pyridine-2-ylmethylene)amino)aniline (5), (E)-N,N-dimethyl-4-((pyridine-4-ylmethylene)amino)aniline (6) and (E)-N,N-dimethyl-4-((pyridine-3-ylmethylene)amino)aniline (7). These compounds were synthesized with microwave method and their structures characterized by FT-IR, 1H-NMR, 13C-NMR, and elemental analysis tecniques. In the theoretical studies, torsional barriers analysis, ground state structure, Fourier Transform Infrared spectra (FT-IR), and Nuclear Magnetic Resonance spectra (NMR) of 5, 6, and, 7 were calculated by Density Functional Theory (DFT) computations. The conformers obtained from the torsional barrier scanning were optimized by B3LYP/6-31G(d,p) level. The harmonic vibrational frequencies, potential energy distribution (PED), infrared intensities, and NMR chemical shifts of the most stable conformers were determined using the B3LYP/6-311++G(d,p). Theoretically, predicted spectral data were compared with experimental results. Antimicrobial studies of the synthesized compounds were performed against various microbial strains. Antimicrobial activities of 5, 6, and, 7 were tested against selected bacteria and yeast through minimum inhibitory concentration (MIC) and diffusion method. Compound 7 was found to be the most active against bacteria and yeast, while compound 5 was found to be moderately active. Compounds 6 (against S. aureus and C. albicans) and, 7 were found to have a very high minimum inhibitory concentration, ranging between 1.95 and 7.81 g/mL (against P. aeruginosa and E. coli). Compounds (6 and 7) showed zone of inhibition values in the range of 10–20 mm against other bacteria except L. monocytogenes and S. thyphimurium. © 2022 Elsevier B.V

Statistical pattern recognition techniques for target differentiation using infrared sensor

This study compares the performances of various statistical pattern recognition techniques for the differentiation of commonly encountered features in indoor environments, possibly with different surface properties, using simple infrared (IR) sensors. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting feature in a way that cannot be represented by a simple analytical relationship, therefore complicating the differentiation process. We construct feature vectors based on the parameters of angular IR intensity scans from different targets to determine their geometry type. Mixture of normals classifier with three components correctly differentiates three types of geometries with different surface properties, resulting in the best performance (100%) in geometry differentiation. The results indicate that the geometrical properties of the targets are more distinctive than their surface properties, and surface recognition is the limiting factor in differentiation. The results demonstrate that simple IR sensors, when coupled with appropriate processing and recognition techniques, can be used to extract substantially more information than such devices are commonly employed for. © 2006 IEEE

Sağlık Pazarlaması Ve Uygulamaları,

Özel alanda sağlık sektöründeki gelişmeler ve özel sağlık kuruluşlarının hızla artması beraberinde artan rekabet ve maliyetler, müşteri memnuniyeti, kaliteli hizmet talepleri vb. nedenlerle sağlık sektöründe pazarlama faaliyetleri her geçen gün daha da önem kazanmaktadır. Bu bağlamda pazarlama anlayışı kapsamında hizmet kalitesi ve sağlık turizmine yönelik olarak katılımcılara 35 sorudan oluşan anket uygulanmıştır. Araştırma Denizli, Ankara, Afyon ve İstanbul ili kapsamında 892 katılımcı ile gerçekleştirilmiştir. araştırmada kullanılan anketin güvenirlik katsayısı 0.896 olarak bulunmuştur. Araştırmadan elde edilen verilerin analizinde SPSS 18 İstatistik programı kullanılmıştır. Analiz kapsamında, güvenilirlik analizi, frekans tabloları, betimleyici istatistikler, Korelasyon analizlerinden faydalanılmıştır. Araştırma sonunda, katılımcıların Türk sağlık turizmine ilişkin görüşleri, alınan hizmet ve tesis fiyatlandırması, sağlık kurumlarının hijyen ve genel özellikleri ile genel bilgi ve görüşler faktörleri arasında cinsiyet, milliyet, hizmet tipi, ülke ve gelire göre orta ve kuvvetli düzeyde pozitif yönlü ilişki bulunmuştur