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Ivan Illich famously coined the term «tools for conviviality» in which he refers to tools that encourage individual creativity and expression, and that can be adapted to people’s needs and situations. This paper describes the design of an interactive adaptable tool, whose applications include education and game play, and contextualizes it within Illich’s framework. The Collaborative Interactive Tabletop for Education (CITE), currently being developed at SUNY Polytechnic, allows users to manipulate digital information through the manipulation of hand-held tabletop objects. Such interfaces, known as Tangible User Interfaces (TUI), are believed to have significant advantages over traditional screen-based interfaces. For example, they have been shown to measurably increase collaborative behavior among users. However, TUIs have not been widely adopted, perhaps because most current TUI designs require some kind of specialized hardware. Our approach is to instead create a system, a set of verified instructions along with open-license software packages, for adapting any combination of standard computer hardware into a highly customizable interactive tabletop.

Satellite image analysis is a research area in which many research studies are carried out for civil and military applications in the field of image processing. Satellite imagery has many applications including recognition, detection and classification of regions, buildings, roads, aircraft and other man-made objects. Among these, especially aircraft detection is strategically important for military applications, and forms the basis of this study. In the first phase of the study, a new dataset of aircrafts is created from Google Earth images to compensate the shortage of data set in this area. In the second stage, the detection of air vehicles was carried out using algorithms based on Convolutional Neural Network (CNN). Region-based Fully Convolutional Network (R-FCN), Single Shot Multi Box Detector (SSD) and Faster R-CNN methods are used for this process. The obtained accuracy rate for R-FCN, SSD and Faster R-CNN are 98.01%, 69.71% and 96.56%, respectively., Uydu görüntü analizi, görüntü işleme alanında sivil ve askeri uygulamalar için birçok çalışmanın yapıldığı geniş bir araştırma alanıdır. Uydu görüntüleri, bölgelerin, binaların, yolların, uçakların ve diğer insan yapımı nesnelerin tanınması, algılanması ve sınıflandırılması olmak üzere birçok uygulamada kullanılmaktadır. Uçak tespiti özellikle askeri uygulamalar için stratejik öneme sahiptir ve bu çalışmanın temelini oluşturmaktadır. Bu çalışmada uçak tespiti uygulamalarına yönelik veri seti eksikliğini telafi etmek için Google Earth görüntülerinden yeni bir uçak veri seti oluşturulmuştur. Hava araçlarının tespiti için Konvolüsyonel Sinir Ağlarına (CNNs) dayalı algoritmalar kullanılmıştır. Uçak tespitine yönelik yapılan deneylerde Bölge Tabanlı Tam Evrişimli Ağ (R-FCN), Tek Atışlı Çoklu Kutu Detektörü (SSD) ve Daha Hızlı R-CNN (Faster R-CNN) yöntemlerinin performansı karşılaştırılmıştır ve sırasıyla % 98.01,% 69.71 ve % 96.56 doğruluk oranları elde edilmiştir.

The event related P300 potentials, positive waveforms in electroencephalography (EEG) signals, are often utilized in brain computer interfaces (BCI). Many studies have been carried out to improve the performance of P300 speller systems either by developing signal processing algorithms and classifiers with different architectures or by designing new paradigms. In this study, a new paradigm is proposed for this purpose. The proposed paradigm combines two remarkable properties of being a 3D animation and utilizing column-only flashings as opposed to classical paradigms which are based on row-column flashings in 2D manner. The new paradigm is utilized in a traditional two-layer artificial neural networks model with a single output neuron, and numerous experiments are conducted to evaluate and compare the performance of the proposed paradigm with that of the classical approach. The experimental results, including statistical significance tests, are presented for single and multiple EEG electrode usage combinations in 1, 3 and 15 flashing repetitions to detect P300 waves as well as to recognize target characters. Using the proposed paradigm, the best average classification accuracy rates on the test data are improved from 89.97% to 93.90% (an improvement of 4.36%) for 1 flashing, from 97.11% to 98.10% (an improvement of 1.01%) for 3 flashings and from 99.70% to 99.81% (an improvement of 0.11%) for 15 flashings when all electrodes, included in the study, are utilized. On the other hand, the accuracy rates are improved by 9.69% for 1 flashing, 4.72% for 3 flashings and 1.73% for 15 flashings when the proposed paradigm is utilized with a single EEG electrode (P8). It is observed that the proposed speller paradigm is especially useful in BCI systems designed for few EEG electrodes usage, and hence, it is more suitable for practical implementations. Moreover, all participants, given a subjective test, declared that the proposed paradigm is more user-friendly than classical ones.

The COVID-19, which has rapidly spread and infected millions of people from all over the world, causes various problems including psychiatric, economic, educational as well as health. Many studies have been reported that COVID-19 can be characterized by vascular damage predominantly involving micro vessels. In this study, we proposed a method to examine whether COVID-19 effects on brain computer interface (BCI) performance or not. We collected P300 based electroencephalogram (EEG) signals from six subjects before and after the COVID-19 infection. For classifying the P300 and non-P300 EEG signals, single output and two-layer artificial neural network was utilized. Based on the t-test analysis, it was observed that there was a significant difference between the before and after COVID-19 infection test groups especially on Oz channel in occipital region for alpha=0.05 percent while that of for alpha=0.01 percent shows no statistical difference for P300 classification results. The latency values, on the other hand, before and after COVID-19 infection did not represent any difference for both significance levels. It is clearly understood from the literature that COVID-19 negatively effects to the microvascular bed. Therefore, it might be expected that it could cause to reduce the P300 based BCI performance. This was the first study to investigate the impact of COVID-19 on P300-based BCI performance, taking into account the EEG signals of the COVID-19 infection. The obtained results showed that although the COVID-19 infection did not generally effected P300 based BCI application performance and latency values, the performance of the occipital region electrodes slightly effected.

Drone detection and classification, important in military and civilian applications, are performed using different sensor signals. Proposed study handles this task using Radio Frequency (RF) signals utilizing basic machine learning methods. It is composed of two main stages as feature extraction succeeded by training/testing of the model. In feature extraction stage, valuable information for classification, contained in the RF signal, is obtained. For this purpose, spectral features, frequently used in speech processing applications, are employed. Specifically, Power Spectral Density (PSD), Mel-Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC) are adopted by adjusting filter bank margins and parameters for this task. In the second stage, a Support Vector Machine (SVM) classifier is first trained based on the obtained features and finally tested to measure its performance. All experimental studies are carried out using publicly available DroneRF dataset. This dataset contains 2-Class, 4-Class and 10-Class samples for drone existence vs. background (BG), drone types and drone operation modes, respectively. The best classification results are obtained using, PSD, MFCC and LFCC based features for 2-Class, MFCC and LFCC based features for 4-Class and LFCC based features for 10-Class. Accuracy rates for 2-Class, 4-Class and 10-Class are 100%, 98.67% and 95.15%, respectively. These results show that the proposed method outperforms the results given in the literature for DroneRF dataset.

In this study, using the polysomnography data set, the classification of sleep stages was realized automatically with supervised learning method. In this study, sleep stage classification was carried out in three stages. In the first stage, the biomedical signal was divided into its independent components by Independent Component Analysis method. In the second stage, feature extraction was performed by using Mel Kepstrum Coefficient method. In the third stage, artificial neural networks were trained by using the extracted features and the sleep phases were estimated by using software architecture called Long ShortTerm Memory. As a result of the classification process performed in this way, the accuracy rate of the ten fold cross validation obtained for the binary classification (asleep / wake) was found to be 97,87%. For the five fold classification problem, the accuracy rate of the subject dependent algorithm and polysomnography data obtained using a single EEG channel was found to be 93,36% as a result of the ten-fold cross validation process.

An efficient de-embedding air-line microwave method has been proposed for accurate relative complex permittivity, e r = e r ′ − i e r ′ ′ , measurement of water-adulteration level within honey. It could be effectively applied to eliminate the errors arising from usage of imperfect calibration standards because it bypasses the requirement of these standards. Its accuracy is improved by utilizing the unitary and similarity properties of a passive two-port network, and then is compared with the accuracy of a calibration-dependent method present in the literature by using normalized root-mean-square-error (N-RMSE) values of e r ′ and e r ′ ′ of distilled water, in reference to the Debye value. From this comparison, it is observed that N-RMSE values calculated for e r ′ (and e r ′ ′ ) by using this calibration-dependent method and the (improved) proposed method are, respectively, around 0.1955 (0.1002) and 0.1962 (1.1067), indicating a good agreement between them. After validation the proposed de-embedding method using distilled water measurements, tested pure honey was adulterated with distilled water by different percentage values δ ranging from 1% to 10% in 1% increments. It is observed that the maximum distance between extracted e r ′ (or e r ′ ′ ) values of adulterated honey by the applied calibration-dependent method and the proposed method is less than 2%. Afterward, an empirical formula was devised to fit e r ′ and e r ′ ′ values from measured e r of water-adulterated honey with various δ levels. It is noted that extracted e r ′ is much more better fitted than extracted e r ′ ′ , especially for δ ≤ 4 . Next, an optimization process is followed to evaluate the frequency for optimum prediction of adulteration levels using the empirical formula based on e r ′ or e r ′ ′ . It is noticed that optimized δ values using the empirical formula based on e r ′ (with an average prediction error of around 0.071 at 4.5 GHz) are superior to optimized δ values using the empirical formula based on e r ′ ′ (with an average prediction error of around 0.085 at 4.2 GHz) for prediction of previously known δ values. Sensitivity and uncertainty analyses were performed to assess and improve the accuracy of the proposed method.

Satellite image analysis is a field of study in the field of image processing for many civil and military applications. Satellite images have many civilian applications, including recognition, detection and classification of regions, buildings, roads, aircrafts and other man-made objects. Among these, especially aircraft detection is strategically important for military applications and forms the basis of this study. In the first phase of the study, a new dataset composed of airplanes at different airports on Google Earth's satellite images is prepared to eliminate the lack of data set in this field. In the second stage, the detection of aircraft was carried out by using algorithms based on Convolutional Neural Network. Transfer Learning was used to improve the performance of this process. The accuracy rate obtained as a result of Transfer Learning with previously trained network Alexnet, was 91%.

Radome (Radar Domes) detection in remote sensing imagery plays a vital role for both civilian and military applications. Several studies have been carried out to detect objects in remote sensing imagery. However, no study has performed for radome detection due to several factors. The most important one is the absence of an existing dataset. This study has two objectives. The first one is to create a dataset of images which all contain single or multiple radome objects and all acquired from Google Earth with different spatial resolution. The second objective is radome location detection with Faster R-CNN. Based on the experimental results the radome detection accuracy was 80% on the constructed dataset, ATA-Radome.

We investigate the effect of fabrication parameters (nonequal surface current densities, impurities inside the structure, etc.) and loss factor on reflectivity and transmittivity measurements from porous silicon Fabry–Perot cavities with finite-size substrate thicknesses. We apply the formalism based on wave cascade matrix method for obtaining dependencies of reflectivity and transmittivity. From our analysis, we note the following results. First, resonance behavior of reflectivity and transmittivity changes only when optical/physical properties of middle layers of the cavity alter. Second, for lossless cavities, while reflectivity and transmittivity considerably change with surface characteristics (optical/physical properties of first layers), transmittivity is significantly modified by a change of optical/physical properties of middle layers (microcavity region). Third, loss inside a FP cavity makes the transmittivity more immune to variations in optical/physical properties of middle layers. Finally and most importantly, transmittivity values at resonance wavelength as well as the resonance wavelength shift can be utilized for the identification of unknown chemical/biological molecules by lossless FP cavities. For validation of these results, we carried out reflectivity and transmittivity measurements from some arbitrarily chosen positions but around the center of two fabricated FP cavities resonating at 1456 nm and at 542 nm.

We propose a novel method for identification of various gases using combined measurements of wavelength shift ( $\Delta \lambda _{0}$ ) and reflectivity difference ( $\Delta R$ ). The method relies on plotting the measurement data on the $\Delta R$ – $\Delta \lambda _{0}$ space and exploits in the identification process the effect of significant loss behavior of bulk silicon near or within the visible range (for lossless gas vapors). It has been validated by reflectivity measurements on a Fabry–Perot cavity resonating around 745 nm for four different gas vapors whose reflective indices are close to each other and by two metrics for separability/identification analysis by the scattering matrix method. It is noted that the combined measurements of $\Delta \lambda _{0}$ and $\Delta R$ result in better identification than that obtained by the measurement of $\Delta \lambda _{0}$ (or $\Delta R$ ) itself.

This study presents a low cost measurement setup for estimating apple bruise volume during drop impact tests. The proposed measurement system consists of three main stages namely recording impact sound, signal processing, and estimating bruise volume. In the recording stage, the impact sound occurred when striking the apple on the surface was recorded by a piezoelectric microphone into computer. The impact sound was converted into Shannon entropy signals based on the Kernel density approach, and two measurable quantities namely entropy peak and pulse-width time were extracted from Shannon entropy signals in the signal processing stage. In the bruise volume estimating stage, the regression curves that map entropy peak and pulse-width features into bruise volume are estimated using measurements data. To evaluate the performance of the proposed method, we conducted several drop experiments with six different drop heights and two different temperatures (1 and 19 °C). Experimental results showed that bruise volume was exponentially proportional to the entropy peak and it was inversely proportional to pulse-width time as power functions, and all the curves were fitted with coefficients of determination of more than 0.98. Moreover, it was found that colder apples developed less bruise volume then warmer ones.

Our research envisions a framework to analyze dynamics of service delivery over different environments including cloud platforms. Our research takes into account the business objectives, service development and deployment dynamics, data center and cloud dynamics for analysis, prediction, simulation and comparison of profits generated by the delivered services to the organization. This paper focuses on estimating the cost of digital services when such services are provided with on-premise IT infrastructure and with on-cloud infrastructure in a hybrid cloud deployment. The required computing resources for delivering the digital service are estimated. After that, the costs are estimated with pay-per-use pricing, subscription pricing and auction pricing models.

Through investigating a particular blog, several factors were found which have significant influence on reader actions. This study uses the term “memetic primers” for those styles that most often cause readers to take notice of a particular comment, remember information included in it, and take action. The memetic primers were derived in a discourse analysis. The study discovered the memetic primers using logic-of-inquire approach to the online comments. While evidence indicated that the usefulness of some primers was low, it emerged those negatively written comments where the most common impact on a comment’s volume.

A free-space measurement method has been proposed for complex permittivity determination of lossy materials at millimeter and terahertz frequencies. The method relies on the Fabry-Perot interference effect between two identical samples separated by a varying air region. We derived a general metric function for fast and accurate complex permittivity determination of lossy materials using intensity data of transmittance power measurements. We have shown that via a graphical method, unique permittivity retrieval is possible using only two measurements of transmittance data corresponding to different distances between identical lossy samples. We have validated the proposed method by performing a numerical analysis.

In this study, an innovative design for porous silicon (PSi) based sensors is proposed to eliminate some problems of conventional PSi sensors such as undesired reflections from imprecise positioning of the fiber optic cable and the PSi structure. Our design has three stages as hole milling for fiber socket, fabrication of filter structure, and integration of the fiber optic cable to the bulk Si. We have carried out reflectivity measurements of alcohols with close refractive index values by the proposed and conventional sensors. From the measurements, we note that both sensors have equal sensitivity in identifying alcohols, that repeatability of our proposed sensor is relatively higher, and that our sensor allows easy positioning and flexibility in sensing applications. Nonetheless, our proposed sensor necessitates a thorough fabrication process and a methodological preparation.

In this paper, we investigate the effect of non-uniformities (enlargement of current passage, non-equal surface current densities, etc.) in axial as well as transverse directions of a porous silicon Fabry-Perot (FP) cavity as well as loss nature of bulk silicon on spectral properties of this cavity, even that cavity is created with an anisotropic etching process. Without correct and comprehensive characterization of such cavities by incorporating these non-uniformities and inherent lossy nature of a cavity, detection and identification of biological and chemical molecules by that cavity may yield unpredictable and misleading results. From our simulations, we note the following two key points. First, effects of the refractive index and the thickness of microcavity region of a lossless or lossy FP cavity on resonance wavelength is more prevailing than those of first and last layers. Second, the effect of some small loss inside the FP cavity is not detectable by the measurement of resonance wavelength whereas the same influence is noticeable by the measurement of reflectivity. We carried out some measurements from two different regions on the fabricated cavities to validate our simulation results. From a practical point of view in correct detection and/or identification of lossy biological or chemical vapor by FP cavities, we conclude that not only the measurement of resonance wavelength as well as its shift but also the reflectivity value at the resonance wavelength or some specific wavelengths should be utilized.

Researchers have argued that video games have great utility for learning. Games promote experiential learning and can be used to facilitate active learning. This paper examines the potential of video games in education. In particular, it examines the benefits of game modding compared to playing and/or creating games. However, video game classes have been primarily attended by male students. This paper looks further into the gender issue regarding the use of video game modding in education. This is demonstrated through a course developed by the authors on game design. The main goal of the course was to introduce middle school and high school female students to IT and assist them in acquiring five basic IT skills. During the course, survey data was collected from participating students. Results from the surveys as well as analysis of student projects and anecdotal evidence suggest that using video game modding is successful in increasing self‐efficacy and motivation as well as teaching female students basic IT skills.

Three difierent techniques are applied for accurate constitutive parameters determination of isotropic split-ring resonator (SRR) and SRR with a cut wire (Composite) metamaterial (MM) slabs. The flrst two techniques use explicit analytical calibration- dependent and calibration-invariant expressions while the third technique is based on Lorentz and Drude dispersion models. We have tested these techniques from simulated scattering (S-) parameters of two classic SRR and Composite MM slabs with various level of losses and difierent calibration plane factors. From the comparison, we conclude that whereas the extracted complex permittivity of both slabs by the analytical techniques produces unphysical results at resonance regions, that by the dispersion model eliminates this shortcoming and retrieves physically accurate constitutive parameters over the whole analyzed frequency region. We argue that incorrect retrieval of complex permittivity by analytical methods comes from spatial dispersion efiects due to the discreteness of conducting elements within MM slabs which largely vary simulated S-parameters in the resonance regions where the slabs are highly spatially dispersive.