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Unmanned aerial vehicles (UAVs) are increasingly significant in modern warfare due to their versatility and capacity to perform high-risk missions without risking human lives. Beyond surveillance and reconnaissance, UAVs with jet propulsion and engagement capabilities are set to play roles similar to conventional jets. In various scenarios, military aircraft, drones, and UAVs face multiple threats while ground radar systems continuously monitor their positions. The interaction between these aerial platforms and radars causes temporal fluctuations in scattered echo power due to changes in aspect angle, impacting radar tracking accuracy. This study utilizes the potential radar cross-section (RCS) dynamics of an aircraft throughout its flight, using ground radar as a reference. Key factors influencing RCS include time, frequency, polarization, incident angle, physical geometry, and surface material, with a focus on the complex scattering geometry of the aircraft. The research evaluates the monostatic RCS case and examines the impact of attitude variations on RCS scintillation. Here, we present dynamic RCS modeling by examining the influence of flight dynamics on the RCS fluctuations of a UAV-sized aircraft. Dynamic RCS modeling is essential in creating a robust framework for operational analysis and developing effective countermeasure strategies, such as advanced active decoys. Especially in the cognitive radar concept, aircraft will desperately need more dynamic and adaptive active decoys. A methodology for calculating target aspect angles is proposed, using the aircraft’s attitude and spherical position relative to the radar system. A realistic 6DoF (6 degrees of freedom) flight data time series generated by a commercial flight simulator is used to derive aircraft-to-radar aspect angles. By estimating aspect angles for a simulated complex flight trajectory, RCS scintillation throughout the flight is characterized. The study highlights the importance of maneuver parameters such as roll and pitch on the RCS measured at the radar by comparing datasets with and without these parameters. Significant differences were found, with a 32.44% difference in RCS data between full maneuver and no roll and pitch changes. Finally, proposed future research directions and insights are discussed.

This research advances millimeter-wave (mmWave) altimetry for unmanned aerial systems (UASs) by optimizing performance metrics within the constraints of inexpensive automotive radars. Leveraging the software-defined architecture, this study encompasses the intricacies of frequency modulated continuous waveform (FMCW) design for three distinct stages of UAS flight: cruise, landing approach, and touchdown within a signal processing framework. Angle of arrival (AoA) estimation, traditionally employed in terrain mapping applications, is largely unexplored for UAS radar altimeters (RAs). Time-division multiplexing multiple input–multiple output (TDM-MIMO) is an efficient method for enhancing angular resolution without compromising the size, weight, and power (SWaP) characteristics. Accordingly, this work argues the potential of AoA estimation using TDM-MIMO to augment situational awareness in challenging landing scenarios. To this end, two corner cases comprising landing a small-sized drone on a platform in the middle of a water body are included. Likewise, for the touchdown stage, an improvised rendition of zoom fast Fourier transform (ZFFT) is investigated to achieve millimeter (mm)-level range accuracy. Aptly, it is proposed that a mm-level accurate RA may be exploited as a software redundancy for the critical weight-on-wheels (WoW) system in fixed-wing commercial UASs. Each stage is simulated as a radar scenario using the specifications of automotive radar operating in the 77–81 GHz band to optimize waveform design, setting the stage for field verification. This article addresses challenges arising from radial velocity due to UAS descent rates and terrain variation through theoretical and mathematical approaches for characterization and mandatory compensation. While constant false alarm rate (CFAR) algorithms have been reported for ground detection, a comparison of their variants within the scope UAS altimetry is limited. This study appraises popular CFAR variants to achieve optimized ground detection performance. The authors advocate for dedicated minimum operational performance standards (MOPS) for UAS RAs. Lastly, this body of work identifies potential challenges, proposes solutions, and outlines future research directions.

Frequency hopping (FH) is a well-known technique that is commonly used in communication systems owing to its many advantages, including its strong anti-jamming capability. In this technique, basically, radio signals are transmitted by switching the carrier between different frequency channels. As a result, the FH signal is not stationary; hence, its spectrum is expected to change over time. Therefore, the task of detection and parameter estimation of FH signals is very challenging in practice. To address this challenge, the study presented in this article proposes a method that detects and estimates the parameters of multiple narrowband FH signals. In the proposed method, first, short-time Fourier transform (STFT) is utilized to analyze FH signals, and a practical binarization process based on thresholding is used to detect FH signals. Then, a new algorithm is proposed to ensure that the center frequencies of the detected signals are successfully separated. Next, another algorithm is proposed to estimate the parameters of the detected signals. After estimating the parameters for the entire spectrum, an approach is used to detect FH signals. Lastly, the hop-clustering process is applied to separate the hops into groups without time overlap. The simulation results show that the proposed method can be an efficient way for the fast and accurate parameter estimation and detection of multiple narrowband FH signals.

The automatic dependent surveillance broadcast (ADS-B) system is one of the key components of the next generation air transportation system (NextGen). ADS-B messages are transmitted in unencrypted plain text. This, however, causes significant security vulnerabilities, leaving the system open to various types of wireless attacks. In particular, the attacks can be intensified by simple hardware, like a software-defined radio (SDR). In order to provide high security against such attacks, radio frequency fingerprinting (RFF) approaches offer reasonable solutions. In this study, an RFF method is proposed for aircraft identification based on ADS-B transmissions. Initially, 3480 ADS-B samples were collected by an SDR from eight aircrafts. The power spectral density (PSD) features were then extracted from the filtered and normalized samples. Furthermore, the support vector machine (SVM) with three kernels (linear, polynomial, and radial basis function) was used to identify the aircraft. Moreover, the classification accuracy was demonstrated via varying channel signal-to-noise ratio (SNR) levels (10–30 dB). With a minimum accuracy of 92% achieved at lower SNR levels (10 dB), the proposed method based on SVM with a polynomial kernel offers an acceptable performance. The promising performance achieved with even a small dataset also suggests that the proposed method is implementable in real-world applications.

Precise altitude data are indispensable for flight navigation, particularly during the autonomous landing of unmanned aerial systems (UASs). Conventional light and barometric sensors employed for altitude estimation are limited by poor visibility and temperature conditions, respectively, whilst global positioning system (GPS) receivers provide the altitude from the mean sea level (MSL) marred with a slow update rate. To cater to the landing safety requirements, UASs necessitate precise altitude information above ground level (AGL) impervious to environmental conditions. Radar altimeters, a mainstay in commercial aviation for at least half a century, realize these requirements through minimum operational performance standards (MOPSs). More recently, the proliferation of 5G technology and interference with the universally allocated band for radar altimeters from 4.2 to 4.4 GHz underscores the necessity to explore novel avenues. Notably, there is no dedicated MOPS tailored for radar altimeters of UASs. To gauge the performance of a radar altimeter offering for UASs, existing MOPSs are the de facto choice. Historically, frequency-modulated continuous wave (FMCW) radars have been extensively used in a broad spectrum of ranging applications including radar altimeters. Modern monolithic millimeter wave (mmWave) automotive radars, albeit designed for automotive applications, also employ FMCW for precise ranging with a cost-effective and compact footprint. Given the technology maturation with excellent size, weight, and power (SWaP) metrics, there is a growing trend in industry and academia to explore their efficacy beyond the realm of the automotive industry. To this end, their feasibility for UAS altimetry remains largely untapped. While the literature on theoretical discourse is prevalent, a specific focus on mmWave radar altimetry is lacking. Moreover, clutter estimation with hardware specifications of a pure look-down mmWave radar is unreported. This article argues the applicability of MOPSs for commercial aviation for adaptation to a UAS use case. The theme of the work is a tutorial based on a simplified mathematical and theoretical discussion on the understanding of performance metrics and inherent intricacies. A systems engineering approach for deriving waveform specifications from operational requirements of a UAS is offered. Lastly, proposed future research directions and insights are included.

This study presents an improved hybrid-flipped classroom (hybrid-FC) education method based on technology-enhanced learning (TEL) along with diluted classes for a course on probability and random processes in engineering. The proposed system was implemented with the participation of two student groups who alternated weekly between attending face-to-face activities and fully online classes as a sanitary measure during the pandemic. The education model was combined with the flipped classroom (FC) approach in order to improve the quality of learning and address the negative effects of remote education. Before the lessons, the students studied the course material, filled a question form, and then took a low-stake online quiz. Then, the students attended a session where the questions reported in the forms were discussed, and they took an online problem-solving session followed by an individual quiz. Class sessions were available to both online and face-to-face students, as well as in the form of video recordings for anyone who missed lessons. Qualitatively and quantitatively, the proposed education method proved to be more effective and comprehensive than conventional online methodologies. The students' performances were evaluated via quizzes and exams measuring the achievement of the course learning outcomes (CLOs). Weekly pre/post-tests were applied to examine the students’ progress in each topic. Midterm and final exams were planned to measure the level of success for all course topics. Additionally, the students' perception was assessed with questionnaires and face-to-face interviews. A performance assessment showed an apparent increase in the success rate, and the students' perception was found to be positive.

In this study, a total of 364 samples were collected and 349 of them belonged to, 349 specimens of Dasyatis pastinaca, 6 of Gymnura altavela, 5 of Torpedo marmorato, 2 of Aetomylaeus bovinus and Mustelus mustelus, were obtained by-catch in trammel nets off the Izmir Bay (Aegean sea coast of Turkey) during period of spring in 2018. Males of D. pastinaca ranged from 28–62 cm and females from 31.5-94 cm total length. The analysis of the angular coefficient (b) demonstrated that growth (in weight) in relation to length was isometric in males and females of D. pastinaca. Morphologic measurements of a total of 60 individuals to represent all individuals of the population, t test in independent groups to determine differences in males and females and all other morphometric measurements were statistically tested by multiple regression analysis (stepwise method). Therefore, difference between mean values obtained according to sexes was not found different. However, A linear correlation between weight- pelvic length for females and weight- preanal length for males according to multiple regression analysis in male and female individuals. Isometric and positive allometric growths were calculated for species of Torpedo marmorata and Gymnura altavela, respectively.

Salespeople are crucial piece of the puzzle in micro business retailing environments of developing countries. With no formal training and education in salesmanship, they practice the modern version of the customer relationship management to achieve success. However, empirical studies regarding the salesperson’s performance in such environments in developing countries are scarce. We contribute to the existing literature by extending the application of the behaviour-based salesperson self-evaluation into micro enterprise environments of under-researched countries in Central America and Western Africa. Using an empirical data collected from two developing nations, we examined the importance ratings of the Bush et al. (1990) behaviour-based scale dimension in micro enterprise retail shops in Ghana and Guatemala. Our results indicate that the dimensionality of the behaviour-based salesperson performance evaluation scale holds in both countries. In addition, the importance of some of the performance evaluation dimensions differed in the countries studied.

Nowadays, Visible Light Communication (VLC) has gained much attention due to the significant advancements in Light Emitting Diode (LED) technology. However, the bandwidth of LEDs is one of the important concerns that limits the transmission rates in a VLC system. In order to eliminate this limitation, various types of equalization methods are employed. Among these, using digital pre-equalizers can be a good choice because of their simple and reusable structure. Therefore, several digital pre-equalizer methods have been proposed for VLC systems in the literature. Yet, there is no study in the literature that examines the implementation of digital pre-equalizers in a realistic VLC system based on the IEEE 802.15.13 standard. Hence, the purpose of this study is to propose digital pre-equalizers for VLC systems based on the IEEE 802.15.13 standard. For this purpose, firstly, a realistic channel model is built by collecting the signal recordings from a real 802.15.13-compliant VLC system. Then, the channel model is integrated into a VLC system modeled in MATLAB. This is followed by the design of two different digital pre-equalizers. Next, simulations are conducted to evaluate their feasibility in terms of the system’s BER performance under bandwidth-efficient modulation schemes, such as 64-QAM and 256-QAM. Results show that, although the second pre-equalizer provides lower BERs, its design and implementation might be costly. Nevertheless, the first design can be selected as a low-cost alternative to be used in the VLC system.

This article presents a preliminary propagation study on the accuracy of empirical path loss models for efficient planning and deployment of a linear wireless sensor network (LWSN) based on long range (LoRa) enabled sensor nodes in suburban and rural environments. Real-world deployment of such network requires accurate path loss modelling to estimate the network coverage and performance. Although several models have been studied in the literature to predict the path loss for LoRa links, the assessment of empirical path loss models within the context of low-height peer to peer configured system has not been provided yet. Therefore, this study aims at providing a performance evaluation of well-known empirical path loss models including the Log-distance, Okumura, Hata, and COST-231 Hata model in a peer to peer configured system where the sensor nodes are deployed at the same low heights. To this end, firstly, measurement campaigns are carried out in suburban and rural environments by utilizing LoRa enabled sensor nodes operating at 868 MHz band. The measured received signal strength values are then compared with the predicted values to assess the prediction accuracy of the models. The results achieved from this study show that the Okumura model has higher accuracy.

Abstract We explored the joint effect of synchronization window and offset/drift mode selection on the time synchronization of linear wireless sensor networks (LWSNs). Recent advances in the field along with the availability of capable hardware led to adoption of LWSNs in diverse areas like monitoring of roads, pipelines, and tunnels. The linear topology applications are susceptible to single point of failure; therefore, energy efficient operation of LWSNs is even more important than the traditional WSNs. To address the challenge, we investigate the time synchronization mode selection for the optimum operation of a multi-hop and low-overhead LWSN. We investigate two modes of synchronization: synchronization by using only offset and synchronization by using offset in addition to the clock drift. Furthermore, we investigate the effects of synchronization window size. Our experimental results reveal that computation of offset alone for smaller window sizes and resynchronization periods is sufficient in achieving acceptable degree of synchronization.

Literatürde, kısa mesafe iç mekân haberleşme linklerinde insan vücudu blokajının sebep olduğu kaybın tahmininde matematiksel olarak sade bir yapıya sahip olması sebebiyle Çift Bıçak Kenarlı Kırınım (ÇBKK) modeli sıklıkla kullanılmaktadır. Fakat modelde insan vücudu benzetimi için kullanılan dikdörtgensel ekran, insan vücudu fiziğini temsil etmek için yeterli olmayabilir. Bu durum, özellikle çoklu insan vücudu blokajı olması durumunda, modelin tahmin doğruluğunu olumsuz etkileyebilir. Öte yandan, insan vücudu benzetiminde Geometrik Kırınım Teorisi (GKT) temelli mükemmel iletken silindir modeli, literatürde sıklıkla kullan bir diğer modeldir. Ancak bu modelin, çoklu insan vücudu blokajı durumunda, yayılım kaybını tahmin etmedeki etkisi henüz çalışılmamıştır. Bu nedenle, sunulan bu kısa çalışmadaki amaç, iletken silindir modelinin, 5G için tahsis edilmesi en muhtemel frekans bantlarından biri olan 28 GHz’de, çoklu insan vücudu blokajının neden olduğu kısa mesafe iç mekân linklerindeki yayılım kaybını tahmin etmedeki doğruluğunu irdelemektir. Bu amaçla, öncelikle, kısa mesafe iç mekân linki bir insan vücudu ile tamamen bloklanmış; aynı anda, link yakınındaki başka bir insan vücudu linke yaklaştırılarak ölçümler yapılmıştır. Sonrasında, yayılım kaybını tahmin etmek için GKT ve ÇBKK modelinden faydalanılmıştır. Tahmin doğruluğu analizi için simülasyon ve ölçüm sonuçları karşılaştırılmıştır. Sonuç olarak deneysel çalışmalar ile literatürde ilk defa, çoklu insan vücudu blokajının GKT modeli ile tahmin doğruluğunun arttığı gözlemlenmiştir.

Radio frequency fingerprinting (RFF) based on RF transients is one of the most effective techniques for improving wireless security. For an efficient RFF development, RF transients need to be accurately detected. However, the detection of the transient starting point remains a main challenge due to the channel noise. In the literature, several methods have been presented to detect the starting point of the transient signals. As an alternative to these methods, this study proposes a method that utilizes Energy Criterion (EC) technique for the first time. In order to test its performance, firstly, an extensive dataset consisting of Wi-Fi signals recorded under realistic Signal-to-Noise Ratio (SNR) conditions is created. Using the provided dataset, the proposed method as well as common transient detection methods are employed for transient start detection. Then, the effect of SNR on the performance of transient start detection is evaluated. Moreover, a performance comparison between the methods is provided based on their respective computational speed and complexity. The results prove the feasibility and efficiency of the proposed method to detect the transient starting point for RFF of Wi-Fi device identification. As to the knowledge of the authors, this study is the first report that comparatively assesses the transient detection methods by using extensive data under realistic noise conditions.

In this study, a novel method is proposed to detect and classify the threats for fiber optic distributed acoustic sensing (DAS) systems. In the study, phase-sensitive optical time-domain reflectometry (phase-OTDR) is realized for the sensing system. The proposed method is consisted of three main stages. In the first stage, Wavelet denoising method is applied for noise reduction in the measured signal, and difference in time domain approach is used to perform high-pass filtering. Autocorrelation is then used for comparing the signal with itself over time in each bin to remove uncorrelated signals. Next, the power of the correlated signals at each bin is calculated and sorted where maximum valued bins are considered as the event signal. In the second stage, Variational Mode Decomposition (VMD) technique is used to decompose the detected event signals into a series of band-limited modes from which the event signals are reconstructed. From the reconstructed event signals, higher order statistical (HOS) features including variance, skewness, and kurtosis are extracted. In the last stage, the threats are discriminated by implementing Linear Support Vector Machine (LSVM)-based classification approach to the extracted features. In order to evaluate the effects of proposed method on the classification performance, different types of activities such as digging with hammer, pickaxe, and shovel collected from various points of a buried fiber optic cable have been used under different Signal-to-Noise Ratio (SNR) levels (-4 to -18 dB). It has observed that the classification accuracy at high/moderate (-4 to -8 dB) and low (-8 to -18 dB) SNR levels are 79.5% and 75.2%, respectively. To the best of authors' knowledge, this research study is the first report to use VMD technique for threat classification in phase-OTDR-based DAS systems.

Brain tumors have been the subject of research for many years. Brain tumors are typically classified into two main groups: benign and malignant tumors. The most common tumor type among malignant brain tumors is known as glioma. In the diagnosis of glioma, different imaging technologies could be used. Among these techniques, MRI is the most preferred imaging technology due to its high-resolution image data. However, the detection of gliomas from a huge set of MRI data could be challenging for the practitioners. In order to solve this concern, many Deep Learning (DL) models based on Convolutional Neural Networks (CNNs) have been proposed to be used in detecting glioma. However, understanding which CNN architecture would work efficiently under various conditions including development environment or programming aspects as well as performance analysis has not been studied so far. In this research work, therefore, the purpose is to investigate the impact of two major programming environments (namely, MATLAB and Python) on the accuracy of CNN-based glioma detection from Magnetic Resonance Imaging (MRI) images. To this end, experiments on the Brain Tumor Segmentation (BraTS) dataset (2016 and 2017) consisting of multiparametric magnetic MRI images are performed by implementing two popular CNN architectures, the three-dimensional (3D) U-Net and the V-Net in the programming environments. From the results, it is concluded that the use of Python with Google Colaboratory (Colab) might be highly useful in the implementation of CNN-based models for glioma detection. Moreover, the 3D U-Net model is found to perform better, attaining a high accuracy on the dataset. The authors believe that the results achieved from this study would provide useful information to the research community in their appropriate implementation of DL approaches for brain tumor detection.

Small-scale fisheries have a high socioeconomic importance for local communities. Therefore, ensuring the sustainability of fisheries resources would contribute for the socioeconomic development of fisheries communities. In this study, traps produced from plastic waste recycle materials which can be used in fishing of economically valued whelks (Hexaplex trunculus and Bolinus brandaris) that are used as natural bait in angling and longline fishing were investigated. These traps in different sizes were examined at the depths of 3-8 m in the Urla coasts of Izmir Bay, Turkey. The soaking times of traps were between 10-48 hours. The same amounts of baits (Sardina pilchardus) were equally placed in all traps. Catch per unit efforts (CPUE) of traps were calculated between 7-34 individuals/trap per day. The experimented traps were found successful for whelks fishing. Traps yield high quality and economically valued catches, related with restricted bycatch and low impact on habitat. Therefore, these traps can be suggested as alternative fishing gears designed for trap fisheries for whelks. This paper provides valuable knowledge for fisheries managers and decision makers to ensure the sustainability of local fisheries resources and small-scale fisheries.

Recently, network security has become a major challenge in communication networks. Most wireless networks are exposed to some penetrative attacks such as signal interception, spoofing, and stray. Radio frequency (RF) fingerprinting is considered to be a promising solution for network security problems and has been applied with various improvements. In this paper, extensive data from Bluetooth (BT) devices are utilized in RF fingerprinting implementation. Hilbert-Huang transform (HHT) has been used, for the first time, for RF fingerprinting of Bluetooth (BT) device identification. In this way, time-frequency-energy distributions (TFED) are utilized. By means of the signals' energy envelopes, the transient signals are detected with some improvements. Thirteen features are extracted from the signals' transients along with their TFEDs. The extracted features are pre-processed to evaluate their usability. The implementation of three different classifiers to the extracted features is provided for the first time in this paper. A comparative analysis based on the receiver operating characteristics (ROC) curves, the associated areas under curves (AUC), and confusion matrix are obtained to visualize the performance of the applied classifiers. In doing this, different levels of signal to noise ratio (SNR) levels are used to evaluate the robustness of the extracted features and the classifier performances. The classification performance demonstrates the feasibility of the method. The results of this paper may help readers assess the usability of RF fingerprinting for BT signals at the physical layer security of wireless networks.

This paper presents a simple approach to characterize the effects of scattering objects around indoor links at 28 GHz while the link is fully blocked by a human body. The effects of scattering objects nearby the link were studied by conducting measurements with a metallic reflector and the human body. Here, the basic mechanisms of wave propagation, such as reflection and diffraction, were accounted for each scattering object. To predict the attenuation caused by the metallic reflector, a specular reflection model was employed in reflection modeling. In diffraction modeling, on the other hand, the double knife-edge diffraction (DKED) model was exploited to predict the attenuation by the human body. Simulations were then compared with measurements to evaluate the prediction accuracy of the models. Results indicate that the presented simple models work well for indoor links. Therefore, the results of this paper could be extended to model multiple human bodies near the indoor links of fifth generation (5G) systems.

Radio frequency fingerprinting (RFF) is based on identification of unique features of RF transient signals emitted by radio devices. RF transient signals of radio devices are short in duration, non-stationary and nonlinear time series. This paper evaluates the performance of RF fingerprinting method based on variational mode decomposition (VMD). For this purpose, VMD is used to decompose Bluetooth (BT) transient signals into a series of band-limited modes, and then, the transient signal is reconstructed from the modes. Higher order statistical (HOS) features are extracted from the complex form of reconstructed transients. Then, Linear Support Vector Machine (LVM) classifier is used to identify BT devices. The method has been tested experimentally with BT devices of different brands, models and series. The classification performance shows that VMD based RF fingerprinting method achieves better performance (at least 8% higher) than time-frequency-energy (TFED) distribution based methods such as Hilbert-Huang Transform. This is demonstrated with the same dataset but with smaller number of features (nine features) and slightly lower (2-3 dB) SNR levels.

This study proposes a simple convolutional neural network (CNN)-based model for vehicle classification in low resolution surveillance images collected by a standard security camera installed distant from a traffic scene. In order to evaluate its effectiveness, the proposed model is tested on a new dataset containing tiny (100 × 100 pixels) and low resolution (96 dpi) vehicle images. The proposed model is then compared with well-known VGG16-based CNN models in terms of accuracy and complexity. Results indicate that although the well-known models provide higher accuracy, the proposed method offers an acceptable accuracy (92.9%) as well as a simple and lightweight solution for vehicle classification in low quality images. Thus, it is believed that this study might provide useful perception and understanding for further research on the use of standard low-cost cameras to enhance the ability of the intelligent systems such as intelligent transportation system applications.

This study presents experimental analysis of range error of modular FMCW (frequency modulated continuous wave) radar operating at C-band. As FMCW utilizes frequency modulation, it can measure both range and speed of targets. One of the problems arising in the design of modular FMCW radar is error due to time delays in components and/or modules (Modular Component Delay-MCD). Then, MCD is taken into account to improve the accuracy of range measurement, and range error is quantized. This form of time delay causes frequency offset in measured beat frequency resulting in range error. Therefore, the resulting frequency offset of the radar is estimated and quantized experimentally. In this way, a mean error of about 11cm was achieved within 9m range measurements for C Band modular FMCW radar.

Bu çalışmada A356 alaşımından üretilen binek araç jantlarının mekanik yükleme altında tasarım ve ağırlık optimizasyonu gerçekleştirilmiştir. Bu kapsamda seçilen bir jant modeli üzerinde geleneksel tasarım uygulamalarında mühendislik tecrübeleri ile gerçekleştirilen tasarım aşamalarında belirlenen ölçüler tasarım parametreleri olarak seçilmiştir. Tasarımda ANSYS Workbench’de “Sayısal Deney Tasarımı” yöntemi ile tasarım noktaları belirlenmiştir. “Cevap Yüzeyi Metodu” kullanılarak en iyi tasarım elde edilmiştir. Sonuç olarak jant ağırlığı ve deformasyonuna en çok etki eden parametreler belirlenmiş, tasarımın mekanik davranışı iyileştirilmiş ve daha hafif bir jant elde edilmiştir. Jantın geometrisi üzerinde gerçekleştirilen iyileştirme ile yorulma davranışı da iyileştirilmiştir.

Due to the environmental pollution threatening human life, clean water accessibility is one of the major global issues. In this context, in literature, there are many portable water disinfection systems utilizing ultraviolet (UV) radiation. UV water disinfection systems employ piezoelectric-based electric power along with UV light-emitting diode (LED) sources. This paper elaborates on the detailed design and parametric optimization of a portable UV disinfection system. The proposed system aims to generate piezoelectric harvesting-based electrical power simply by shaking, and the generated power is then used to supply UV-LEDs for water disinfection. To this end, overall system parameters along with a physical-mathematical model of mechanical, electrical and biochemical aspects of the system are fully developed. Moreover, the main design parameters of the developed model are derived for optimal operation of the system by employing Genetic Algorithm (GA). Finally, optimal design parameters were identified for three different cost scenarios. The model can further be improved for practical implementation and mass production of the system.

Purpose: The purpose of this study is to explore Generation Z (Gen Z) consumers' sustainable consumption behaviors at a university campus by examining various antecedent and moderating influencers of their sustainable consumption behaviors. Design/methodology/approach: A quantitative research methodology is used in this study. A structured questionnaire was administered ("n" = 279) to young college students (Gen Z consumers in the context of this study) enrolled at a large state university in the USA. The structural equation model was used to test the hypothesized relationships in the conceptualized model. Findings: Study findings show that social responsibility (feelings, engagement and expectations) and external incentives (material and social) positively influence Gen Z consumers' sustainability interests, which in turn influences their sustainable consumption behaviors (actions). Collectivist cultural values did not appear to have any statistically significant effect on Gen Z consumers' sustainable consumption interests. Moreover, learned helplessness, perceived barriers and the awareness of the consequences of sustainability consumption actions did not have any significant moderating effect on Gen Z consumers' sustainable consumption behavior. Research limitations/implications: The questionnaire was only sent to students majoring in business degrees, which may limit the generalizability of this study to broader Gen Z consumer populations. Expanding this study to include non-business students may be valuable as a next step. Replicating this study in different cultural environments of international countries could enhance the relationships identified in this study. Practical implications: Consumer social responsibility education along with material and social incentives will encourage Gen Z consumers' participation in sustainable behaviors at college campuses. Originality/value: This research provides valuable insights into understanding the importance of consumer social responsibility and external incentives in influencing Gen Z consumers' sustainable consumption intentions and behaviors. Accordingly, consumer social responsibility education and incentive programs need to be developed to encourage the participation of Gen Z consumers in sustainable consumption.

Radio frequency fingerprinting (RFF) is a promising physical layer protection technique which can be used to defend wireless networks from malicious attacks. It is based on the use of the distinctive features of the physical waveforms (signals) transmitted from wireless devices in order to classify authorized users. The most important requirement to develop an RFF method is the existence of a precise, robust, and extensive database of the emitted signals. In this context, this paper introduces a database consisting of Bluetooth (BT) signals collected at different sampling rates from 27 different smartphones (six manufacturers with several models for each). Firstly, the data acquisition system to create the database is described in detail. Then, the two well-known methods based on transient BT signals are experimentally tested by using the provided data to check their solidity. The results show that the created database may be useful for many researchers working on the development of the RFF of BT devices.

Radio frequency fingerprinting (RFF) is one of the communication network’s security techniques based on the identification of the unique features of RF transient signals. However, extracting these features could be burdensome, due to the nonstationary nature of transient signals. This may then adversely affect the accuracy of the identification of devices. Recently, it has been shown that the use of variational mode decomposition (VMD) in extracting features from Bluetooth (BT) transient signals offers an efficient way to improve the classification accuracy. To do this, VMD has been used to decompose transient signals into a series of band-limited modes, and higher order statistical (HOS) features are extracted from reconstructed transient signals. In this study, the performance bounds of VMD in RFF implementation are scrutinized. Firstly, HOS features are extracted from the band-limited modes, and then from the reconstructed transient signals directly. Performance comparison due to both HOS feature sets is presented. Moreover, the lower SNR bound within which the VMD can achieve acceptable accuracy in the classification of BT devices is determined. The approach has been tested experimentally with BT devices by employing a Linear Support Vector Machine (LSVM) classifier. According to the classification results, a higher classification performance is achieved (~4% higher) at lower SNR levels (−5−5 dB) when HOS features are extracted from band-limited modes in the implementation of VMD in RFF of BT devices.

In this study, a method for determining scattering center (or center of scattering points) of a multipath is proposed, provided that the direction of arrival of the multipath is known by the receiver. The method is based on classical electromagnetic wave principles in order to determine scattering center over irregular terrain. Geometrical optics (GO) along with Fresnel zone concept is employed, as the receiver, the transmitter positions and irregular terrain data are assumed to be provided. The proposed method could be used at UHF bands, especially, operations of radars and electronic warfare applications.

Removal of Pb(II) ions from electroplating wastewater of Bursa, an industrial city in Turkey, was investigated in fixed-bed column. The experiments were conducted to study the effect of important design parameters such as column bed height and flow rate. The breakthrough profiles were obtained in these studies. At a bed height of 14 cm and flow rate of 6 mL/min, the metal-uptake capacity of poly(ethylene glycol dimethacrylate-1-vinylimidazole) [poly(EGDMA-VIM)] beads for Pb(II) ions was found to be 90 mg/g. Bed Depth Service Time (BDST) model was used to analyse the experimental data and evaluate the performance of adsorption column. For various flow rates, adsorption capacity per unit bed volume (N0) and adsorption rate constant (ka) are in the range of 2370–3560 mg/mL and 0.0225–0.0616 L/mg h, respectively. The saturated column was easily regenerated by 0.1 M HNO3 and the poly(EGDMA-VIM) beads in fixed-bed column could be reused for Pb(II) ions removal.

A modified two-step seeded polymerization was used for the preparation of porous mono-sized microspheres of poly(ethylene glycol dimethacrylate- co -1-vinylimidazole) [poly(EGDMA- co -VIM)] of 7 μm in size. The specific surface area and the EGDMA/VIM molar ratio of the microspheres were determined as 64.5 m 2 /g and 1.5:1.0 mol/mol, respectively. The adsorption of Pb(II) ions from aqueous solution onto the porous mono-sized poly(EGDMA- co- VIM) microspheres was examined. The maximum adsorption capacities of the microspheres towards Pb(II) ions were determined as 21.74, 30.52, 49.23 and 56.48 mg/g at 293 K, 303 K, 318 K and 338 K, respectively. The dynamic and equilibrium adsorption behaviours of the system were adequately described by the pseudo-second-order kinetic model and the Langmuir isotherm, respectively. The apparent activation energy was determined as 6.212 kJ/mol, which is characteristic of a chemically controlled reaction. The values of E fe obtained from the Dubinin–Radushkevich equation in the non-linear form were 9.153 kJ/mol at 293 K, 10.34 kJ/mol at 303 K, 10.18 kJ/mol at 318 K and 10.91 kJ/mol at 333 K, thereby indicating that the adsorption of Pb(II) ions onto the porous monosized poly(EGDMA- co -VIM) microspheres occurred via a chemical process at all the temperatures studied. Various thermodynamic parameters, such as the Gibbs' free energy change (ΔG 0 ), the standard enthalpy change (ΔH 0 ) and the standard entropy change (ΔS 0 ) were also determined. The thermodynamic parameters obtained indicated that the adsorption process was endothermic in nature. These results demonstrate that the material studied could be used as a purifier for the removal of Pb(II) ions from water and wastewater.