Browsing by Author "Kara, Ali"
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Article A Hybrid-Flipped Classroom Approach: Students’ Perception and Performance Assessment(Ingeniería e Investigación, 2023-08-04) Yalçınkaya Gökdoğan, Bengisu; Çoruk, Remziye Büşra; Benzaghta, Mohamed; Kara, AliThis 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. Este estudio presenta un método educativo mejorado de aula invertida híbrida (hybrid-FC) basado en el aprendizaje mejorado por tecnología (TEL) junto con clases diluidas para un curso sobre probabilidad y procesos aleatorios. El sistema propuesto se implementó con la participación de dos grupos de estudiantes que alternaban semanalmente entre asistir a actividades presenciales y totalmente en línea como una medida sanitaria durante la pandemia. El modelo educativo se combinó con el enfoque de aula invertida (FC) para mejorar la calidad del aprendizaje y hacer frente a los efectos negativos de la educación a distancia. Antes de asistir a clase, los estudiantes estudiaban el material del curso, completaban un formulario de preguntas y luego tomaban un quiz en línea de bajo impacto. Luego, los estudiantes asistían a una sesión en la que se discutían las respuestas en los formularios, y tomaban una sesión en línea de resolución de problemas, seguida de un cuestionario individual. Las clases estaban disponibles tanto para los estudiantes en línea como para aquellos que asistían de forma presencial; también había grabaciones de video para quien faltara a clases. Cualitativa y cuantitativamente, el método educativo propuesto demostró ser más efectivo y completo que los métodos online convencionales. El desempeño de los estudiantes se evaluó mediante cuestionarios y exámenes que medían el logro de los resultados de aprendizaje del curso (CLO). Se realizaron exámenes previos y posteriores semanales para examinar el progreso de los estudiantes en cada tema. Se planificaron exámenes parciales y finales para medir el nivel de éxito de todos los temas del curso. Además, la percepción de los estudiantes se evaluó con cuestionarios y entrevistas presenciales. La evaluación del desempeño mostró un aumento aparente en la tasa de éxito, y se encontró que la opinión de los estudiantes era positiva.Article A Radio Frequency Fingerprinting-Based Aircraft Identification Method Using ADS-B Transmissions(Aerospace, 2024-03-17) Gürer, Gürsu; Dalveren, Yaser; Kara, Ali; Derawi, MohammadThe 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.Item AN EXPERIMENTAL STUDY ON TIME SYCHRONIZATION IN LINEAR SPANNING TREE WIRELESS SENSOR NETWORK(2022-02-17) Erpay, Ahmet; Kara, AliIn this study, time synchronization in a linear spanning tree wireless sensor network is studied. The method is applied on a PIC based platform where time synchronization process and temperature effects on it are focused. Outdoor experiments are fused with temperature change and the aim is to see performance of the wireless sensor network and to overcome possible challenges.Item ANALYSIS AND QUANTIZATION OF RANGE ERROR OF MODULAR FMCW RADAR AT C BAND(2022-02-28) Abdulrazigh, Mahfoud Y. A.; Kara, AliThis thesis presents the experimental analysis of range error of modular FMCW (frequency modulated continuous wave) radar operating at C-band. FMCW radar is a type of radar where frequency modulation is used in signal transmission. FMCW radar can measure both range and speed of targets. One of the problems in the design of modular FMCW radar is errors due to time delays in components and/or modules such as cables, PA, LNA and antenna (Measurement Instrumentation Delay-MID). Then, MID is taken into account to improve the accuracy of range measurement, and range error is quantized. Time delay (MID) 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.Item ASSESSMENT OF FEATURES AND CLASSIFIERS FOR BLUETOOTH RF FINGERPRINTING(2022-02-14) Ali, Aysha; Kara, AliIn this thesis, we introduced a novel technique to enhance the security at physical layer of wireless networks. This is based on the use of radio freqency (RF) fingerprinting for Bluetooth (BT) signals. BT signal records are acquired from twenty different cell phone brands, models, and serial numbers. One hundred fifty records are collected from each device. For the first time, Hilbert Huang Transform (HHT) are used for the BT device identification with such huge data set. By means of the signals’ energy envelopes with some improvements, the transient signals are detected accurately. Through the Empirical mode decomposition (EMD) and Hilbert Transform (HT), the HHT is implemented to obtain Time Frequency Energy Distributions (TFED) of the detected transients. Thirteen features are extracted from the signals’ transients and their TFEDs. The extracted features are pre-processed to enhance their usability. Different classifiers are employed with the extracted features for device identification, and comparative analysis of the classifiers is also provided. The classifier performance is examined for different SNR levels from 8 dB to 35+ dB . The identification performance demonstrates the feasibility of the method.Article Convolutional Neural Network-Based Vehicle Classification in Low-Quality Imaging Conditions for Internet of Things Devices(Sustainability, 2023-11-24) Maiga, Bamoye; Dalveren, Yaser; Kara, Ali; Derawi, MohammadVehicle classification has an important role in the efficient implementation of Internet of Things (IoT)-based intelligent transportation system (ITS) applications. Nowadays, because of their higher performance, convolutional neural networks (CNNs) are mostly used for vehicle classification. However, the computational complexity of CNNs and high-resolution data provided by high-quality monitoring cameras can pose significant challenges due to limited IoT device resources. In order to address this issue, this study aims to propose a simple CNN-based model for vehicle classification in low-quality images collected by a standard security camera positioned far from a traffic scene under low lighting and different weather conditions. For this purpose, firstly, a new dataset that contains 4800 low-quality vehicle images with 100 × 100 pixels and a 96 dpi resolution was created. Then, the proposed model and several well-known CNN-based models were tested on the created dataset. The results demonstrate that the proposed model achieved 95.8% accuracy, outperforming Inception v3, Inception-ResNet v2, Xception, and VGG19. While DenseNet121 and ResNet50 achieved better accuracy, their complexity in terms of higher trainable parameters, layers, and training times might be a significant concern in practice. In this context, the results suggest that the proposed model could be a feasible option for IoT devices used in ITS applications due to its simple architecture.Article Deployment and Implementation Aspects of Radio Frequency Fingerprinting in Cybersecurity of Smart Grids(Electronics, 2023-12-06) Awan, Maaz Ali; Dalveren, Yaser; Çatak, Ferhat Özgür; Kara, AliSmart grids incorporate diverse power equipment used for energy optimization in intelligent cities. This equipment may use Internet of Things (IoT) devices and services in the future. To ensure stable operation of smart grids, cybersecurity of IoT is paramount. To this end, use of cryptographic security methods is prevalent in existing IoT. Non-cryptographic methods such as radio frequency fingerprinting (RFF) have been on the horizon for a few decades but are limited to academic research or military interest. RFF is a physical layer security feature that leverages hardware impairments in radios of IoT devices for classification and rogue device detection. The article discusses the potential of RFF in wireless communication of IoT devices to augment the cybersecurity of smart grids. The characteristics of a deep learning (DL)-aided RFF system are presented. Subsequently, a deployment framework of RFF for smart grids is presented with implementation and regulatory aspects. The article culminates with a discussion of existing challenges and potential research directions for maturation of RFF.Item DESIGN AND IMPLEMENTATION OF HIGH GAIN MICROSTRIP ANTENNAS FOR SUB-GHZ BANDS(2022-02-14) Bilgin, Gulsima; Aydın, Elif; Kara, AliThis thesis presents design and production of multilayer microstrip antennas working at sub-GHz (the especially unlicensed band of ISM), yielding high gain. To increase the gain for the sub-GHz band, the multilayer antenna structure, keeping the dimen sion as small as possible, is considered the most appropriate method. Firstly, general background information about microstrip antenna is proposed and then, the theory of multilayer microstrip antenna is handled in detailed. In accordance with this purpose, different types of multilayer antenna designs are performed and one easy to fabricate is produced and measured. In addition, to investigate aesthetics and environmen tal adaptivity of the antenna, the antenna is placed into a metal box and covered by an outer substrate to be protected from harsh environmental conditions. This thesis also presents the electromagnetic numerical methods both theoretically and experi mentally. Finite element method (FEM) and finite integration technique (FIT) are explained and compared experimentally by using two commercial tools.Item DESIGN AND IMPLEMENTATION OF MODULAR FRONT END FOR RF FINGERPRINTING OF BLUETOOTH SIGNALS(2022-02-24) Uzundurukan, Emre; Kara, AliIn wireless networks, physical layer security would be complementary when higher level software based security approaches are inadequate. One of the physical layer methods is Radio Frequency (RF) fingerprinting. In RF fingerprinting method, data acquisition phase plays a critical role in precisely capturing signals for extracting the fingerprints. In this thesis, a low-cost modular RF front-end designed with commercial of the shelf (COTS) is presented. In order to design this RF front end, computer based design tool is used. Three different data acquisition methods including proposed RF front end usage is also presented. Moreover, assessment of the RF front end for the RF fingerprinting performance with respect to three different data sets are presented. Transient signal detection, feature extraction and classification algorithms are implemented on these data sets. To do classification support vector machine (SVM) and neural networks (NN) are used. Results of the study show that the designed RF front end would work well in RF fingerprinting, and accurate classification of BT devices.Item DEVELOPMENT AND PERFORMANCE ASSESSMENT OF SIGNAL DETECTION METHODS FOR TRANSIENT SIGNAL STARTS(2022-01-11) MOHAMED, ISMAIL; Kara, AliRadio frequency fingerprinting (RFF) is one of the most effective techniques for improving wireless security. RF transient detection process is considered as important phase for establishing an efficient RFF security system. In this thesis, an approach to detect WiFi transient signal starts has been proposed. It is named as Energy Criterion (EC) method, and it can exploit both amplitude and/or phase characteristics of RF transient signal. The main idea behind the proposed method that the most significant change point in the signal sequence is associated with the significant change point in its signal energy. The methods reported in the literature have been evaluated and compared experimentally with the EC method in terms of accuracy, complexity and constraints. The performance assessment was implemented under different levels of signal to noise ratio (SNR). To this end, the collected WiFi signals were passed through pre-processing steps in order to extract transient signals more efficiently. It has been proven experimentally that the EC method is easy to implement as it needs no threshold, and it has a better performance for low SNR signals. Moreover, the EC is much faster than the other methodsItem IMPLEMENTATION OF MATCHED FILTER FOR RADAR RECEIVER ON FPGA(2022-02-16) Shfat, Mohamed Salem A.; Özbek, Mehmet Efe; Kara, AliThe underlying thesis scouts about a radar receiver in general and the Matched Filter and the implementation of this filter on the field programmable gate array FPGA. The practical implementation of the radar system is not as simple as its basic concept might seem. The Radar is operated by detection of the echo which is returned from reflecting objects (targets) and radiating the electromagnetic energy.The information about the target is provided by the nature of the echo signal such as position, velocity, and perhaps size. Several sources exist which are able to affect and confuse the quality of the radar these include undesired interference and echo The Matched Filter is a kind of filter helps to separate the reflected signal from the clutters and undesired echoes by increasing the SNR of the received echo in order to give the radar high ability to detect the targets successfully. The FPGA have been chosen as a platform for this work because of its high quality and speed in processing especially in digital systems design. In this work, the Matched filter is implemented on FPGA by using the Verilog language and MATLAB for generating the echo signals. Throughout this study, it was clearly shown that all the selected tools and language are suitable for this practical work.Article Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images(Diagnostics, 2023-02-09) Yılmaz, Vadi Su; Akdağ, Metehan; Dalveren, Yaser; Doruk, Reşat Özgür; Kara, Ali; Soylu, AhmetBrain 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.Item MODELING AND CHARACTERIZATION OF HUMAN BODY SHADOWING AT MILLIMETER WAVES(2022-01-26) Bin-Alabish, Ahmed H. A.; Kara, Ali; Dalveren, YaserAs 5G communication may use Millimetre waves (mmWave) bands, it is essential to estimate short range indoor links from blockage point of view. This study presents some initial studies for characterizing effects of human body movement on short range link. To the best of our knowledge, this study is the first to experimentally examine the effects of human body movement at this band. This study also presents a simple approach to characterize the effects of scattering objects around indoor links at 28 GHz while the link is fully blocked by human body. The effects of scattering objects close to the link were carried out by performing measurements with a metallic reflector and human body. Here, fundamental mechanisms of wave propagation such as reflection and diffraction were accounted for each scattering object. To predict the attenuation produced by metallic reflector, specular reflection model was used in reflection modelling. In diffraction modelling, on the other hand, the double knife-edge diffraction (DKED) model was exploited to predict the attenuation by human body. Simulations were then compared with measurements to estimate the prediction accuracy of the models. Results indicate that presented simple models work well for indoor links. Therefore, the results of this study could be extended to model multiple human body near the indoor links of fifth generation (5G) systems.Article Modelling and Design of Pre-Equalizers for a Fully Operational Visible Light Communication System(Sensors, 2023-06-14) Bostanoğlu, Murat; Dalveren, Yaser; Çatak, Ferhat Özgür; Kara, AliNowadays, 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.Item MODELLING AND OPTIMIZATION OF HARNESS DESIGN AND COST ANALYSIS USING SOFTWARE TOOLS(2022-01-11) Tankut, Mehmet Kemal; Lotfisadigh, Bahram; Kara, AliA harness is a cable web or an assembly of cables that transmits electrical power, signals, or information between two or multiple sources. It also may have some other subcomponents such as connectors, backshells, wires, boots, transitions, shields, jackets, and splices. Since a harness may consist of various materials and subcomponents, determining required materials is critical in the harness design and production process. The selection of appropriate components requires an excessive amount of time to browse through standards and datasheets, therefore. Time losses are depending heavily on the designer's experience level and complexity of the ordered harness. In this research, to reduce time losses during harness design and meanwhile reduce the risk of error in component selection software is developed. This study encapsulates the impacts and results of such an attempt to improve harness design process considering harness complexity, user experience. According to experimental results, consumed time on projects per month for each designer is calculated and a cost model developed. Finally, the contribution of prepared software is evaluated and results show that application of this software in the design and development process of harness regardless of harness complexity and user experience is beneficial.Item PERFORMANCE ANALYSIS OF SIGNAL DE-NOISING TECHNIQUES IN DISTRIBUTED ACOUSTIC SENSING SYSTEMS(2022-01-21) Abufana, Saleh; Kara, AliIn this thesis, it is aimed to propose a novel method to detect and classify the threats for fiber optic distributed acoustic sensing (DAS) systems based on phase OTDR. In the first stage of the proposed method, Wavelet de-noising method is applied to remove the noise from the measured backscattered signal, and difference in time domain approach is used to perform high-pass filtering. In this stage, autocorrelation is also used for improving interferometric visibility of the events in all range bins. Further, the power of the correlated signals at each bin is calculated and sorted. Hence, the maximum valued bins are considered to be the event signal. In the second stage, the detected event signals are decomposed into a series of band-limited modes by using VMD technique, and from these modes, the enhanced event signals are reconstructed. Moreover, from the reconstructed event signals, higher order statistical (HOS) features are extracted. In the last stage, Linear Support Vector Machine (LSVM)-based classification approach is implemented to the extracted features for discriminating the threats. In order to measure the effects of the proposed method on the classification performance, different types of activities collected from various points of a fiber optic cable have been used under different SNR levels. The results show the effectiveness of the proposed method in threat detection and classification.Article Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars(Drones, 2024-03-11) Awan, Maaz Ali; Dalveren, Yaser; Kara, Ali; Derawi, MohammadPrecise altitude data are indispensable for flight navigation, particularly during the au tonomous landing of unmanned aerial systems (UASs). Conventional light and barometric sensors employed for altitude estimation are limited by poor visibility and temperature conditions, respec tively, whilst global positioning system (GPS) receivers provide the altitude from the ean 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 al timeter 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 ap plications 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.Item USE OF WAVELET DECOMPOSITION IN RADIO FREQUENCY FINGERPRINTING OF BLUETOOTH SIGNALS(2022-01-10) AL-MASHAQBEH, HEMAM; Dalveren, Yaser; Kara, AliThis thesis addresses a new Open Systems Interconnection (OSI) Physical (PHY) layer scheme for extract and exploits Radio Frequency Fingerprinting (RFF) to uniquely identify specific devices such as cell phones based on their Bluetooth (BT) signals. Firstly, fingerprint features were extracted from BT transient signals experimentally collected from cell phones. After analysing transient Bluetooth signals, Dual-Tree Complex Wavelet Transform (DT-CWT) has been used to decompose Bluetooth signals. Feature extraction was performed from both time domain (TD) and wavelet-domain (WD) signals. Then, for classification, supported vector machine (SVM) classifier was used. Next, classification results achieved for time domain (TD) and wavelet domain (WD) BT signals were compared. The experiments were performed under different transient durations with different SNR levels such as low SNR (0 < SNR< 5 dB), moderate SNR (5 < SNR < 15 dB), high SNR (15 < SNR < 25 dB), and very high SNR (25 < SNR < 35 dB). Results show that it is possible to achieve reasonable accuracy in WD (at least 88%) even with short transient durations at low SNR levels. When compared to the results achieved for TD BT signals, better detection accuracy is clearly observed for WD BT signals. Therefore, it concluded that the use of DT-CWT can be evidently used in RFF of BT signals.Item VARIATIONAL MODE DECOMPOSITION BASED RADIO FREQUENCY FINGERPRINTING OF BLUETOOTH DEVICES(2022-01-26) Aghnaiya, Alghannai; Kara, AliIn this thesis, we evaluated the performance of RF fingerprinting method based on variational mode decomposition (VMD). 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. 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 VMD-reconstructed transients and VMD-modes. 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 reconstructed transients 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 lowers (2-3 dB) SNR levels. For the same dataset the classification performance demonstrates that VMD-modes method achieves better performance (4% higher) than VMD-reconstructed transient method.