LEE- Elektronik Mühendisliği-Doktora
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ÖgeHigh speed data acquisition techniques for pipelined analog to digital converters in IHP SiGe BiCMOS 0.13 µm(Graduate School, 2024-04-29)The resolution of the analog-to-digital converter market may be categorized into 8-b, 10-b, 12-b, 14-b, 16-b, and other options. The incorporation of several resolutions arises from the demands of different applications. In 2018, the 12-b resolution lead the market for analog-to-digital converters. The 16-b type is expected to overtake and become the dominant force in the future. The use of 12-b for 5G connectivity presents a favorable opportunity for market expansion. Texas Instruments unveiled a groundbreaking ADC in May 2019, boasting the industry's largest bandwidth, lowest power consumption, and fastest sampling rate. This converter is anticipated to assist engineers in attaining optimal measurement precision for 5G testing, oscilloscopes, and direct X-band sampling in radar applications. In this work, a one way 11-b pipeline ADC, designed in a SiGe BiCMOS 0.13 μm, is presented. It has sampling frequencies up to 1.6 GS/s and can provide above 8-b ENOB for the low input signal frequency and 6.4-b ENOB for the highest input frequency of 799 MHz according to the simulation results obtained without calibration. For our ADC, sample-and-hold amplifier-less (SHA-less) architecture was preferred since the SHA was one of the most power-consuming sub-blocks, and brought inevitable noise and distortion. A composite ADC architecture, having 8x 1.5-b cascaded stages and a back-end 3-b flash ADC is designed to reach up to 11-b physical resolution. A novel MDAC is proposed to mitigate ISI. Moreover, a novel BiCMOS residue amplifier (RA), which performs 6.43 GHz UGB and 80 dB DC gain, is implemented. A non-overlapping clock generation architecture at 1.6 GHz is devised, incorporating a differential clock driver and clock level converters.The SNRjitter of the clock generation system is 57 dBc at a clock signal frequency of 1.6 GHz. The results of the measurements carried out at ITU VLSI are included in the thesis. SiGe BiCMOS 0.13 μm process is utilized to fabricate the complete ADC, which has a 1.6 V supply and a silicon area of 2.2 mm × 3 mm.
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ÖgeMulti - capsule endoscopy: Demonstrations of inter - capsular control and (tactile) sensing(Graduate School, 2023-12-19)Wireless Capsule Endoscopy (WCE) is an emerging Gastrointestinal (GI) tract imaging and treatment method that is developed to be a better alternative to the traditional endoscopy/colonoscopy devices and procedures. Thanks to pill shaped design and dimensions (22-26 mm in length, 10-13 mm in diameter), patient can easily swallow a single WCE where the capsule travels through the GI tract, and leaves the body. The complete non-invasive monitoring of the small bowel is possible in this way which is not possible in conventional endoscopy/colonoscopy. Even tough the WCE has very important role in the GI diagnostics in the clinics, there are several limitations to its usage. First of all, it is still cannot take any biopsy sample or make therapeutic actions in the clinical applications. Nonetheless, clinical operator can not control locomotion of the capsule through GI tract, which affect the monitoring process negatively. Also, WCE cannot perform sunctioning, flushing etc. which can be possible with conventional endoscopy. Due to its size limitation because of the patient comfort, battery life is limited and this might result with an incomplete examination. There are exhaustive studies to find solution to these shortcomings in the literature which we will be also working with the similar aim. Commercial capsules have a CMOS camera integrated on both tips and takes video stream and/or photographs while travelling. A clinician interprets this visual data to diagnose any issues with GI tract. Besides imaging, numerous literary studies on capsule endoscopy have demonstrated drug delivery, navigation strategies, tactile sensing for tumor diagnosis, and biopsy to increase the functionality of the WCE. While each function can work individually, using these in conjunction is needed to achieve complex treatment methods without any invasive process. Yet, the size limitation due to patient comfort hampers the availability of multiple features within a single capsule. In the first two parts of our study, in an effort to increase the space and functionality, we propose the usage of multiple capsules in conjunction. For our method, capsules together form a capsule-train in GI tract, whose wagons are connected with magnetic push/pull forces without any contact to each other. We focused on contactless/wireless connection between capsuels due to possible tissue damage. By knowing the distance between capsules, several functions can work in junction, e.g. the first capsule uses camera to find a tumor and second capsule takes a biopsy sample accurately thanks to known distance between capsules. For the first section, we have used passive magnets to achieve a wireless force connection between two capsules. After trying several magnet arrangamets on the tips of the capsules, we finalized a design where two capsules has a balanced constant distance in-between by using both pulling and pushing forces. We have used two large ring magnets that pulls each other and two cylindrical magnets that pushes each other. Cylindrical magnets are placed on the tips of the capsules while the ring magnets are placed with more distance to each other. Here, pulling force is dominant at large distances due to ring magnets and pushing force is dominant at smaller distances due to cylindrical magnets. This arrangament achieves constant distance in-between capsules without any energy consumption. Distance value is determined by the magnet sizes and arrangement. However, this method only works in thight tube-like shapes where tips of the capsules can not misalign, which might result with a clinch between a tip capsule with a ring capsule. To test the passive connection, we have used straight plastic tubes where we move capsules together with a constant in-between distance. Here, we pulled one of the capsules with a stepper motor and monitored in between distance with a camera placed above the test setup where an image processing code is ran to monitor the distance. We have achieved the capsule train without any connection breaks for typical bowel movement speed. As the second section, we improved our capsule train model to be more applicable on more challenging real life environments. Since typical human bowel diameter is ~25 mm while WCE diameter is ~12 mm, two capsules will have an angle between their tips, which results with a difference in between force due to angle. Here, we demonstrate an active distance control model with a closed loop control via the placement of a sphere permanent magnet on one capsule and a solenoid on the other capsule. Hall Effect Sensors have employed to determine distance between capsules. A PID controller have been developed to achieve stabilized desired distance between capsules by manipulating solenoid current. Experiments were conducted by pulling the leading capsule at typical human peristalsis speed. An inter-capsule distance of 1.94 mm was achieved on the average for the desired distance as 2 mm on 3D-printed plastic phantoms, while 0.97 ± 0.28 mm of distance was observed for the ex-vivo bovine tissue, for a set distance of 1mm. By achieving successful demonstration of inter-capsule control, this work substantiates realizability of multi capsule endoscopy for future studies. As the third part of our study, we focused on to develop a novel diagnostic tactile sensing method to use in capsule endoscopy, which supports our multi-capsule approach by adding a new method to WCE functional palette. Since palpation is a widespread diagnostics method for clinicians, using this method inside GI tract will be an useful addition since some of the abnormalities such as inflammation of early stage tumors cannot be seen on visual imagery while having higher elasticity modulus than healthy tissue. Planned tactile sensing model measures the tissue elasticity modulus. In our fourth part this study, we will be presenting our tactile sensing mechanism that adopts the atomic force microscopy (AFM) methodology and fits it into a single WCE volume. AFM is used to achieve surface imaging up to nanoscale levels of resolution by scanning a moving cantilever through targeted area. On each discrete scanning position on the sample, cantilever base moves down to make cantilever tip interact with the surface. Tip deflection occurring due to interaction between the cantilever tip and the surface gets recorded. Mechanical surface properties such as topology, elasticity, adhesivity etc. can be deducted from this data by Hertz contact model to be used to inspect the tissue healthiness. Main difference of our model is having a different cantilever tip displacement measurement method. Conventional AFM uses a lazer beam reflecting from the cantilever tip and lands on a 4 part photodiode sensor where the sensor output is directly related to cantilever tip deflection. Since using such system in a single capsule is not possible with current hardware, we focused on using a different sensing method and decided to use a piezoelectric material attached onto cantilever. As cantilever tip bends, piezomaterial placed onto cantilever is also bends and generates charge. Amount of generated electric charge, therefore the current output, is directly related to piezo deformation. By using this mechanism, we can use the same elasticity modulus measurement procedure with AFM approach, which is widely used in practical applications. We planned to use a micro stepper motor in the capsule due to size limitations. We have 3D modeled the cantilever and inner-capsule holding parts around the typical WCE and motor dimensions. Since each manufactured cantilever and piezo material is unique, we needed to calibrate each assembly on a glass surface where no indentation occurs. Also, we needed a charge amplifier circuit to read piezo signal output since the used piezo sheets has ~10 mm3 area with a very small electrical charge generation. The test procedure calculates the stiffness of the cantilever by finding it's resonant frequency. We need this value to find force acting on the tip of the capsule. By knowing the piezo output on glass and the stiffness of the cantilever, we were able to start our test on different materials and tissues. We have prepared two jellies with different densities to experiment on. Also, we used chicken breast, bovine liver, sheep stomach, cow stomach, and human colon tissue as tissue tests. After all measurements, we were able to compare real elasticity values of jellies, chicken breast and bovine liver tissues to measured values by the cantilever. After all test, we have obtained 30% maximum error on chicken breast, 15% on bovine liver, %33 on stiffer jelly and %49 on the second jelly. Since the tissues with inflammation or tumors have 4x to 5x larger elasticity values than healthy tissues, there results would be usable to determine the state of the inspected tissue which is our main motivation for this section. As an additional study, which can be identified as the fourth section of this study is about 3D locomotion of a single WCE (with passive magnets inside) in stomach by an externally controlled electromagnet array used by an clinician. Thanks to this mobility, clinician can see any desired area in the stomach. Here, WCE position and angle is controlled by 5 electromagnet array placed under the patient in lying position. Capsule tip levitation is controlled by a single, larger electromagnet placed above the patient. As my study, I have simulated the angular position of WCE by alternating the electromagnet currents to achieve any desired distance. Later on, I conducted rotation, levitation and displacement experiments on 3D printed PLA bovine stomach model. Also, same experiments done in ex-vivo environment where bovine stomach tissue is paved into 3D printed PLA model. These experiments succesfully valiated the applicability and accuracy of 3D control with 6 magnet array arrangement.
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ÖgeNovel fractional order calculus-based audio processing methods and their applications on neural networks for classification and synthesis problems(Graduate School, 2023-10-24)This thesis dissertation aims to explore the application of the Fractional Order Calculus (FOC) framework in addressing contemporary problems in audio signal processing. One crucial aspect of present audio signal processing approaches is their reliance on large amounts of data, which necessitates appropriate tools for increasing amount of data. Another important aspect is in relation to the methods used to produce inference models. The neural network approaches dominating the field often require optimization of a large number of parameters. As a result, digital signal processing (DSP) tools are being repurposed to reduce the parameters of neural models. The introductory chapter provides an overview of the dissertation's purpose, which is to investigate whether FOC can provide novel methods to solve problems in neural network-based audio signal classification and reconstruction. Chapter 2 introduces the FOC framework by explaining its capabilities and complexities. While the complexities of FOC have often caused it to be overlooked in engineering applications, its capabilities have attracted the interest of many researchers in various fields, including audio processing, time series estimation, and image enhancement. Providing examples of FOC based applications on audio signal processing, this chapter aims to provide fammiliarity to the FOC concept. The dissertation is structured such that each chapter focuses on a specific application of audio signal processing. Chapter 3 tackles the problem of audio classification, which is categorised by being speech, music or environmental sound signals. Due to the limited availability of data for environmental sound signals, data augmentation methods remain crucial for Environmental Sound Classifaciton (ESC) problems. The chapter presents three FOC based data augmentation methods: Fractional Order Mask, Fractional Order Frequency Scale, and Fractional Order Mel Scale. Fractional Order Mask and Fractional Order Mel Scale methods are applied to Mel Spectrogram and Log-Mel Spectrogram representations of envrionmental sound data. Experiments on ESC problem with neural architectures demonstrate their effectiveness as data augmentation tools in improving the accuracy of neural network models. The findings indicate that employing a data augmentation procedure in combination with the proposed methods can yield a boost of approximately 7.7% in performance for a 5-layer CNN when Log-Mel Spectrograms are used as input. Similarly, the augmented dataset resulted in a increase of over 9% in performance for a 18-layer ResNet. Chapter 4 delves into audio synthesis and its importance in reconstructing time domain representations of audio signals. The history of vocoding methods and their relation to signal reconstruction approaches are discussed. The chapter focuses on phase reconstruction with methods such as SPSI and spectral consistency based iterative methods such as the Griffin-Lim Algorithm (GLA) and its novel forms. In this chapter a FOC based method is proposed. The FOC based method models a signal's Power Spectral Density Function (PSDF) using Fractional Differential Equations (FDE), estimating the instantaneous frequency of a peak in a windowed audio spectrum. This method proves effective in phase reconstruction. The results show the usage of FOC framework provided up to 4% better quality than SPSI. The experiments also highlight the proposed method's effectiveness as an initial phase estimator for spectral consistency based iterative methods. Chapter 5 explores the contemporary research topic of Neural Audio Synthesis (NAS).
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ÖgeFully simulated and model based power consumption estimation of internet of things devices(Graduate School, 2024-09-11)The rapid proliferation of Internet of Things (IoT) devices, driven by the demand for efficient computing units integrated into various networks, underscores the critical need for energy efficiency. As these devices often operate in mobile settings, optimizing energy consumption becomes paramount to minimize maintenance costs associated with battery replacement or recharge. Additionally, power efficiency directly impacts the portability of IoT devices, enhancing their usability and effectiveness in diverse environments. Software and hardware factors are influential on the energy consumption of IoT devices. Due challenges of battery replacement, IoT devices rely heavily on software-based power management for optimization. Thus, software updates playing a significant role in battery life expectancy. To plan maintenance processes effectively, manufacturers and service providers must accurately estimate energy consumption and battery lifetime, necessitating a holistic approach to power estimation. Traditional methods of energy consumption estimation, reliant on physical measurements, are impractical due to the extensive hardware and software design iterations required. Consequently, a fully simulated, model-based approach to power consumption estimation emerges as essential, especially considering the frequent update requirements of IoT devices. Such an approach enables accurate estimation throughout design changes and updates, facilitating efficient planning and management of power consumption across various scenarios. This thesis proposes a comprehensive energy consumption model tailored for IoT devices, complemented by fully simulated, model-based system-level power estimation approaches. By leveraging simulation environments like Open Virtual Platform (OVP), the proposed methodology achieves approximately \%97 accuracy in typical real-life scenarios. Notably, the methodology eliminates the need for completed hardware and software designs, enabling efficient power estimation throughout the development and operational phases of IoT devices. In conclusion, the study contributes a novel methodology for accurate power consumption estimation in IoT devices, addressing the challenges posed by evolving hardware and software requirements. By embracing simulation-based modeling and system-level approaches, the proposed methodology offers a practical and efficient solution for managing power consumption in IoT devices, ultimately enhancing their usability, reliability, and sustainability in diverse application domains. All these advantages have been validated through different applications, and the results have been shared within the scope of the thesis study.
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ÖgeA robust framework covering measures developed using EVM metric against jamming attacks in next-generation communication systems(Graduate School, 2024-08-07)In the face of escalating threats posed by malicious jamming in next-generation communication systems, this thesis presents a comprehensive framework comprising jamming signal detection, jamming signal classification, jammer localization, and two anti-jamming strategies to address these challenges effectively. The proposed error vector magnitude vs. resource block (EVM vs. RB) methodology for jamming signal detection, unlike traditional approaches commonly use received signal strength (RSS) and bit error rate (BER), examines the effects of jamming signals on IQ symbols using the EVM metric. Our methodology, which is able to capture even small changes imposed by jamming signals on IQ symbols, provides significant advantages in terms of sensitivity compared to the conventional approaches. Moreover, the proposed methodology has a low-complexity of the order of O(N), which is especially important for next-generation communication systems known for their strict latency requirements. By utilizing IQ symbols that are natively generated in the data transmission system, our methodology seamlessly integrates into existing 5G and LTE systems without imposing additional overhead, facilitating practical deployment in real-world scenarios. RBs represent the frequency domain for next-generation wireless communication systems and the proposed methodology measures the EVM for each RB in the received signal. In this way, our approach not only detects jamming signals but also provides vital frequency information associated with the jammer. This information enhances counteraction capabilities, enabling targeted responses to mitigate the impact of jamming attacks. Furthermore, the proposed method demonstrates stability across varying system parameters, including modulation type and code rate, thereby contributing to adaptability in dynamic communication environments. The effectiveness of the proposed detection methodology is validated through extensive theoretical analysis, simulation studies, and laboratory experiments. Theoretical analyses substantiate the advantages of our approach, reinforcing its validity and reliability. Simulation results showcase the robustness and stability of our method across diverse scenarios, highlighting its practical utility in real-world applications. Laboratory experiments provide empirical evidence of its effectiveness, further validating its potential for deployment in operational communication systems. Beyond the jamming signal detection, our jamming signal classification methodology offers a comprehensive solution to accurately characterize and classify various jamming signals by utilizing Symbol-RB-EVM which is another measurement we developed. The Symbol-RB-EVM measurement is created by accumulating the EVM vs. RB data obtained for each OFDM symbol time into a matrix and provides a nuanced understanding of jamming signal behavior across time and frequency domains. Unlike traditional measurements such as spectrogram, RSS, and BER, the proposed measurement offers superior sensitivity and specificity in capturing the intricacies of jamming signals. After creating the dataset using the Symbol-RB-EVM results, we take advantage of machine learning algorithms for jamming signal classification. Thanks to the fact that Symbol-RB-EVM efficiently provides useful features of jamming signals, the proposed methodology enables precise classification of jamming types with high accuracy, thereby minimizing false alarms. This property of Symbol-RB-EVM also enables lower complexity machine learning methods to produce successful results even with minimal training data. Add to this the fact that Symbol-RB-EVM is computed with low computational complexity (O(N)), and we can say that the proposed methodology is in a very valuable position in terms of overall complexity. Extensive simulations demonstrate the superior performance of the proposed metodology in accurately characterizing diverse jamming signal types across varying scenarios and environmental conditions. In addition to the detection and classification, the EVM metric is also considered to provide effective results for jammer localization problem. The localization process begins with the detection of jamming signals using the EVM vs. RB methodology. EVM contours are then drawn on the map using the EVM data acquired from user equipments (UEs). In this approach, which has a range-free feature, the contours are concentrated towards the jammer source, providing a coarse estimate of the location of the jammer. WhentheUEdensitysurroundingthejammerissufficiently high, accurate localization can be swiftly achieved using these contours alone, eliminating the need for further operations. However, in cases where the UE density is not sufficient for an accurate localization, we take our methodology to a hybrid structure by also using Time Difference of Arrival (TDOA), a range-based technique, to improve localization accuracy. With the help of the coarse location information from the EVM contours, the right one is selected quickly among the sensitive solutions offered by the TDOA. Thanks to such an innovative approach, the quickness of the range-free technique and the high accuracy of the range-based technique are combined. Extensive simulations demonstrate the localization success of the proposed methodol ogy across diverse network densities and environmental conditions, underscoring its robustness and reliability in real-world deployment scenarios. By offering both high accuracy and low complexity, our methodology promises to bolster the resilience of 5G networks against malicious jamming attacks, ensuring uninterrupted communication services and safeguarding critical data transmission pathways. Effective solutions presented in the fields of jamming signal detection, classification and jammer localization encourage us to develop successful anti-jamming solutions. The first anti-jamming proposal provides a robust method designed to protect next-generation communication systems from malicious jamming. At the beginning of this methodology is the identification of RBs affected by jamming attacks through the EVM vs. RB measurement. By leveraging this measurement, which provides insight into the frequency domains targeted by jammers, our methodology effectively discerns clean RBs from those under jamming influence. Building upon this insight, we propose an RB sharing strategy aimed at optimizing resource allocation and protecting UE from jammer interference. The strategy prioritizes the allocation of clean RBs to UEs closest to the jammer, thereby isolating their signals from jamming attacks and ensuring uninterrupted communication. Acknowledging the finite nature of RB resources, our research endeavors to assess jammed RBs and allocate them to UEs farthest from the jammer whenever possible. Alternatively, we are also investigating data rate reduction strategies that can be realized for these RBs in order to increase their resistance to jamming. Key to the success of our methodology is its low-complexity decision-making process, which eliminates the need for extensive training and ensures rapid response capabilities—critical attributes in the context of next-generation communication systems characterized by low-latency requirements. Moreover, our approach seamlessly integrates with existing system architectures, leveraging IQ data obtained from the inherent system flow for necessary EVM measurements. Simulation results underscore the efficacy of the proposed methodology in maintaining maximum UE throughput, even in the face of sustained jamming attacks. By optimizing RB resource utilization and minimizing disruptions caused by jamming interference, our approach promises to bolster the resilience of next-generation communication systems against evolving threats and ensuring uninterrupted service delivery. Our second anti-jamming proposal introduces a novel methodology engineered to confront the challenges posed by malicious jamming attacks head-on. At its core lies a sophisticated approach that harnesses transmitted and received IQ symbols to train a linear regression algorithm, enabling the system to adapt and neutralize the disruptive effects of jamming signals on the IQ symbol packets. Utilization of the EVM metric gauges the training performance of the linear regression algorithm. Through an iterative process, the algorithm assimilates the impact of jamming signals on IQ symbols, effectively deciphering their disruptive effects and restoring communications for jammer-occupied resources. One of the key strengths of our methodology lies in its adaptability to diverse jamming signals, ensuring robust protection against a wide range of jamming tactics. By efficiently restoring communications for jammer-occupied resources, our approach minimizes the impact of jamming attacks on network performance, ensuring uninterrupted service delivery for end-users. Moreover, the low-complexity implementation is facilitated by leveraging linear regression and EVM techniques. Theoretical analyses and simulation results confirm the effectiveness of the proposed methodology and underline its potential to increase the resilience of communication infrastructures against malicious interference.