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Piezoelectric Plate Sensor
1) A method was developed to detect transrenal DNA mutations using a piezoelectric plate sensor with attomolar sensitivity and specificity to detect mutant DNA in a background of wild type DNA.
2) Signal processing algorithms and a moving window approach were optimized to reduce noise and accurately detect mutation peaks with concentrations as low as 60 copies/ml.
3) Laminar flow and temperature conditions were identified that maximized specificity for detecting mixtures of mutant and wild type target DNA.
4) The method was validated through two-color fluorescent microsphere reporter hybridization and applied to detect double-stranded target DNA and DNA extracted from cell cultures.
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Piezoelectric Plate Sensor
1) A method was developed to detect transrenal DNA mutations using a piezoelectric plate sensor with attomolar sensitivity and specificity to detect mutant DNA in a background of wild type DNA.
2) Signal processing algorithms and a moving window approach were optimized to reduce noise and accurately detect mutation peaks with concentrations as low as 60 copies/ml.
3) Laminar flow and temperature conditions were identified that maximized specificity for detecting mixtures of mutant and wild type target DNA.
4) The method was validated through two-color fluorescent microsphere reporter hybridization and applied to detect double-stranded target DNA and DNA extracted from cell cultures.
Certificate
This verified certificate recognizes that Ceyhun Ekrem Kirimli successfully completed and passed the online course "6.02x: Embedded Systems - Shape the World" offered through UTAustinX, an online learning initiative of The University of Texas System partnered with edX. The certificate is signed by the Executive Director of the Institute for Transformational Learning at UT System, the Vice Provost for Higher Education Policy and Research and Executive Director of the Center for Teaching and Learning at UT Austin, a Senior Lecturer at UT Austin, and a Professor of Electrical Engineering at UT Austin.
Certificate
Michel Maharbiz, Associate Professor of Electrical Engineering and Computer Science at UC Berkeley, and Tom Zajdel, Graduate Student Researcher at UC Berkeley, verify that Ceyhun Ekrem Kirimli successfully completed and passed the course EE40LX: Electronic Interfaces: Bridging the Physical and Digital Worlds, offered by BerkeleyX through edX, and was issued a certificate on May 08, 2015.
Certificate
Rafael A. Irizarry is a professor of biostatistics at both Harvard T.H. Chan School of Public Health and Dana Farber Cancer Institute. This certificate verifies that Ceyhun Ekrem Kirimli successfully completed and passed the course PH525.2x: Introduction to Linear Models and Matrix Algebra offered by HarvardX, Harvard University's online learning initiative through edX, on May 26, 2015.
Certificate
Professor Rafael A. Irizarry is a professor of biostatistics at both Harvard T.H. Chan School of Public Health and Dana Farber Cancer Institute. This certificate verifies that Ceyhun Ekrem Kirimli successfully completed and passed the course PH525.1x: Statistics and R for the Life Sciences offered by HarvardX, an online learning initiative of Harvard University through edX, on May 26, 2015.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...
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Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
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Certificate
This verified certificate recognizes that Ceyhun Ekrem Kirimli successfully completed and passed the online course "6.02x: Embedded Systems - Shape the World" offered through UTAustinX, an online learning initiative of The University of Texas System partnered with edX. The certificate is signed by the Executive Director of the Institute for Transformational Learning at UT System, the Vice Provost for Higher Education Policy and Research and Executive Director of the Center for Teaching and Learning at UT Austin, a Senior Lecturer at UT Austin, and a Professor of Electrical Engineering at UT Austin.
Certificate
Michel Maharbiz, Associate Professor of Electrical Engineering and Computer Science at UC Berkeley, and Tom Zajdel, Graduate Student Researcher at UC Berkeley, verify that Ceyhun Ekrem Kirimli successfully completed and passed the course EE40LX: Electronic Interfaces: Bridging the Physical and Digital Worlds, offered by BerkeleyX through edX, and was issued a certificate on May 08, 2015.
Certificate
Rafael A. Irizarry is a professor of biostatistics at both Harvard T.H. Chan School of Public Health and Dana Farber Cancer Institute. This certificate verifies that Ceyhun Ekrem Kirimli successfully completed and passed the course PH525.2x: Introduction to Linear Models and Matrix Algebra offered by HarvardX, Harvard University's online learning initiative through edX, on May 26, 2015.
Certificate
Professor Rafael A. Irizarry is a professor of biostatistics at both Harvard T.H. Chan School of Public Health and Dana Farber Cancer Institute. This certificate verifies that Ceyhun Ekrem Kirimli successfully completed and passed the course PH525.1x: Statistics and R for the Life Sciences offered by HarvardX, an online learning initiative of Harvard University through edX, on May 26, 2015.
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Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
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model is rigorously trained and evaluated, exhibiting remarkable performance
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our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
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TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
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