Faculty of Computer Science and Engineering
Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5
The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest
faculties in that field in the region.
The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.
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Item type:Publication, A 12-Bit 20-kS/s 640-nW SAR ADC With a VCDL-Based Open-Loop Time-Domain Comparator(Institute of Electrical and Electronics Engineers (IEEE), 2022-02); ; Zhang, YanlongThis brief presents a 12-bit ultra-low-power asynchronous successive approximation register (SAR) analog-to-digital converter (ADC). A voltage-controlled delay line (VCDL) based open-loop time-domain comparator is proposed and analyzed, achieving low noise and ultra-low power performance. By employing the mixed switching scheme, the segmented capacitive digital-to-analog converter (CDAC) arrays as well as the synchronous data-weighted averaging (DWA) calibration block, the proposed SAR ADC can operate from 1.8 V down to 0.8 V at 20–200 kS/s. The designed ADC is fabricated in a 0.18- μm CMOS process and the measurement results show the proposed SAR ADC achieves an SNDR of 65-dB with power consumption of 647 nW from a 0.8 V power supply at 20 kS/s. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Detection of Premature Heartbeats(IEEE, 2022-05-23)Premature heartbeats are those that appear earlier than the regular ones due to contractions not originating from the Sinus Atrial Node, out of the normal heart rhythm. Although one might think this is a trivial task to detect, the distribution of premature heartbeats in the benchmark electrocardiograms shows it is not the case. We aim at finding the optimal method to detect premature heartbeats, threshold value and context level for temporal based approach. Methodology: Several methods are specified to calculate the relation of the premature heartbeat to a set of several previous instances and conduct experiments to present which averaging method (arithmetic, geometric and harmonic mean) delivers the best solution. Then, we calculate the deviation ratio to the average of these beats that affect the prematurity condition. Particularly, the goal is to find an average AV G of previous k beat-to-beat intervals RR, such that the ratio between the difference dRR of the analyzed RR and AV G versus AV G is more than a specific threshold T hr. Data: The comprehensive MIT-BIH Arrhythmia Electrocardiogram benchmark database is used in our evaluation. The analysis is conducted on the array of beat-to-beat intervals for the heartbeats preceding the premature one. Conclusion: The results show that the optimal method is based on the arithmetic mean AM We found that the larger k, the better performance is achieved. A sufficient performance with F1 score over 85% is achieved for k = 5 and T hr = 15%. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparing AWS Streaming Services: A Use Case on ECG Data Streams(IEEE, 2022-05-23) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Network Traffic Impact on Cloud Usage at Different Providers(IEEE, 2022-05-23); Markozanov, V.The research question focuses on finding an optimal cost-less and efficient solution, analyzing the differences between uploading and downloading data between different cloud solutions. The conducted experiments compare AWS cloud platform offered by Amazon and use two of its products: computational cloud platform EC2 and storage cloud platform S3. We explore three different versions of S3 storage and S3 Standard, each of them with its own purposes, characteristics and costs. Additionally we test the data transfer and computational capabilities of a t2.micro instance of the EC2 virtual machine. Then, we compare the experiment on the European Cloud provider - Scaleway Elements and use two of its products - Virtual Instances based on the Stardust instances for computations and Object Storage a S3 compatible storage platform.We conclude that the network connection between Amazon nodes is far superior than any other, both in performance and costs. Fetching results from an AWS computational service (EC2) is much cheaper and faster when using an AWS platform as an intermediary (S3) compared to a direct transfer to the local machine. We observe a similar superior network performance in the comparison of Scaleway Cloud Infrastructure. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Detecting Ventricular Beats with Machine Learning Models(IEEE, 2022-05-23) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Parallelization of Dijkstra’s Algorithm for Robot Motion Planning: Is It Worth Increasing Speed Without Losing Accuracy?(IEEE, 2022-11-15); ; This paper examines the problem of parallelizing Dijkstra’s algorithm, as an algorithm for robot motion planning was a challenge to twist the algorithm in such a way so it can be executed in parallel, rather than sequentially. In this paper, we test the validity of a research hypothesis that the speed of Dijkstra’s algorithm can be increased by parallelizing and keeping the same accuracy. The developed use-case of Dijkstra’s algorithm as a robot motion planning algorithm was tested against performance, both in execution speed and accuracy. Finally, we elaborate on the benefits of this parallelizing approach. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, CardioHPC: Serverless Approaches for Real-Time Heart Monitoring of Thousands of Patients(IEEE, 2022-11); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, From Continuous ECG Signals to Extracted Features for Machine Learning Models and Arrhythmia Annotations(IEEE, 2022-11-15); This paper describes the process of transforming an ECG signal as a continuous stream of numbers representing measured electrical voltages between the ECG electrodes into an output indicating the existence of arrhythmia. The ECG data stream is a structured array of converted analog signal values to digital data. Although this stream uses a continuous structure of numbers within a given range that depends on the bit resolution during conversion, it is still unstructured as a representation of the appearance of arrhythmia since it does not contain information about detected arrhythmia. This paper presents how to process ECG data, detect heartbeat annotations, and calculate various parameters for tabular-based data with a fixed number of columns to be used as input into ML-based algorithms. Our use case addresses an ML algorithm to detect atrial fibrillation arrhythmia, as an irregular heart rhythm. Practically a set of numbers in the ECG samples, which do not have structured arrhythmia annotations, is transformed into structured annotations. Experiments are conducted on the well-known ECG benchmark MIT-BIH Arrhythmia Database. The input data is resampled from 360 Hz to 125 Hz signal, and a signal processing algorithm is used to detect heartbeats, extracting a fixed set of features, and systematically forwarded to the feature selection ML methodologies to obtain atrial fibrillation annotations. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Sampling Rate Impact on Heart Rate Variability(IEEE, 2022-11-15); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Effect of Bézier Interpolation on Similarity of Heartbeats in Electrocardiograms(IEEE, 2022-11-15);