Iskanje po repozitoriju

A+ | A- | | SLO | ENG

Povzetek: This study introduces hypothetical circuit elements, specifically the Constant Phase Element (CPE) and Zeroth-Order Approximation of a RC Circuit (ZARC), into the SPICE circuit simulation environment to enhance Electrochemical Impedance Spectroscopy (EIS) analysis. EIS, a critical method for understanding electrochemical processes in fields such as fuel cell analysis, corrosion studies, and biomaterials, relies on fitting measured impedance curves to Equivalent Electrical Circuit (EEC) models. However, existing approaches require expert knowledge and significant mathematical effort, limiting automation. By integrating CPE and ZARC into SPICE, this work bridges the gap between EIS analysis and advanced automatic circuit design methodologies, enabling efficient model selection and parameter determination. Experimental results demonstrate the accuracy of the implemented elements through a series of case studies, evaluated using Sheppard’s criteria function. This integration marks a significant step toward automated EIS model fitting and optimization, with potential implications for advancing electrochemical and materials research.
Ključne besede: electrochemical impedance spectroscopy, circuit simulators, hypothetical circuit elements, equivalent electronic circuits
Objavljeno v DiRROS: 18.06.2026; Ogledov: 151; Prenosov: 177
Celotno besedilo (639,78 KB)
Gradivo ima več datotek! Več...

Povzetek: A dual-band, single-layer reflect array offering high gain has been developed for dual-polarized drone tracking applications. The reflect array unit comprises two center-notched spiral structures aligned orthogonally and is separated by an independent notchperfect rectangle element. Based on the reflect array unit design, a single layer 7 x 7 array, totally 49 elements for Horizontally Linearly Polarized (HLP) Ku-band transmission (12.4 – 14.8 GHz) and 49 elements for Vertically Linearly Polarized (VLP) X-band reception (10.7 – 12 GHz is designed with the phase changes of over 75o . This phase changes is obtained by adjusting the length of the notched spiral patch within the unit cell. Two distinct horn sources has been implemented to energize the orthogonal ports, supplying separate linear polarization for each frequency band. The efficiency is about 18.2 to 22% across different frequency bands, underscoring the array’s suitability for Drone Tracking applications. From the performance measures, it is found that the proposed dual-band reflect array is more suitable for drone ground station tracking applications. Additionally, this proposed structure minimizes design complexity and meets specifications of a low profile antenna with a simple structure and reduced weight.
Ključne besede: dual-band, reflect array, orthogonally linear polarization, single reflecting layer, low profile
Objavljeno v DiRROS: 18.06.2026; Ogledov: 112; Prenosov: 162
Celotno besedilo (2,45 MB)
Gradivo ima več datotek! Več...

Povzetek: This paper presents the design and optimization of a parametric multiple-channel Double Dynamic Switching Biased Complementary Folded-Cascode Amplifier with switched capacitor integrator application in 32nm FinFET technology. The LTspice simulations demonstrate that the amplifier can attain an open-loop DC gain of 44.8dB, and a phase margin of about 87.8° with ±0.5V supply voltages. Moreover, the amplifier power consumption is measured 246µW including bias circuitry and a Gain-Bandwidth Product (GBW) of 77.45MHz under a 5pF load capacitor. The circuit’s stability enables it to offer diverse design capabilities tailored to specific application needs. This novel design is capable of reducing supply voltages and power dissipation
Ključne besede: 32nm finfet technology, complementary folded-cascode amplifier, double dynamic switching bias, self-cascode, switched capacitor
Objavljeno v DiRROS: 18.06.2026; Ogledov: 147; Prenosov: 137
Celotno besedilo (1,68 MB)
Gradivo ima več datotek! Več...

Povzetek: In Modern world, Fifth Generation (5G) technology is ubiquitous, so it is necessary to meet all of its service requirements. Hence, resource allocation for every service is very important. This research addresses the problem of resource allocation for both Enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communication (URLLC) users. The work comprises both static and dynamic resource allocation for eMBB and URLLC users. This research aims to minimize latency for URLLC users by taking into account the time and energy constraints of both eMBB and URLLC in static resource allocation, formulating these constraints as a convex optimisation problem. The results show that the static resource allocation strategy performs better than the fixed bandwidth and Central Processing Unit (CPU) cycle schemes. However, the use of static resource allocation becomes inefficient as the number of users increases. We propose a dynamic resource allocation strategy to address this issue, which uses online conformal prediction to schedule URLLC traffic on top of eMBB. The dynamic resource allocation strategy outperforms all previous resource allocation methods, ensuring 67% eMBB efficiency and 1 millisecond latency for URLLC users.
Ključne besede: 5G, eMBB, URLLC, conformal prediction, resource allocation
Objavljeno v DiRROS: 18.06.2026; Ogledov: 145; Prenosov: 136
Celotno besedilo (1,56 MB)
Gradivo ima več datotek! Več...

Povzetek: Network slicing (NS) divides the physical network into many logical networks in order to support the variety of new applications with higher performance and flexibility needs. As a result of these applications, a massive amount of data has been generated with a huge number of mobile phones. Due to this, NS performance has been greatly impacted and extreme challenges have been created. To efficiently handle the challenges, this paper proposes a novel Optimal Network slice Classification Using Deep learning (ONE-CLOUD) technique, which integrates the Coati Optimization Algorithm (COA), GhostNet, and Gated Dilated Convolutional Neural Network (CNN). COA optimizes features such as user device type, packet loss ratio, and delay rate, employing GhostNet model, and Gated Dilated CNN for network slice classification. The proposed method classifies network slices into enhanced Mobile BroadBand (eMBB), Ultra-Reliable and Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC). The effectiveness of the suggested approach has been evaluated using the 5G-SliciNdd dataset, utilizing evaluation criteria like accuracy, precision, recall, sensitivity, specificity, throughput, and reduced latency. The overall accuracy of the proposed method is 5.78%, 2.78% and 4.70% higher than the existing DQN-E2E, DRL, and AAA techniques respectively.
Ključne besede: network slicing, deep learning, GhostNet, gated dilated, CNN, Coati optimization
Objavljeno v DiRROS: 18.06.2026; Ogledov: 162; Prenosov: 165
Celotno besedilo (1,75 MB)
Gradivo ima več datotek! Več...