Performance Analysis Of Architecture Research Articles

Design and Performance Analysis of a Low Jitter Charge Pump-Phase Locked Loop Architecture and Loop Filter in CMOS Process

Design and Performance Analysis of a Low Jitter Charge Pump-Phase Locked Loop Architecture and Loop Filter in CMOS Process

Performance analysis of multi-folded pipelined successive cancellation decoder architecture for polar code

Performance analysis of multi-folded pipelined successive cancellation decoder architecture for polar code

Performance analysis of convolutional neural network architectures over wireless capsule endoscopy dataset

Performance analysis of convolutional neural network architectures over wireless capsule endoscopy dataset

Performance Analysis of YOLO Architectures for Surgical Waste Detection in Post-COVID-19 Medical Waste Management

In the wake of the COVID-19 outbreak, there has been a dramatic uptick in the need for efficient medical waste management, making it imperative that more surgical waste management systems are developed. Used surgical masks and gloves are examples of potentially infectious materials that are the subject of this research. By utilizing its real-time object detection capabilities, the You Only Look Once (YOLO) deep learning-based object detection algorithm is used to identify surgical waste. Using the MSG dataset, a deep dive into the performance of three different YOLO architectures (YOLOv5, YOLOv7, and YOLOv8) was undertaken. According to the findings, YOLOv5-s, YOLOv7-x, and YOLOv8-m all perform exceptionally well when it comes to identifying surgical waste. YOLOv8-m was the best model, with a mAP of 82.4%, among these three. To mitigate post-COVID-19 infection risks and improve waste management efficiency, these results can be used to the creation of automated systems for medical waste sorting.

Performance Analysis of a Novel Multiple Access Spatial Modulation Architecture based on STBC-OFDM and CDMA over Nakagami-m fading Channel

One of the schemes which are likely to be adopted in the forthcoming wireless communication standards is the so-called spatial modulation (SM). However, the wireless channels pertaining to such standards will mostly be frequency selective because of the involved high data rates, and SM technique alone can not overcome the resulting signal distortion. In this context, orthogonal frequency division multiplexing (OFDM) has been used as an efficient solution to combat this impediment, and space-time block coding (STBC)-OFDM has followed as an innovative approach merging the viable features of OFDM and the reliability enhancement capabilities of STBC. Obviously, SM solutions in the presence of multiple users communicating in frequency selective channels should be targeted. To fulfill such a need, a new SM-based architecture incorporating STBC-OFDM and CDMA is proposed in this work and is shown to exhibit a performance which is superior to state-of-the-art alternatives. Furthermore, its sensitivity to the aforementioned adverse parameters is studied in Nagakami channel.

Performance Analysis of Deep Convolutional Network Architectures for Classification of Over-Volume Vehicles

The number of vehicle accidents has increased in recent years due to overloaded goods carriers. Off-road driving, mountain roads, and sharp edges on a road are the main causes of an imbalance in overloaded trucks. In rural areas, where smaller roads cannot accommodate high volume vehicles, such vehicles cause many problems for cars, bikes, bicycles, and other small vehicles, as well as an increase in traffic congestion in those areas. This has become a major problem in the daily lives of drivers in rural areas as well as major urban areas. Solutions are needed to detect over-volume goods carriers and alert drivers to slow down or control the volume in their trucks. This work mainly focuses on a solution that uses deep CNN models. In this work, different deep convolutional architectures are evaluated for their ability to classify goods based on their volume. The model implemented is based on a dataset-specific transfer learning process with CNN layers generated in ImageNet in which only dense layers are learned. The primary objective of this work is to identify a classification method that exhibits proven results with respect to the accuracy parameters. In this work, different deep architectures were tested, and among the efficient networks, Net-B3 was found to perform with 95% accuracy on average. The different architectures were evaluated based on their accuracy, confusion matrix, ROC curve, and AUC score with a real-time dataset.

A product and organisational architecture analysis of the performance of Southeast Asian airlines

In this study, the operating environment's attributes and its influence on the performance of airlines in Southeast Asia are assessed through a comprehensive literature review and performance analysis. As a first step, a Pareto Analysis is performed to assess the capacity contribution of registered airlines in the Southeast Asia region, totalling 65 in 2019. Then, seven of the largest Southeast Asian airlines, representing 80% of total capacity in the region, are taken forward into an in-depth benchmarking and Product and Organisational Architecture (POA) performance analysis. The research pinpoints key differences between Full-Service Carriers and Low-Cost Carriers in the region through a series of 13 metrics applied in the POA. It was found that the region's FSCs scored better at Product Architecture, which includes convenience, comfort and connectivity, whilst the LCCs strength was in the Organisational Structure component, namely fleet and labour productivity, sales and distribution, and airport attractiveness, especially in the case of AirAsia.

Performance Analysis of a RTK Vector Phase-Locked Loop Architecture for GPS Signal Tracking in Degraded Environments

Performance Analysis of a RTK Vector Phase-Locked Loop Architecture for GPS Signal Tracking in Degraded Environments

Performance analysis of an outdoor Li-Fi system-based AO-OFDM architecture under different FSO turbulence and weather conditions

The Li-Fi system is being considered as an excellent alternative to the RF in the wireless network for the past few years due to its many benefits. Free space optics (FSO) is a type of communication where the light signal is sent over an unguided and unlimited path. Atmospheric attenuation, such as scintillation and weather or meteorological conditions, are critical characteristics affecting the reliability and quality of Li-Fi systems. This study extensively examined and simulated the atmosphere attenuation with various conditions and evaluated the influence of these conditions on different laser beam wavelengths. The performance of a proposed outdoor Li-Fi system based on all-optical OFDM to transmit 640 Gbps generated from eight orthogonal channels under the attenuated modelled channel situations was tested and analyzed. The system demonstrated a decent performance with BER at approximately 6 × 10−4 and received a signal of − 35 dBm for 10 Km and significant turbulence. VPI photonics design package and MATLAB were utilized to evaluate the system performance and channel modeling.

Performance analysis of deep learning architectures for plant leaves disease detection

Plant disease is a major concern in agriculture field. Different pathogens are causes of plant disease. Manual inspection sometimes can cause errors in the disease detection over large cultivation area. Large number of resources and high level of expertise is required for correct prediction of diseases. Sometime the experts are not available in the nearby. Some diseases are so common and have same symptoms even the different experts may give different opinion about the same disease.In this work different deep architectures are studied and tested for plant leaves disease detection. For carrying out the study plant village dataset of 20640 images are used which represents 15 class and 3 species namely pepper, potato, and tomato. Total images in the dataset are divided in three sets i.e., 70% for training, 20% for validation and 10% for testing purpose. MATLAB R2019b is utilized for experimentation. Appropriate value of hyper parameter is determined initially by varying number of epochs, learning rate. Performance of Adam optimizer is compared with Sgdm Optimizer. Finally, accuracy of eight deep learning architectures is calculated at 16 & 32 minibatch size.

Performance Analysis of Neural Network Architecture in Developing Real-Time Malaysian River Water Quality Model

According to the world health organization, 485,000 people died each year due to water-related health diseases which are mainly contributed by poor river water quality. As a result, water quality monitoring stations have been deployed across the world. Unfortunately, due to the complex nature of the off-site water quality parameters, the water quality index (WQI) cannot be assessed in real-time. This has led to a significant push for the scientific community to develop an accurate and robust water quality prediction model. The dynamic and nonlinear nature of water quality parameters are major challenges for traditional machine learning algorithms such as multi linear regression to capture. In this study, the water quality index prediction model was developed using the feedforward artificial neural network (FANN) algorithms utilizing only on-site parameters. The performance of different nonlinear activation functions in the hidden neurons was thoroughly analysed which includes rectified linear unit (ReLU), scaled exponential linear unit (SELU), and exponential linear unit (ELU). Additionally, various initialization and optimization algorithms were also evaluated for maximum performance and efficiency. The results shows that FANN-ELU model coupled with Glorot initialization technique and AdaGrad optimizer outperformed other model combinations with an R2 value of 0.88 and mean squared error (MSE) value of 22.74.

Containerized Architecture Performance Analysis for IoT Framework Based on Enhanced Fire Prevention Case Study: Rwanda.

Nowadays, building infrastructures are pushed to become smarter in response to desires for the environmental comforts of living. Enhanced safety upgrades have begun taking advantage of new, evolving technologies. Normally, buildings are configured to respond to the safety concerns of the occupants. However, advanced Internet of Things (IoT) techniques, in combination with edge computing with lightweight virtualization technology, is being used to improve users’ comfort in their homes. It improves resource management and service isolation without affecting the deployment of heterogeneous hardware. In this research, a containerized architectural framework for support of multiple concurrent deployed IoT applications for smart buildings was proposed. The prototype developed used sensor networks as well as containerized microservices, centrally featuring the DevOps paradigm. The research proposed an occupant counting algorithm used to check occupants in and out. The proposed framework was tested in different academic buildings for data acquisition over three months. Different deployment architectures were tested to ensure the best cases based on efficiency and resource utilization. The acquired data was used for prediction purposes to aid occupant prediction for safety measures as considered by policymakers.

Performance analysis of transformer-based architectures and their ensembles to detect trait-based cyberbullying

Performance analysis of transformer-based architectures and their ensembles to detect trait-based cyberbullying

Performance Analysis of Various 5G Mobile Architectures

The field of Telecommunication has progressed and emerged to be a successful field of technology. From communicating via pigeons and cannon balls to video conferencing for a meeting, the communication has improved surpassing every user’s expectation. With the introduction of wireless communication, the field of communication took a different turn. Just when we thought we reached the zenith of wireless communication, an esoteric concept of StandAlone (S.A.) and Non-StandAlone (N.S.A.) opened the gates to a more efficient communication, expanding the horizons of technology. The 5G network has started developing over some regions in the world but the real question remains; which is, whether an efficient communication is worth the economy spent on developing it or certain advantages of this networks should be given up to protect the country’s economy.The 3rd Generation Partnership Project (3GPP) presents two essential architecture for 5G deployment from LTE: Non-Standalone (NSA) and Standalone (SA). NSA empowers quick 5G benefit arrangement with minimum investment by leveraging the existing LTE infrastructures. SA comprises of a single Radio Access Technology (RAT), meaning it is conceivable to supply full 5G upgrades designed to work only in the 5G New Radio (NR) This analytical paper elaborates the performance of these two architectures for the implementation of 5G cellular technology, where in the first one would be highly technology centric approach with less cost efficiency and the other one would be the economy savvy aspect with little bit negotiation with technology upgrades

A MILP approach to DRAM access worst-case analysis

The Dynamic Random Access Memory (DRAM) is among the major points of contention in multi-core systems. We consider a challenging optimization problem arising in worst-case performance analysis of systems architectures: computing the worst-case delay (WCD) experienced when accessing the DRAM due to the interference of contending requests. The WCD is a crucial input for micro-architectural design of systems with reliable end-to-end performance guarantees, which is required in many applications, such as when strict real-time requirements must be imposed. The problem can be modeled as a mixed integer linear program (MILP), for which standard MILP software struggles to solve even small instances. Using a combination of upper and lower scenario bounding, we show how to solve realistic instances in a matter of few minutes. A novel ingredient of our approach, with respect to other WCD analysis techniques, is the possibility of computing the exact WCD rather than an upper bound, as well as providing the corresponding scenario, which represents crucial information for future memory design improvements.

Hybrid-electric and hydrogen powertrain modelling for airplane performance analysis and sizing

This paper describes a framework for parametric modelling of hybrid-electric powertrain components for innovative airplane configurations. These models are used in scalability studies and performance analysis of novel propulsion architecture. The methodology involves culmination of these models in a set of tools specifically developed to study the initial and conceptual sizing of hybrid-electric aircraft. This allows quick parametric evaluation of various configurations based on components at different technology readiness levels, such as batteries and fuel cells. Characteristics and performance of the power-train components are evaluated using computational analysis as well as laboratory tests. This information is used to develop numerical models described in the paper and to validate the sizing of fundamental propulsion components. Applications to two variants of a commuter aircraft are given, one using a serial hybrid-electric architecture based on a thermal engine, and the other using a fuel-cell system fed by a gaseous hydrogen tank.

Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images.

Atherosclerotic plaque deposit in the carotid artery is used as an early estimate to identify the presence of cardiovascular diseases. Ultrasound images of the carotid artery are used to provide the extent of stenosis by examining the intima-media thickness and plaque diameter. A total of 361 images were classified using machine learning and deep learning approaches to recognize whether the person is symptomatic or asymptomatic. CART decision tree, random forest, and logistic regression machine learning algorithms, convolutional neural network (CNN), Mobilenet, and Capsulenet deep learning algorithms were applied in 202 normal images and 159 images with carotid plaque. Random forest provided a competitive accuracy of 91.41% and Capsulenet transfer learning approach gave 96.7% accuracy in classifying the carotid artery ultrasound image database.

Performance Analysis of Low Power Interference Cancellation Architecture for OFDM System

Orthogonal Frequency Division Multiplexing (OFDM) is a wireless communication technology that is used for highly reliable and high data rate communication. In a multi-user OFDM system, the interference has occurred in the receiver side between the consecutive OFDM symbols. This interference reduces the performance of the OFDM system. To achieve good quality in received symbols the interference level should be minimized. The conventional cancellation system requires higher interference reduction time and power. These limitations of the conventional interference cancellation architectures for OFDM systems are overcome by proposing efficient and low power interference cancellation architecture. Hence, this paper proposes a novel and efficient architecture based on logic gates for interference cancellation in multi-user OFDM systems. The proposed design consist of multiplexers, inverters and OR gate. The heuristic parameters for the proposed cancellation architecture are computed by performing an XOR operation. Compared to existing architecture, the proposed interference cancellation architecture consumes 7 mW of power consumption in the Virtex processor, 33.59 mW of power consumption in Spartan 3E processor and 0.029 mW of power consumption in the CPLD processor. This proposed interference cancellation architecture consumes fewer hardware resources and consumes low power. The proposed system is designed using Verilog High Definition Language (HDL) and synthesized in Xilinx Project Navigator 12.1i. Further, this paper also proposes gate diffusion input (GDI) based implementation of proposed interference cancellation architecture to analyse a delay and power consumption compared to other logic style implementation.

Performance Analysis of Fuzzy Logic-Based Digital Library Architecture Using n-ary Huffman Coding, Regression and Fairness Measures

Performance Analysis of Fuzzy Logic-Based Digital Library Architecture Using n-ary Huffman Coding, Regression and Fairness Measures

Transfer modeling of 1T1R crossbar arrays with line resistances based on matrix algebra method

Progress of neuromorphic computing and next-generation information storage technologies hinges on the development of emerging nonvolatile memory (eNVM) devices, which are typically organized employing the crossbar array architecture. To facilitate quantitative performance analysis of eNVM crossbar array architecture, this paper proposes a way to study the one-transistor-one-resistor (1T1R, R: eNVM devices) crossbar arrays based on matrix algebra method. The comparative analysis of 1T1R crossbar array modeling based on matrix algebra method and compact-model SPICE simulations verifies the accuracy of the proposed method, which can be directly used for static quantitative analysis and evaluation of 1T1R crossbar array performance. With the proposed method, the optimization of array operation schemes and current backflow issue are discussed. Our analysis indicates that the proposed method is capable of flexibly adjusting array parameters and consider the influence of line resistance on array operation, and can provide guidance for improving the sensing margin of the array through multi-parameter co-simulation. The proposed matrix algebra-based 1T1R crossbar array modeling method can bridge the gap between the accuracy and flexibility of the available methods.