Miracle-dz/UniFAR-FTUs huggingface dataset:
Breast Cancer Detection using IR-UWB with Deep Learning

This paper proposes breast cancer positioning detection using the IR-UWB system with deep learning, which is an interesting alternative method. When compared to ultrasound, x-ray mammogram, and CT-scan, there are several advantages to using IR-UWB, including low cost, less energy required, less long-term effect, portability, and providing much more breast cancer screening access for patients. Nowadays, the IR-UWB system has many techniques for processing IR-UWB signals, and one of the most interesting technique is using deep learning. In this study, we collected data from nine IR-UWB antennas. Then, the prepared data is fed through Deep Neural Networks to find the hidden patterns of signal and predict the cancer position which are 16 of breast cancer positions and one of undetected, also known as 17 classes. The model gave an average accuracy up to 95.60%.
Leveraging Deep Learning for Computer Vision: A Review

The usability of computer vision is everywhere, whereas deep learning revolutionized the concept of artificial intelligence including computer vision. This paper discusses the leverages of deep learning for computer vision. At first, the background details of computer vision and deep learning have been discussed. Important tasks of computer vision like image classification, object localization, object detection, segmentation are briefly explained. Various types of deep learning algorithms have been described. The architecture of the convolutional neural network has been explained briefly. Applications of deep learning in different fields of computer vision like image classifications, human activity recognition, scene text detection and recognition, object tracking, visual question answering, etc., have been discussed with the references of the recent state-of-the-art works.
Development of hybrid constrained genetic algorithm and particle swarm optimisation algorithm for load flow

The calculation of active and reactive power flow in an electric network to ensure satisfactory voltage profile and loading of transmission circuits is of utmost importance in power system planning, operation and control. Conventional methods such as the Newton-Raphson and the fast decoupled load flow methods have been widely used by the power utilities. However, when a power system becomes highly stressed, it will be difficult for conventional methods to converge. Furthermore, as more and more non-linear devices, for instance Flexible AC transmission system (FACTS) devices, are used in power transmission networks, conventional load flow methods may have difficulties in solving the load flow problem and hence difficult to use these methods to determine the maximum loading points and to assess the static voltage stability of a power system. To overcome the above mentioned problems, this thesis is devoted to the development of alternative approach in dealing with the load flow problem based on evolutionary computation. In particular, this thesis reports work on the formation of a hybrid algorithm comprising of the constrained genetic algorithm and Particle Swarm Optimisation. Based on the virtual population concept embedded in a constrained genetic algorithm for load flow previously developed, the Particle Swarm Optimisation method is utilised as an efficient means to generate high quality candidate solutions in the virtual population during the optimisation process in seeking for the load flow solution. An experimental approach is reported in the thesis on finding the best parameter settings for use in the Particle Swarm Optimisation part of the hybrid algorithm. The performance of the developed algorithm is demonstrated using the IEEE 30-, 57- and 118-bus systems. The results in finding the maximum loading points using the new hybrid method are presented and discussed. The use of the hybrid algorithm in determining the Type-1 load flow solutions for voltage stability assessment is also demonstrated. The new hybrid algorithm developed in the thesis is found to be powerful in solving the load flow problem for heavy-loaded systems and is efficient in locating the Type-I load flow solutions and determining the maximum loading point of a power system.
Damage Manipulation and In Situ Repair of Composite T-Joints

A novel method for the in situ repair of subcritical damage in complex composite structures (T-joints) is demonstrated. In situ repair is achieved through the use of embedded microvascular networks within the structure. Once failure occurs, the embedded vascules are ruptured, providing a route for the injection of a repair agent directly into the damage site. The efficacy of these networks at infiltrating damage is assessed for a number of configurations of vascularized T-joints. The effect of vasculature on the mechanical performance of the component is assessed both numerically and experimentally. Two- and three-dimensional thermomechanical finite element analyses are performed to determine the influence of vasculature on thermal residual stresses and joint strength under 90 deg tensile (pulloff) loading. Failure mechanisms observed during 90 deg tensile pulloff testing agree well with the prediction from finite element analyses. In all cases, successful interaction of vascules with the damage is achiev.
Optimization of Power Dispatch With Load Scheduling for Domestic Fuel Cell-Based Combined Heat and Power System

This study proposes an optimization scheme of thermal power and electric power dispatch integrated with load scheduling for the domestic fuel cell-based combined heat and power (DFCCHP) system. The scheme is implemented in home energy management systems installed in smart homes. To provide accurate energy cost evaluation for the optimization scheme, the nonlinear electric efficiency characteristics of the fuel cell are approximated with a polynomial expression. Along with the inclusion of thermal power dispatch in the scheme, the nonlinear relationship between the thermal power and the electric power of the fuel cell is incorporated to design temperature constraints in the DFCCHP system. On top of the nonlinearity and complexity of the fuel cell, residential electric load scheduling and other energy sources including the power grid, PV panels, and battery energy storage are considered to ensure that the scheme takes a comprehensive energy management approach. Because of the nonlinearity that exists in the modeling of the DFCCHP system, a mixed-integer nonlinear programming formulation is utilized to solve the optimization problem. The scheme is tested in a day-ahead environment with time-varying electricity prices and natural gas prices. The optimization aims to minimize the electricity cost and natural gas cost. It is shown in the simulation that optimization scheme dispatches the electric power and thermal power in an optimal way so that the energy cost due to time-varying electricity prices and natural gas prices is minimized. The electricity cost optimization puts both power purchase and power selling into consideration. It is also shown in the simulation that the household loads are scheduled in an optimal way to the time slots with lower electricity prices in accordance with the optimal thermal and electric power dispatch.
A Resilient Sequential Service Restoration Framework for Unbalanced Active Distribution Networks under System Uncertainties

The resilience of an active distribution network (ADN) is continuously threatened by extreme weather, natural disaster, and cyber-physical incidents. An automatic restoration framework is thus desired to optimally integrate the emerging smart-grid technologies with the grid-edge flexibility to self-heal the ADNs. This paper proposes a framework for sequential ADN restoration by coordinating different distributed energy resources (DERs) on the grid-edge. The uncertainty of the renewable DERs is, however, a challenge. Therefore, the proposed framework is formulated as a chance-constrained optimization problem (CCOP). The chance-constraints use a parametrized response policy for the control during fluctuations and are reformulated for an efficient solution. The proposed CCOP’s solution provides a sequence of control actions at each time-step that coordinates the network switches and DERs to form multiple islanded microgrids. Its effectiveness is evaluated on IEEE 123 node ADN.
Multimodal Melanoma Detection with Federated Learning

Melanoma disease analysis is increasingly approached using statistical machine learning techniques, including deep learning. These techniques require large sizes of datasets. However, health institutions are inhibited from sharing their patients' data due to concerns regarding the privacy of subjects. This paper presents a methodology that utilizes Federated Learning (FL) in ensuring the preservation of subjects' privacy during training. We fused two modalities: skin lesion images and their corresponding clinical data. The performance of the global federated model was compared with the results of a Centralized Learning (CL) scenario. The FL model is on-par with the CL model with only 0.39% and 0.73% higher F1-Score and Accuracy performances, respectively, obtained by the CL model. Through extended fine-tuning, the performance difference could be further minimized. Moreover, the FL model was 3.27% more sensitive than the CL model, hence correctly classified more positives than the CL model. Our model also obtained competitive performance when compared with other models from literature. The results indicate the capability of federated learning in effectively learning high predictive models while ensuring no training data is shared among the participating clients.
A feedback design to a CAD-guided area sensor planning system for automated 3D shape inspection

A feedback-based area sensor planning system is introduced in this paper. This system is developed for automated 3D shape inspection. A CAD-guided area sensor planning system had been developed previously. However, physical properties of a part surface are usually difficult be modeled for an area sensor. Shadows, reflections, and image quality are three problems related to surface measurement quality, which cannot be predicted from a CAD model. Feedback from a measured 3D point cloud can be used for dealing with those problems by adding more viewpoints in iterations. In feedback mode, the inspection system generates viewpoints according to point clouds measured in each step. The loop stops when the obtained point clouds satisfy the measurement quality requirements. In this paper, a general framework of such area sensor planning system is introduced. The developed feedback system has been implemented for a robot-aided dimensional inspection system.
Land - Use and Land - Cover Classification Methods: A Review

The physical cover that present on the earth's surface is referred as land cover. It includes forests, water bodies, grass, asphalt, bare ground etc. The data acquisition of the land cover is mostly done by remote sensing. The acquired data is then categorized into different land covers, by the process of land cover classification. The selection of land cover classification method and its complexity depends upon the landscape under consideration. Various researches are going on the field of land cover classification as its application is spreading in to various areas like disaster management, urban planning, management in various fields like ecology, wild life and so on. This paper presents a review on various recent approaches for land cover classification which includes the statistical, machine learning and deep learning methods and its efficiency in classifying different terrains.
Business Model for Post-industrial Tourism from a System Dynamics Perspective

The research in this paper uses system dynamics in proposing a business model in post-industrial tourism that aims to create a Theme Park in a mining perimeter in Romania. The proposed model can contribute to a better understanding of how the tourism business works and creates value for stakeholders, as it shows the causal relationships between the variables of the business system. The simulation adds value to this model, as it allows experimentation with alternative growth strategies and, as a result, the identification of those performance patterns that lead to sustainable development. The current hyper-competitive business environment requires tourism organizations to be agile and to adapt quickly to the ever-changing environment.
Traffic and road sign recognition using deep convolutional neural network

Automatic road sign recognition is one of the most important steps to help a driver prevent accidents. In this paper, the deep convolutional neural network (Deep CNN) was used for the autonomous traffic and road signs detection and recognition system. The proposed system works in real-time recognition of the image of road signs. The contribution of this article is that it consists of a newly developed datasets for 42 different road signs using combination of existing datasets and collected local road signs. The images were taken from different angles and included other parameters and conditions. More than 30K images were collected to create the dataset, which is named Uzbekistan traffic and road signs (UZ-TRS-2021). The CNN architecture was used with various parameters to achieve the best recognition speed. Experimental results show that the proposed CNN architecture achieved 98% accuracy.
Analysis of Environmental Factors for Smart Farming: An Internet of Things Based Approach

The Internet of Things also referred to as IoT plays an important role in our everyday lives by allowing us to control electrical gadgets through networks. Controlling is accomplished by meticulously watching key parameters that produce critical information about the operation of this electrical equipment. At the same time, the critical information will transfer from the transmitting device and preserved on the cloud for apps and secondary processes to access. This investigation connects the results of environmental measures such as temperature and humidity taken by making use of sensors. The data acquired might be put to good use to create activities such as faraway dominating long-term statistics, cooling, or heating devices that can be utilised to govern the situation. The observed data is transferred to a cloud storage service through the internet and linked to an Android application. Arduino UNO and sensor devices, and ESP8266 Wi-Fi module are used in the system. Using Arduino UNO. The testing and findings will disclose the live temperature and humidity of the surroundings, as well as the soil moisture of sample soil. The sensor element is used to check the humidity and temperature using the DHT 11 sensor. The sensors are used to take temperature readings from the environment and store the data on Thinkspeak Cloud. The ESP8266 Wi-Fi module was utilized to connect with the internet and store the data on the Thinkspeak cloud in this project.
Uncertainty-aware Weighted Fair Queueing for Routers Based on Deep Reinforcement Learning

In current computer communication networks, the increasing packet loss and delay caused by the increasing traffic becomes the bottleneck for the desired Quality of Service (QoS). Weighted Fair Queueing can be used to provide differentiated services according to the Service Level Agreement (SLA) associated with each packet. However, due to inaccurate measurements of queue usage, drop rate and delay in real routers, and the intrinsic property of a real network system that there will always be some unpredictable traffic patterns, current methods for WFQ updating can be improved and extended further. In this work, an uncertainty-aware soft actor-critic agent is introduced. First, the learned weights updating strategy is a maximum entropy policy, which is robust under estimation and model error. Second, the technique of model uncertainty estimation is adopted into the agent so that it is capable of detecting novel states that are unseen during the training period, which facilitates a strategy switching framework. The proposed algorithm shows the potential of using reinforcement learning for WFQ weights updating and is compatible with existing techniques by monitoring the model uncertainty, which makes a more robust and stable system. The benefits of applying the proposed algorithm is validated through the simulation studies, showing a promising direction for further exploration.
Distributed Control of Aggregated Smart Buildings for Frequency Regulation

Increased penetration level of renewable energy and its intermittency make the power grid more prone to frequency fluctuation. Demand response of the aggregated smart buildings can provide ancillary secondary frequency regulation. Distributed control methods for the aggregated buildings are attractive due to plug-and-play scalability, high reliability, no single point of failure, and lower communication requirements, compared to the conventional centralized control. This paper identifies three major control objectives for this distributed control problem including balancing the state-of-energy of the buildings, tracking frequency regulation signal, and eliminating circulating power. The control problem is challenging as these control objectives need to be achieved simultaneously in a distributed fashion. This paper proposes a new distributed control method to achieve the three objectives at the same time. The plug-and-play scalability and the robustness to communication-link failure of the proposed control are verified through simulations. Moreover, using data from California Independent System Operator (CAISO), a three-area power system is simulated with heterogeneous building parameters to demonstrate the effectiveness of the proposed control method, which achieves the three control objectives and reduces the frequency deviation by more than 30% with the aggregated buildings.
Power Allocation Effect on Capacity of Single Carrier Power Domain Non-Orthogonal Multiple Access (NOMA)

Non-Orthogonal Multiple Access (NOMA) has become one of the most discussed topics nowadays in the mobile cellular world. Plenty of papers say the NOMA system is superior to the previous technology used in fourth-generation (4G) and due to its high spectral efficiency makes it a promising candidate for the fifth generation (5G) mobile communication system. However, NOMA is simulated with only two or three users in a single carrier in most of those works, making readers wonder how many users it can support. In this paper, the NOMA system with a hundred users is simulated to see each sum capacity or spectral efficiency in bit per second per Hz. Both fixed power allocation and dynamic power allocation methods are used in order to compare the performance. Channel State Information (CSI) based power allocation method, which is the dynamic power allocation, outperforms the Fixed Power Allocation (FPA) method. The resulting graphic shows that FPA increases sum capacity to one peak point but decreases as more users are added. On the other hand, CSIbased power allocation significantly increases the sum capacity to one point and then remains stable as the number of users is added.
Optimized Distributed Linear Block Codes For Single Relay Cooperative Wireless Communications

Cooperative communication strategies have long been touted as a solution to increase performance in wireless networks. We propose a simple form of coded cooperation using block codes and optimized selection at the relay node in this paper. The optimized distributed linear block codes for single relay cooperative technique enhances the performance by extending the maximum free distance of the overall code. MATLAB simulations proved that the proposed scheme outperforms the sub-optimal cooperation where there is no optimized selection at the relay node.
Investigating Square Slot Unit Cell for Low-Cost Phase-Gradient Metasurfaces

The paper presents a unit-cell element having multiple square slots in metal layers on thin dielectric sheets to develop low-cost metasurfaces. The suitability of the cells is investigated for phase-gradient metasurfaces (PGMs) used in beam-steering antenna applications. Multiple identical layers of cells were used to achieve high-transmission magnitude with 360-degree transmission phase shift at the center operating frequency of 20GHz. The cell with three layers is used to design a PGM, which tilts a normal beam at 15 deg offset angle.
Anaerobic digestion of green waste as an alternative for the reduction of greenhouse gas emissions in landfills (In Spanish)

Landfilling is the main disposal technology for municipal solid wastes (MSW) in Colombia. Currently, about 50% of the landfill systems are close to the end of their useful life. It is necessary to evaluate alternative of disposal and treatment for MSW. Municipal solid wastes generated in Colombia contain a high proportion of organic material, which contributes to the generation of greenhouse gas emissions (GHG) in landfills. In this regard, anaerobic digestion is an alternative technology for waste treatment. Additionally, the produced methane (CH4) could be used as an energy source. This paper seeks to make a direct comparison between GHG emissions produced in landfills and the anaerobic digestion process as options for the disposal and treatment of municipal organic wastes in Colombia. The emissions of GHG were calculated considering a study case in the landfill disposal of garden wastes in the city of Manizales, Colombia. Theoretical models were used to quantify the emissions of methane and carbon dioxide. The calculation methodologies applied in this study showed considerable differences in its results, mainly for the assessment of anaerobic digestion by emission factors. However, it was possible to estimate that about 50 tons of methane and 1200 tons of carbon dioxide, would be avoided annually in the city of Manizales if the anaerobic digestion was used as an alternative to the current green waste disposal technology.
Inverter-induced bearing currents – A thorough study of the cause-and-effect chains

In the last three decades, the study of inverter-induced bearing currents has become a very important line of research, and hundreds of works related to this theme can be found in the literature. Thus, several contributions concerning these currents were made, such as in the areas of modeling and simulation techniques, selection of appropriate mitigation method, developments of new mitigation devices and damage monitoring within the bearings, among others. In this context, much information related to bearing currents cause-and-effect chains is available. However, many contributions that have been developed in recent years for describing the mechanisms behind the bearing currents occurrence can only be found in a fragmented way. Thus, great efforts must be employed to provide a clear comprehension of each bearing current type and all the corresponding issues. To overcome this problem, this paper provides a broad overview of the physical principles related to the inverter induced bearing current, by means of a detailed and didactic approach. A huge volume of information was compiled, describing the expected currents maximum amplitudes, their probability of occurrence and the motor size which they are more expected to occur, among other relevant issues.