Miracle-dz/UniFAR-FTUs huggingface dataset:
Prosumer Driven Virtual Power Plant Profit Maximization Considering Demand Response

The Energy systems are changing Dramatically from downstream power delivery to smart distribution and bidirectional power flows, from top-down topologies to decentralized local structure. These changes break monopoly in power generation and enables energy democracy due to renewable based power generation and integration. A Virtual Power Plant (VPP) is an innovative approach to power generation and energy trading that connects multiple decentralized generation units, such as solar panels farm, wind turbines, and micro-turbines, through cloud-based software control systems. By aggregating and coordinating these units, a VPP acts as a flexible power plant that can respond to market demands and optimize operations in real-time. The primary purpose of a VPP is to participate in energy trading, specifically addressing uncertainties associated with Renewable Energy Sources (RESs), by balancing the intermittent nature of renewables and maximizing their utilization. To optimize electricity consumption, the paper proposes a conventional demand response approach called Time of Use (TOU). It divides the day into two time zones: the excessive zone (E-Zone) and the non-excessive zone (NE-Zone). Shifting electricity consumption between these zones based on pricing and demand patterns enables effective load scheduling. The research aims to demonstrate how the VPP can enhance its participation in the day-ahead energy market, ensuring cost-effective and reliable electricity supply. This work contributes to the development of advanced energy management strategies that promote renewable energy integration and facilitate a more sustainable and efficient energy future. The optimization is carried out in a General algebraic modeling system (GAMS) environment using CPLEX Solver. The VPP Profit is observed before and after the implementation of Demand Response.
A Haar-Cascading and Deep Learning Driven Enhanced Mechanism on Cloud Platform for Real-Time Facial Emotion Detection

Real-time emotion detection utilizes computer vision that aims to recognize and categorize emotions. However, it contains challenges, including the intricate nature of emotions and the variations in their expression across individuals. The accuracy of the system can also be affected by environmental factors such as background noise, lighting, and other people. Having access to a diverse and comprehensive dataset is essential for training algorithms that can accurately detect emotions in real time. In order to tackle these challenges, we utilized CNN and seven emotions to identify their corresponding facial expressions. By implementing appropriate activation functions like ELU, ReLU, and Leaky ReLU, and training with the dataset Facial Expression Recognition 2013 (FER-2013) that contains over 30,000+ images of facial expressions and emojis, as we increase the epoch the accuracy of this model increases, we achieved an impressive accuracy of 94.74% by using ReLU on 100 enochs.
Mobility and Resource Allocation with Intelligent Clustering in UAVs Assisted VANETs

In the past decade, there has been significant development and utilization of Unmanned Aerial Vehicles (UAVs) in Vehicular Ad Hoc Networks (VANETs) across various applications. The integration of UAVs in VANETs has provided vehicles with enhanced performance by enabling communication through an aerial medium, thereby bypassing ground-level obstacles. Efficient management of mobility and network resources becomes crucial when dealing with a large number of highly dynamic vehicles. To address this, the proposed approach in this paper is Mobility and Resource Allocation with Intelligent Clustering in UAVs-assisted VANETs (MRAIC-UAVs). The key components of the proposed approach include the network model, mobility model, clustering strategy, and UAVs Cluster Head (CH) selection process. The selection of CHs is based on the evaluation of parameters such as residual energy, UAVs mobility, UAVs degree difference, distance, and UAVs stability. This approach significantly improves network energy efficiency and packet delivery ratio. The simulation is conducted using OMNET++ with the SUMO mobility generator, and a comparison is made with earlier models such as PRO-UAVs and RJEDC-UAVs. The performance analysis considers parameters such as packet delivery ratio, end-to-end delay, energy efficiency, and energy consumption. The simulation results demonstrate that the proposed MRAIC-UAVs approach achieves higher energy efficiency and packet delivery ratio while exhibiting lower end-to-end delay and energy consumption compared to earlier approaches.
Disturbance Rejection for Robust Distributed Learning via Time-Vertex Filtering

Distributed learning has attracted considerable interests in literatures because the collaborations of multiple agents would help to solve complicated engineering problems. Robustness issue plays an important role in distributed learning since attacks on agents would strongly affect the convergence performance and even lead the collaboration to an incorrect global solution. In this paper, we consider the attacks as disturbance and propose a joint time-graph filtering to defend against the attacks in distributed learning. The coefficients of joint filtering can be determined based on the coefficients of time-domain and graph-domain filters that are designed separately. If there is no attack in distributed learning, the joint time-graph filtering can also contribute to the convergence performance acceleration. Numerical studies demonstrate that the joint filtering in both time domain and graph domain can defend against attacks with noise and outperforms several existing algorithms.
Estimating Continuous Blood Pressure from Photoplethysmogram Signals for Non-invasive Devices by Convolutional Neural Network

Blood pressure (BP) is a crucial indicator of abnormal levels of stress on blood vessel walls, including hypertension or hypotension. Although several studies have attempted to predict BP based on photoplethysmogram (PPG), accuracy and resource consumption remain significant challenges. To address these challenges, we propose a flexible approach employing a deep convolutional neural network (CNN) with high accuracy. Our proposed deep-learning model delivers mean errors ± standard deviations of 0.18 ± 5.91 mmHg, -0,09 ± 3.21 mmHg, and 0.001 ± 3.82 mmHg for Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Mean Arterial Pressure (MAP), respectively. Furthermore, our method meets the standards set by the Advancement of Medical Instrumentation (AAMI) and achieves an "A" grade performance, as required by the British Hypertension Society (BHS) standard. Compared with previous benchmarks, our model achieves greater accuracy with fewer parameters, offering potential for real-time non-invasive BP monitoring using wearable devices.
Using Control Signals to Obtain Synchronization of Transmitters in Indoor VLC systems

The target of this work is to reduce the effect of multipath propagation in indoor visible light communication (VLC) systems. This is accomplished by giving a control signal (CS) for each transmitter of the systems. The aim of this CS is to synchronize the transmitted data from transmitters. In addition, CSs are used to make the controller, which is used to manage the communication, knows all information about transmitters. This leads to eliminate the difference time propagation between transmitters and reduce the effect of multipath propagation. Results revel that using CSs leads to improve channel bandwidth and signal to noise ratio (SNR) when the system operates at high data rate of 1 Gb/s.
Sensitivity Assessment of Dielectrically Modulated Tri-Material Hetero Stack Gate MOSFET biosensor

In this work, a dielectrically modulated tri-material hetero stack gate (DM-TMHSG) MOSFET biosensor is designed for biosensing application to improve the sensitivity of the device. So, here the device is developed for the recognition of analytes such as Uricase, Ferro-cytochrome-C, Streptavidin, and Protein to assess the features of the biosensor. The various performance constraints like threshold voltage, threshold voltage sensitivity, current, current sensitivity, Subthreshold swing (SS), and SS sensitivity are investigated in the presence of only neutral biomolecules. All the constraints are simulated using the 2D TCAD platform. Simulation results showed that protein biomolecules offer better sensitivity as compared to other biomolecules in terms of current, threshold voltage, and subthreshold swing.
inSTEP: An In Situ Stochastic Approximation based Scalar Quantizer for Narrowband IoT

Lack of long range connectivity and low bandwidth of LoRa networks have hindered NB-IoT deployments in large geographical areas. TV White Spaces (TVWS) offer a viable solution to address the limitations of LoRa by allowing unutilized TV spectrum to be shared with NB-IoT devices on an ad hoc basis. This is enabled by a geolocation database that will broadcast the utilized TV bands to IoT clients through a rate-constrained channel. However, there are regulatory and practical challenges in using the TVWS that are to be addressed. To overcome these challenges, we propose an adaptive data-driven quantizer design algorithm for sharing the utilized TV bands’ data with IoT clients through the rate-constrained channel. This design is compliant with the regulatory norms and is capable of allocating unused TV bands dynamically. The performance results of our quantizer are discussed in the paper based on two TVWS datasets.
Detection of Diabetic Maculopathy

The macula is an oval-shaped central part of the retina that controls the eye’s central vision. Macular degeneration caused by diabetes or age-related macular degeneration can result in blurry vision and even blindness. The macula of the eye is impacted by diabetes-related retinal degeneration known as diabetic macular edema (DME). That could cause serious issues or perhaps result in total blindness. Hence it is very important to understand and treat the problem early. Early detection of diabetic maculopathy (DM) is possible through regular checkups of diabetic patients but the availability of ophthalmologists is significantly less compared to the number of diabetic patients. It has created great interest in the automatic detection of diabetic maculopathy systems. In this paper, we have performed a review of different methods used in the detection of DM along with mentioning its pre-processing techniques and databases used. The results of each method are discussed.
Robot Programming by Demonstration

Also referred to as learning by imitation, tutelage, or apprenticeship learning, Programming by Demonstration (PbD) develops methods by which new skills can be transmitted to a robot. This book examines methods by which robots learn new skills through human guidance. Taking a practical perspective, it covers a broad range of applications, including service robots. The text addresses the challenges involved in investigating methods by which PbD is used to provide robots with a generic and adaptive model of control. Drawing on findings from robot control, human-robot interaction, applied machine learning, artificial intelligence, and developmental and cognitive psychology, the book contains a large set of didactic and illustrative examples. Practical and comprehensive machine learning source codes are available on the books companion website: http://www.programming-by-demonstration.org
Classification of Micro-Moment-Based Anomalous Power Consumption Using Transfer Learning

The identification of unusual power usage in buildings is crucial for improving energy efficiency. Using an electrical consumption monitoring system can help with energy conservation by identifying unusual energy consumption patterns. This paper suggests a micro-moment-based methodology for detecting abnormal power use. This study makes use of a benchmark dataset called SimDataset, which is used in most of the micro-moment classification-related works. On the images created from the dataset labeled with two classes and five classes, binary and multi-class classifications have both been used. Transfer learning is used by employing pre-trained CNN models, namely DenseNet121, ResNet50V2, and Xception model. The results depicted that the DenseNet121 model has outperformed all other models by giving the best accuracy of 99% and F1-score of 0.984.
Breast Tumor Detection and Classification Using ABC-ELM Algorithm

Analyzing medical images and deciding on health issues require a hybrid intelligence system. Today, cancer is the illness that kills the most people worldwide. The most common type of tumor sickness is breast cancer. The previously employed algorithms for detecting and classifying breast tumors kept getting better until they produced successful results. Machine learning techniques for detecting and classifying breast tumors have been the subject of numerous studies. For breast tumor identification and classification, we proposed the Artificial Bee Colony Extreme Learning Machine (ABC-ELM) algorithm. We aim to improve the breast tumor identification and classification algorithm using the Morlet wavelet transform for feature extraction and tumor segmentation. Breast images are classified as malignant or non-cancerous by extracting pertinent features and improving the ELM classifier's parameters using the ABC algorithm. As a result, the proposed method performs best compared to earlier comparative research, achieving an accuracy of 98.2%, Sensitivity is 98.5%, and precision 98.5%.
Face recognition method based on GA-BP neural network algorithm

Abstract In order to recognize faces, face recognition methods need to be studied. When a face is identified by the current method, the image denoising effect is poor, the face image recognition result thus has error, the time used to recognize the face image is long, the signal to noise ratio, the recognition result and the recognition efficiency are low. Based on the GA-BP neural network algorithm, a face recognition method is proposed. A mixed denoising model of face images is constructed by combining dictionary based sparse representation with non-local similarity. The principal component analysis method is used to extract the feature of the face image after denoising and staining the eigenvector of the face image. The GA-BP neural network algorithm is used to optimize the initial weights and thresholds so as to achieve the optimal value. The feature vectors of face images are ted into the genetic neural network to complete face recognition. Experimental results show that the proposed method has high signal-to-noise ratio, accuracy and recognition efficiency.
Intent-Based Networking for Zero-Touch Optical Networking

The Intent-Based Networking (IBN) paradigm targets at defining high-level abstractions, so network operators can define what are their desired outcomes without specifying how they would be achieved. The latter can be achieved by leveraging network programmability, monitoring and data analytics, as well as the key assurance component. In this paper, we introduce the IBN paradigm and its application to optical networking, highlighting the benefits that Machine Learning (ML) algorithms can provide to IBN. Illustrative examples of intent-based operation are presented for proactive self-configuration and cooperative intent operation.
Autoencoder-Based PPG Compression for Resource-Constrained Device

PPG-enabled wearables are increasingly prevalent and have the potential to improve remote healthcare systems significantly. However, their implementation can be challenging under resource constraints. To alleviate these constraints, this article proposed a lightweight autoencoder-based lossy PPG compression system for data streaming applications. The system reduces the amount of transmitted data by a factor of 8 while maintaining the information with the signal RMSE at 0.026 and peak MAE at 0.020. This is possible through the loss-conditional training approach on specific task learning. Its practical functionality and advantages are verified on a resource-constrained Docker container and an Arduino Nano 33 BLE microcontroller board. This system cut down transmission energy consumption by 84%.
Analysis of New Embedded Simulation Technology based on Smart Internet of Things

The integration of Intelligent Internet of Things (IoT) and Embedded Systems has made the design and development of embedded systems more intelligent and efficient. In embedded systems, simulation technology is an important tool that can be used to verify and test the correctness and reliability of the system. This article introduces a new embedded simulation technology based on the intelligent Internet of Things, which can be used to complete simulation testing more quickly, improve the accuracy and precision of simulation, and reduce the cost of system development and testing. The core of this technology is to utilize the data collection, transmission, and processing functions of the intelligent Internet of Things to input data from physical reality into the simulation system, achieving rapid construction of the simulation system and more accurate simulation results. At the same time, this technology also supports remote monitoring and control, enabling embedded systems to dynamically adjust and optimize during operation, improving system performance and reliability.
Comparative Analysis of Dual-edge Triggered and Sense Amplifier Based Flip-flops in 32 nm CMOS Regime

The article summarizes the outcomes of extensive research carried out on dual-edge triggered flip-flops (DETFFs) and sense amplifier based flip-flops (SAFFs) in relation to their power consumption, delay measurements and area requirements. The research focused on four of the most advanced flip-flops (FFs) currently available, and simulations were conducted in SPICE using 32 nm CMOS technology. The results showed that Lee's FF had better power efficiency at data activities of greater than 25% whereas Lapshev's FF could be used for circuits with less data activity. Jeong's FF had better speed efficiency at higher voltages. Lee's FF also had the best power delay product (PDP) followed closely by Jeong's FF at nominal conditions. Additionally, Jeong's FF outperformed other designs in terms of area overhead.
A Multi-Objective Home Energy Management System based on Non-Intrusive Load Monitoring and Heat Pump Control

Grid modernization and digitalization gradually alter the traditional operation of power systems. Demand side management has attained significant attention and the deployment of smart meters creates opportunities for new data-driven services. Towards this direction, home energy management systems (HEMS) can play a pivotal role by enabling residential demand response applications. This paper introduces a HEMS aiming at low energy cost, improved thermal comfort and scheduling of appliance operation according to end-user habits. To this end, a multi-objective optimization problem is formulated by employing a non-intrusive load monitoring mechanism and a control scheme for heat pumps. The conducted analysis investigates the effect of each objective on the overall system performance under different dynamic tariff schemes.
Prediction of Drought - A Machine Learning Approach using Time Series Data

In order to forecast drought in a certain area, this research employed three different machine learning models, namely ARIMA (Auto Regressive Integrated Moving Average Model), VAR (Vector Auto Regressive model), and Prophet. These models are used to forecast upcoming drought occurrences and are trained using historical data on a variety of meteorological factors, including temperature, precipitation, humidity, and wind speed. Different statistical metrics, such as mean absolute error, root mean squared error, and coefficient of determination, are used to assess each model's performance. The findings indicate that all three models are capable of accurately and reliably forecasting drought episodes, with Prophet surpassing the other models. According to the findings of the study, machine learning models can be useful tools for forecasting drought episodes in a particular region, giving decision-makers a head start on taking the necessary actions to lessen the effects of drought on agriculture, water resources, and society. Among the three time series models experimented, Prophet model exhibited the accuracy of 92.01% which is the best when compared with the other two models. The choice of selecting a model for predicting the drought depends on the type of data being used for experimentation.
Improving Sparse Point Cloud Classification Performance Through Image Inpainting

With the increasing number of platforms using LiDAR, robots using LiDAR have become more easily accessible in our daily lives. LiDAR sensors are essential sensors for robots, much like eyes, but their limitations are also evident. Due to the nature of LiDAR sensors, the amount of points that can be obtained from distant objects is sparse, which has a significant negative impact on the object classification performance through LiDAR. This paper proposes augmenting the point cloud through image inpainting to overcome this limitation. The point cloud collected through LiDAR is transformed into a 2D image, restored through image inpainting, and then validated for performance using the Point-wise Classification technique.
Cooperative Spectrum Sensing Approach in C-V2X based on Multi-Agent Reinforcement Learning

In cellular vehicle-to-everything (C-V2X) Mode 4, the autonomous mode is based on spectrum sensing for vehicles selecting spectrum resources, however, the spectrum sensing process may be imprecise, leading to frequent packet collisions especially when vehicles are in the congested scenario with high speeds. In this paper, we propose a cooperative spectrum sensing approach based on multi-agent reinforcement learning (MARL) to improve the spectrum sensing accuracy. The proposed algorithm includes the sense channels selection procedure and the cooperative users selection procedure. Specifically, we imitate the process of vehicles selecting sense channels as Indian buffet process (IBP) to predict the channel selection probability, and establish the belief scheme of cooperative users based on the historical sensing results, then exploit deep Q-learning network (DQN) to select sense channels based on the channel selection probability and select cooperative users based on the belief. Simulation results show that the proposed algorithm can improve success sensing rate and reducing collision rate compared with other cooperative and non-cooperative methods.