Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Optimization of Battery Energy Storage System and Fans for Frequency Reserve Capacities Allocation and Day-Ahead Energy Management

In this paper, we propose a novel optimization strategy for energy-efficient day-ahead energy management and economic power reserve capacity allocation for frequency regulation, by jointly utilizing battery energy storage systems (BESSs) and demand-responsive (DR) fans at the distribution level. Specifically, we consider a smart building with renewable energy integration. The building owns a BESS and is willing to take part in frequency regulation service by allocating a proportion of its BESS capacity as power reserve capacity and receiving monetary reservation fee in return. Due to the high cost of BESS, its limited capacity, and significant degradation caused by excessive charging and discharging, we consider DR fans as a viable low-cost capability of the smart building for energy cost saving and frequency regulation. Particularly, we consider modern fans of the heating, ventilation, and air conditioning (HVAC) systems that can vary their speeds (and thus energy consumption), subject to satisfying user comfort constraints. Simulations results based on the real renewable energy and load data show that our proposed joint optimization strategy is effective in reducing the total energy cost of the building and at the same time providing a higher power reserve capacity to the grid.
How to Select the Appropriate Pattern of Human-Computer Interaction?: A Case Study with Junior Programmers

In the process of designing user interfaces (UI), designers must be aware of the many factors and different contexts that influence design, which are established in the Software requirements. The objective of this Case study is to analyze the identification of patterns of Human-Computer Interaction (HCI) through the understanding of functional requirements by junior programmers. The above, through the identification of important aspects such as tasks, the type of user, and context of use. Twenty-two students were invited to participate in the Case study and generated eight Software Development projects from which their perception was obtained regarding these activities, which will be considered for the classification of Software requirements.
DC4LED: A Hierarchical VDTN Routing for Data Collection in Smart Cities

In smart cities, the mobility of vehicles can be used to collect data produced by connected objects and to deliver them to several applications which are delay tolerant. The Vehicular Delay Tolerant Networks (VDTN) can be utilized for such services. This paper introduces DC4LED (Data Collection for Low Energy Devices): a hierarchical VDTN routing which takes advantage of the specific mobility patterns of the various type of vehicles. It provides a low-cost delivery service for applications that need to gather data produced from the field. The idea is to propose a simple routing scheme where cars, taxis, and buses route data hierarchically in a store-carry-forward mechanism to any of the several available Internet Point-of-Presences in the city. This paper aims to explore the possibility of implementing a very simple mechanism for very simple services, as opposed to utilizing complex VDTN mechanisms proposed in the literature. Our simulations compare the performance of DC4LED routing with two legacy VDTN routing schemes which represent the extreme ends of VDTN routing spectrum: First-contact and Epidemic routing. It show that DC4LED has much lower network overhead in comparison with the two legacy routing schemes, which is advantageous for its implementation scalability. The DC4LED also maintains comparable data delivery probability and latency to Epidemic routing.
Speech Signal Based Broad Phoneme Classification and Search Space Reduction for Spoken Term Detection

Depending on the acoustic properties, sound units can be grouped into six broad categories as Vowels (V), Nasals (N), Fricatives (F), Approximants (A), Plosives (P) and Silence (S). This paper proposes a set of signal based features for broad phoneme classification. A Deep Neural Network (DNN) based Broad Phoneme Classifier (BPC) is trained to predict the broad phone class labels in continuous speech. Across the TIMIT test data, the BPC gives an overall frame level accuracy of 75%. After smoothing the BPC labels, 83% of the broad phone regions are correctly detected by the system on an average. These broad class labels are useful for applications like speech recognition, and Spoken Term Detection (STD). Effectiveness of the proposed broad phoneme classifier labels for search space reduction in STD is illustrated using TIMIT database. Around 82% of search space reduction can be achieved for detecting 78% of the keyword locations from the TIMIT test dataset.
State Observability through Prior Knowledge: A Conceptional Paradigm in Inertial Sensing

Inertial Navigation Systems suffer from unbounded errors on the position and orientation estimate. Exteroceptive sensors may not always be available to correct the error. Applications in the literature overcome this problem by fusing IMU data with prior knowledge in an ad-hoc fashion. In different applications, various knowledge is available, which allows to correct the erroneous state estimate. In this position paper, we argue that the fusion of knowledge and inertial sensor data should be viewed as a paradigm and that the observability of systems with prior knowledge should be analysed theoretically. With a theoretical foundation, application design will be simplified and verifiable. We show methods to start the analysis and give a first proof with practical insight.
Time-varying Parameter-based Synchrosqueezing Wavelet Transform with the Approximation of Cubic Phase Functions

Synchrosqueezing wavelet transform (SST) is a linear time-frequency analysis method, which aims to sharpen the distribution of the continuous wavelet transform (CWT) and separate multicomponent signals. In this paper, we propose a new SST algorithm with the time-varying parameters and second-order phase transform. More precisely, we analyze the separation abilities and conditions of CWT and SST by approximating a non-stationary signal with cubic phase functions. The optimal time-varying parameters are also proposed. The experimental results show the validity and efficiency of the proposed method, especially for multicomponent signals with fast varying frequencies.
Joint Embedding with Multi-Task Learning for Multi-Label Zero-Shot Action Recognition

Action recognition, one of the most significant fields of computer vision, has gain great success over the past few years mainly due to the popularity of deep and data-driven architecture. However, when it comes to a real-world scenario that includes unseen actions, the previous methods which are supervised directly by labels may lead to two main problems, namely domain adaptation and laborious annotation. Therefore, we propose a novel joint space model of visual data and semantic information to address zero-shot action recognition problems. Furthermore, in order to take better advantage of semantic relationship between seen and unseen classes by word vectors, auto-encoder is introduced to our framework to narrow the semantic gap and improve the recognition accuracy. Moreover, we utilize the ε-greedy based max-pooling technique to select the most relevant visual segment from an instance according to the label. Finally, we employ multi-task learning to overall optimize classification and ranking tasks in joint latent space. We evaluate our work on a weakly annotated human action dataset Charades. The experimental results demonstrate that the proposed method significantly outperforms the state-of-the-arts in both accuracy and efficiency.
Temporal Pyramid Pooling Based Relation Network for Action Recognition

Efficient spatiotemporal representations play a vital role in video understanding. In this study, we propose a novel temporal pyramid pooling based relation network (TPPRN) to learn spatiotemporal representations in an end-to-end fashion. More specifically, TPPRN poolings high-level features derived from a convolutional neural network of sampling frames at multi-scale. Then, the features of same-length segments are concatenated to reason about the relation within the same-length segments. Finally, different relations are aggregated to make predictions comprehensively. Our first contribution is the carefully-designed sampling strategy. It splits a video into three clips evenly and samples four frames at each clip uniformly, which allows the model to reduce computation and memory cost. Our second contribution is the temporal pyramid pooling at multi-scale. It provides various-grained segments for relation module to reason about their relations. The experimental results on two standard benchmarks, HMDB-51 and UCF-101, demonstrate the effectiveness of the learned spatiotemporal representations. And the proposed TPPRN achieves the comparable performance with the state-of-the-art.
ML-Based Approach to Detect DDoS Attack in V2I Communication Under SDN Architecture

The need for Internet-based services is increasing at a tremendous pace in smart cities. The driver and occupants of the vehicle access Internet, and different intelligent transportation system (ITS) related services such as real-time traffic information, parking space availability, downloading the map, etc., in a vehicle to infrastructure communication (V2I) mode. In a highly dynamic network environment like vehicular network, software-defined networking (SDN) promises to be an ideal solution. However, it also opens doors for various distributed denial of service (DDoS) attacks. An attacker can easily flood short-lived spoofed flows and exhaust network resources. This motivates us to find a solution to detect the attacks in a V2I communication under SDN. In this paper, we propose a machine learning (ML) based DDoS attack detection. The proposed system uses various ML schemes, and few of them found to be accurate with a high detection rate and a relatively low false alarm rate.
A control strategy for DC Smart Grids operation

Currently, most of available architectures for electrical energy distribution are mainly centralized with AC topology. However, decentralized generation growing significantly and grid-connected system being proposed in most applications have leaded to challenge for the electric power system operation. The intermittent and unpredictable nature of renewable energy sources, such as wind turbine and photovoltaic, remains an issue for their integration into the public grid resulting in: fluctuations of voltage and/or frequency, harmonic pollution, difficult load management. On the other hand, recently there has been significant increase in direct current (DC) loads in domestic appliance. This requires AC-DC converters, causing great amount of power loss, due to the AC reactive power losses, and the converter losses. To increase the integration level of renewable energy and energy efficiency, the smart grid based on distributed DC management is proposed.
MR-Edge: a MapReduce-based Protocol for IoT Edge Computing with Resource Constraints

Edge computing is proposed to remedy the Cloud-only processing architecture for Internet of Things (IoT) because of the massive amounts of IoT data. The challenge is how to deploy and execute data processing tasks on heterogeneous IoT edge network. As MapReduce is a well-known model in Cloud computing for distributed processing of big data, this paper aims to devise a MapReduce-based protocol to achieve IoT edge computing. Our design is built upon the novel Information Centric Networking (ICN), which supports function naming and forwarding so as to facilitate task distribution among edge devices. To guarantee the correctness of task execution, a tree topology is formed in our approach to establish the logical connection between different types of edge devices, namely processing-capable nodes and forward-only ones. Moreover, the proposed protocol includes a task maintenance scheme that enables the coexistence of multiple IoT computation jobs. A testbed is implemented on ndnSIM to verify the feasibility of our design. The results show our approach could significantly decrease the network traffic compared with centralized data processing.
Sound Classification Using Convolutional Neural Networks

The performance of Sound Classification like Urban Sound Classification, Environmental Sound Classification, etc. has been significantly improved with the evolution of Deep Neural Networks (DNN). In this paper, we propose a model which uses Convolutional Neural Networks (CNN) for classification of sound based on the spectrograms obtained for different sound samples. The model can be used for Deforestation detection, Gunshot detection in urban areas and also for detecting unusual sounds in streets like a cry for help, tyres screeching etc. at odd hours.
Estimating Distance of Going Up and down Stairs in a Building Using the Smartphone’s Rotation Vector Sensor

Localization of persons in the building after a disaster occurs is one of prominent issues for rescuing them and also it is technically challenging, considering that communication infrastructures such as cellular or WiFi would be often unavailable due to the severe damage or heavy traffic concentration. This paper focuses on estimating person’s moving distance in the stairs and presents a scheme to estimate only with rotation vector sensor, being typically installed in the recent smartphone. Performance evaluation by experiments shows that the proposed scheme can estimate the number of floors by which the person moved with error of almost zero.
A SIFT-Based Image Fusion Method

Aimed at the wrong matching appearance when applying Scale Invariant Feature Transform (SIFT) to image fusion. In this paper, we propose to use the SIFT to find the featured points and matched featured points primarily for image fusion. The double Otsu method and average distance threshold segmentation method are used to remove mismatching points and realize an accurate image registration. Finally, we apply the weight average fusion method to blend source images to obtain the final fused image. The results show that the proposed method can automatically realize high precision in fused images and achieve natural image fusion.
Latency in Verifying Connection Setup in Dynamic Optical Networks

We consider dynamic applications that require new connections to be established with very low setup times. One challenge is determining when to initiate transmission, which can be based on a timing mechanism or based on receiving positive acknowledgment that the connection has been properly established. We investigate the latter strategy and analyze the added latency that is imposed by requiring verification of a newly established path. We show that with a distributed connection-setup architecture, verification potentially adds a negligible amount of latency, whereas with centralized operation, the added latency can be appreciable, especially in a backbone network.
Advanced machine learning discriminant analysis models for face retrieval system

advanced machine learning discriminant analysis models for face retrieval system has been implemented in this paper. The image can be portioned into different subband system to extract the features. To reduce the dimensionality to extract a few features. So, it takes more computational time to extract the features. The proposed method can be implemented in one of the most popular methods is dimensionality reduction. Discriminate analysis models are widely applied on linear and non-linear expressions to extract the feature basis on scatter matrix. Different preserving projection models are used for extract the features from local and non-local matrices. In this paper, we proposed the advanced machine learning discriminate analysis models based on discriminant analysis and preserving projection based on the ORL, YALE database. This paper gives improved results regarding of face recognition parameters as compared to existing models.
Traffic signs recognition with deep learning

In this paper, a deep learning based road traffic signs recognition method is developed which is very promising in the development of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. The system architecture is designed to extract main features from images of traffic signs to classify them under different categories. The presented method uses a modified LeNet-5 network to extract a deep representation of traffic signs to perform the recognition. It is constituted of a Convolutional Neural Network (CNN) modified by connecting the output of all convolutional layers to the Multilayer Perceptron (MLP). The training is conducted using the German Traffic Sign Dataset and achieves good results on recognizing traffic signs.
Exact Performance of NOMA with Full-Duplex Energy Harvesting Relaying in Nakagami-m Frequency-Selective Fading Channel

In this paper, the NOMA transmission with full-duplex energy harvesting relaying (FDEHR) is investigated. Closed-form expressions for the outage probability of the near and far users are presented over Nakagami-m frequency-selective fading channel. Monte Carlo simulations are provided throughout to verify the accuracy of the derivations. Simulation and numerical results demonstrate that the near user without FDEHR possesses lower outage probability than the far user. While the outage performance of the far user could be improved and better than the near user with FDEHR.
Experimental study on polishing process of cylindrical roller bearings

The surface quality and accuracy of the geometry of the cylindrical rollers are important factors for bearing life. This paper presents effects of machining parameters on the surface roughness, topography and roundness of cylindrical rollers through the lapping and polishing experiments. And then the surface roughness of the cylindrical rollers is optimized. The results found that the surface roughness of rollers is significantly changed in lapping process with different abrasive particle sizes, while the surface roughness has slightly reduced in polishing process. It also indicated that the smoother surfaces with better roughness can be obtained after lapping and polishing process. In addition, the surface roughness of cylindrical rollers is rapidly reduced from Ra of 0.5 µm to Ra of 0.063 µm after the 3-h lapping process and Ra of 0.013 µm after the 1-h polishing process. The surface topography of rollers can be achieved by the smoother surface when loads are from 25 to 35 N in lapping process, and the loads are from 35 to 40 N in polishing process. Finally, the Taguchi method is applied to optimize the surface roughness in polishing process. The result found that the optimal surface roughness achieves 0.015 µm with respect to the time of 35 min and type of 4000# Al2O3.
Fog Computing Framework for Internet of Things Applications

Within the Internet of Things (IoT) era, a big volume of data is generated/gathered every second from billions of connected devices. The current network paradigm, which relies on centralised data centres (a.k.a. Cloud computing), becomes impractical solution for IoT data storing and processing due to the long distance between the data source (e.g., sensors) and designated data centres. In other words, by the time the data reaches a far data centre, the importance of the data would be vanished. Therefore, the network topologies have been evolved to permit data processing and storage at the edge of the network, introducing what so-called "Fog computing". The later will obviously lead to improvements in quality of service (QoS) via processing and responding quickly and efficiently to varieties of data processing requests. Therefore, understanding Fog computing architecture and its role in improving QoS is a paramount research topic. In this research, we are proposing a Fog computing architecture and framework to improve QoS for IoT applications. Proposed system supports cooperation among Fog nodes in a given location, in order to permit data processing in a shared mode, hence satisfies QoS and serves largest number of service requests. The proposed framework could have the potential in achieving sustainable network paradigm and highlights significant benefits of Fog computing into the computing ecosystem.
GAN-NL: Unsupervised Representation Learning for Remote Sensing Image Classification

Recently, deep learning methods have greatly enhanced the classification performance because of their strong representation ability in the local receptive field. However, the non-local spatial information always exist in the images. Moreover, the limited amount of the labeled data imposes great challenges on the supervised representation learning model, especially the remote sensing images. With the consideration, we propose a generative adversarial network with non-local spatial information (GAN-NL) for remote sensing image classification. Specifically, a non-local layer is incorporated into a generative adversarial network for unsupervised representation learning. Then, a classification network is designed to infer the labels of the images. The classification results on the challenging NWPU-RESISC45 remote sensing image dataset show that our proposed method performs favorably against the state-of-the-art methods in terms of the classification accuracy without any pre-training.
User Behavior Analysis with Machine Learning Techniques in Cloud Computing Architectures

This paper presents the use of machine learning algorithms to analyze the behaviors of users working in a distributed computer environment. The objective consists in discriminating groups of close users. These groups are composed of users with similar behaviors. Event related to the user’s behaviors are recorded and transferred to a database. An approach is developed to determine the groups of the users. A non-parametric method of estimating a probability density is used to predict application launches and session openings in an individual way for each user. These algorithms have been implemented and demonstrated their effectiveness within a complete virtualization environment for workstations and applications under real conditions in a hospital.
New MPPT Sliding Mode Approach for Grid Connected PV System

In this paper, a new sliding mode approach is proposed to track the maximum power point of a photovoltaic system connected to utility grid. The duty cycle is generated automatically by the Maximum Power Point Tracking (MPPT) system. The photovoltaic array is connected to the alternative current grid via a three-phase three-level voltage source converter and a boost converter. The three phase voltage source converter converts the direct current link voltage to alternative current and keeps unity power factor. The voltage source converter is controlled with two control loops: the current control loop which regulates the active and reactive currents grid components and the DC link voltage control loop. The unity of the power factor is assured by setting the reactive current reference equal to zero. The simulations results demonstrate the performance of the proposed MPPT controller in presence of both changes in irradiance level and ambient temperature.