Miracle-dz/UniFAR-FTUs huggingface dataset:
IOT Multitasking: Design of Smart phone application for systematic execution and scheduling in real time environment

Scheduling plays a vital role in real time embedded systems to prepare a schedule dynamically and share resources effectively. The scheduling algorithms involve priorities in mixed fashion depending on hard and soft deadlines. Real time scheduling systems basically depends on logical computation and time of the results produced. Multitasking involves parallel execution of request and response based on the scheduling algorithm. It brings automation in many areas with reference to performance parameters like reliability and sensitivity. The major use of scheduling is Effective utilization of resources and avoiding task conflicts. In the proposed work the concept of Scheduling in real time systems is applied in transportation system application for efficient service and effective utilization of resources. One of the best transportation system in the world is railways, however in present days this transport service is under pressure due to mismanagement of operations resulting in customer complaints and long waiting list of passengers. The proposed system is used to accommodate the passengers based on the request and resource availability.In this work Internet of Things (IoT) based smart phone application system is designed to introduce automation in addressing the emergency service to the passenger requests in the moving train. The proposed system consists of raspberry pi as a server for maintaining the database and provides wireless communication between the passenger and the ticket collector of train. The aim of this work is to help passengers to have safe and comfortable journey in the train. The user interface designed in each compartment is used to send the request from the passenger to the train employee for allocating the seat in the train in emergency situations. On the request received from the passenger the response is given depending on the resource list loaded in the server. The fair distribution of the seat to the passengers is scheduled using composite scheduling for multitask (CSM) algorithm based on database resource list. The algorithm defines the task priority as its own unique priority level for every task and assign the same priority to the multiple tasks if more number of tasks are introduced in scheduling then they are either time sliced or work round robin fashion. The results obtained in the proposed work justify the performance parameters in real time for the algorithm used.
Token-Based Lightweight Authentication to Secure IoT Networks

The rapid growth of Internet of Things (IoT) technology offers huge opportunities and also brings many new challenges related to the authentication in (IoT) devices. Using passwords or pre-defined keys have drawbacks that limit their use for different (IoT) applications like smart hotel and smart office. In fact, they didn’t provide temporary access to data in such reservation systems. Thus, authenticating users basing on password mechanism is not feasible. In this paper, we propose a new Token-Based Lightweight User Authentication (TBL UA) for (IoT) devices, which is based on token technique in order to enhance the robustness of authentication. Security analysis shows the security strength of the proposed scheme such as token security, Perfect Forward Secrecy (PFS), etc. In addition, the presented performance analysis shows that it is a strong competitor among existing ones for user authentication in (IoT) environments.
Automatic Detection of Insulting Sentences in Conversation

An overall goal of our work is to use machine-learning based solutions to assist children with communication difficulties in their communication task. In this paper, we concentrate on the problem of recognizing insulting sentences the child says, or insulting sentences that are told to him. An automated agent that is able to recognize such sentences can alert the child in real time situations, and can suggest how to respond to the resulting social situation. We composed a dataset of 1241 non-insulting and 1255 insulting sentences. We trained different machine learning methods on 90% randomly chosen sentences from the dataset and tested it on the remaining. We used the following machine learning methods: Multi-Layer Neural Network, SVM, Naive Bayes, Decision Tree, and Tree Bagger for the task. We found that the best predictors of the insulting sentences, were the SVM method, with 80% recall and over 75%precision, and the Multi-Layer Neural Network and the Tree Bagger, with precision and recall exceeding 75%, We also found that adding additional data to the learning process, such as 9500 labeled sentences from twitter, or adding the word “positive” and the word “negative” to sentences including positive or negative words, respectively, slightly improves the results in most of the cases. Our results provide the cornerstones for an automated system that would enable on-line assistance and consultation for children with communication disabilities, and also for other persons with communication problems, in a way that will enable them to function better in society through this assistance.
Green Mesh Network of UAVs: A Survey of Energy Efficient Protocols across Physical, Data Link and Network Layers

Green communication is one of the major research areas of future wireless networks including multi-UAV flying ad-hoc networks. The UAVs working in a collaborative manner are limited with onboard energy resources affecting the network performance. Achieving energy efficiency by proper resource management is even more critical in cases where UAVs are being used in public safety applications. Radio Resource Management conserves network power, improving the energy efficiency and network QoS by adapting the network parameters to the load variations from component level to the network level. Research work is being carried out on optimization strategies to improve the overall energy consumption in such networks. This paper presents a survey of network deployment, network management and radio resource management topics to achieve energy efficiency in multi-UAV ad-hoc networks. It provides an analysis of the energy efficient protocols across physical, data link and network layers presented in the literature to draw the energy efficiency enablers.
Structuring Elements in the Watershed Algorithm for the Segmentation of Mammography Images

Reading mammography images has always been a challenging task even for experienced radiologists. With the advancements in computer technology, machine tools such as the Computer Aided Detection and Diagnosis (CAD) systems are widely engaged as a second reader to assist radiologists in image reading. One of the important processes in the CAD machine is the segmentation process. The morphological watershed algorithm is one of the hybrid technique that combines boundary and region criteria, but this algorithm has several drawbacks such as over-segmentation and sensitive to noise. In this research, the denoising method applies the Principal Component Analysis (PCA) filtering. Prior to the segmentation by the watershed algorithm, the Fuzzy C-Means (FCM) clustering algorithm is used to identify the image foreground, which is the region of interest (abnormality region). A marker-controlled watershed algorithm is implemented to overcome the over-segmentation drawback. Furthermore, applying a suitable shape of structuring element in the watershed algorithm has the same effect of reducing the over-segmentation problem. Thus, three shapes of structuring elements, which are the disk, diamond, and octagon are tested and compared. The aim of this research is to find a suitable shape of structuring element for the marker-controlled watershed algorithm. For the evaluation of the segmentation performance, three evaluation methods are used, which are the Jaccard Index (JI), Dice Similarity Coefficient (DSC) and Figure of Merit (FOM). The result of the comparison shows that the diamond-shaped structuring element is a suitable shape for the segmentation of mammography images.
Geo-Fencing in Wireless LANs with Camera for Location-Based Access Control

This paper proposes a camera-based geo-fencing system for wireless local area networks (WLANs) which enables geo-location based wireless access control to intuitively manage the area where the WLANs are available. The proposed system leverages camera to localize WLAN users accurately and estimates the proximity of users to objects in the real world. Meanwhile, conventional geo-location based access control suffers from low accuracy of RSSI based localization. As an example of geo-location based access control, we execute a WLAN activation control which allows STAs to pre-activate WLAN and associates with access points (APs) so that the power consumption and time to obtain contents are reduced. Experimental results show the feasibility of camera-based geo-fencing.
Early Fire Detection System Using Wireless Sensor Networks

Every year millions of hectares of forest are devastated by fire, which causes huge damage to forest and nature especially in a country such as Algeria where the size of our forests are in degradation. Existed methods for forest monitoring and fire detection are traditional and based on humans observation, which is inefficient due to the size of controlled regions. Therefore the deployment of automatic fire detection system is unavoidable to allow early and fast fire extinction. In this paper we present an early fire detection system based on Wireless Sensor Network.
A Privacy-Preserving Incentive Mechanism on Multi-Bid Crowdsourced Spectrum Sensing

Crowdsourced spectrum sensing (CSS)has been considered a great technique for increasing current spectrum service. Researchers have brought up some auction-based mechanisms for crowdsourced spectrum sensing system. However, the bid privacy of participants in multi-bid scenario has never been taken into consideration. In this paper, we propose BPriCSS, the first mechanism to protect the bid privacy during the process of selecting spectrum-sensing participants in CSS system. We prove that BPriCSS can simultaneously achieve differential bid privacy and truthfulness by theoretical analysis and simulation.
Transmission Timing on Optical Data-Center RotorNet for Reduced Cost, Energy and Latency

RotorNet comprises switches that provide direct all-to-all connectivity among data-center nodes by cycling among all cyclic shift permutations. Combined with two-hop routing, whereby packets traverse the optical network twice, they provide high throughput regardless of the traffic pattern and with no need for centralized control. Although being a circuit-switched optical interconnect intended primarily for latency-insensitive traffic, reducing latency saves energy and buffer space, and is thus important. We show that latency can be reduced by properly timing the transmissions by the sender, and offer insights along with some useful results.
OPTIMAL TASK OFFLOADING IN FOG-ENABLED NETWORKS VIA INDEX POLICIES

Fog computing has been considered to be a potential solution to enable computation-intensive and latency-critical application at the battery-empowered mobile devices. Task offloading could take advantages of available neighboring computational resources to achieve low latency. By exploiting the temporal correlation of the states at fog nodes, a sequential decision-making problem that aims to optimize the long-term task offloading performance is considered in this paper. Such a problem is a partially observed Markov decision process with an exploitation-exploration tradeoff that is difficult to analyze. We address this tradeoff in task offloading under the framework of restless multi-armed bandits (RMAB). The indexability analysis of this task offloading problem is then provided. Meanwhile, an index policy, which is asymptotically optimal and has remarkably low computation complexity, is established based on the Whittle’s index to solve the task offloading problem. Numerical results show the superiority of the proposed task offloading method.
Designing and Implementation of Microcontroller Based Non-Invasive Health Monitoring System

Over the last few decades, design and implementation of non-invasive techniques for instant health condition measurement has become a huge demand. And in our day to day life, instant detection of heart rate, blood glucose level and body temperature has become a laidback task with the advancement of technology in medical science. And in this paper, we proposed a model based on microcontroller that can instantly detect human body temperature, heart beats per minute (BPM) and blood glucose level with a non-invasive technique. Biomedical equipment like Glucometer can only be used to detect blood glucose level through an invasive technique, digital thermometers can be used to detect only body temperature and detecting BPM requires costly blood pressure measurement kit. But in our model, we accumulated all the three mechanisms together on a single device to measure body temperature, BPM and blood glucose level instantly. This design is not only easy to use by a person with non-medical background but will also be cost-effective as well and it is accessible by low income households. From an environmental aspect, this eco-friendly design utilizes solar PV technology to charge the 6V battery that powers all the electronic parts associated with the device. The module includes a GSM based message system that allows the user to send a message expressing the health condition of the user to a physician. The entire setup is achieved via a control unit consisting of a groove ear clip, Arduino Uno, GSM module, buzzer, MQ2 gas sensor, LM35 temperature sensor and a 6V battery with a solar PV charging unit with a charge controller.
Do Lower Minimum Wages for Young Workers Raise Their Employment? Evidence from a Danish Discontinuity

Abstract We estimate the impact of youth minimum wages on youth employment by exploiting a large discontinuity in Danish minimum wage rules at age 18, using monthly payroll records for the Danish population. The hourly wage jumps by 40% at the discontinuity. Employment falls by 33%, and total input of hours decreases by 45%, leaving the aggregate wage payment almost unchanged. We show theoretically how the discontinuity may be exploited to evaluate policy changes. The relevant elasticity for evaluating the effect on youth employment of changes in their minimum wage is in the range 0.6 to 1.1.
Automatic Speaker Recognition using Stationary Wavelet Coefficients of LP Residual

Automatic speaker recognition is a challenging task when the duration of the speech utterances are very short ie. , a few seconds. Source features extracted from speech utterances are shown to be effective for such cases. This paper proposes a system based on LP residual for text independent speaker recognition. Parameterization of LP residual is attempted here using Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT). Feature obtained from different levels of decomposition are used for implementing an i-vector/PLDA based speaker recognition system. Effectiveness of the system is evaluated for 10 sec training - 10 sec testing task of NIST SRE 2010 database. Features obtained from SWT level-2 decomposition gives the lowest Equal Error Rate (EER) of 39.
Genetic Algorithms for Discovering Community Cores Hidden within Multidimensional Social Networks

Multidimensionality in social networks is a significant problem that appeared owing to the fact that most social networking sites such as YouTube, Twitter, and Facebook enable people to interact with each other through various social activities, which reflects various types of relationships between them. Each type of these relationships is represented by a network dimension. In resent years, discovering community cores hidden within multidimensional social networks has become essential in social network analysis field. When dealing with this type of networks, the definition of community detection problem changes to be, the discovery of the shared community cores across all network dimensions. Many studies presented on the topic of community detection, but communities uncovering in multidimensional networks has not deeply investigated yet. In this paper algorithms to solve community detection problem are proposed as a single-objective and as a multi-objective optimization problem by applying genetic algorithms. The most popular objective functions proposed over the past years are used. The performance, the limitation of each objective, and how those objectives correlate with each other has been clarified. Experiments in synthetic and real world networks showed the ability of the proposed algorithms to correctly discover community structures that maximize modularity across all network dimensions, compared with others on the literature. The proposed algorithms have the advantages that it doesn’t need any prior knowledge about number of the hidden community cores.
Indoor Positioning with Reference Nodes Selection in Wireless Networks

This study explores the indoor positioning in wireless networks. We used the Minimum Mean Square Error (MMSE) algorithm for triangulation positioning with the received signal strength (RSS). Through previous research, it is found that the accuracy of positioning is not only related to the distance between the reference point and the positioning node, but also the variation of the distance between the reference point and the positioning node. It also affects the accuracy. Therefore, considering the distance between the reference point and the positioning node, and the distance variation between the reference points, it is divided into the internal distance variation (IDV) and the external distance variation (EDV). We proposed a reference nodes selection (RNS) with the weighted variation mechanism. Simulation results show that the effective reference improvement can be obtained by the weighting RNS method of the EDV.
Compressed sensing cardiac DTI reconstruction using nonlocal low-rank regularization

Diffusion Tensor Imaging (DTI) is a promising tool for investigating the fiber architectures of the human heart noninvasively. However, DTI requires long acquisition time to obtain images with high quality in practical applications. Compressed sensing (CS) framework provides a new and feasible way for faster imaging with high quality. In this study, we proposed a CS-DTI reconstruction method for human heart based on nonlocal low rank regularization to reduce therefore the acquisition time. Such regularization exploits the global correlation of diffusion weighted(DW) images along different diffusion gradient directions and nonlocal self-similarity across spatial of DW images. The experiment results on real cardiac diffusion weighted images shown that, comparing with some previously proposed CS-based DT-MR image reconstruction methods, the proposed method achieved the better performance with lower reconstruction errors on both visual and quantitative DTI indices, compared to some previously proposed CS-based DT-MR image reconstruction methods.
SPECTRAL CLUSTERING FOR BEAM-FREE SATELLITE COMMUNICATIONS

This paper introduces the notion of beam-free satellite systems and it investigates different scheduling algorithms for this architecture. Attending to the current satellite gateway cloudification, this paper assumes that users from different beams can be scheduled over the same frame. Indeed, considering full frequency reuse among beams and on ground precoding, we show that whenever the scheduler is able to freely group users independently of their beam location, large attainable rates are obtained. In addition, we also consider that the gateway is able to select a number of simultaneous transmissions which leads to a substantial sum-rate increase. A scheduling scheme based on spectral clustering is proposed and it shows a higher performance compared to other state-of-the-art alternatives. In addition, our method is able to deal with different user terminal traffic classes. Based on the numerical results obtained considering a close-to-real multibeam satellite pattern, we point out that the current per-beam scheduling process is an inefficient network management for multi-beam satellite systems using precoding.
Improved Image Expansion for Preserving Sharpness Without Jagged Edges

Image expansion is an interpolation problem which has been known to cause blurred and jagged edges. A number of methods have been developed to preserve sharp edges while preventing jaggedness, but the results are often unsatisfactory. In this paper, we propose an image expansion technique which can preserve sharpness of an image without jagged edges, while maintaining smoothness in other parts of the interpolated image. The input image is first expanded by the nearest neighbour interpolation method in order to preserve the original edges. It is also interpolated by another interpolation algorithm with a different degree of edge preservation. The absolute difference between the two outputs produces a residual image of edges with a magnitude which increases with the sharpness of an original edge. This edge residual image is then used to adaptively adjust the gamma value of a novel modified gamma contrast enhancement algorithm so that the edges can regain their original sharpness. A halo effect suppressor has also been introduced to suppress halo effects by avoiding over-sharpening of the edges. It has been shown that our algorithm can produce sharp images without jagged edges and outperform the other benchmarking algorithms visually and quantitatively.
Misbehavior Detection of Embedded IoT Devices in Medical Cyber Physical Systems

We propose a lightweight specification-based misbehavior detection technique to efficiently and effectively detect misbehavior of an IoT device embedded in a medical cyber physical system through automatic model checking and formal verification. We verify our specification-based misbehavior detection technique with a patient-controlled analgesia (PCA) device embedded in a medical health monitoring system.
Weather Classification using Convolutional Neural Networks

Deep learning is one of the most popular fields in recent years and is applicable to many fields, such as computer vision and image processing. In this paper, we describe a new weather image classification technique using Alexnet and Resnet convolutional neural networks (CNN) combined with a multi- class Support Vector Machine (SVM). Experimental results in weather database, desnownet, and d-hazy datasets demonstrate that the proposed method achieves good performance in weather classification.
An Impedance-Based Stability Assessment Methodology for DC Distribution Power System With Multivoltage Levels

Impedance-based methods are effective for stability assessment of cascaded systems. However, the dc distribution power system (DPS) is becoming increasingly complex, incorporating multivoltage levels, many dc buses, and numerous converters. Consequently, conventional stability criteria, such as Middlebrook criterion and its extensions, may not have the ability to judge stability very succinctly. Thus, more research work related to stability evaluation of dc DPS with multivoltage levels needs to be carried out. This article proposes an impedance-based stability assessment methodology. First of all, a unified form of the system is obtained regardless of its structures and operating modes, based on classification of any converter as either a bus voltage-controlled converter or a bus current-controlled converter. Then, according to the two-port small-signal model, the equivalent loop gain of the system is derived precisely. It should be noted that intermediate bus converter could adopt output current or output voltage control modes, which will have influence on the equivalent loop gain expression. After that, the stability requirement of the system is given by introducing the Nyquist criterion to the equivalent loop gain. Finally, an experimental prototype is established to validate the effectiveness of the proposed criterion.