Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Multivariate-Polynomial-Based Membership Authentication and Key Establishment for Secure Group Communications in WSN

The primary task of secure group communications in wireless sensor networks (WSNs) is to securely transmit various types of data, for example weather data, traffic data, etc. Collected data in WSNs is different from most data transmitted in digital communication applications. Most collected data in WSNs contains only few bits of information. Conventional protocols are not suitable for WSNs since WSNs need more fast and lightweight protocols for secure group communications. User authentication and key establishment are two fundamental security services in secure communications for WSNs. The user authentication allows communication entities to authenticate their communication partners and the key establishment allows a secret session key to be shared among all communication entities. The session key can be used to protect exchange information in the communication. Communication has been moved from traditional one-to-one communications to many-to-many communications, also called group communications. Traditional user authentication which authenticates one user at one time is no longer suitable for a group communication which involves multiple users. In this paper, we propose a protocol which provides both membership authentication and key establishment simultaneously for WSNs. However, all existing solutions can only provide either user authentication or key establishment separately. Furthermore, our proposed membership authentication has complexity <inline-formula> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the number of users in a group communication, which is different from all existing user authentication schemes which are one-to-one authentications with complexity <inline-formula> <tex-math notation="LaTeX">$O(n^{2})$ </tex-math></inline-formula>.
An Ensemble based Machine Learning model for Diabetic Retinopathy Classification

As technology and digitization grows, there is a huge surge in digital storage of health records. Machine learning has an important role in uncovering patterns existing in these health records providing interesting insights to medical practitioners for assistance in the diagnosis of various ailments. Due to the sensitivity of the health records, the machine learning algorithms often fail to predict the diseases accurately. In present work, an ensemble based machine learning model comprising of the Machine Learning (ML) Algorithms namely Random Forest classifier, Decision Tree Classifier, Adaboost Classifier, K-Nearest Neighbour classifier, Logistic Regression classifier is experimented on diabetic retinopathy dataset. As a first step, normalization is done on the diabetic retinopathy dataset by min-max normalization method. This normalized dataset is then trained the proposed ensemble model. The performance of the proposed model is finally evaluated against the individual machine learning algorithms. The comparative analysis reveals the fact that the ensemble machine learning model outperforms the individual machine learning algorithms.
Simulation of LTE-Network for Throughput Improving Using FDD and TDD Carrier Aggregation

In this paper, the LTE network simulation using frequency division duplex (FDD) and time division duplex (TDD) with carrier aggregation (CA) was analyzed in order to improve the throughput of the network. The analysis is divided into two sections which are non-carrier aggregate for FDD or TDD, and another with carrier aggregate for FDD and TDD. In both sections, the ns-3 program was used for the LTE system simulation for comparing throughputs. Finally, the results showed that carrier aggregate for FDD and TDD can improve the throughput by 74% on FDD and 134% on TDD used in the LTE mobile cellular network.
Multipath Based Localization of Indoor Target Shadowed by Non-Penetrating Obstacles

Through-the-wall radar imaging attempts to locate the targets behind the wall based on backscattered waves from the targets directly. However, conventional Born approximation imaging method is not applicable when indoor targets are covered by non-penetrating obstacles such as metallic walls. In this paper, a multipath based method is proposed to locate the indoor targets behind metallic obstacles. Specifically, the second-order multipath between the target and the side wall is considered, and the multipath imaging method based on the classical geometrical optical theory (GO) is modeled. Numerical simulation results based on finite difference time domain (FDTD) are presented to show the effectiveness of the proposed method.
Hybrid Routing for Man-in-the-Middle (MITM) Attack Detection in IoT Networks

Affordable and expandable low power networks such as 5G and Low Power Wide Area Networks (LPWAN) in the public and private network areas have improved network bandwidth capacities and processing performance. Internet of Things (IoT) technologies are increasing in popularity with numerous applications and devices being developed for smart environments and health-related applications. This raises security concerns in these networks, as many IoT devices handle confidential information such as IP/MAC addresses, which could be used to identify a user's location. As a result, there is vulnerability to data tampering by man-in-the-middle (MITM) attacks, which feature two observable characteristics: (1) there is a measurable delay in the session and (2) has unusual travel times compared to prior normal transactions. To improve the detection of these attacks, this paper proposes a novel scheme using a hybrid routing mechanism, which involves appointing dedicated nodes for enforcing routing between IoT devices and users with minimal intervention and workload to the network. The function of dedicated devices with more computational and battery power can provide three advantages: (1) determine secured paths within the network by avoiding suspicious nodes and networks, (2) provide stable travel times (less fluctuations) for a trusted time server (TTS) to improve the accuracy of estimated travel times, and (3) provide packet inspection for security checks. This proposed solution contributes towards increasing the security of IoT networks by enabling the real-time detection of intruders.
Classification of the Wind Turbine Generated Radar Detections by Artificial Intelligence

this paper presents the architecture of an artificial intelligence classification network designed to mitigate the wind turbine generated plots. Exploiting high resolution Doppler spectrum in addition to the radar measurements attributes, the classification network is trained and validated on real recordings of medium range multimission air defense radar with a high rate of success (Abstract)
Dictionary Learning for Radar Classification of Multiple Micro-Drones

Micro-Doppler signatures (MDS) for classifying micro-drones has been the subject of much research in recent years. Most existing work on radar classification of micro-drones assumes that the received signal is reflected from a single micro-drone. However, this assumption may be violated in a number of practical cases. When multiple micro-drones exist in the observed scene, the superimposition of their micro-Doppler signatures greatly increases the difficulty of their classification. In particular, it is challenging to determine if a specific kind of micro-drone exists when there is more than one type of drone observed. In this paper, a method for detection and classification of multiple micro-drones based on their micro-Doppler signatures via dictionary learning is proposed. First, the dictionary is learned for each kind of micro-drone based on cadence-velocity diagrams (CVD) of training samples. Then, a sparse representation of CVD for multiple micro-drones is obtained via the orthogonal matching pursuit (OMP) algorithm. Finally, a threshold detector is applied to the sparse solution in order to recognize the components of multiple micro-drones. The results of experiments, using a 25GHz radar and three types of micro-drone, show that the proposed method achieves a successful classification performance of 92%.
Bi-level Model for Demand Side Management in Integrated Energy System

The integrated energy system (IES) has realized the comprehensive utilization of energy, and the interaction between the supply side and the demand side is more complex. Therefore, a bi-level model for demand side management in IES, considering the income of energy suppliers, the comfort level and the cost of users, is constructed in this paper. In the upper level, energy suppliers optimize the subsidy price to improve their income. In the lower level, the users will adjust their consumption plans based on the subsidy electricity price from the energy suppliers. A solution method based on genetic algorithm and big-M method is used to solve the proposed bi-level model. And the model and solution method are verified in a 24-hour simulation. Through the simulation, the optimal subsidy price and users’ energy-use plan can be obtained.
Online Detection of Soft Internal Short Circuit in Lithium-Ion Batteries at Various Standard Charging Ranges

Soft internal short circuit (ISCr) in lithium-ion batteries is a latent risk, and it is a primary reason for thermal runaway with blaze and explosion. Early detection of ISCr is necessary to ensure safe utilization of the batteries. Based on the applications of batteries, load currents are considerably diverse and occasionally do not satisfy the persistent excitation condition, resulting in inaccurate detection of soft ISCr with existing model-based methods. Using constant current for standard charging of the batteries, this study proposes a novel and accurate model-based algorithm to detect soft ISCr online irrespective of the specific type of load currents. An equivalent circuit model of the battery with ISCr is used to extract open circuit voltage of the battery. Enhanced relationship between open circuit voltage and state of charge is obtained to estimate ISCr resistance as a fault index. To improve estimation accuracy of the fault index, factors affecting the ISCr resistance are analyzed and considered. Experiments incorporating various charging ranges and soft ISCr conditions below 100 $\Omega $ are configured, and the proposed method is verified with experimental data. The results of the study indicate that the relative error of the estimated fault index does not exceed 6.4%; thereby, the battery management system is enabled to accurately detect an ISCr early.
Prediction of Bitcoin Price Change using Neural Networks

In recent years, Bitcoin is rising and become an attractive investment for traders. Unlike stocks or foreign exchange, Bitcoin price is fluctuated, mainly because of its 24-hours a day trading time without close time. To minimize the risk involved and maximize capital gain, traders and investors need a way to predict the Bitcoin price trend accurately. However, many previous works on cryptocurrency price prediction forecast short-term Bitcoin price, have low accuracy and have not been cross-validatedThis paper describes the baseline neural network models to predict the short-term and the long-term Bitcoin price change. Our baseline models are the Multilayer Perceptron (MLP) and the Recurrent Neural Networks (RNN) models. Data used are Bitcoin's blockchain from August 2010 until October 2017 with 2-days period and the total amount of 1300 data. The models generated are predicting both for short-term and long-term price change, from 2-days until 60-days.The result shows that long-term prediction has a better result than short-term prediction, with the best accuracy in Multilayer Perceptron when predicting the next 60-days price change and Recurrent Neural Networks when predicting the next 56-days price change. Multilayer Perceptron outperforms Recurrent Neural Networks with accuracy of 81.3 percent, precision 81 percent, and recall 94.7 percent.
Social Media Data Analysis for Intelligent Transportation Systems

Transportation is the major tribulations in urban cities. Traditional transportation approach are not smart for traffic management so Intelligent transport systems works with automated system and gives better solutions for smart parking, electronic toll collection, traffic monitoring and planning etc by accessing data from different sources. Among passive data, Social media data is widely sourced for Intelligent Transportation Systems (ITS), users communicate and interact online by writing blogs through face book, twitter and other social media applications. This data from social media can be used for transportation analysis like traffic prediction, accident zones analysis etc. unfortunately the data sourced is highly unstructured and massive in volume leading to complex processing for data usage. Big data helps in processing, storing and analyzing this data. In this paper we summarize main research on review topics in the field of data preprocessing techniques for noisy data removal, data concentration methods for filtering the data, feature selection methods to extract the main features, abnormal behavior of data to identify the suspicious behavior accounts and road condition prediction for detecting the road patterns and traffic behavior by supervised learning and unsupervised machine learning algorithms, this paper discusses towards the end on future research directions in social media analysis in ITS.
Analysis of Machine Learning Algorithms on Cancer Dataset

The most promising of all cancers that are prevailing among and the primary source of women’s deaths worldwide is the cancerous breast cells. Accurate discovery of this type of cancer cells is essential in its early stages, which can be attained via. various data mining and machine learning techniques. Therefore, a comparative analysis among different machine learning techniques such as Random Forest, Support Vector Machine, Naive Bayes, Decision Tree, Neural Networks and Logistic Regression is conducted. It is determined using the WEKA tool. Also, the selected machine learning algorithms are evaluated based on accuracy in prediction results and performance comparison of each classifier with a ROC curve on multiple classifiers is performed.
IoT Based: Improving Control System For High-Quality Beef in Supermarkets

Supermarkets are modern markets that sell various household needs ranging from equipment items to various food products, one of which is beef. Consuming beef can have good or bad effects depending on the quality of the freshness of the beef itself. Therefore, before the beef is sold and consumed by the customer, it is necessary first to control the quality of the freshness of the beef. In supermarkets, the process of controlling the quality of beef still uses manual methods, so that the output produced is inaccurate and requires a long time. The presence of IoT technology can overcome these problems, so the process of controlling the quality of beef can be faster and more accurate. In this study, the controlling system is proposed to identify the quality of freshness of beef, the quantity of beef and temperature control in the sales rack. This controlling system utilizes NodeMCU microcontroller, three gas sensors (MQ-136, MQ-137, and TGS-2602), TCS-3200 color sensors, LED lights, buzzers, HC-SR04 ultrasonic sensors and LM35 temperature sensors. The workings of this system are to detect the gas and color produced from the beef and detect the temperature and presence of beef on each side of the rack. It can be concluded that the success of the proposed system has fairly good representation, in accordance with the theory regarding the level of freshness of beef that already exists.
Design, Simulation and Comparative Evaluation of Active Filters Using 32-nm CNTFET-OTA

In this paper an efficient simulation of low voltage and low power operational transconductance amplifiers (OTA) using carbon nanotube based field effect transistor (CNTFET at 32-nm technology node has done. The performance of the CNTFET-OTA is estimated by altering number of carbon nano tubes (CNTs) N, inter CNTs pitch (S) and CNT diameter (DCNT). The performance of CNTFET-OTA module has been thoroughly investigated it has been found that DC gain has hiked by 280%, transconductance and output resistance have increased by 256% and 262% respectively than bulk driven complementary metal oxide semiconductor OTA (CMOSOTA). A 136 times lesser power consumption and a total harmonic distortion (THD) of CNTFET-OTA which is 0.37 lesser than the conventional CMOS-OTA has been achieved. Further, Two voltage mode Active filters using bulk driven CMOS-OTA and proposed CNTFET-OTA topology are simulated. A significant increase in cut-off frequency is achieved using CNTFET-OTA second order filter structures.
Approach to Estimating the Probability of Providing “Cloud” Services in the SDN

The subject of the study is “cloud” services that are provided to customers in software-defined networks. The purpose of the work is to offer mathematical software for calculating the channel resource when implementing a “cloud” service at the client's request and assess the possibility of providing it, taking into account the specifics of software-defined networks. The methodological basis of the work is made up of methods of theoretical analysis, mathematical modeling and method of generalization. The mathematical model is based on a proven method of sifted load for a multiservice network. Mathematical modeling was carried out for a generalized network structure using the MATLAB mathematical package. The results were obtained for the most frequently used number of channel units (from 1 to 10) and different network load, which makes it possible to put the proposed method into practice. Based on the results of an experimental study, conclusions and recommendations are made.
Small-Signal Modeling of Off-Grid Integrated Energy System Considering Internal Disturbances

The development of the integrated energy system (IES) has become a hot spot in the energy field. According to the existing research, less research focus on off-grid mode modeling, but it play an important impact in operation and control of the system. In this paper, small signal modeling of off-grid IES considering external disturbances is studies. First, a small-signal state space model for four sub-modules including inverters, networks, phase-locked loop (PLL) and loads is established on its individual reference frame. On this basis, according to the coordinate transformation and intermediate equations, a complete small signal model is established on a common coordinate system by connecting all the sub-modules. Moreover, the internal disturbances of IES such as loads disturbances is added in the small-signal model by assuming the virtual resistor and disturbance load current. The modular small-signal model proposed in this paper can greatly simplify the small-signal modeling of IES, and this small-signal model can also provide theoretical basis for the state and stability evaluation of the system under internal disturbance.
Automated Cardiac Health Diagnosis: A Time-Domain Approach

Cardiological problems are one of the leading causes of human fatality. Electrocardiogram is a major noninvasive tool for monitoring heart conditions. The human vision is not suitable to identify the minute changes in Electrocardiogram wave amplitude and time intervals; hence an automatic diagnostic tool is necessary for precise abnormality detection. This paper presents a classification method to classify seven heartbeat conditions-normal and six classes of abnormalities. The algorithm implements a time domain approach to obtain the statistical features from the Electrocardiogram beats extracted from the arrhythmia database. This objective of this work is to find the suitability of time domain features to arrhythmia classification with machine learning. The statistical features are extracted from raw ECG signal, the time derivative, time integral and 5-point first derivative stencil of the ECG data. The cardiac abnormality classification is implemented with Support Vector Machine. The attained classification accuracy is upto 93% for chosen input feature pairs for binary Support Vector Machine.
Instability changes the MAGIC NAND gate to the NOR gate

Memristor-based stateful logic demonstrates a method of in-memory computing, which is a promising way to overcome the data-transfer bottleneck in the current von Neumann computer architecture. However, due to the instability, the memristor device exhibits an inherent stochastic switching behavior especially when the applied voltage is in the switching range of unsafe writing. In such case, the delicate design of stateful memristor gates could suffer the reliability problem. Here, such unsafe writing impacts on the memristor-based logic operation is systematically analyzed. Through establishing the Markov chain model of unsafe writing effects, we deduce the mathematical relationship between the memristor-aided logic (MAGIC) gate reliability and switching probability. It reveals that unsafe writing with enough operation time would make the MAGIC NAND gate converge to always NOR logic. The best operation time for the unsafe write is then proposed to improve the probability of right logic function and avoid the undesired logic result, which is demonstrated with simulation.
Green Point Energy-efficient Analysis of Non-Orthogonal Multiple Access (NOMA) over OFDMA

Non-orthogonal multiple access is emerging as a promising technology for 5G with its superior spectral efficiency and user fairness. NOMA has recently gained ever-growing attention from both industry and academia and thus is proved to be an excellent candidate with superior performance in energy and spectrum efficiency to support the massive connectivity of users. Yet, the current system of NOMA needs to be optimized with respect to total energy consumption. Hence, in this paper, energy-efficient analysis of NOMA is correlated with OFDMA for various system bandwidth. This analysis is carried out by utilizing the derivative function of energy and spectrum efficiencies and then analyzed at the green point value where the energy efficiency is maximum. Simulation results demonstrate the superiority and efficiency of the NOMA over OFDMA in terms of energy efficiency and thus is an emerging noble candidate for future mobile communication.
An Enhanced Secured IOT Model for Enterprise Architecture

Enterprise architecture (EA) is a comprehensive way of helping an organization to achieve its desire objectives. Recent research shows the requirement of EA in small and medium-sized enterprises (SMEs). There is strong empirical evidence affirming that SMEs are a major job creation in any economy. However, the analysis also clearly shows that the sector is very diverse. With the advent of the Internet of Things (IoT), a new paradigm for driving digital innovation in EA has emerged. IoT enabled interactions between machines and devices to communicate globally. It also spurred new digital innovation used to deliver value-added services in a wide range of industries. There is a risk that promoting IoT in EA due to their large contribution to employment, without adequate provision for security may result in a trade-off between quantity and the security of EA. In view of this, this paper proposed an enhanced IoT ecosystem architecture with a security layer to enforce the security policies for each of EA IoT layers. The paper also presents the IoT service business model.