SecSPS: A Secure and Privacy-Preserving Framework for Smart Parking Systems

Smart parking systems are a crucial component of the “smart city” concept, especially in the age of the Internet of Things (IoT). They aim to take the stress out of finding a vacant parking spot in city centers, due to the increasing number of cars, especially during peak hours. To realize the concept of smart parking, IoT-enabling technologies must be utilized, as the traditional way of developing smart parking solutions entails a lack of scalability, compatibility with IoT-constrained devices, security, and privacy awareness. In this paper, we propose a secure and privacy-preserving framework for smart parking systems. The framework relies on the publish/subscribe communication model for exchanging a huge volume of data with a large number of clients. On one hand, it provides functional services, including parking vacancy detection, real-time information for drivers about parking availability, driver guidance, and parking reservation. On the other hand, it provides security approaches on both the network and application layers. In addition, it supports mutual authentication mechanisms between entities to ensure device/ data authenticity, and provide security protection for users. That makes our proposed framework resilient to various types of security attacks, such as replay, phishing, and man-in-the-middle attacks. Finally, we analyze the performance of our framework, which is suitable for IoT devices, in terms of computation and network overhead.

<i>Helicobacter pylori</i>microbe and detecting with data mining algorithms

Nowadays medicines believe that the only definite method to diagnose the existence of Helicobacter pylori microbe is performing endoscope, however it’s painful and insufferable for young children. Thus in this paper we used data mining algorithms to diagnose the existence of this microbe and eventually we succeeded in predicting the existence of this bacterium in stomach that guides medicines to perform Endoscopy just in cases where percentage of finding this bacterium is high.

Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

Characteristics of Multi-Objective Linear Programming Problem and Multi-Objective Linear Fractional Programming Problem Taking Maximum Value of Multi-Objective Functions

In this paper, a new statistical averaging technique is proposed for finding an optimal solution to a multi-objective linear fractional programming problem (MOLFPP) and multi-objective linear programming problem (MOLPP) by using new arithmetic averaging method and new geometric averaging method. It is significantly noticeable same characteristics among all the technique while taking maximum or minimum among all optimized values for multi-objective functions using simplex algorithm. The characteristics provided from the problems are verified by the numerical examples.

Numerical Investigation of Thermal Behavior of CNC Machine Tool and Its Effects on Dimensional Accuracy of Machined Parts

The dimensional accuracy of machined parts is strongly influenced by the thermal behavior of machine tools (MT). Minimizing this influence represents a key objective for any modern manufacturing industry. Thermally induced positioning error compensation remains the most effective and practical method in this context. However, the efficiency of the compensation process depends on the quality of the model used to predict the thermal errors. The model should consistently reflect the relationships between temperature distribution in the MT structure and thermally induced positioning errors. A judicious choice of the number and location of temperature sensitive points to represent heat distribution is a key factor for robust thermal error modeling. Therefore, in this paper, the temperature sensitive points are selected following a structured thermomechanical analysis carried out to evaluate the effects of various temperature gradients on MT structure deformation intensity. The MT thermal behavior is first modeled using finite element method and validated by various experimentally measured temperature fields using temperature sensors and thermal imaging. MT Thermal behavior validation shows a maximum error of less than 10% when comparing the numerical estimations with the experimental results even under changing operation conditions. The numerical model is used through several series of simulations carried out using varied working condition to explore possible relationships between temperature distribution and thermal deformation characteristics to select the most appropriate temperature sensitive points that will be considered for building an empirical prediction model for thermal errors as function of MT thermal state. Validation tests achieved using an artificial neural network based simplified model confirmed the efficiency of the proposed temperature sensitive points allowing the prediction of the thermally induced errors with an accuracy greater than 90%.

The Simulation of Rumor’S Spreading and Controlling in Micro-Blog Users’ Network

In this work, a rumor’s spreading and controlling in a directed Micro-blog user network being consisted with 580 000 nodes are simulated. By defining some authority nodes that release anti-rumor information as the prevention strategy, the effect of the nodes’ role in network on rumor’s suppression is studied. The findings show that rumor will be spread out fast and reach a stable level within limited steps. The suppression of rumor is more predominated by the intervening opportunity, the earlier the intervention strategy was implemented, the better the rumor’s controlling could be achieved. The controlling effect is less relevant with the role of the authority nodes in network.

Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve

The rapid expansion of Internet of Things(IoT)devices deploys various sensors in different applications like homes,cities and offices.IoT applications depend upon the accuracy of sensor data.So,it is necessary to predict faults in the sensor and isolate their cause.A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults.This technique identifies the faulty sensor and determines the correct working of the sensor.Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form.Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described.There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique.So,some solutions are provided to overcome the limitations of the fall curve technique.In this paper,a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years.Its novelty is to predict a fault before its occurrence by looking at the fall curve.The sensing of current flow in devices is important to prevent a major loss.So,the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices.The analysis result proved that if any of the current sensors gets faulty,then the fall curve will differ and the value will immediately drop to zero.Various evaluation metrics for fault prediction are also described in this paper.At last,this paper also addresses some possible open research issues which are important to deal with false IoT sensor data.

Laser Interferometer Based Measurement for Positioning Error Compensation in Cartesian Multi-Axis Systems

Accuracy is one of the most important key indices to evaluate multi-axis systems’ (MAS’s) characteristics and performances. The accuracy of MAS’s such as machine tools, measuring machines and robots is adversely affected by various error sources, including geometric imperfections, thermal deformations, load effects, and dynamic disturbances. The increasing demand for higher dimensional accuracy in various industrial applications has created the need to develop cost-effective methods for enhancing the overall performance of these mechanisms. Improving the accuracy of a MAS by upgrading the physical structure would lead to an exponential increase in manufacturing costs without totally eliminating geometrical deviations and thermal deformations of MAS components. Hence, the idea of reducing MAS’s error by a software-based alternative approach to provide real-time prediction and correction of geometric and thermally induced errors is considered a strategic step toward achieving the full potential of the MAS. This paper presents a structured approach designed to improve the accuracy of Cartesian MAS’s through software error compensation. Four steps are required to develop and implement this approach: (i) measurement of error components using a multidimensional laser interferometer system, (ii) tridimensional volumetric error mapping using rigid body kinematics, (iii) volumetric error prediction via an artificial neural network model, and finally (iv) implementation of the on-line error compensation. An illustrative example using a bridge type coordinate measuring machine is presented.

Hardness Profile Prediction for a 4340 Steel Spline Shaft Heat Treated by Laser Using a 3D Modeling and Experimental Validation

Laser surface transformation hardening becomes one of the most effective processes used to improve wear and fatigue resistance of mechanical parts. In this process, the material physicochemical properties and the heating system parameters have significant effects on the characteristics of the hardened surface. To appropriately exploit the benefits presented by the laser surface hardening, it is necessary to develop a comprehensive strategy to control the process variables in order to produce desired hardened surface attributes without being forced to use the traditional and fastidious trial and error procedures. The paper presents a study of hardness profile predictive modeling and experimental validation for spline shafts using a 3D model. The proposed approach is based on thermal and metallurgical simulations, experimental investigations and statistical analysis to build the prediction model. The simulation of the hardening process is carried out using 3D finite element model on commercial software. The model is used to estimate the temperature distribution and the hardness profile attributes for various hardening parameters, such as laser power, shaft rotation speed and scanning speed. The experimental calibration and validation of the model are performed on a 3 kW Nd:Yag laser system using a structured experimental design and confirmed statistical analysis tools. The results reveal that the model can provide not only a consistent and accurate prediction of temperature distribution and hardness profile characteristics under variable hardening parameters and conditions but also a comprehensive and quantitative analysis of process parameters effects. The modelling results show a great concordance between predicted and measured values for the dimensions of hardened zones.

Geometric Computing Based on Computerized Descriptive Geometric

Computer-aided Design （CAD）, video games and other computer graphic related technology evolves substantial processing to geometric elements. A novel geometric computing method is proposed with the integration of descriptive geometry, math and computer algorithm. Firstly, geometric elements in general position are transformed to a special position in new coordinate system. Then a 3D problem is projected to new coordinate planes. Finally, according to 2D/3D correspondence principle in descriptive geometry, the solution is constructed computerized drawing process with ruler and compasses. In order to make this method a regular operation, a two-level pattern is established. Basic Layer is a set algebraic packaged function including about ten Primary Geometric Functions （PGF） and one projection transformation. In Application Layer, a proper coordinate is established and a sequence of PGFs is sought for to get the final results. Examples illustrate the advantages of our method on dimension reduction, regulatory and visual computing and robustness.

Towards the Design of Ethics Aware Systems for the Internet of Things

The Internet of Things promises to offer numerous societal benefits by providing a spectrum of user applications.However,ethical ramifications of adopting such pervasive technology on a society-wide scale have not been adequately considered.Smart things endowed with artificial intelligence may carry out decisions that entail ethical consequences.It is assumed that the functioning of a smart device does not involve any ethical responsibility vis-a-vis its application context.Such a perspective may precipitate situations that endanger essential human values or cause physical or emotional harm.Therefore,it is necessary to consider the design of ethics within intelligent systems to safeguard human interests.In order to address these concerns,we propose a novel method based on Boolean algebra that enables a machine to exhibit varying ethical behaviour by employing the concept of ethics categories and ethics modes.Such enhancement of smart things offers a way to design ethically compliant smart devices and paves way for human friendly technology ecosystems.

ESSD: Energy Saving and Securing Data Algorithm for WSNs Security

The Wireless Sensor Networks(WSNs)are characterized by their widespread deployment due to low cost,but the WSNs are vulnerable to various types of attacks.To defend against the attacks,an effective security solution is required.However,the limits of these networks’battery-based energy to the sensor are the most critical impediments to selecting cryptographic techniques.Consequently,finding a suitable algorithm that achieves the least energy consumption in data encryption and decryption and providing a highly protected system for data remains the fundamental problem.In this research,the main objective is to obtain data security during transmission by proposing a robust and low-power encryption algorithm,in addition,to examining security algorithms such as ECC and MD5 based on previous studies.In this research,the Energy Saving and Securing Data algorithm(ESSD)algorithm is introduced,which provides the Message Digest 5(MD5)computation simplicity by modifying the Elliptic Curve Cryptography(ECC)under the primary condition of power consumption.These three algorithms,ECC,MD5,and ESSD,are applied to Low Energy Adaptive Clustering Hierarchy(LEACH)and Threshold-sensitive Energy Efficient Sensor Network Protocol(TEEN)hierarchical routing algorithms which are considered the most widely used in WSNs.The results of security methods under the LEACH protocol show that all nodes are dead at 456,496,and 496,respectively,to ECC,MD5,and ESSD.The results of security methods under the TEEN protocol show that the test ends at 3743,4815,and 4889,respectively,to ECC,MD5,and ESSD.Based on these results,the ESSD outperforms better in terms of increased security and less power consumption.In addition,it is advantageous when applied to TEEN protocol.

Test Generation and Design-for-Testability Based on Acyclic Structure with Hold Registers

We present a method of test generation for acyclic sequential circuits with hold registers. A complete (100% fault efficiency) test sequence for an acyclic sequential circuit can be obtained by applying a combinational test generator to all the maximal time-expansion models (TEMs) of the circuit. We propose a class of acyclic sequential circuits for which the number of maximal TEMs is one, i.e, the maximum TEM exists. For a circuit in the class, test generation can be performed by using only the maximum TEM. The proposed class of sequential circuits with the maximum TEM properly includes several known classes of acyclic sequential circuits such as balanced structures and acyclic sequential circuits without hold registers for which test generation can be also performed by using a combinational test generator. Therefore, in general, the hardware overhead for partial scan based on the proposed structure is smaller than that based on balanced or acyclic sequential structure without hold registers.

Obtaining Crisp Priorities for Triangular and Trapezoidal Fuzzy Judgments

This paper proposes anoptimal fuzzy-based model for obtaining crisp priorities for Fuzzy-AHP comparison matrices.Crisp judgments cannot be given for real-life situations,as most of these include some level of fuzziness and com-plexity.In these situations,judgments are represented by the set of fuzzy numbers.Most of the fuzzy optimization models derive crisp priorities for judgments repre-sented with Triangular Fuzzy Numbers(TFNs)only.They do not work for other types of Triangular Shaped Fuzzy Numbers(TSFNs)and Trapezoidal Fuzzy Numbers(TrFNs).To overcome this problem,a sum of squared error(SSE)based optimization model is proposed.Unlike some other methods,the proposed model derives crisp weights from all of the above-mentioned fuzzy judgments.A fuzzy number is simulated using the Monte Carlo method.A threshold-based constraint is also applied to minimize the deviation from the initial judgments.Genetic Algorithm(GA)is used to solve the optimization model.We have also conducted casestudiesto show the proposed approach’s advantages over the existingmethods.Results show that the proposed model outperforms other models to minimize SSE and deviation from initial judgments.Thus,the proposed model can be applied in various real time scenarios as it can reduce the SSE value upto 29%compared to the existing studies.

An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder

Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice.Encryption ofmedical images is very important to secure patient information.Encrypting these images consumes a lot of time onedge computing;therefore,theuse of anauto-encoder for compressionbefore encodingwill solve such a problem.In this paper,we use an auto-encoder to compress amedical image before encryption,and an encryption output(vector)is sent out over the network.On the other hand,a decoder was used to reproduce the original image back after the vector was received and decrypted.Two convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder,which is utilized to compress and encrypt the images,and the other assesses the classification accuracy of the image after decryption and decoding.Different hyperparameters of the encoder were tested,followed by the classification of the image to verify that no critical information was lost,to test the encryption and encoding resolution.In this approach,sixteen hyperparameter permutations are utilized,but this research discusses three main cases in detail.The first case shows that the combination of Mean Square Logarithmic Error(MSLE),ADAgrad,two layers for the auto-encoder,and ReLU had the best auto-encoder results with a Mean Absolute Error(MAE)=0.221 after 50 epochs and 75%classification with the best result for the classification algorithm.The second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error(MSE),RMSprop,three layers for the auto-encoder,and ReLU,which had the best classification accuracy of 65%,the auto-encoder gives MAE=0.31 after 50 epochs.The third case is the worst,which is the combination of the hinge,RMSprop,three layers for the auto-encoder,and ReLU,providing accuracy of 20%and MAE=0.485.

Scanning Based Induction Heating for AISI 4340 Steel Spline Shafts-3D Simulation and Experimental Validation

This paper presents an investigation of non-stationary induction heating process applied to AISI 4340 steel spline shafts based on 3D simulation and experimental validation. The study is based on the knowledge, concerning the form of correlations between various induction heating parameters and the final hardness profile, developed in the case of stationary induction heating. The proposed approach focuses on analyzing the effects of variation of frequency, power and especially scanning speed through an extensive 3D finite element method simulation, comprehensive sensitivity study and structured experimental efforts. Based on coupled electromagnetic and thermal fields analysis, the developed 3D model is used to estimate the temperature distribution and the hardness profile. Experimentations conducted on a commercial dual-frequency induction machine for AISI 4340 steel splines confirm the feasibility and the validity of the proposed modelling procedure. The 3D model validation reveals a great concordance between simulated and measured results, confirms that the model can effectively be used as framework for understanding the process and for assessing the effects of various parameters on the hardening process quality and performance and consequently leads to the most relevant variables to use in an eventual hardness profile prediction model.

DDGrid： a grid computing system for drug discovery and design

This paper presents DDGrid, a novel grid computing system for drug discovery and design. By utilizing the idle resources donated by the clusters that scatter over the Intemet, DDGrid can implement efficient data-intensive biologic applications. P2P high-level resource management framework with a GridP2P hybrid architecture is described. With P2P technologies, some problems which are inevitable in the master-slave model can be avoided, such as single point of failure or performance bottleneck. Then an agent-based resource scheduling algorithm is presented. With this scheduling algorithm, the idle computational resources are dynamically scheduled according to the real-time working load on each execution node. Thus DDGrid can hold an excellent load balance state. Furthermore, the framework is introduced into the practical protein molecules docking applications. Solid experimental results show the load balance and robustness of the proposed system, which can greatly speed up the process of protein molecules docking.

Authentication and Secret Message Transmission Technique Using Discrete Fourier Transformation

In this paper a novel technique, Authentication and Secret Message Transmission using Discrete Fourier Transformation (ASMTDFT) has been proposed to authenticate an image and also some secret message or image can be transmitted over the network. Instead of direct embedding a message or image within the source image, choosing a window of size 2 x 2 of the source image in sliding window manner and then con-vert it from spatial domain to frequency domain using Discrete Fourier Transform (DFT). The bits of the authenticating message or image are then embedded at LSB within the real part of the transformed image. Inverse DFT is performed for the transformation from frequency domain to spatial domain as final step of encoding. Decoding is done through the reverse procedure. The experimental results have been discussed and compared with the existing steganography algorithm S-Tools. Histogram analysis and Chi-Square test of source image with embedded image shows the better results in comparison with the S-Tools.

A Predictive Modeling Based on Regression and Artificial Neural Network Analysis of Laser Transformation Hardening for Cylindrical Steel Workpieces

Laser surface hardening is a very promising hardening process for ferrous alloys where transformations occur during cooling after laser heating in the solid state. The characteristics of the hardened surface depend on the physicochemical properties of the material as well as the heating system parameters. To exploit the benefits presented by the laser hardening process, it is necessary to develop an integrated strategy to control the process parameters in order to produce desired hardened surface attributes without being forced to use the traditional and fastidious trial and error procedures. This study presents a comprehensive modelling approach for predicting the hardened surface physical and geometrical attributes. The laser surface transformation hardening of cylindrical AISI 4340 steel workpieces is modeled using the conventional regression equation method as well as artificial neural network method. The process parameters included in the study are laser power, beam scanning speed, and the workpiece rotational speed. The upper and the lower limits for each parameter are chosen considering the start of the transformation hardening and the maximum hardened zone without surface melting. The resulting models are able to predict the depths representing the maximum hardness zone, the hardness drop zone, and the overheated zone without martensite transformation. Because of its ability to model highly nonlinear problems, the ANN based model presents the best modelling results and can predict the hardness profile with good accuracy.

Framework Development Using Data Mining Techniques to Predict Mortality Risk during Pandemic

The corona virus, which causes the respiratory infection Covid-19, was first detected in late 2019. It then spread quickly across the globe in the first months of 2020, reaching more than 15 million confirmed cases by the second half of July. This global impact of the novel coronavirus (COVID-19) requires accurate forecasting about the spread of confirmed cases as well as continuation of analysis of the number of deaths and recoveries. Forecasting requires a huge amount of data. At the same time, forecasts are highly influenced by the reliability of the data, vested interests, and what variables are being predicted. Again, human behavior plays an important role in efficiently controling the spread of novel coronavirus. This paper introduces a sustainable approach for predicting the mortality risk during the pandemic to help medical decision making and raise public health awareness. This paper describes the range of symptoms for corona virus suffered patients and the ways of predicting patient mortality rate based on their symptoms.