mohammad reza mosavi

Since some GPS signals are unencrypted and available to the public, they are subject to some interference that can cause errors or deception in GPS signals. We discuss the effect of Time Synchronization Attack (TSA) on the clock offset of commercial GPS receivers with crystal and atomic oscillators. Then, a new innovative algorithm based on Linear Regression Prediction (LRP) is proposed. With this algorithm, we can predict the clock offset and compare it with the clock offset estimated by the GPS receiver. In this case, if a spoofing attack occurs, we can detect it and then correct the clock offset. The efficiency of the proposed algorithm has been compared with other methods such as predictive Multi-Layer Perceptron Neural Network (MLP NN). A proper and deep understanding of the clock offset behavior is the key to providing a simpler algorithm with greater efficiency and accuracy than earlier methods. Due to its simplicity and the need for less training data, the proposed algorithm has a 3-4 times higher learning speed than the other predictive methods. Less computational complexity, more prediction horizon, and high prediction accuracy are other features of the proposed LPR algorithm.

Global Positioning System (GPS)-based positioning has become an indispensable part of our daily lives. A GPS receiver calculates its distance from a satellite by measuring the signal reception delay. Then, after determining its position relative to at least four satellites, the receiver obtains its precise location in three dimensions. There is a fundamental flaw in this positioning system, namely that satellite signals at ground level are very weak and susceptible to interference in the bandwidth; therefore, even a slight interference can disrupt the GPS receiver. In this paper, spoofing detection based on the Cross Ambiguity Function (CAF) is used. Furthermore, a dimension reduction algorithm is proposed to improve the speed and performance of the detection process. The reduced-dimensional images are trained by a Convolutional Neural Network (CNN). Additionally, a modified CNN model as Transformed-CNN (TCNN) is presented to enhance accuracy in this paper. The simulation results show a 98.67% improvement in network training speed compared to images with original dimensions, a 1.16% improvement in detection accuracy compared to the baseline model with reduced dimensions, and a 9.83% improvement compared to the original dimensions in detecting spoofing, demonstrating the effectiveness of the proposed algorithm and model.

 Awareness of patient rights and respecting them are both an important and a fundamental duty of healthcare staff. This study aimed to assess nurses' awareness of the patient bill of rights and their adherence to it from the perspective of hospitalized patients.

 This cross-sectional descriptive study was conducted in 2022 on 168 nurses and 383 patients admitted to Qa’em Hospital, in the city of Asadabad, Hamadan. The participants were selected using convenience sampling. The questionnaires "The Awareness of the Patient Bill of Rights" by Ruintan et al and "The Level of Compliance with Patient Bill of Rights" by Basiri Moghadam were used in this study. Data analysis was done using SPSS 18, considering a significance level of less than 0.05.

Ethical Considerations: 

In all the stages of this study, ethical principles and data confidentiality were observed and the questionnaires were completed after obtaining informed consent and explaining the research objectives to the participants.

A positive but non-significant correlation was observed between the nurses' awareness of the patient bill of rights and the level of compliance with it from the patients' perspective at a significance level of 0.05 (r=0.04, p=0.61). The mean and standard deviation of nurses' awareness of the patient bill of rights were 9.44±4.34, indicating an average level. The mean and standard deviation of compliance with the patient bill of rights in the hospital from patients' perspective were 32.90±7.73, which was considered optimal. The mean score of nurses' awareness of the patient bill of rights was higher in nurses aged 31-40 years, and this difference was statistically significant (p=0.01). There was a significant difference in the mean score of compliance with the patient bill of rights in the hospital from the perspective of patients across different age groups; the highest mean score was reported by patients over 40 years of age and the lowest, by patients aged 20-30 years (p=0.005).

 The results of the study showed a correlation between nurses' awareness of the patient bill of rights and the level of compliance with it from patients' perspective. It is necessary for the healthcare planners and policymakers to consider interventions in developing and implementing in-service training programs, providing incentives to motivate employees, and establishing strict rules to improve compliance with the patient bill of rights in hospitals. To examine the impact of factors, other than awareness, on the level of compliance with the patient bill of rights in hospitals, it is recommended that interventional and qualitative studies be conducted in the futur.

The spoofing subject is becoming ever increasingly more severe. In addition to the flow of technology, the availability of software-defined radio platforms has increased. Usually, detecting the spoofing is performed by introducing the features that are difficult for the deceiver to counterfeit. Spoofing and countering can be performed in different parts of a GPS receiver. In recent years, less attention has been paid to defense at cold-start. This research presents that the spoofing attack can be diminished during the initial start-up process with a very short effective time. This low-cost method introduces a new decision rule based on a multiple statistical hypothesis test to identify fake peaks in correlation output of acquisition and extract the authentic peaks utilizing the wavelet transform or peak removal process. The main distinction of this method with previous works is investigating different amplitude ratios of spoofing signal to authentic. Simulation results on 10 data sets show that the probability of correct detection and mitigation is more than 90%.

A common technique for navigation and positioning applications is the Global Positioning System (GPS)/Inertial Navigation System (INS) integration, which combines the strengths of GPS and INS to offer accurate and reliable information. As a standalone system, the performance of the INS deteriorates as time is passed. Kalman Filter (KF) is used for GPS/INS integration, and its performance is excellent for simple data. However, in a complex and natural set environment, its performance degrades when the system performs relatively long; therefore, resolving the long-time problem for the GPS/INS system is challenging. The novelty of this paper is GPS/INS integration with the Faded Kalman Filter (FKF). In the FKF, the measurement updates are weighted differently to adapt to changes in the system. This approach allows the filter to adapt to changes or uncertainties in the system dynamics. GPS/INS integration performance is significantly improved using this algorithm rather than a simple KF. An average of 45% reduces the positioning errors compared to traditional KF.

In the spoofing attack, the attacker sends his fake request messages to the destination server computer and pretends that the requests were sent from a source that is a node in the network with a valid and reliable IP address. In this article, an intelligent method is introduced to detect the presence of several fake users. Evolutionary processing has also been used to improve the accuracy and precision of detecting the number of attackers. The artificial neural network reveals the spoofing attack by detecting the change in the signal pattern of the IP packets received from a node in the access point. The metaheuristic algorithm of imperial competition also determines the number of attackers using clustering. By extracting the features of signal strength, signal phase, and received signal energy, this algorithm can cluster the signal pattern of users with a specific IP address. In the worst case, it correctly detects attacks with a probability of 98. As a result, if the number of clusters is more than the number of active IP addresses of the wireless network, this number of excess clusters shows the number of attackers. The simulation results of the proposed algorithm show that it has 99% precision and 98% accuracy in detecting attacks.

Today, positioning has attracted special attention as one of the fundamental aspects of various systems. Over time, the navigation system's error, especially in the Inertial Navigation System (INS), has significantly increased. This error increase can lead to serious issues in navigation processes, especially on long routes. To ensure the high accuracy and stability of navigation on long routes, the use of advanced and effective auxiliary systems is necessary. The goal of this article is to present an innovative method for positioning by combining camera and inertial sensor data to enhance accuracy in this vital process. This method is particularly important in self-driving vehicles, domestic robots, and search and rescue robots operating in enclosed environments or areas without active GPS coverage. Consequently, there is a crucial need for a real-time positioning system for these systems. In this article, we have implemented a real-time Visual-Inertial Odometry (VIO) navigation system on a PC platform, which performs significantly better than the INS system alone. In this system, cameras are used to correct the accumulated INS error, and the fusion of cameras and inertial data is done using a graph. To validate and evaluate the system, practical data collected by the system are used, demonstrating that the proposed VIO system exhibits more than a 90% improvement compared to INS.

Each software Global Positioning System (GPS) receiver consists of three sections called acquisition, tracking, and navigation. The acquisition section is known as the first part of the receiver that has some serious problems to increase the speed of acquiring the GPS signals. In some cases, the Doppler Effect (DE) that causes the relative motion between satellite and receiver leads to transforming the carrier frequency of the GPS receiver. In this study, we propose new method to achieve fast acquisition in GPS receivers. In our presented method, the Doppler frequency band has a deviation of ; so that it has been considered in the worst case. In the proposed method, we limit the frequency band by using a smaller filter and then completely cover the space of by shifting the filter. In each shift, the satellites in Line of Sight (LOS) are detected and separated from the collection list of satellites. In this method, the best filter that reduces the acquisition time is selected. To validate proposed method, we utilize five real datasets that two of them were collected from around Iran University of Science and Technology. The experimental results represent that our suggested method outperforms the conventional band-fixed method in terms of the execution time and computational complexity by approximately 22% and 19%, respectively.

Passive Millimeter Wave (PMMW) imaging makes it possible to acquire images of objects that are concealed underneath clothing by measuring the radiometric temperatures of different objects on a human subject, that provides a great advantage for security systems in special places like airports. Due to the inherent physical properties of PMMW imaging and the associated hardware, images have poor contrast and low signal to noise ratio. The fundamental operation underlying the sensing operation is the low-pass filtering effect due to the finite size of the antenna lens. The image at the output of the imaging system is a low-pass filtered and blurred version of the original scene. In order to improve the resolution and the fusion quality of the image, some methods of image restoration, super-resolution and fusion will be needed. In the process of restoration, the point spread function is used for the inverse restoration of the original image. Image fusion is the technique of quality improvement that intelligently integrates and processes the multi-source images from various sensors and obtains more accurate, complete and dependable descriptions about the same scene compared with than any of the individual source images. In this paper, various methods for image restoration, super-resolution and fusion are reviewed. Then, appropriate methods in both contexts are selected and modified for the special considered usage. Also, an appropriate generic procedure is designed for PMMW quality improvement in concealed object detection applications. By implementing this procedure, noticeable visual improvement is found in the output image quality.

Accurate timing is one of the key features of the Global Positioning System (GPS), which is employed in many critical infrastructures. Any imprecise time measurement in GPS-based structures, such as smart power grids, and Phasor Measurement Units (PMUs), can lead to disastrous results. The vulnerability of the stationary GPS receivers to the Time Synchronization Attacks (TSAs) jeopardizes the GPS timing precision and trust level. In this paper, the PMU receiver clock deviation monitoring method is used. In this method, a deception and fraud reduction algorithm is presented based on clock deviation observations. A multi-layer perceptron neural network is trained to track clock behavior information behavior and maintain a valid trend under time-synchronization attack conditions that can dramatically mimic clock deviation. Finally, the results were compared with a strong and low-memory RE estimator, which is one of the most recent methods of counteracting TSA, as well as an extended Kalman filter and a Luenberger observer. This indicates the good performance of the proposed method.

In this paper, a new histogram-based method is introduced to make object detectors resistant to hostile attacks. In the following, this method was applied to two object detector models, YOLOV5 and FRCNN, and in this way, two models resistant to attacks were introduced. In order to verify the performance of the mentioned models, we performed the adversarial training process of these models with three targeted attacks TOG-vanishing, TOG-mislabeling, and TOG-fabrication and one untargeted attack, DAG. We have checked the efficiency of the introduced models on two data sets MSCOCO and PASCAL VOC, which are among the most famous data sets in the field of object recognition. The results show that this method, in addition to improving the adversarial accuracy, also improves the clean accuracy of the object detector models to some extent. The average clean accuracy of the YOLOv5-n model for the PASCAL VOC dataset, if adversarial attacks are applied to it, in the case where no defense method is applied, is 85.5%, and in the case where the histogram method is applied, the average accuracy is equal to with 87.36%. In the YOLOv5-n model, according to the results, the best adversarial accuracy of this model, which has increased compared to other models, is in TOG-vanishing and TOG-fabrication attacks, which are 48% and 52.36%, respectively.

The spoofing attack is one of the most serious interferences in the Global Positioning System (GPS). By propagating a signal structurally similar to the original GPS signal, the spoofers try to influence the function of different parts of the receiver and force it to make a wrong positioning. This study focus on the acquisition stage. During the acquisition process, GPS receivers estimate the values of Doppler frequency and Pseudo Random Noise (PRN) code phase of the received signal, which are necessary for tracking the GPS satellite signals. One of the effects of the spoofing signal in the acquisition unit of the receiver is to increase the interactions in the Quadrate correlation taps (Q-correlation tap). In 2018, adding a denoising unit on the Q-correlation tap in the acquisition stage to reduce the interactions mentioned above was presented as a spoofing mitigation method. In this paper, the mentioned method is placed as the primary basis of the work. Here, by using powerful methods of evolutionary computing, the denoising unit added in the Q-correlation tap is tried to be optimally adjusted to mitigate the spoofing attack. Specifically, to achieve a more efficient denoising method for spoofing mitigation, the Particle Swarm Optimization (PSO) algorithm is proposed to determine the critical parameters of the Discrete Wavelet Transform (DWT) based on the Haar wavelet. In order to evaluate the proposed method, first, the noise reduction performance of the algorithm is measured on four benchmark signals, namely Blocks, Bumps, Heavy Sine, and Doppler. Then, compared to four traditional methods, namely, Rigrsure, Heursure, Sqtwolog, and Minimaxi, the developed de-nosing method outperformed the former methods by 47.3%, 38.4%, 47,3%, and 30%, respectively. Finally, the proposed algorithm was placed in the Q-correlation tap of the GPS receiver acquisition stage, and its performance in reducing the spoof effects was investigated. The results show that the proposed algorithm is 37.74% more efficient compared to the method that was considered the primary method.

In this paper, to increase the navigation accuracy in the integrated Global Positioning System and Inertial Navigation System, a new direct filtering approach called Interacting Multiple Model-Refined Strong Tracking Extended Kalman Filter has been developed. In the proposed method, while using inertial navigation equations and tracking equations in order to improve the accuracy of position and velocity, to increase the accuracy of orientation, attitude estimation methods based on the gyroscope, accelerometer, and global positioning system have been used. In addition, in order to enhance the Extended Kalman Filter robustness against modeling error, the Refined Strong Tracking method has been used. The aircraft then verified the proposed method using data collected in a real field experiment. The results of the proposed method were compared with the results of the conventional indirect filtering method Kalman Filter, direct filtering Unscented Kalman Filter, and Interacting Multiple Model - Extended Kalman Filter. The results show the more accurate performance of the proposed method compared to the previous three methods in the Global Positioning System and Inertial Navigation System integration.

Inertial Navigation System (INS) is one of the navigation systems widely used in various land-based, aerial, and marine applications. Among all types of INS, Microelectromechanical System (MEMS)-based INS can be widely utilized, owing to their low cost, lightweight, and small size. However, due to the manufacturing technology, MEMS-based INS suffers from deterministic and stochastic errors, which increase positioning errors over time. In this paper, a new effective noise reduction method is proposed that can provide more accurate outputs of MEMS-based inertial sensors. The intelligent method in this paper is a combined denoising method that combines Wavelet Transform (WT), Permutation Entropy (PE), Support Vector Regression (SVR), and Genetic Algorithm (GA). Firstly, WT is employed to obtain a time-frequency representation of raw data. Secondly, a four-element feature vector is formed. These four features are (1) amplitude of frequency, (2) its ratio to mean of amplitudes of all frequencies, (3) location of frequency in time-frequency representation, and (4) judgment on behaviors of frequency that is obtained by utilizing PE. Thirdly, based on the feature vector, the GA-SVR algorithm predicts amplitudes of all frequencies in the time-frequency representation of the denoised signal. Finally, by employing inverse WT the denoised signal is obtained. In this work, the outputs of the Inertial Measurement Unit (IMU) in ADIS16407 sensor, as a low-cost and MEMS-based INS, have been utilized for data collection. The proposed method has been compared with other noise reduction methods and the achieved results verify superior improvement than other methods.

Automated Guided Vehicles (AGVs) have been widely used in industrial factories and seaport due to their high efficiency and ability to move in recent years. Nevertheless, controlling AGVs to keep moving in the predefined (correct) direction is one of their most important challenges. In this paper, an automated guided control system is designed using the direct current of servomotor. In order to guide the automatic control robot in the predefined and rotational paths, the Proportional-Integral-Derivative (PID) control system with proportional, integral, and derivative parts is used. Adaptive adjustment of PID controller coefficients is difficult due to the nature of the used control system location. Hence in this study, an optimally set up of the coefficients of PID controller is used in control system of AGV by applying adaptive Radial Basis Function Neural Network (RBFNN). The simulation results indicate that the proposed controlling scheme can optimally increase the accuracy rather than traditional PID-based method. In addition, the proposed scheme is practically implemented as a prototype robot leading to provide an experimental validation to be used as an applicable solution in AGV control systems due to the low cost of design and optimal performance.

The navigation system is one of the primary and most essential parts of a positioning system in autonomous vehicles. Inertial navigation systems (INS) based on inertial sensors are among the most common navigation systems, which have received much attention due to their low cost. However, what limits the use of this navigation system is the presence of unavoidable errors caused by inertial sensors, which grow over time. Therefore, to reduce and eliminate this error, this article proposes a suitable integrated INS/image navigation aid system for utilization in a vehicle.</strong>In this article, without the intervention of the global positioning system (GPS), the proposed system gets the map of the places our car has passed through.</strong> This map can be used to monitor the places the driver travels in order to make the traffic police systems intelligent. The vehicle is equipped with a camera and inertial sensors to take pictures of the environment. This study uses the data from the inertial sensors to increase the output data's accuracy. The photos from the camera are processed by a convolutional neural network (CNN) and give us a series of geometric features. These features are in the form of a tensor of numbers that, together with the IMU data, form the inputs of the </strong>long short-term memory</strong></em> (LSTM) netwo</strong>rk. Finally, this network gives us the vehicle's location at any moment while outperforming ORB-SLAM, CL-VO, DeepVO, and VISO2_M and is 36% more accurate than DeepVO.</strong>

With the expansion of the use of Internet of Things (IoTs) devices and the connection of all devices to the Internet, the need to provide security in this area has been felt, which the reason Physical Unclonable Functions (PUFs) were introduced. PUF is a lightweight and popular hardware security primitives for use in IoT devices. By scrutiny the benefit and weakness of PUF types, it was found that RO-PUF has a more suitable structure for implementation on FPGA because it has high reliability and it is easier to provide circuital symmetry. Therefore, it was selected for further studies. But on the other hand, RO-PUF have small CRP space and not attractive for authentication applications. By focusing on RO-PUF and reviewing the proposed structures introduced to improve RO-PUF and modify the CRO-PUF structure, a structure is created that exponentially increases CRP space and also reduces power consumption. The average reliability of the responses is 98.33% and the uniqueness of the response bits is 47.54%. Also, the average uniformity of response bits is 50.57%. The implementation results show that the proposed design is superior to other structures.

Due to the increasing use of Inertial Navigation Systems (INSs) in various fields, increasing the accuracy and efficiency of this system is significant. INS is a standalone system that works in any weather conditions and can provide continuous navigation information of position, velocity, and attitude without affecting the environment. However, due to errors in gyroscope and accelerometer sensors, the accuracy of INS sensor performance decreases over time. Therefore, the main problem in using INS for navigation systems is the unlimited errors that occur over time. In other words, over time, its performance suffers from errors that are increasing rapidly. Therefore, to achieve the desired outputs, noise removal must be performed. In this paper, according to the types of sensors, the best noise removal method has been presented. The wavelet noise removal based on the lifting scheme is utilized for de-noising. The lifting method is real-time and does not require blocking the received data. Also, we have leveraged some multi-level liftings. Further, we propose an optimization method based on the genetic algorithm to increase accuracy by combining the results of multi-level liftings. For evaluation, two types of dynamic and static data have been used. The achieved results have clarified the effectiveness of our proposed method. Therefore, a suitable method for good, reliable, and fast noise removal is presented.

Millimeter waves have unique properties such as the ability to penetrate the fibers of clothing that lead to the detection of hidden objects under clothing. For this reason, the millimeter wave imaging system is used in the public security departments of important centers, especially airports. Under security conditions and in order to increase intelligence for monitoring criminals, detecting hidden objects under clothing with high accuracy and speed will increase police authority. By introducing methods for detecting objects based on deep convolutional neural networks, these methods can be used to meet the needs of high accuracy and speed. In this paper, the YOLOv3 algorithm is used as the base of object detection algorithm to detect targets due to its high accuracy and speed. In order to increasing the mean average precision of the YOLOv3 algorithm and also increasing the accuracy of small object detection, we add the spatial pyramid pooling module in the network structure of the YOLOv3 algorithm and also use the suitable anchor boxes with the purposes within the dataset. The smaller structure of YOLOv3 is also used to reduce computational complexity as well as detection time. Finally, the efficiency of the proposed method is compared with the original YOLOv3 algorithm. The results show that the proposed method, in addition to increasing the mean average precision by 4.04%, also consumes a detection time of 14 milliseconds.

Due to the complex physical properties of the detected targets using sonar systems, identification and classification of the actual targets is among the most difficult and complex issues of this field. Considering the characteristics of the detected targets and unique capabilities of the intelligent methods in classification of their dataset, these methods seem to be the proper choice for the task. In recent years, neural networks and support vector machines are widely used in this field. Linear methods cannot be applied on sonar datasets because of the existence of higher dimensions in input area, therefore, this paper aims to classify such datasets by a method called Online Multi Kernel Classification (OMKC). This method uses a pool of predetermined kernels in which the selected kernels through a defined algorithm are combined with predetermined weights which are also updated simultaneously using another algorithm. Since the sonar data is associated with higher dimensions and network complexity, this method has presented maximum classification accuracy of 97.05 percent. By reducing the size of input data using genetic algorithm (feature selection) and statistical moments (feature extraction), eliminating the existing redundancy is crucial; so that the classification accuracy of the algorithm is increased on average by 2% and execution time of the algorithm is declined by 0.1014 second at best.

This paper presents an accurate Differential Global Positioning System (DGPS) using multi-layered Neural Networks (NNs) based on the Back Propagation (BP) and Imperialistic Competition Algorithm (ICA) in order to predict the DGPS corrections for accurate positioning. Simulation results allowed us to optimize the NN performance in term of residual mean square error. We compare results obtained by the NN technique with BP and ICA. Results show a good improvement obtained by the application of the NN trained by the ICA. The experimental results on measurement data demonstrate that the prediction total RMS error using NN trained by the ICA learning algorithm are 0.8273 and 0.7143 m, before and after selective availability, respectively.

Global Positioning System (GPS) is proven to be an accurate positioning sensor. However, there are several sources of errors such as ionosphere and troposphere effects, satellite time errors, errors of orbit data, receiver''s errors, and errors resulting from multi-path effect which reduce the accuracy of low-cost GPS receivers. These sources of errors also limit the use of single-frequency GPS receivers due to their less accurate data. Therefore, it''s important to reduce the effect of errors on GPS systems. In order to cope with these errors and enhance GPS system''s accuracy, Differential GPS (DGPS) method can be used. The problem with this method is slow updating process of differential corrections. In this paper, three algorithms based on Kalman Filtering (KF) are proposed to predict real-time corrections of DGPS systems. The efficiency of proposed algorithms is verified based on the collected of actual data. The experimental results carried out in field tests assure the high potential of these methods to get accurate positioning data. The results show that KF with variable transition matrix is better than other methods; so that it''s possible to reduce the Root Mean Square (RMS) of positioning errors in low-cost GPS receivers to less than one meter.