Physical Unclonable Functions (PUFs) are hardware-based mechanisms that exploit inherent manufacturing variations to generate unique identifiers for devices. Dynamic Random Access Memory (DRAM) has emerged as a promising medium for implementing PUFs, providing a cost-effective solution without the need for additional circuitry. This makes DRAM PUFs ideal for use in resource-constrained environments such as Internet of Things (IoT) networks. However, current DRAM PUF implementations often either disrupt host system functions or produce unreliable responses due to environmental sensitivity. In this paper, we present EPUF, a novel approach to extracting random and unique features from DRAM cells to generate reliable PUF responses. We leverage bitmap images of binary DRAM values and their entropy features to enhance the robustness of our PUF. Through extensive real-world experiments, we demonstrate that EPUF is approximately 1.7 times faster than existing solutions, achieves 100% reliability, produces features with 47.79% uniqueness, and supports a substantial set of Challenge-Response Pairs (CRPs). These capabilities make EPUF a powerful tool for DRAM PUF-based authentication. Based on EPUF, we then propose a lightweight authentication protocol that not only offers superior security features but also surpasses state-of-the-art authentication schemes in terms of communication overhead and computational efficiency.

Abstract In 2018, Abbasinezhad‐Mood and Nikooghadam (IEEE Transaction on Smart Grid, pp 6194–6205, 9(6), 2018) proposed an ultra‐lightweight secure scheme for neighbourhood area network () communications in smart grid. They have claimed that their protocol is secure against all known attacks in environment by providing informal security analysis besides a formal analysis which was done by using an automatic verification tool. However, by performing several attacks, this study shows that their scheme has serious security flaws. After performing each attack, lightweight countermeasures is proposed for securing their protocol against that attack.

Abstract It is well known that in high‐speed movements, the positioning accuracy of global positioning system (GPS) receivers decreases drastically. The models presented so far to describe high‐speed motion do not represent the state of the system precisely, so positioning accuracy with the methods based on these models is not appropriate. Here, a comprehensive method is proposed to solve the accuracy issue of the single‐frequency GPS receiver at high‐speed motions without increasing the computational complexity. Suitable modelling of the GPS receivers at high‐speed motion, using the sequential extended Kalman filter, correct determination of the process noise covariance matrix and accurately estimating the variance of the observations are the basics of the proposed approach. Simulations with different data and motion scenarios (at speeds from 100 to 7300 m/s) show that the proposed method, while not increasing the computational cost, improves the accuracy of positioning more than 70% when compared to the conventional methods.

The extensive use of Internet of things (IoT) devices demonstrates an increasing demand for secure frameworks. Position altering attacks are amongst the most severe vulnerabilities in two‐way time of arrival distance measurement technique, which is the most common in distance measurements by IoT devices. The overshadowing attack as a distance enlargement attack replays the authentic signal with a certain amount of delay and sensible power amplification. Therefore, the attack detection is difficult and requires a precise model to estimate its behaviour. The contribution of this study is a location estimation method based on the overshadowing attack probabilistic model that estimates the attack outcome delay and corrects the erroneous measurements. The overshadow delay, which is the delay between the authentic signal and the attacked one, plays a key role in this process. The method has been evaluated by three attack scenarios and compared to the conventional maximum likelihood (CML) and mixture secure location of things (M‐SLOT) methods. The results of the proposed method demonstrate root mean squared error (RMSE) of 0.48 in position estimation. With the same dataset, RMSEs of M‐SLOT and CML methods are 4.69 and 9.84, respectively, which confirm the better performance of the proposed method.

Antenna arrays have been widely used for space and space–time processing to estimate the desired signals in the presence of narrowband and wideband interferences. Estimating the optimal weight vector is a challenging problem in space and space–time processing due to its high computational complexity. The problem escalates when the number of antennas and taps of filters for time‐domain processing increases. In this study, a convolutional neural network is employed to estimate nearly‐optimal weight vectors even when the number of available snapshots of the received signal is as low as 400 and a signal‐to‐noise ratio as low as −5 dB. Unlike the conventional processors, the authors proposed method requires no prior knowledge about the direction of arrival of the desired signal. In addition, due to its parallel architecture, the computational complexity of the neural processors is reasonable using a single graphical processing unit on a PC to run the algorithms. Through simulations of the uniform linear array in the presence of narrowband and wideband interferences, they demonstrate that the output signal‐to‐interference plus noise ratio (SINR) is very close (by <0.5 dB) to the max SINR obtained by the optimal weight vector of antenna arrays.

Automatic detection and tracking of a small target in infrared (IR) images are of great importance. Toggle operator (TO) is the newest class of non‐linear operator morphology that has been widely used in detection and tracking the target in IR images. The most important problem in improving the efficiency of the TO is to use structural elements (SEs) in accordance with signal‐to‐clutter ratio (SCR) of each image. Generally, the clutters and targets are different in case of each image; therefore, for images with different SCRs, using SEs with fixed pixels and dimensions cannot lead to successful target detection. In this study, a new method is presented based on genetic algorithm to achieve adaptive SE for target detection in IR images. In this method, by designing the SE in accordance with the characteristics of each image, a large amount of background clutter and noise is suppressed and the contrast between target and background is increased. The results of a large set of real IR images including moving targets show that the proposed algorithm is effective in target detection. In the proposed method, the contrast between the target and background clutter is greatly increased while maintaining a low false alarm rate.

In this study, a new hybrid anti‐jamming system is proposed for kinematic global positioning system receivers. The proposed system employs a short‐time Fourier transform (STFT)‐based pre‐correlation block to guarantee that the receiver can acquire at least four satellites in jamming environments. It also employs a discrete wavelet transform‐based denoising block in the navigation unit of the receiver to increase positioning accuracy, which was degraded due to the jamming and also due to the movement of the receiver. Simulation results demonstrate that the proposed system has a better anti‐jamming performance compared with previous methods. They show that the average positioning accuracies of the proposed system are 47, 45, and 43% better than the standard STFT‐based mitigation method, wavelet‐packets transform (WPT)‐assisted filter, and WPT‐based hybrid system, respectively.

Given high uncertainty in medicine, it is essential to use some approaches to deal with this uncertainty. Therefore, this article utilizes computing with words (CWW) in fuzzy cognitive maps (FCMs) for the application of a new medical decision support system. In this framework, expressed as CWW FCM, all concepts and the weights of connecting links between them are described based on interval type-2 membership functions (IT2 MFs) expressed as a set of words. This type of FCM structure shows high performance by taking into account uncertainties in experts' opinions and in system parameters in the process of modeling. In this paper, we utilize CWW FCM to classify celiac disease (CD), a chronic disorder. Thus, to illustrate the behavior and performance of the proposed model in classifying CD, we have collected a real dataset from patients with different types of CD. Applying the model on this dataset, acceptable accuracy in classification is achieved.

Image fusion is a process to enhance the human perception of different images from the same scene. Nowadays, two popular methods in the signal/image fusion, namely, multi‐scale transform (MST) and sparse representation (SR) are being used. This study uses an image energy approach to enhance a fusion rule based on the combination of MST and SR methods. Each source image is first decomposed to its sub‐bands using the selected MST method. Then, SR is applied to the low‐pass band and maximum absolute (max‐abs) rule merges the high‐pass bands. The activity level of the sparse coefficients is measured based on the energy differences of the source images. When the gap energy is high enough, a coefficient with maximum L 2 ‐norm is selected; otherwise, maximum L 1 ‐norm is considered. Finally, by applying inverse MST to the attained bands, the fused image is reconstructed. The popular MSTs, such as discrete wavelet transform, dual‐tree complex wavelet transform and non‐sub‐sampled contourlet are used. The experiments are carried out on several standard and real‐life images. The measurement results confirm that the proposed method has enhanced the contrast, clarity and visual information of the fused results.

Detection of small targets in an infrared (IR) image with high reliability is very important for defence systems. Small targets in an IR image are defined as salient features which attract the attention of human visual system. In this study, a robust method for detection of small targets in an IR image is proposed based on HV attention. In this method, first, the Gaussian‐like feature maps are extracted from the original image. Then, saliency maps (SMs) are created based on pulsed discrete cosine transform, in which the target is salient and background clutter is suppressed. Finally, to increase the contrast between target and background clutter and to raise robustness of this method against false alarms, SMs are fused adaptively. Experiments are carried out on the data set including real‐life IR images with small targets as well as various and complicated backgrounds. Qualitative and quantitative assessments show that the proposed method can detect small targets in IR image with high reliability and is more effective compared with other methods based on HV attention. Therefore, it can be used in many applications for detection of small targets in IR image with minimum false alarms.

Multipath (MP) interference in urban canyons is one of the major sources of the positioning error. Among different methods for MP mitigation, vectorised receiver (VR) is a promising one in which channels can share their information, and as a result the stronger channels can aid the weaker or affected ones to be tracked more accurately. This study proposes a weighted VR (WVR) with the strategy of giving more weights to the observations with lower vulnerability to MP. To increase the number of immune satellites to MP, global positioning system, and global navigation satellite system integration in WVR has been discussed. The performance is also compared with conventional VR. The experimental results show that the proposed method both in a static mode under an exaggerated MP condition and in a drive through an urban canyon could find the position, respectively, with about 10 and 13% improvements.

This study aims to use the image-based visual servoing (IBVS) method for smart control of a flying quadrotor robot. The fuzzy cognitive map (FCM) is a causal diagram which shows the relationship among the main components in the system. The relationships in an FCM model are determined by experts who have knowledge of system components and their governing relations. Since an FCM entails the main advantages of both fuzzy logic and neural networks, it is a suitable choice for designing intelligent vision-based systems. In this article, it is assumed that image characteristics lie within the field of vision of the camera. Maintaining image features within the field of vision of the camera is of particular importance in an underactuated quadrotor robot. The reason is that, to produce translation in the robot, the quadrotor roll and pitch angles must be changed. We solve this problem by limiting the input accordingly. First, certain features were presented via combining the perspective image moments. Subsequently, these features were deployed in the IBVS using FCM servoing. The obtained simulation results showed that, in spite of the existing challenges, the proposed method was implemented successfully to control the quadrotor robot.

Detection of dim small targets in infrared (IR) images with high reliability is very important in defence systems. In this study, a new method is introduced based on human visual system and saliency maps fusion to detect the small target in IR images with high reliability. By using the static and motion saliency maps fusion, emphasizing the obtained saliencies from one method to another and applying the information and benefits of all maps in the saliency map fusion, this method suppresses the background clutter and noise with high reliability, makes the target more prominent and finally increases the contrast among them. The experiments are carried out on some real‐life data of IR images containing the moving target. The obtained results show the efficiency and robustness of the proposed method so that this method improves the target signal to the background noise and clutter and increases the contrast between them and also detects the small target in IR images with low false alarm rates and high reliability.

In this paper, the authors describe a new pre‐correlation mitigation technique for global positioning system (GPS) receivers. Proposed mitigation technique is based on wavelet packet coefficients thresholding. This technique employs adaptive thresholding to mitigate GPS jammers. The authors compare proposed technique against discrete wavelet coefficient thresholding method. In this paper, the interferers are generated by an RF signal generator. The received GPS signal and interference are combined in a combiner. Then, the combined signal is processed by a software GPS receiver which includes the proposed mitigation technique. Simulation results show benefits of the proposed method in terms of signal‐to‐noise power ratio improvement and mean square error (MSE) in contrary to previous methods. Comparison of presented method and the discrete wavelet method illustrates that the average improvement is 50% and 80% in terms of SNR improvement and MSE, respectively.

Global Positioning System (GPS) satellites signal processing to obtain all in view satellite measurements and to use them to find a solution and to do integrity monitoring forms a major component of the load on the receiver's processing element. If processing capability is limited there is restriction on the number of measurements which can be obtained and processed. Alternatively, the number of measurements can be restricted and the resulting saving in load on the processor can be used to offer more spare processing time which can be used for other user specific requirements. Thus if m visible satellites can provide measurements only n measurements can be used (n < m). The arrangement and the number of GPS satellites influence measurement accuracy. Dilution of Precision (DOP) is an index evaluating the arrangement of satellites. Geometric DOP (GDOP) is, in effect, the amplification factor of pseudo-range measurement errors into user errors due to the effect of satellite geometry. The GDOP approximation is an essential feature in determining the performance of a positioning system. In this paper, knowledge-based methods such as neural networks and evolutionary adaptive filters are presented for optimum approximation of GDOP. Without matrix inversion required, the knowledge-based approaches are capable of evaluating all subsets of satellites and hence reduce the computational burden. This would enable the use of a high-integrity navigation solution without the delay required for many matrix inversions. Models validity is verified with test data. The results are highly effective techniques for GDOP approximation.

Ionospheric errors are the most influential source of the GPS positioning errors. In an attempt to at least partially improve the GPS positioning process, a global ionospheric correction model is introduced within the standard GPS positioning service. Referred to as the Klobuchar model after its inventor, this model appeared as a compromise between computation complexity and correction accuracy. Neural Networks (NNs) are ideal tool for the approximation of single-frequency GPS receivers ionospheric time-delay behaviour, which has nature highly non-linear. A major advantage of using NNs for the approximation of single-frequency GPS receivers ionospheric time-delay behaviour over analytical methods is that no previous knowledge of the nature of the non-linear relationships is required. This paper presents the use of NN modeling to approximate single-frequency GPS receivers ionospheric time-delay to reduce GPS signal propagation error. The ionospheric time-delay is approximated utilizing the Radial Basis Function (RBF) NN approach. The NN-based approach reduces the computational burden respect to Klobuchar model. The method employed here is applicable on the L1 GPS receivers.

Systematic errors in Global Positioning System (GPS) were related to the atmosphere, imprecise orbit, satellite distribution geometry, multipath, satellite and receiver clock, and selective availability (SA). Using DGPS corrections prediction can reduce these errors to a certain extent, with the exception of SA. This makes it possible to enhance the accuracy of GPS positioning by a factor of twenty five. This paper presents a recurrent wavelet neural network (RWNN) for improving positioning accuracy. Method validity is verified with experimental data from an actual data collection, before and after SA The results show very clearly the dramatic change in the error value. It also shows that by using proposed method over a long period of time, 0.7 m GPS accuracy can be achieved. Also, it is shown that RWNN outperforms single WNN and RNN.

The Global Positioning System (GPS) is a network of satellites, whose original purpose was to provide accurate navigation, guidance, and time transfer to military users. The past decade has also seen rapid concurrent growth in civilian GPS applications, including farming, mining, surveying, marine, and outdoor recreation. One of the most significant of these civilian applications is commercial aviation. A stand-alone civilian user enjoys an accuracy of 100 meters and 300 nanoseconds, 25 meters and 200 nanoseconds, before and after Selective Availability (SA) was turned off. In some applications, high accuracy is required. In this paper, five Neural Networks (NNs) are proposed for acceptable noise reduction of GPS receivers timing data. The paper uses from an actual data collection for evaluating the performance of the methods. An experimental test setup is designed and implemented for this purpose. The obtained experimental results from a Coarse Acquisition (C/A)-code single-frequency GPS receiver strongly support the potential of methods to give high accurate timing. Quality of the obtained results is very good, so that GPS timing RMS error reduce to less than 120 and 40 nanoseconds, with and without SA.