This paper presents a new sinusoidal position-tracking control scheme with a resonant controller for linear motor drive systems. The sinusoidal tracking controller is designed without any added algorithm for system identification and requires only approximate values of the mechanical parameters. Therefore, the controller is simple and robust to parameter variations. The proposed sinusoidal tracking resonant-based controller (STRC) is designed to track reference positions using a cascade control structure with an inner current/force control with hysteresis current control followed by a speed control loop with a resonant controller, and an outer position loop with a proportional and velocity-feedforward controller. The stability of the cascade feedback scheme and its parameter tuning are analyzed using the Routh–Hurwitz criterion. The performance of the proposed control scheme is validated using simulations and experiments on a voice-coil linear stage. The proposed STRC strategy is characterized by ease of implementation and shows excellent performance with fast response and high accuracy at different frequencies with a maximum error of 0.58% at 0.25 Hz. Full article

The widely used countermeasures against fault attacks are based on spatial, temporal, or information redundancy. This type of solution is very efficient, but it can be very expensive in terms of implementation cost. Thus, trying to propose a secure and efficient countermeasure for a lightweight cipher is a hard challenge, as the goal of a lightweight cipher is to be the lightest possible. This paper considers information redundancy based on parity bit code, with code-abiding transformations of the operations. This error detection code, with the code-abiding notion added, is very efficient against single fault injection and has a small overcost. The solution is tested on the LED lightweight cipher to measure its overhead. Moreover, a bitslice version of the cipher is used with the parity bit code applied to be robust against all the single-word fault injections. The challenge is to adapt the cipher functions in a way in which the parity bit is always considered, but without considering a heavy implementation. The advantage of our solution is that this countermeasure leads to a 100% fault coverage, with a reasonable overhead. Full article

In recent years, Federated Learning has attracted much attention because it solves the problem of data silos in machine learning to a certain extent. However, many studies have shown that attacks based on Generative Adversarial Networks pose a great threat to Federated Learning. This paper proposes Defense-GAN, a defense method against Generative Adversarial Network attacks under Federated Learning. Under this method, the attacker cannot learn the real image data distribution. Each Federated Learning participant uses SHAP to explain the model and masks the pixel features that have a greater impact on classification and recognition in their respective image data. The experimental results show that while attacking the federated training model using masked images, the attacker cannot always obtain the ground truth of the images. At the same time, this paper also uses CutMix to improve the generalization ability of the model, and the obtained model accuracy is only 1% different from that of the model trained with the original data. The results show that the defense method proposed in this paper can not only resist Generative Adversarial Network attacks in Federated Learning and protect client privacy, but also ensure that the model accuracy of the Federated model will not be greatly affected. Full article

This paper focuses on the development of a virtual training system by applying simulation techniques such as: Full Simulation and Hardware-in-the-Loop (HIL). This virtual reality system is intended to be a teaching and learning tool focused on the area of industrial robotics. For this purpose, mathematical models (kinematic and dynamic) have been considered. These models determine the characteristics and restrictions of the movements of a Scara SR-800 robot. The robot is then virtualized to simulate position and trajectory tasks within virtual environments. The Unity 3D graphic engine (Unity Software Inc., San Francisco, CA, USA), allows the design and development of the training system which is composed of a laboratory environment and an industrial environment. The same that contribute to the visualization and evaluation of the movements of the robot through the proposed control algorithm using the mathematical software (MatLab, manufactured by MathWorks, USA), through shared memories. This software in turn can be linked to an electronic board (Raspberry Pi) for data acquisition through a wireless connection. Finally, the stability and robustness of the implemented controllers are analyzed, as well as the correct operation of the virtual training system. Full article

With the advancement of internet technologies and multimedia applications, the spectrum scarcity problem is becoming more acute. Thus, spectral-efficient schemes with minimal interference for IoT networks are required. Device-to-device communication (D2D) technology has the potential to solve the issue of spectrum scarcity in future wireless networks. Additionally, throughput is considered a non-convex and NP-hard problem, and heuristic approaches are effective in these scenarios. This paper presents two novel heuristic approaches for throughput optimization for D2D users with quality of service (QoS)-aware wireless communication for mobile users (MU): the modified whale colony optimization algorithm (MWOA) and modified non-domination sorted genetic algorithm (MNSGA). The performance of the proposed algorithms is analyzed to show that the proposed mode selection technique efficiently fulfills the QoS requirements. Simulation results show the performance of the proposed heuristic algorithms compared to other understudied approaches. Full article

Cancer is one of the leading causes of death worldwide. In the last few decades, cancer treatment has come a long way, but multidrug resistance (MDR) in cancer still has low survival rates. It means that much research is required for an accurate diagnosis and effective therapy. The new era of cancer research could include theranostic approaches and targeted delivery of chemotherapeutic agents utilizing the nanoparticulate system. Recently, there has been much interest gained among researchers for carbon-based and graphene-based quantum dots due to their higher biocompatibility and ease of biofunctionalization compared to conventional heavy metal quantum dots. Moreover, these quantum dots have various interesting utilities, including bioimaging, biosensing, quantum dots-mediated drug delivery, and their role in photodynamic therapy (PDT) and photothermal therapy (PTT). The current review highlighted the utility of hybrid quantum dots as a theranostic system in different cancers and discussed the various bio-molecules conjugated hybrid quantum dots investigated for diagnostic/therapeutic applications in cancer. The influence of conjugation of different biomolecules, such as folic acid, PEG, etc., with hybrid quantum dots on their biopharmaceutical attributes (such as aqueous solubility, tumor penetrability, stability of loaded therapeutics in the tumor microenvironment), delivery of drugs specifically to tumor tissues, and its therapeutic outcome in different cancer has also been discussed. Full article

The design and investigation of an intelligent controller for hardware-in-the-loop (HIL) implementation of hybrid electric vehicles (HEVs) are proposed in this article. The proposed intelligent controller is adopted based on the enhancement of a model predictive controller (MPC) by an artificial neural network (ANN) approach. The MPC-based ANN (NNMPC) is proposed to control the speed of HEVs for a simulation system model and experimental HIL test systems. The HIL is established to assess the performance of the NNMPC to control the velocity of HEVs in an experimental environment. The real-time environment of HIL is implemented through a low-cost approach such as the integration of an Arduino Mega 2560 and a host Lenovo PC with a Core i7 @ 3.4 GHz processor. The NNMPC is compared with a proportional–integral (PI) controller, a classical MPC, and two different settings of the ANN methodology to verify the efficiency of the proposed intelligent NNMPC. The obtained results show a distinct behavior of the proposed NNMPC to control the speed of HEVs with good performance based on the distinct transient response, minimum error steady state, and system robustness against parameter perturbation. Full article

Recently, many works have been carried out to find effective ways that can allow for plausibly effective interactions of social intelligent agents (SIAs) in unpredictable environments in a reasonable time. Behavior trees (BTs) allow for knowledge to be modeled as a graph representation and provide a way for SIAs to effectively interact with the received information. BTs can store past social experiences that can then be used by SIAs to provide adequate human-like interactions when facing new social situations (query). One challenge appears when a social agent with vast past experiences—represented as a forest of BTs—tries to retrieve a similar BT to learn from in order to provide plausible interactions in the current situation in a cost-effective manner. Cognitive scripts with their inherent temporal structure can address this challenge where they can facilitate the use of contextual retrieval techniques on BTs represented as cognitive scripts. This paper introduces novel hybrid retrieval techniques that use agglomerative hierarchical clustering (H-clustering) and similarity-based algorithms: map-and-reduce and least common parent (LCP) to effectively retrieve similar BTs to a specific query BT in a reasonable time. The model groups BTs, represented as cognitive scripts, into compact clusters that can then be used to retrieve the most similar BT to a query one in real time without noticeable delay. A comparison was done between the performance of the proposed hybrid-retrieval techniques using a semi-structured dataset of cognitive scripts. The results showed that H-clustering-map-and-reduce is more cost-effective than H-clustering-LCP as it allowed for a low average retrieval time of 8 × 10⁻³ s compared to 3.1 s, respectively. Full article

In recent years, some research results have been achieved in the field of image steganalysis. However, there are still problems of difficulty in extracting steganographic features from images with low embedding rates and unsatisfactory detection performance of steganalysis. In this paper, we propose an image steganalysis method based on the attention mechanism and transfer learning. The method constructs a network model based on a convolutional neural network, including a preprocessing layer, a transposed convolutional layer, an ordinary convolutional layer, and a fully connected layer. We introduce the efficient channel attention module after the ordinary convolutional layer to focus on the steganographic region of the image, capture the local cross-channel interaction information, realize the adaptive adjustment of feature weights, and enhance the ability to extract steganographic features. Meanwhile, we apply the transfer learning method to use the training model parameters of high embedding rate images as the initialization parameters of the training model of the low embedding rate to achieve feature migration and further improve the steganalysis performance of the low embedding rate. The experimental results show that compared to the typical Xu-Net and Yedroudj-Net models, the detection accuracy of the proposed method is improved by 16.36% to 30.66% and by 35.59 to 37.83% for the embedding rates of 0.05 bpp, 0.1 bpp, and 0.2 bpp, respectively. Compared to the state-of-the-art Shen-Net model with low embedding rates, the detection accuracy is improved by 3.43% to 6.41%. This demonstrates the higher detection performance of the proposed method for steganalysis of low embedding rate images. Full article

With the development of Industry 4.0, hard-cut materials such as titanium alloys have been widely used in the aerospace industry. However, due to the poor rigidity of titanium alloy parts, deformation and vibration easily occur during the cutting process, which affects the accuracy, surface quality and efficiency of part machining. Therefore, in this paper, tool runout and workpiece deformation are introduced into the milling process of flat-end mills. Based on the tool’s hypocycloid motion, a geometric parameter model of the milling process is established, and the undeformed cutting thickness model is obtained considering the tool runout and workpiece deformation. Finally, the milling force model for side-milling titanium alloy thin-walled parts was established. The accuracy of the force model is verified through experiments. The error of the proposed model is far less than that of the traditional basic method. The maximum error of the traditional basic method is 87.09%. However, the maximum error of the proposed model is only 66.54%. The results show that the proposed force model considering tool runout and workpiece deformation can provide more accurate milling force prediction. Full article

Safe and accurate landing is crucial for Unmanned Aerial Vehicles (UAVs). However, it is a challenging task, especially when the altitude of the landing target is different from the ground and when the UAV is working in adverse environments, such as coasts where winds are usually strong and changing rapidly. UAVs controlled by traditional landing algorithms are unable to deal with sudden large disturbances, such as gusts, during the landing process. In this paper, a reliable vision-based landing strategy is proposed for UAV autonomous landing on a multi-level platform mounted on an Unmanned Ground Vehicle (UGV). With the proposed landing strategy, visual detection can be retrieved even with strong gusts and the UAV is able to achieve robust landing accuracy in a challenging platform with complex ground effects. The effectiveness of the landing algorithm is verified through real-world flight tests. Experimental results in farm fields demonstrate the proposed method’s accuracy and robustness to external disturbances (e.g., wind gusts). Full article

Two algorithms have been extensively studied for motor control: Field Oriented Control (FOC) and Direct Torque Control (DTC). Both control algorithms use a Voltage Source Inverter (VSI) to drive a Permanent Magnet Synchronous Motor (PMSM). To prevent short-arm short-circuit accidents when driving PMSM using VSI, a dead time is used to turn off the TOP and BOTTOM switches of each arm at the same time. However, this dead-time technique causes an unexpected pole voltage to be applied to the PMSM on the VSI output voltage, causing distortion and resulting in control nonlinearity. The disturbance voltage that causes nonlinearity is difficult to measure directly with the sensor. Therefore, this paper analyzes the nonlinearity of the controller due to the distorted voltage caused by the dead time during PMSM operation using the DTC algorithm and predicts the distorted output voltage using the extended Kalman Filter (EKF) to improve control stability. As a result, The algorithm proposed in this paper has verified the improvement of torque ripple and stator flux ripple through experiments and simulations. Full article

Drowsy driving causes many accidents. Driver alertness and automobile control are challenged. Thus, a driver drowsiness detection system is becoming a necessity. In fact, invasive approaches that analyze electroencephalography signals with head electrodes are inconvenient for drivers. Other non-invasive fatigue detection studies focus on yawning or eye blinks. The analysis of several facial components has yielded promising results, but it is not yet enough to predict hypovigilance. In this paper, we propose a “non-invasive” approach based on a deep learning model to classify vigilance into five states. The first step is using MediaPipe Face Mesh to identify the target areas. This step calculates the driver’s gaze and eye state descriptors and the 3D head position. The detection of the iris area of interest allows us to compute a normalized image to identify the state of the eyes relative to the eyelids. A transfer learning step by the MobileNetV3 model is performed on the normalized images to extract more descriptors from the driver’s eyes. Our LSTM network entries are vectors of the previously calculated features. Indeed, this type of learning allows us to determine the state of hypovigilance before it arrives by considering the previous learning steps, classifying the levels of vigilance into five categories, and alerting the driver before the state of hypovigilance’s arrival. Our experimental study shows a 98.4% satisfaction rate compared to the literature. In fact, our experimentation begins with the hyperparameter preselection to improve our results. Full article

For classifying brain tumors with small datasets, the knowledge-based transfer learning (KBTL) approach has performed very well in attaining an optimized classification model. However, its successful implementation is typically affected by different hyperparameters, specifically the learning rate (LR), batch size (BS), and their joint influence. In general, most of the existing research could not achieve the desired performance because the work addressed only one hyperparameter tuning. This study adopted a Cartesian product matrix-based approach, to interpret the effect of both hyperparameters and their interaction on the performance of models. To evaluate their impact, 56 two-tuple hyperparameters from the Cartesian product matrix were used as inputs to perform an extensive exercise, comprising 504 simulations for three cutting-edge architecture-based pre-trained Deep Learning (DL) models, ResNet18, ResNet50, and ResNet101. Additionally, the impact was also assessed by using three well-known optimizers (solvers): SGDM, Adam, and RMSProp. The performance assessment showed that the framework is an efficient framework to attain optimal values of two important hyperparameters (LR and BS) and consequently an optimized model with an accuracy of 99.56%. Further, our results showed that both hyperparameters have a significant impact individually as well as interactively, with a trade-off in between. Further, the evaluation space was extended by using the statistical ANOVA analysis to validate the main findings. F-test returned with p < 0.05, confirming that both hyperparameters not only have a significant impact on the model performance independently, but that there exists an interaction between the hyperparameters for a combination of their levels. Full article

This manuscript presents a fully differential difference transconductance amplifier (FDDTA) architecture based on CMOS inverters. Designed in a 130-nm CMOS process it operates in weak inversion when supplied with 0.25 V. In addition, the FDDTA requires no supplementary external calibration circuit, like tail current or bias voltage sources, since it relies on the distributed layout technique that intrinsically matches the CMOS inverters. For analytical purposes, we carried out a detailed investigation that describes all the concepts and the whole operation of the FDDTA architecture. Furthermore, a comparison between the modeling equations and measured data assures high performance. Full article