In a typical Wireless Sensor Network (WSN), thousands of sensor nodes can be distributed in the environment. Then, each sensor node transmits its detected data to the base station with the help of cooperation. In this type of network, data aggregation protocols are used to increase the network’s lifetime and reduce each sensor node’s communication load and energy consumption. With Data Clustering, the density of data circulating in the network is reduced, thus increasing the network’s life. Energy, delay, and efficiency are essential criteria in Data Clustering; however, security is another crucial aspect to be considered. A comprehensive solution for secure data clustering has yet to be seen when the literature is examined. In the solutions developed, data availability, which means that the WSN is resistant to Denial of Service (DOS) attacks, has been neglected too much, even though confidentiality, integrity, and authentication are met with different algorithms. This study developed a comprehensive, secure clustering protocol by considering all security requirements, especially data availability. The developed protocol uses the blowfish encryption algorithm, EAX mode, and RSA algorithm. The proposed protocol was theoretically analyzed, empirically evaluated, and simulated from many perspectives. Comparisons were made with LSDAR, SUCID, and OOP-MDCRP protocols. As a result of the study, a comprehensive security solution is provided and more successful results were obtained according to Energy Efficiency, Network Lifetime, Average Delay, and Packet delivery ratio criteria. Full article

Renewable Energy Sources are an effective alternative to the atmosphere-contaminating, rapidly exhausting, and overpriced traditional fuels. However, RESs have many limitations like their intermittent nature and availability at far-off sites from the major load centers. This paper presents the forecasting of wind speed and power using the implementation of the Feedforward and cascaded forward neural networks (FFNNs and CFNNs, respectively). The one and half year’s dataset for Jhimpir, Pakistan, is used to train FFNNs and CFNNs with recently developed novel metaheuristic optimization algorithms, i.e., hybrid particle swarm optimization (PSO) and a Bat algorithm (BA) named HPSOBA, along with a modified hybrid PSO and BA with parameter-inspired acceleration coefficients (MHPSO-BAAC), without and with the constriction factor (MHPSO-BAAC-χ). The forecasting results are made for June–October 2019. The accuracy of the forecasted values is tested through the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The graphical and numerical comparative analysis was performed for both feedforward and cascaded forward neural networks that are tuned using the mentioned optimization techniques. The feedforward neural network was achieved through the implementation of HPSOBA with a mean absolute error, mean absolute percentage error, and root mean square error of 0.0673, 6.73%, and 0.0378, respectively. Whereas for the case of forecasting through a cascaded forward neural network, the best performance was attained by the implementation of MHPSO-BAAC with a MAE, MAPE and RMSE of 0.0112, 1.12%, and 0.0577, respectively. Thus, the mentioned neural networks provide a more accurate prediction when trained and tuned through the given optimization algorithms, which is evident from the presented results. Full article

Sharing prior knowledge across multiple robotic manipulation tasks is a challenging research topic. Although the state-of-the-art deep reinforcement learning (DRL) algorithms have shown immense success in single robotic tasks, it is still challenging to extend these algorithms to be applied directly to resolve multi-task manipulation problems. This is mostly due to the problems associated with efficient exploration in high-dimensional state and continuous action spaces. Furthermore, in multi-task scenarios, the problem of sparse reward and sample inefficiency of DRL algorithms is exacerbated. Therefore, we propose a method to increase the sample efficiency of the soft actor-critic (SAC) algorithm and extend it to a multi-task setting. The agent learns a prior policy from two structurally similar tasks and adapts the policy to a target task. We propose a prioritized hindsight with dual experience replay to improve the data storage and sampling technique, which, in turn, assists the agent in performing structured exploration that leads to sample efficiency. The proposed method separates the experience replay buffer into two buffers to contain real trajectories and hindsight trajectories to reduce the bias introduced by the hindsight trajectories in the buffer. Moreover, we utilize high-reward transitions from previous tasks to assist the network in easily adapting to the new task. We demonstrate the proposed method based on several manipulation tasks using a 7-DoF robotic arm in RLBench. The experimental results show that the proposed method outperforms vanilla SAC in both a single-task setting and multi-task setting. Full article

The test vector leakage assessment (TVLA) is a widely used side-channel power leakage detection technology which requires evaluators to collect as many power traces as possible to ensure accuracy. However, as the total sample size of the power traces increases, the amount of redundant information will also increase, thus limiting the detection efficiency. To address this issue, we propose a principal component analysis (PCA)-TVLA-based leakage detection framework which realizes a more advanced balance of accuracy and efficiency. Before implementing TVLA to detect leakage, we project the original power data onto their most significant feature dimensions extracted by the PCA procedure and screen power traces according to the magnitude of their corresponding components in the variance of the projection vector. We verified the overall performance of the proposed framework by measuring the detection capability and efficiency with t-values and the required time, respectively. The results show that compared with similar existing schemes, under the best circumstances, the proposed framework decreases the t-value by 4.3% while saving time by 25.2% on the MCU platform and decreases the t-value by 2.4% while saving time by 38.0% on the FPGA platform. Full article

Recently, artificial intelligence (AI)-enabled technologies have been widely employed for complex industrial applications. AI technologies can be utilized to improve efficiency and reduce human labor in industrial applications. At the same time, fault diagnosis (FD) and detection in rotating machinery (RM) becomes a hot research field to assure safety and product quality. Numerous studies based on statistical, machine learning (ML), and mathematical models have been available in the literature for automated fault diagnosis. From this perspective, this study presents a novel sandpiper optimization with an artificial-intelligence-enabled fault diagnosis (SPOAI-FD) technique for intelligent industrial applications. The aim is to detect the existence of faults in machineries. The proposed model involves the design of a continuous wavelet transform (CWT)-based pre-processing approach, which transforms the raw vibration signal into a useful format. In addition, a bidirectional long short-term memory (BLSTM) model is applied as a classifier, and the Faster SqueezeNet model is applied as a feature extractor. In order to modify the hyperparameter values of the BLSTM model, the sandpiper optimization algorithm (SPOA) can be utilized, showing the novelty of the work. A wide range of simulation analyses were conducted on benchmark datasets, and the results highlighted the supremacy of the SPOAI-FD algorithm over recent approaches. Full article

With progress in fifth-generation techniques, more advanced techniques are available for disabled people. Disability table tennis has also benefited from the new technology. In this paper, a portable and low-cost triboelectric nanogenerator for wheelchair table tennis monitoring systems is proposed. It was applied for wheelchair table tennis athletes’ monitoring. The portable and low-cost triboelectric nanogenerator consists of Kapton, polyurethane triboelectric films, and a foam supporting layer. The materials have flexible and low-cost characteristics. Therefore, the device has no influence on exercise performance. Due to triboelectric and electrostatic induction, the portable and low-cost triboelectric nanogenerator can convert biomechanical signals into electric signals. The electric signal is used as a sensing signal and is transformed in a computer by an Analog-to-Digital acquisition module. The coach acquires motion information in real time from a terminal device regarding force, exercise amplitude, and stability of the athlete. Meanwhile, the electric signal provides also sustainable energy for the microelectronic device. It can light 20 LEDs easily and power a calculator and a watch. This portable and low-cost self-powered triboelectric nanogenerator offers a new approach to the field of motion monitoring for disabled people. Full article

In this work, we present a 2 × 2 near-field multi-input multiple-output (MIMO) prototype for bit-error-rate (BER) and error vector magnitude (EVM) measurements in a metal enclosure. The near-field MIMO prototype was developed using software-defined-radios (SDRs) for over-the-air transmission of QPSK modulated baseband waveforms. We checked the near-field MIMO BER and EVM measurements in three different scenarios in a highly reflecting metal enclosure environment. In the first scenario, the line-of-sight (LOS) communication link was investigated when the mode stirrer was stationary. In the stationary channel conditions, near-field MIMO BER and EVM measurements are performed. In the second scenario, BER and EVM measurements were performed in dynamic channel conditions when the mode stirrer was set to move continuously. In the third scenario, LOS communication near-field MIMO BER and EVM measurements were performed in stationary channel conditions but now in the presence of MIMO interference. In three different scenarios, near-field MIMO BER and EVM measurements were investigated at different Tx USRP gain values and in the presence of varying levels of MIMO interference. Full article

The paper is devoted to the problem of the decentralized control of unmanned aerial vehicle (UAV) formation in the case of parametric uncertainty. A new version of the feedback linearization approach is proposed and used for a point mass UAV model transformation. As result, a linear model is obtained containing an unknown value of the UAV mass. Employing the speed-gradient design method and the implicit reference model concept, a combined adaptive control law is proposed for a single UAV, including the UAV’s mass estimation and adaptive tuning of the controller parameters. The obtained new algorithms are then used to address the problem of consensus-based decentralized control of the UAV formation. Rigorous stability conditions for control and identification are derived, and simulation results are presented to demonstrate the quality of the closed-loop control system for various conditions. Full article

In music information retrieval (MIR), beat tracking is one of the most fundamental tasks. To obtain this critical component from rhythmic music signals, a previous beat tracking system of hidden Markov model (HMM) with a recurrent neural network (RNN) observer was developed. Although the frequency of music beat is quite stable, existing HMM based methods do not take this feature into account. Accordingly, most of hidden states in these HMM-based methods are redundant, which is a disadvantage for time efficiency. In this paper, we proposed an efficient HMM using hidden states by exploiting the frequency contents of the neural network’s observation with Fourier transform, which extremely reduces the computational complexity. Observers that previous works used, such as bi-directional recurrent neural network (Bi-RNN) and temporal convolutional network (TCN), cannot perceive the frequency of music beat. To obtain more reliable frequencies from music, a periodic recurrent neural network (PRNN) based on attention mechanism is proposed as well, which is used as the observer in HMM. Experimental results on open source music datasets, such as GTZAN, Hainsworth, SMC, and Ballroom, show that our efficient HMM with PRNN is competitive to the state-of-the-art methods and has lower computational cost. Full article

Homography estimation of infrared and visible images is a highly challenging task in computer vision. Recently, the deep learning homography estimation methods have focused on the plane, while ignoring the details in the image, resulting in the degradation of the homography estimation performance in infrared and visible image scenes. In this work, we propose a detail-aware deep homography estimation network to preserve more detailed information in images. First, we design a shallow feature extraction network to obtain meaningful features for homography estimation from multi-level multi-dimensional features. Second, we propose a Detail Feature Loss (DFL), which utilizes refined features for computation and retains more detailed information while reducing the influence of unimportant features, enabling effective unsupervised learning. Finally, considering that the evaluation indicators of the previous homography estimation tasks are difficult to reflect severe distortion or the workload of manually labelling feature points is too large, we propose an Adaptive Feature Registration Rate (AFRR) to adaptive extraction of image pair feature points to calculate the registration rate. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on synthetic benchmark dataset and real dataset. Full article

Autonomous intravitreal injection in ophthalmology is a challenging surgical task as accurate depth measurement is difficult due to the individual differences in the patient’s eye and the intricate light reflection or refraction of the eyeball, often requiring the surgeon to first preposition the end-effector accurately. Image-based visual servo (IBVS) control does not rely on depth information, exhibiting potential for addressing the issues mentioned above. Here we describe an enhanced IBVS strategy to achieve high performance and robust autonomous injection navigation. The radial basis function (RBF) kernel with strong learning capability and fast convergence is used to globally map the uncertain nonlinear strong coupling relationship in complex uncalibrated IBVS control. The Siamese neural network (SNN) is then used to compare and analyze the characteristic differences between the current and target poses, thus making an approximation of the mapping relationships between the image feature changes and the end-effector motion. Finally, a robust sliding mode controller (SMC) based on min–max robust optimization is designed to implement effective surgical navigation. Data from the simulation and the physical model experiments indicate that the maximum localization and attitude errors of the proposed method are 0.4 mm and 0.18°, exhibiting desirable accuracy with the actual surgery and robustness to disturbances. These results demonstrate that the enhanced strategy can provide a promising approach that can achieve a high level of autonomous intravitreal injection without a surgeon. Full article

A serious problem affecting the local radio industry in Cyprus is radio interference from the Middle East. This problem is especially acute on the highway along the coast during the summer months because interference degrades the sound quality in vehicle receivers due to chirping noise, hissing, fading and distortion. During the last decade the issue was explored by monitoring the field strength intensity of unwanted signals with respect to atmospheric conditions that excacerbate interference. According to the research outcomes presented in this paper, severe interference occurs due to “Tropospheric Ducting”, i.e., radio energy becomes trapped between two boundaries in the lower layers of Earth’s atmosphere. Thereby, this phenomenon acts as a waveguide that favors radio waves to propagate beyond the horizon with very strong field strength intensity where under certain atmospheric conditions exceeding the expected free space theoretical value. Because the commercial Band FM is already oversaturated for years, it was considered important to expand this research to explore the impact of interference on the new digital DAB, DAB+ radio, that will soon be launched in Cyprus, as it is discussed below. Full article

Liver segmentation from abdominal computed tomography (CT) images is a primary step in the diagnosis and treatment of liver cancer, but previous liver segmentation methods have the problems of excessive demand for prior knowledge, under- and oversegmentation, and boundary leakage. To solve these problems, this paper proposes a new method of liver segmentation to assist doctors in medical judgment. Firstly, a liver reconstruction algorithm is proposed to obtain the approximate initial region of the liver, which reduces the requirement of prior knowledge and can reconstruct the liver region closer to the liver boundary. Then, we refine the edge of the liver region based on the reaction diffusion level set (RD level set). This edge refinement method can effectively deal with the weak boundary problem, prevent under- and oversegmentation, and obtain a more accurate liver region. Our method is verified on the clinical and public datasets, respectively. The segmentation results in terms of mean VOE, RVD, ASD, RMSD, and MSD on dataset Sliver07 are 5.1%, −0.1%, 1.0 mm, 2.0 mm, and 18.2 mm, and on dataset 3Dircadb are 8.1%, −0.2%, 1.5 mm, 2.4 mm, and 20.8 mm, respectively. Compared with the previous algorithms, the experiment results show that this method has a great improvement in accuracy with less prior knowledge. The liver reconstruction algorithm proposed in this paper can obtain a more accurate initial liver region, reduce the requirement for prior knowledge, and reduce time costs compared with the level set algorithm. Full article

Dual-stage cascaded dc systems are some of the most widely applied power interfaces in dc distributed power systems. However, in some practical situations, these systems might be unstable, especially if they incorporate tightly regulated load converters that operate as constant power loads (CPLs), whose power fluctuations could exert a cascading impact on the operation of the systems. Existing studies tend to describe the instability phenomena using bifurcation diagram analysis and the loci of eigenvalue analysis. However, it is usually difficult to derive the explicit expressions of the stability criterion. This paper addresses the large-signal stability issue of the dual-stage cascaded dc systems from a standpoint of load power and obtains the explicit form large-signal stability boundary in terms of load power by using Lyapunov-type mixed potential theory. Moreover, the prototype dual-stage cascaded dc system, in which the control strategies for the feeder converter and the load converter are different, is used as an example in this study. According to the results, the system remains stable when the load power is in [5.8, 23.2] W. When load power is less than 5.8 W or increased to [23.2, 32.8] W, the system is in a period-2 subharmonic oscillation state. Moreover, when the load power exceeds 32.8 W, the system falls into a chaotic state. The deduced boundary is highly consistent with the analysis results of both a bifurcation diagram and Jacobian matrix based analysis. Finally, both circuit-level simulation and experimental results validate the effectiveness of the load power stability boundary. Full article

Robotic manipulation, an area inside the field of industrial automation and robotics, consists of using a robotic arm to guide and grasp a desired object through actuators such as a vacuum or fingers, among others. Some objects, such as fragile ceramic pieces, require special attention to the force and the gripping method exerted on them. For this purpose, two grippers were developed, where one of them is a rotary vacuum gripper and the other is an impact gripper with three fingers, each one equipped with a load sensor capable of evaluating the values of load exerted by the grip actuators onto the object to be manipulated. The vacuum gripper was developed for the purpose of glazing a coffee saucer and the gripper with three fingers was developed for the purpose of polishing a coffee cup. Being that the impact gripper with sensorial feedback reacts to the excess and lack of grip force exerted, both these grippers were developed with success, handling with ease the ceramic pieces proposed. Full article