Electronics

In multiplierless finite impulse response (FIR) filters, the product accumulation block (PAB) could be the major contributor to hardware complexity, especially for high-order filters. In this paper, an optimization scheme where the constant multiplication block and the PAB are jointly optimized at the bit-level is proposed to minimize the hardware complexity. In the proposed joint optimization, the multiple constant multiplications (MCM) block is rearranged into several MCM sub-blocks. The products are summed locally before accumulation to reduce the word-length of the structural adders. It is shown that the symmetric property of linear phase FIR filters can be utilized in some cases to further reduce the complexity of the constant multiplications. Quantitative analyses are also presented to study the relationship between the optimum group size and the coefficient values as well as the filter orders. It is shown that there is no fixed optimum structure for filters with different coefficient word-lengths and filter orders, and each filter needs to be optimized specifically to achieve the minimum hardware complexity. Implementation results are presented to validate the effectiveness of the proposed method. Full article

Multicriteria methods have gained traction in academia and industry practices for effective decision-making. This systematic review investigates and presents an overview of multi-criteria approaches research conducted over forty-four years. The Web of Science (WoS) and Scopus databases were searched for papers on multi-criteria methods with titles, abstracts, keywords, and articles from January 1977 to 29 April 2022. Using the R Bibliometrix tool, the bibliographic data was evaluated. According to this bibliometric analysis, in 131 countries over the past forty-four years, 33,201 authors have written 23,494 documents on multi-criteria methods. This area’s scientific output increases by 14.18 percent every year. China has the highest percentage of publications at 18.50 percent, followed by India at 10.62 percent and Iran at 7.75 percent. Islamic Azad University has the most publications with 504, followed by Vilnius Gediminas Technical University with 456 and the National Institute of Technology with 336. Expert Systems With Applications, Sustainability, and the Journal of Cleaner Production are the top journals, accounting for over 4.67 percent of all indexed works. In addition, E. Zavadskas and J. Wang have the most papers in the multi-criteria approaches sector. AHP, followed by TOPSIS, VIKOR, PROMETHEE, and ANP, is the most popular multi-criteria decision-making method among the ten nations with the most publications in this field. The bibliometric literature review method enables researchers to investigate the multi-criteria research area in greater depth than the conventional literature review method. It allows a vast dataset of bibliographic records to be statistically and systematically evaluated, producing insightful insights. This bibliometric study is helpful because it provides an overview of the issue of multi-criteria techniques from the past forty-four years, allowing other academics to use this research as a starting point for their studies. Full article

Adopting cognitive radios (CRs) having multiple antennas in blind non-cooperative and cooperative spectrum sensing (CSS) under fading channels has gained attention due to higher detection performances provided by the spatial diversity gain of multi-sensors in different geographical locations and lower complexity, respectively. However, most studies do not consider sensing scenarios of more practical significance: for example, sometimes adopting only uncalibrated antenna arrays and sometimes only correlated signals at antenna arrays of CRs, but almost always, none of these impairments. Therefore, this paper studies these combined impairments on the performances of two blind techniques in centralized CSS with decision fusion (DF) and data fusion for different numbers of CRs and antennas per CR, both under frequency selective fading channels. One is the circular folding cooperative power spectral density split cancellation (CFCPSC), and the other is the generalized likelihood ratio test (GLRT). Extensive numerical results show, for instance, that with sample fusion (SF) and calibrated antennas, GLRT outperforms CFCPSC independently of correlation, numbers of antennas, or CRs. However, uncalibrated antennas severely penalize GLRT while surprisingly benefiting CFCPSC. Correlation is detrimental to GLRT and CFCPSC with SF but may help CFCPSC in DF and GLRT in DF or eigenvalue fusion. Generally, CFCPSC outperforms GLRT. Full article

Some downstream tasks often require enough data for training in deep learning, but it is formidable to acquire data in some particular fields. Generative Adversarial Network has been extensively used in data augmentation. However, it still has problems of unstable training and low quality of generated images. This paper proposed Data Augmentation Based on Generative Adversarial Network with Mixed Attention Mechanism (MA-GAN) to solve those problems. This method can generate consistent objects or scenes by correlating the remote features in the image, thus improving the ability to create details. Firstly, the channel-attention and the self-attention mechanism are added into the generator and discriminator. Then, the spectral normalization is introduced into the generator and discriminator so that the parameter matrix satisfies the Lipschitz constraint, thus improving the stability of the model training process. By qualitative and quantitative evaluations on small-scale benchmarks (CelebA, MNIST, and CIFAR-10), the experimental results show that the proposed method performs better than other methods. Compared with WGAN-GP (Improved Training of Wasserstein GANs) and SAGAN (Self-Attention Generative Adversarial Networks), the proposed method contributes to higher classification accuracy, indicating that this method can effectively augment the data of small samples. Full article

Industrial control systems (ICSs) for critical infrastructure are extensively utilized to provide the fundamental functions of society and are frequently employed in critical infrastructure. Therefore, security of these systems from cyberattacks is essential. Over the years, several proposals have been made for various types of cyberattack detection systems, with each concept using a distinct set of processes and methodologies. However, there is a substantial void in the literature regarding approaches for detecting cyberattacks in ICSs. Identifying cyberattacks in ICSs is the primary aim of this proposed research. Anomaly detection in ICSs based on an artificial intelligence algorithm is presented. The methodology is intended to serve as a guideline for future research in this area. On the one hand, machine learning includes logistic regression, k-nearest neighbors (KNN), linear discriminant analysis (LDA), and decision tree (DT) algorithms, deep learning long short-term memory (LSTM), and the convolution neural network and long short-term memory (CNN-LSTM) network to detect ICS malicious attacks. The proposed algorithms were examined using real ICS datasets from the industrial partners Necon Automation and International Islamic University Malaysia (IIUM). There were three types of attacks: man-in-the-middle (mitm) attack, web-server access attack, and telnet attack, as well as normal. The proposed system was developed in two stages: binary classification and multiclass classification. The binary classification detected the malware as normal or attacks and the multiclass classification was used for detecting all individual attacks. The KNN and DT algorithms achieved superior accuracy (100%) in binary classification and multiclass classification. Moreover, a sensitivity analysis method was presented to predict the error between the target and prediction values. The sensitivity analysis results showed that the KNN and DT algorithms achieved R2 = 100% in both stages. The obtained results were compared with existing systems; the proposed algorithms outperformed existing systems. Full article

Aiming at the problem that the vertex detection method is difficult to deal with the high-efficiency detection of the large-scale spherical lenslet array, a contact off-axis measuring method is proposed and the measurement accuracy and detection efficiency are verified by experiments. Firstly, by analyzing the 3D model of the relationship between a spherical lens and probe of profilometer, the mathematical model of probe trajectory arc is established based on the off-axis trajectory characteristics of the spherical lenslet array. Then, the error mechanism under the off-axis condition is analyzed, and a mathematical algorithm is proposed to restore the structural parameters at the main optical axis of the lens by using the process design value of the unit lens aperture. Finally, a comparative experiment is carried out between the off-axis detection method and vertex detection method. The experimental results demonstrate that: Compared with the coaxial detection method, the relative errors of the measured lens curvature radius R₀ and the lens vector height f₀ under the off-axis detection method are 1.71% and 1.95%, respectively. Under the sampling measurement scale, the detection efficiency of the off-axis detection method is 41 times higher than that of vertex detection method. Full article

A compressive sensing-based target localization method based on hidden semi-Markov model (HsMM) is proposed to address problems like unpredictable data and the multipath effect of the Receive Signal Strength (RSS) in indoor localization. The method can achieve both coarse and precise positioning by combining HsMM and the compressive sensing algorithm. Firstly, the hidden semi-Markov model is introduced to complete the coarse positioning of the target, and a parameter training method is proposed; secondly, the Davies-Bouldin Index and the Calinski-Harabasz Index based on the Euclidean distance and on the proposed connection distance herein are introduced; then, on the basis of coarse positioning, a precise positioning method based on compressive sensing is proposed; in the compressive sensing method, Gaussian matrix is introduced and a selection method of two screening matrices of the deterministic matrix is proposed; finally, the performance of coarse positioning is verified by experimental data for Hidden Markov Model (HMM) and HsMM, respectively, and the performance of the compressive sensing algorithm based on the two screening matrices of Gaussian matrix and deterministic matrix is respectively verified; the effectiveness of the proposed algorithm is experimentally verified. Full article

In order to achieve the classification of mitral regurgitation, a deep learning network VDS-UNET was designed to automatically segment the critical regions of echocardiography with three sections of apical two-chamber, apical three-chamber, and apical four-chamber. First, an expert-labeled dataset of 153 echocardiographic videos and 2183 images from 49 subjects was constructed. Then, the convolution layer in the VGG16 network was used to replace the contraction path in the original UNet network to extract image features, and depth supervision was added to the expansion path to achieve the segmentation of LA, LV, and MV. The results showed that the Dice coefficients of LA, LV, and MV were 0.935, 0.915, and 0.757, respectively. The proposed deep learning network can achieve simultaneous and accurate segmentation of LA, LV, and MV in multi-section echocardiography, laying a foundation for quantitative measurement of clinical parameters related to mitral regurgitation. Full article

Object detection plays an essential role in the computer vision domain, especially the machine learning-based approach, which has developed rapidly in the past decades. However, the development of convolutional neural networks in the marine field is relatively slow, such as in ship classification and tracking. In this paper, ship detection is considered as a central point classification and regression task but discards the non-maximum suppression operation. We first improved the deep layer aggregation network to enhance the feature extraction capability of tiny targets, then reduced the number of parameters through the lightweight convolution module, and finally employed a unique activation function to enhance the nonlinearity of the model. By doing this, the improved network not only suits unbalanced sample ratios in classifying, but is more robust in scenarios where both the number and resolution of samples are unstable. Experimental results demonstrate that the proposed approach obtains outstanding performance and especially suits tiny object detection compared with current advanced methods. Furthermore, in contrast to the original CenterNet framework, the mAP of the proposed approach increased by 5.6%. Full article

Today, although laser engraving technology is widely used in 2D image engraving, when the image is larger and more complicated, most existing algorithms for engraving path planning have a huge computational burden and reduced engraving efficiency. Accordingly, this article addresses the trajectory optimization problem in large-scale image engraving. First, we formulate the problem as an improved model based on the large-scale traveling salesman problem (TSP). Then, we propose a three-layered algorithm called 3L-MFEA-MP, structured as follows: an upper layer, the genetic algorithm (GA); a middle layer, the GA; and a bottom layer, the parallel multifactorial evolutionary algorithm. Experiments on four classic large-scale TSP datasets show that our algorithm exhibits superior performance in terms of the path length and engraving time compared with other algorithms. In particular, compared with the single-thread algorithm, the proposed parallel algorithm reduced the engraving time by 80%. Moreover, the engraving machine experiment demonstrated that the engraving time of our algorithm on mona-lisa 100K, vangogh 120K, and venus 140K was approximately one tenth that of the traditional dot engraving method. The results indicate that the proposed algorithm can reduce the computational burden and improve engraving efficiency in engraving path planning. Full article

The multilevel back-to-back cascaded H-bridge converter (CHB-B2B) presents a significantly reduced components per level in comparison to other classical back-to-back multilevel topologies. However, this advantage cannot be fulfilled because of the several internal short circuits presented in the CHB-B2B when a conventional PWM modulation is applied. To solve this issue, a powerful math tool known as graph theory emerges as a solution for defining the converter switching matrix to be used with an appropriate control strategy, such as the model-based predictive control (MPC). Therefore, this research paper proposes a MPC with the graph theory approach applied to CHB-B2B which capable of not only eliminating the short circuit stages, but also exploring all the switching states remaining without losing the converter controllability and power quality. To demonstrate the proposed strategy applicability, the MPC with graph theory approach is tested in four different types of SST configurations, input-parallel output-parallel (IPOP), input-parallel output series (IPOS), input-series output-parallel (ISOP), and input-series output series (ISOS), attending distribution grids with different voltage and power levels. Real-time experimental results obtained in a hardware-in-the-loop (HIL) platform demonstrate the proposed strategy’s effectiveness, such as DC-link voltages regulation, multilevel voltage synthesis, and currents with reduced harmonic content. Full article

To fundamentally improve the efficiency of content distribution in the network, information-centric networking (ICN) has received extensive attention. However, the existence of a large number of IP facilities in the current network makes the smooth evolution of the network architecture a realistic requirement. The ICN architecture that separates the process of name resolution and message routing is widely accepted for its better compatibility with IP networks. In this architecture, the user first obtains the locator of the content replica node from the name resolution system (NRS) and then completes the data transmission through the locator. In data transmission, receiver-driven congestion control algorithms need to be studied. Therefore, we introduce the Copa algorithm into ICN and propose an improved Copa-ICN algorithm. Experiments show that the Copa-ICN algorithm has a high convergence speed and fairness, and when there is a transmission process in the opposite direction, it can still have a high throughput different from the original Copa algorithm. Full article

We propose a new parametrization of motion primitives based on Bézier curves that suits perfectly path-planning applications (and environment exploration) of wheeled mobile robots. The individual motion primitives can simply be calculated taking into account the requirements of path planning and the constraints of a vehicle, given in the form of the starting and ending orientations, velocities, turning rates, and curvatures. The proposed parametrization provides a natural geometric interpretation of the curve. The solution of the problem does not require optimization and is obtained by solving a system of simple polynomial equations. The resulting planar path composed of the primitives is guaranteed to be $C^2$ continuous (the curvature is therefore continuous). The proposed primitives feature low order Bézier (third order polynomial) curves. This not only provides the final path with minimal required turns or unwanted oscillations that typically appear when using higher-order polynomial primitives due to Runge’s phenomenon but also makes the approach extremely computationally efficient. When used in path planning optimizers, the proposed primitives enable better convergence and conditionality of the optimization problem due to a low number of required parameters and a low order of the polynomials. The main contribution of the paper therefore lies in the analytic solution for the third-order Bézier motion primitive under given boundary conditions that guarantee continuous curvature of the composed spline path. The proposed approach is illustrated on some typical scenarios of path planning for wheeled mobile robots. Full article

This paper suggests a novel method for diagnosing planetary gearbox faults. It addresses the issue of network bandwidth limitation during wireless data transmission and the problem of relying on expert experience and insufficient training samples in traditional fault diagnosis. The continuous wavelet transform was combined with the AlexNet convolutional neural network using transfer learning and the compressed theory of sense. The original vibration signal was compressed and reconstructed using the compressed sampling orthogonal matching pursuit reconstruction algorithm. A continuous wavelet transform was used to convert the compressed signal into a time–frequency image. The pretrained AlexNet model was selected as the migration object, the network model was fine-tuned and retrained, and the trained AlexNet model was used to diagnose the fault using the model-based migration method. It was demonstrated by the experimental results when the compression ratio CR = 0.5. Compared to other network models, the classification accuracy rate is 97.78%. This method has specific reference value and application prospects and good feature extraction and fault classification capabilities. Full article

This paper presents a high-speed successive approximation register (SAR) analog-to-digital converter (ADC) that takes advantage of both asynchronous SAR ADC and loop-unrolled (LU) SAR ADC. By utilizing the output of the dynamic amplifier (DA) to generate an asynchronous clock, the reset time for the DA can be hidden behind the comparator latching time. Dedicated latches for each digital-to-analog converter (DAC) element eliminate the need for DAC switching logic. The proposed inverter-inserted three-stage comparator significantly reduces the input-referred offset of the comparator. The prototype 6-bit 700 MS/s SAR ADC was implemented in a 28 nm CMOS process and has a small 0.0012 mm² area. The measured peak DNL and INL without any mismatch calibration were 0.33 and 0.27 LSB, respectively. With Nyquist input, the measured signal-to-noise and distortion ratio (SNDR) and spurious-free dynamic range (SFDR) were 34.07 and 47.52 dB, respectively. The power consumption was 1 mW under a supply voltage of 1.0 V, leading to a Walden figure of merit (FoM) of 34.6 fJ/conversion-step at 700 MS/s. Full article

Automatic text summarization is a method used to compress documents while preserving the main idea of the original text, including extractive summarization and abstractive summarization. Extractive text summarization extracts important sentences from the original document to serve as the summary. The document representation method is crucial for the quality of the generated summarization. To effectively represent the document, we propose a hierarchical document representation model Long-Trans-Extr for Extractive Summarization, which uses Longformer as the sentence encoder and Transformer as the document encoder. The advantage of Longformer as sentence encoder is that the model can input long document up to 4096 tokens with adding relative a little calculation. The proposed model Long-Trans-Extr is evaluated on three benchmark datasets: CNN (Cable News Network), DailyMail, and the combined CNN/DailyMail. It achieves 43.78 (Rouge-1) and 39.71 (Rouge-L) on CNN/DailyMail and 33.75 (Rouge-1), 13.11 (Rouge-2), and 30.44 (Rouge-L) on the CNN datasets. They are very competitive results, and furthermore, they show that our model has better performance on long documents, such as the CNN corpus. Full article

Smart social systems are ones where a number of individuals share and interact with each other via various networking devices. There exist a number of benefits to including smart-based systems in networks such as religions, economy, medicine, and other networks. However, the involvement of several cyber threats leads to adverse effects on society in terms of finance, business, liability, economy, psychology etc. The aim of this paper is to present a secure and efficient medical Internet of Things communication mechanism by preventing various cyber threats. The proposed framework uses Artificial Intelligence-based techniques such as Levenberg–Marquardt (LM) and Viterbi algorithms to prevent various social cyber threats during interaction and sharing of messages. The proposed mechanism is simulated and validated with various performance metrics compared with the traditional mechanism. Full article

We report the development of a planar 4-Josephson-junction nanoscale superconducting quantum interference device (nanoSQUID) that is self-biased for optimal sensitivity without the application of a magnetic flux of Φ₀/4. The nanoSQUID contains novel NbN-TiN-NbN nanobridge Josephson junctions (nJJs) with NbN current leads and electrodes of the nanoSQUID body connected by TiN nanobridges. The optimal superconducting transition temperature of ~4.8 K, superconducting coherence length of ~100 nm, and corrosion resistance of the TiN films ensure the hysteresis-free, reproducible, and long-term stability of nJJ and nanoSQUID operation at 4.2 K, while the corrosion-resistant NbN has a relatively high superconducting transition temperature of ~15 K and a correspondingly large energy gap. FIB patterning of the TiN films and nanoscale sculpturing of the tip area of the nanoSQUID’s cantilevers are performed using amorphous Al films as sacrificial layers due to their high chemical reactivity to alkalis. A cantilever is realized with a distance between the nanoSQUID and the substrate corner of ~300 nm. The nJJs and nanoSQUID are characterized using Quantum Design measurement systems at 4.2 K. The technology is expected to be of interest for the fabrication of durable nanoSQUID sensors for low temperature magnetic microscopy, as well as for the realization of more complex circuits for superconducting nanobridge electronics. Full article

The advantages of economies of scale in marine logistics and transportation can be obtained by building bigger ships. As ship sizes increase, so do their propulsion systems, requiring that stable and high-efficiency power must continuously be supplied. In general, ships’ operations require power lower than the installed generator capacity. However, when the generator is operated at a low load, its efficiency decreases. In this study, based on actual operation data, the load requirements for each operation mode were analyzed, and a diesel-generator-based power system was designed. We present a generator capacity optimization calculation method through generator capacity. The proposed strategy maximizes the space utilization and efficiency of the ship while minimizing the generator’s power consumption. The generator’s fuel consumption, operating time, and efficiency were compared and analyzed to verify the proposed strategy’s efficacy. In conclusion, the proposed strategy demonstrated the effect of reducing fuel consumption by 2.2%, increasing generator efficiency by 8.4%, and reducing costs by 5.14% compared to the existing onboard generator capacity for the same vessel. Full article