Electronics, Volume 11, Issue 10 (May-2 2022) – 150 articles

A broadband metamaterial absorber (MA) structure for Ultrahigh-Frequency (UHF) band was proposed, and the miniaturization of the unit was realized by combining the method of bending metal wires and loading metal vias. The size of the unit cell is 0.040 λ_L × 0.040 λ_L × 0.075 λ_L (λ_L is the wavelength corresponding to the lowest frequency of 0.5 GHz). The simulation results show that the bandwidth of the MA is from 0.50 GHz to 1.33 GHz, and the relative bandwidth is 90.7%. Polarization insensitivity of the MA was realized through assembling a 2 × 2 orthogonal array. TE and TM polarizations maintain more than 80% of the absorptance in the range of 40° at oblique incidence. The consistency of full-wave simulation, circuit simulation and measured results is high, which verifies the broadband absorption characteristics of the proposed MA. Full article

Weighting the Hanwoo (Korean cattle) is very important for Korean beef producers when selling the Hanwoo at the right time. Recently, research is being conducted on the automatic prediction of the weight of Hanwoo only through images with the achievement of research using deep learning and image recognition. In this paper, we propose a method for the automatic weight prediction of Hanwoo using the Bayesian ridge algorithm on RGB-D images. The proposed system consists of three parts: segmentation, extraction of features, and estimation of the weight of Korean cattle from a given RGB-D image. The first step is to segment the Hanwoo area from a given RGB-D image using depth information and color information, respectively, and then combine them to perform optimal segmentation. Additionally, we correct the posture using ellipse fitting on segmented body image. The second step is to extract features for weight prediction from the segmented Hanwoo image. We extracted three features: size, shape, and gradients. The third step is to find the optimal machine learning model by comparing eight types of well-known machine learning models. In this step, we compared each model with the aim of finding an efficient model that is lightweight and can be used in an embedded system in the real field. To evaluate the performance of the proposed weight prediction system, we collected 353 RGB-D images from livestock farms in Wonju, Gangwon-do in Korea. In the experimental results, random forest showed the best performance, and the Bayesian ridge model is the second best in MSE or the coefficient of determination. However, we suggest that the Bayesian ridge model is the most optimal model in the aspect of time complexity and space complexity. Finally, it is expected that the proposed system will be casually used to determine the shipping time of Hanwoo in wild farms for a portable commercial device. Full article

Software-defined networking (SDN) and network virtualization (NV) are key technologies for future networks, which allow telecommunication service providers (TSPs) to share network resources with users in a flexible manner. Since TSPs have limited virtualized network resources, it is critical to develop effective virtual network embedding (VNE) algorithms for an SDN network to improve resource utilization. However, most existing VNE algorithms ignore the security issues of SDN networks, which may be subject to malicious attacks due to their openness feature. Therefore, it is necessary to develop secure VNE (SVNE) for SDN networks. In this paper, we researched the relationship between resource value and node security-level, and we found that there are differences in the resource value of different nodes. Based on this analysis, we define the evaluation indicators considering differences in resource value for the SVNE problem. Then, we present a mixed-integer linear program (MILP) model to minimize the cost of SVNE. As the formulated optimization problem cannot be solved conveniently, we design two node-ranking approaches to rank physical and virtual nodes, respectively, and we propose two novel SVNE algorithms based on the node ranking approaches. Finally, simulation results reveal that our proposed algorithm is superior to other typical algorithms. Full article

In recent years, intelligent sensing has gained significant attention because of its autonomous decision-making ability to solve complex problems. Today, smart sensors complement and enhance the capabilities of human beings and have been widely embraced in numerous application areas. Artificial intelligence (AI) has made astounding growth in domains of natural language processing, machine learning (ML), and computer vision. The methods based on AI enable a computer to learn and monitor activities by sensing the source of information in a real-time environment. The combination of these two technologies provides a promising solution in intelligent sensing. This survey provides a comprehensive summary of recent research on AI-based algorithms for intelligent sensing. This work also presents a comparative analysis of algorithms, models, influential parameters, available datasets, applications and projects in the area of intelligent sensing. Furthermore, we present a taxonomy of AI models along with the cutting edge approaches. Finally, we highlight challenges and open issues, followed by the future research directions pertaining to this exciting and fast-moving field. Full article

Personalized trip recommendation attempts to recommend a sequence of Points of Interest (POIs) to a user. Compared with a single POI recommendation, the POIs sequence recommendation is challenging. There are only a couple of studies focusing on POIs sequence recommendations. It is a challenge to generate a reliable sequence of POIs. The two consecutive POIs should not be similar or from the same category. In developing the sequence of POIs, it is necessary to consider the categories of consecutive POIs. The user with no recorded history is also a challenge to address in trip recommendations. Another problem is that recommending the exact and accurate location makes the users bored. Looking at the same kind of POIs, again and again, is sometimes irritating and tedious. To address these issues in recommendation lies in searching for the sequential, relevant, novel, and unexpected (with high satisfaction) Points of Interest (POIs) to plan a personalized trip. To generate sequential POIs, we will consider POI similarity and category differences among consecutive POIs. We will use serendipity in our trip recommendation. To deal with the challenges of discovering and evaluating user satisfaction, we proposed a Serendipity-Oriented Personalized Trip Recommendation (SOTR). A compelling recommendation algorithm should not just prescribe what we are probably going to appreciate but additionally recommend random yet objective elements to assist with keeping an open window to different worlds and discoveries. We evaluated our algorithm using information acquired from a real-life dataset and user travel histories extracted from a Foursquare dataset. It has been observationally confirmed that serendipity impacts and increases user satisfaction and social goals. Based on that, SOTR recommends a trip with high user satisfaction to maximize user experience. We show that our algorithm outperforms various recommendation methods by satisfying user interests in the trip. Full article

Assessing security awareness among users is essential for protecting industrial control systems (ICSs) from social engineering attacks. This research aimed to determine the effect of cyber security awareness on the emergency response to cyber security incidents in the ICS. Additionally, this study has adopted a variety of cyber security emergency response process measures and frameworks and comprehensively proposes a new organizational model of cyber security incident response. The corresponding measures are evaluated based on the MP²DR² risk control matrix model to assess their practical value in the evaluation stage. This study found that after adding security awareness measures to response control measures, the influential value ranking of other control measures changed. The practical value of security awareness control measures was given a higher priority than that of other control measures. The research results highlight the importance of cyber security awareness and aim to inspire ICSs to place a higher priority on staff cyber security awareness in relation to cyber security incidents, which can effectively prevent the occurrence of cyber security incidents and make the field of industrial control application agency respond to incidents faster to restore the regular progress of all works. Full article

Quantum-dot cellular automata is a novel nanotechnology that has the advantages of low energy dissipation, easy integration, and high computing speed. It is regarded as one of the powerful alternative technologies for the next generation of integrated circuits because of its unique implementation concept. In this paper, two XOR/XNOR gates are proposed. Level-sensitive T flip-flops, negative edge-trigger T flip-flops, two-to-one multiplexers, reversible gates, and (8, 4) polar encoders are implemented based on these two proposed logic gates. Simulation results show that, compared with the existing level-sensitive T flip-flops, the second proposed level-sensitive T flip-flop has fewer cells and lower energy dissipation; compared with the best (8, 4) polar encoder, the cell count and area of the second proposed (8, 4) polar encoder are decreased by 13.67% and 12.05%, respectively. The two XOR/XNOR gates have a stable output and low energy dissipation, which can be flexibly designed into complex quantum-dot cellular automata circuits. Full article

This study aims to demonstrate how to compute the damping coefficient of a continuously variable damper for semi-active preview control suspensions while considering the sprung-mass jerk and the controller’s performance advantage. Optimal control theory is used to derive and validate the proposed preview approach to future road disturbances. Despite reduced body acceleration, semi-active suspensions with preview control display an increase in body jerk, implying that ride comfort may not be improved in practice. The optimal preview jerk controller for a semi-active system, on the other hand, can improve ride comfort without degrading road holding by minimizing the performance index that comprises the RMS value of jerk in addition to the RMS values of other outputs. The anti-jerk preview control suspension simulations considering frequency characteristics reveal a difference between suspension systems that consider jerk and those that ignore jerk. The time-domain simulations suggest that the proposed preview control strategy effectively to reduce body jerk, which other controllers cannot. Full article

The Conformer enhanced Transformer by using convolution serial connected to the multi-head self-attention (MHSA). The method strengthened the local attention calculation and obtained a better effect in auto speech recognition. This paper proposes a hybrid attention mechanism which combines the dynamic convolution CNNs and multi-head self-attention. This study focuses on generating local attention by embedding DY-CNNs in MHSA, followed by parallel computation of the globe and local attention inside the attention layer. Finally, concatenate the result of global and local attention to the output. In the experiments, we use the Aishell-1 (178 hours) Chinese database for training. In the testing folder dev/test, 4.5%/4.8% CER was obtained. The proposed method shows better performance in computation speed and the number of experimental parameters. The results are extremely close to the best result (4.4%/4.7%) of the Conformer. Full article

During partial shading conditions (PSCs), the power-voltage curve becomes more complex, having one global maximum power (GMP) and many local peaks. Traditional maximum power point tracking (MPPT) algorithms are unable to track the GMP under PSCs. Therefore, several optimization tactics based on metaheuristics or artificial intelligence have been applied to deal with GMP tracking effectively. This paper details how a direct control cuckoo search optimizer (CSO) is used to track the GMP for a photovoltaic (PV) system. The proposed CSO addresses the limitations of traditional MPPT algorithms to deal with the PSCs and the shortcomings of the particle swarm optimization (PSO) algorithm, such as low tracking efficiency, steady-state fluctuations, and tracking time. The CSO was implemented using MATLAB/Simulink for a PV array operating under PSCs and its tracking performance was compared to that of the PSO-MPPT. Experimental validation of the CSO-MPPT was performed on a boost DC/DC converter using a real-time Hardware-In-the-Loop (HIL) simulator (OPAL-RT OP4510) and dSPACE 1104. The results show that CSO is capable of tracking GMP within 0.99–1.32 s under various shading patterns. Both the simulation and experimental findings revealed that the CSO outperformed the PSO in terms of steady-state fluctuations and tracking time. Full article

To address the problem of strong uncertainty in the high proportion of new energy output, an improved convolutional long- and short-term memory (CLSTM) hybrid neural network is proposed for PV power combination prediction. Firstly, considering the large impact of weather changes on PV power output, a fluctuation feature identification model is used to classify historical PV power series samples into slow weather change type and severe weather change type. Secondly, taking into account the multimodal characteristics of PV power output, an improved variational modal decomposition technique is used to adaptively determine the number of modal components, K, and decompose the two types of samples. Regarding the existence of the low-frequency steady state component and the high-frequency fluctuation component of PV power output, the high-frequency component is used to train the long- and short-term memory (LSTM) model and the low-frequency component is used to train the convolutional neural network (CNN) model. The improved sparrow search algorithm (SSA) is used to optimize the parameters of the LSTM and CNN models during the training process. Finally, the predicted component values of each model are superimposed and reconstructed to obtain PV power prediction values. The actual operation data of a PV plant in northern China were used for comparison and validation, and the experiments showed that the accuracy of the prediction results, based on the improved SSA to optimize the parameters of the CLSTM hybrid neural network for predicting PV output, was significantly better than that of the BP, CNN, LSTM single neural network prediction results, and of the prediction accuracy of the unoptimized CLSTM hybrid neural network. At the same time, compared with the above single neural network and unoptimized hybrid prediction model, the proposed method converged faster and was more adaptable to weather changes. Full article

Nowadays, the data flow architecture is considered as a general solution for the acceleration of a deep neural network (DNN) because of its higher parallelism. However, the conventional DNN accelerator offers only a restricted flexibility for diverse network models. In order to overcome this, a reconfigurable convolutional neural network (RCNN) accelerator, i.e., one of the DNN, is required to be developed over the field-programmable gate array (FPGA) platform. In this paper, the sparse optimization of weight (SOW) and convolutional optimization (CO) are proposed to improve the performances of the RCNN accelerator. The combination of SOW and CO is used to optimize the feature map and weight sizes of the RCNN accelerator; therefore, the hardware resources consumed by this RCNN are minimized in FPGA. The performances of RCNN-SOW-CO are analyzed by means of feature map size, weight size, sparseness of the input feature map (IFM), weight parameter proportion, block random access memory (BRAM), digital signal processing (DSP) elements, look-up tables (LUTs), slices, delay, power, and accuracy. An existing architectures OIDSCNN, LP-CNN, and DPR-NN are used to justify efficiency of the RCNN-SOW-CO. The LUT of RCNN-SOW-CO with Alexnet designed in the Zynq-7020 is 5150, which is less than the OIDSCNN and DPR-NN. Full article

With the expansion of the Industrial Internet of Things (IIoT), real-time data collected by smart sensors deployed in factories are shared over open channels , which may cause unauthorized access of transmitted messages by adversaries, thus causing the problem of privacy leakage. User authentication is the first line of defense for security protection in the IIoT environment. In this paper, we propose a cloud—assisted authentication scheme based on Chebyshev polynomial encryption, in which only authorized users can access the sensing devices in the Internet of Things (IoT) to obtain real-time data. The scheme uses fuzzy extraction technology to verify biometric characteristics. There are three factors to verify the user’s login request: the smart card, password and the user’s personal biometrics. The commonly adopted formal security analysis, the ROR model, is applied to prove the semantic security of session key, and a detailed informal security analysis is performed to show that the proposed scheme can withstand multiple known attacks. Compared with other related user authentication schemes, the proposed scheme provides several extra functionality features, including offline sensor node registration, updating user passwords and biometrics, adding new sensor node deployment, user anonymity and untraceability. In addition, the cost of computation, communication and security is compared with similar schemes, and results show that our scheme has more security performance while the cost is acceptable. Full article

We report a controllable and scalable fabrication approach for the superhydrophobic hierarchical structures and demonstrate the excellent ability to harvest water energy when applied to water-solid contact triboelectric nanogenerator (TENG). A strategy combined with multiple photolithography and micromolding process was developed to accurately regulate the diameters and the center distances of the two-level micropillars. A variety of hierarchical structures were successfully fabricated and presented the advantages of structure control, large scale, high accuracy, and high consistency. The hydrophobic property characterizations were conducted, and the results indicated that the hierarchical structures showed a larger contact angle than the single-level structures and achieved superhydrophobicity. Then the hierarchical structures were applied to water-TENGs with flowing water continuously dripping on, and the effect of the structure parameter on the triboelectric output was analyzed. The hierarchical structures exhibited a superior ability to harvest water energy than the flat film and the single-level structures due to the enhanced friction area and superhydrophobic property. At a flowing velocity of 8 mL/s, the hierarchical structure generated the output voltage of approximately 34 V and the short-circuit current of around 5 μA. The water-TENG device exhibited a power density peak of 7.56 μW/cm² with a resistive load of 16.6 MΩ at a flowing velocity of 10 mL/s. These findings shed light on the potential applications of the hierarchical structures-based water-TENGs to water energy harvesting and self-powered sensor devices. Full article

The quest for better education and knowledge acquisition has triggered the introduction, acceptance and incorporation of e-learning into Nigerian learning. The introduction of the concept of e-learning to Nigerian learning can be dated back to the 1980s, when reputable Nigerians enrolled in several universities in London. In addition, the introduction of e-learning to a premier university in Nigeria, rooted in the college of Ibadan, led to greater interest, causing locals to seek extramural work and other studies at Oxford University. This study examines the impacts that proper educational administration, policy making and implementation, as well as the adoption of e-learning, can have to fix the dilapidated Nigerian educational structure. A quantitative method of data collection was used, through well-structured questionnaires for both administrators and students issued to the four universities sampled in this study. A total of 240 questionnaires were issued to respondents, with 60 each to the different universities and with 30 each for both students and administrators. A total of 180 were retrieved, and descriptive analysis was carried out with SPSS (23). Internal consistency was determined with Cronbach’s alpha, having an internal consistency of 0.78. The findings show that all the administrators were graduates with a minimum of a Bachelor’s degree. It was revealed that 32 (17.8%) of the students possessed smartphones as gadgets for e-learning and that administrators contributed to the enhancement of student performance, hence creating impacts in their examination grades, with a mean of 2.66, being rated ‘Good’ for their performance. Unfavorable government policies and unprofessionalism of administrators in e-learning implementations were the major constraints, with a mean of 4.6. The cost of the procurement of the needed resources (data) for e-learning also impacts e-learning. Internet resources used by the students contributed to huge success in e-learning for 28 (24.6%) and 24 (21.9%) students. Although the constraints limit the effectiveness of e-learning in Nigeria, it also impacts student advancement compared with the face-to-face learning process. The government’s proactive measures will improve e-learning. Full article

Perineural invasion (PNI), a sign of poor diagnosis and tumor metastasis, is common in a variety of malignant tumors. The infiltrating patterns and morphologies of tumors vary by organ and histological diversity, making PNI detection difficult in biopsy, which must be performed manually by pathologists. As the diameters of PNI nerves are measured on a millimeter scale, the PNI region is extremely small compared to the whole pathological image. In this study, an efficient deep learning-based method is proposed for detecting PNI regions in multiple types of cancers using only PNI annotations without detailed segmentation maps for each nerve and tumor cells obtained by pathologists. The key idea of the proposed method is to train the adopted deep learning model, U-Net, to capture the boundary regions where two features coexist. A boundary dilation method and a loss combination technique are proposed to improve the detection performance of PNI without requiring full segmentation maps. Experiments were conducted with various combinations of boundary dilation widths and loss functions. It is confirmed that the proposed method effectively improves PNI detection performance from 0.188 to 0.275. Additional experiments were also performed on normal nerve detection to validate the applicability of the proposed method to the general boundary detection tasks. The experimental results demonstrate that the proposed method is also effective for general tasks, and it improved nerve detection performance from 0.511 to 0.693. Full article

During the outbreak of the COVID-19 pandemic, social networks became the preeminent medium for communication, social discussion, and entertainment. Social network users are regularly expressing their opinions about the impacts of the coronavirus pandemic. Therefore, social networks serve as a reliable source for studying the topics, emotions, and attitudes of users that have been discussed during the pandemic. In this paper, we investigate the reactions and attitudes of people towards topics raised on social media platforms. We collected data of two large-scale COVID-19 datasets from Twitter and Instagram for six and three months, respectively. This paper analyzes the reaction of social network users in terms of different aspects including sentiment analysis, topic detection, emotions, and the geo-temporal characteristics of our dataset. We show that the dominant sentiment reactions on social media are neutral, while the most discussed topics by social network users are about health issues. This paper examines the countries that attracted a higher number of posts and reactions from people, as well as the distribution of health-related topics discussed in the most mentioned countries. We shed light on the temporal shift of topics over countries. Our results show that posts from the top-mentioned countries influence and attract more reactions worldwide than posts from other parts of the world. Full article

With the increasing need for eye tracking in head-mounted virtual reality displays, the gaze-based modality has the potential to predict user intention and unlock intuitive new interaction schemes. In the present work, we explore whether gaze-based data and hand-eye coordination data can predict a user’s interaction intention with the digital world, which could be used to develop predictive interfaces. We validate it on the eye-tracking data collected from 10 participants in item selection and teleporting tasks in virtual reality. We demonstrate successful prediction of the onset of item selection and teleporting with an 0.943 $F_1$-Score using a Gradient Boosting Decision Tree, which is the best among the four classifiers compared, while the model size of the Support Vector Machine is the smallest. It is also proven that hand-eye-coordination-related features can improve interaction intention recognition in virtual reality environments. Full article

This paper proposes a phase current reconstruction strategy based on a dc bus using a single current sensor for a surface permanent magnet synchronous motor (SPMSM). The method of a single current sensor reduces the number of mechanical hall sensors and shunt resistors by using a modified current reconstruction algorithm. The information of rotor position is estimated by the sliding mode observer for its rapid response and strong anti-interference ability, and the observer needs to detect voltage and current components from α − β coordinate system. In order to reduce the buffeting problem of sliding mode observers, an adaptive neural network is introduced, by the way of extracting angle speed estimated values from sliding mode observers, and these values are trained to obtain the compensate angular velocity and minus index value to suppress speed value. The performance of this sensorless speed regulation strategy in the high-speed region using a single current sensor with an optimized adaptive neural network is verified and evaluated by PSIM simulation and experiments. Full article

In this paper, we present a metasurface composed of complementary units that can realize orthogonal linear and linear-to-circular polarization conversion in multi-band. Linear polarization conversion has seven high-conversion frequency bands: 9.1–9.7 GHz, 15.6–17.6 GHz, 19.4–19.7 GHz, 21.2–23.1 GHz, 23.5–23.8 GHz, 26.2 GHz, and 27.9 GHz. Linear-to-circular polarization conversion also has seven frequency bands with axial ratios (ARs) less than 3 dB: 8.9–9.0 GHz, 9.9–14.7 GHz, 19.1–19.3 GHz, 23.2–23.35 GHz, 23.4 GHz, 24.1–25.4 GHz, and 27.2–27.8 GHz, with the generation of multiple bands extended by the combination of complementary units. Then, we utilize the combined polarization conversion unit’s mirror placement to form a 4 × 4 array to realize the phase difference cancellation of the reflective field, giving the metasurface the radar cross section (RCS) reduction function and the dual-band 10-dB monostatic RCS reduction bandwidth: 8.9–9.7 GHz and 15.5–26.1 GHz. The measured and simulated results were essentially identical. Because the design uses the complementary units to form an array to expand the polarization conversion frequency bands, it provides a novel idea for future designs and can be applied to multiple microwave frequency bands. Full article

Radar target characteristics need to be accurately extracted to enhance the role of high-frequency (HF) radar target recognition technology in modern radar, sea and air monitoring, and other applications. The pole characteristics of radar targets have become a mainstream research focus because of their inherent advantages for target recognition. However, existing pole extraction methods for complex targets generally have problems in early- and late-time responses aliasing and target information loss. To avoid this problem, this study proposes a new method to extract radar target poles based on the special particle swarm optimization algorithm (SPSO) and an autoregressive moving average (ARMA) model. This method, which does not involve the distinction between early-and late-time responses, is used to estimate an approximation of the entire scattering echo of the target. Then the parameters of the model are precisely optimized with the help of a particle swarm optimization algorithm combined with opposition-based learning and inertia weight decreasing. Strategy. Owing to the characteristics of the azimuth consistency of the target poles, a sliding window is used to calibrate the positions of multi-azimuth poles in the complex plane. The method was demonstrated to be feasible with good performance when it was applied to extract the pole features of ships at different azimuths in the high-frequency band. Full article

A mixed-signal spiking neural network (SNN) chip is presented, and its radiation effect-Total Ionizing Dose (TID) was studied. The chip was fabricated in a 180 nm silicon-on-insulator (SOI) integration process with an area of 3.75 mm²; the total doses were set at 300 krad (Si), 500 krad (Si), and 1 Mrad (Si). The TID radiation experimental results showed that the average spike frequency and spike amplitude of the output signal of the SNN circuit decreased after the irradiation because of the leakage current caused by the charge trapped in the buried oxide. Sensitive nodes were identified through the analysis of the critical path of the circuit, and guidance toward a radiation-hardening neuron circuit was proposed. The proposed circuit maintains good robustness with firing frequency variation. Full article

Cell detection in microscopy images can provide useful clinical information. Most methods based on deep learning for cell detection are fully supervised. Without enough labelled samples, the accuracy of these methods would drop rapidly. To handle limited annotations and massive unlabelled data, semi-supervised learning methods have been developed. However, many of these are trained off-line, and are unable to process new incoming data to meet the needs of clinical diagnosis. Therefore, we propose a novel graph-embedded online learning network (GeoNet) for cell detection. It can locate and classify cells with dot annotations, saving considerable manpower. Trained by both historical data and reliable new samples, the online network can predict nuclear locations for upcoming new images while being optimized. To be more easily adapted to open data, it engages dynamic graph regularization and learns the inherent nonlinear structures of cells. Moreover, GeoNet can be applied to downstream tasks such as quantitative estimation of tumour proportion score (TPS), which is a useful indicator for lung squamous cell carcinoma treatment and prognostics. Experimental results for five large datasets with great variability in cell type and morphology validate the effectiveness and generalizability of the proposed method. For the lung squamous cell carcinoma (LUSC) dataset, the detection F1-scores of GeoNet for negative and positive tumour cells are 0.734 and 0.769, respectively, and the relative error of GeoNet for TPS estimation is 11.1%. Full article

The reduction in sound pressure level is the focus of noise reduction in construction machinery, but the sound quality parameters can better describe the operator’s subjective perception of noise. This paper proposes a sound quality control method for the cab, which is based on complementary ensemble empirical mode decomposition for signal decomposition and reconstruction and an adaptive control algorithm error filter. Firstly, a subjective and objective prediction model was created to identify the target parameters for the sound quality control in the cab. Secondly, the noise was reconstructed based on a complementary ensemble empirical mode decomposition method, thus evaluating the influence of each component on the sound quality and determining the frequency interval. Lastly, the active sound quality control was completed based on the variable step size filter-error least mean square algorithm. The experiments were performed in the cab of a mini-excavator to verify the method’s effectiveness. It was verified that the loudness peak drops by 0.95 sones under stationary idle working conditions. The results demonstrate that the above methods play a guiding role in the actual application of sound quality control for the cab of construction machinery. Full article

Malware detection is a major security concern and has been the subject of a great deal of research and development. Machine learning is a natural technology for addressing malware detection, and many researchers have investigated its use. However, the performance of machine learning algorithms often depends significantly on parametric choices, so the question arises as to what parameter choices are optimal. In this paper, we investigate how best to tune the parameters of machine learning algorithms—a process generally known as hyper-parameter optimisation—in the context of malware detection. We examine the effects of some simple (model-free) ways of parameter tuning together with a state-of-the-art Bayesian model-building approach. Our work is carried out using Ember, a major published malware benchmark dataset of Windows Portable Execution metadata samples, and a smaller dataset from kaggle.com (also comprising Windows Portable Execution metadata). We demonstrate that optimal parameter choices may differ significantly from default choices and argue that hyper-parameter optimisation should be adopted as a ‘formal outer loop’ in the research and development of malware detection systems. We also argue that doing so is essential for the development of the discipline since it facilitates a fair comparison of competing machine learning algorithms applied to the malware detection problem. Full article

The aim of this paper is to establish mathematical modelling and simulation for the voltage induced during movement of the moveable magnet in a double-sided magnetic spring, being part of the energy harvesting system. For various configurations of the set of permanent magnets, the repulsive forces of magnetic spring and induced voltage in energy harvester winding will be calculated. Changing the geometrical dimensions and shape of permanent magnets allows one to control the stiffness of the so-called double-sided magnetic spring, and furthermore, allows one to change the natural frequency of the energy harvester system. Properly chosen stiffness in the energy harvester system is the crucial issue for high efficiency in energy recovery. In a given case, the energy harvester consists of three permanent magnets inserted into a tube with coils wound on it. To calculate the force between the magnets and the magnetic flux in the coils, the ANSYS program was used. The voltages induced in coils for various configurations of the magnets were simulated in the MATLAB program. Full article

The reliability of memory is an important issue. The rapid development of transistor technology makes the memory more prone to soft errors. Several recent efforts have proposed various designs to avoid the corruption of stored data by using Error Correction Codes (ECC). However, these designs tend to focus on one indicator, which means they cannot balance the electrical timing, area and power consumption constraints with the increasing of the chip-scale and the operating frequency. In this paper, we propose a design named TECED: A Two-Dimensional Error-Correction Codes Based Energy-Efficiency SRAM Design. We achieve higher energy-efficiency and lower hardware cost by using a two-dimensional error correction codes, and evaluate the design by considering the overall system performance. Comparing with the traditional Hamming code, the evaluation shows that the TECED reduces most of fifty percent of the area overhead and twenty-eight point five percent power consumption of the memory at a specific storage capacity. Full article

With the spread of eyewear devices, people are increasingly using information devices in various everyday situations. In these situations, it is important for eyewear devices to have eye-based interaction functions for simple hands-free input at a low cost. This paper proposes a gaze movement recognition method for simple hands-free interaction that uses eyewear equipped with an infrared distance sensor. The proposed method measures eyelid skin movement using an infrared distance sensor inside the eyewear and applies machine learning to the time-series sensor data to recognize gaze movements (e.g., up, down, left, and right). We implemented a prototype system and conducted evaluations with gaze movements including factors such as movement directions at 45-degree intervals and the movement distance difference in the same direction. The results showed the feasibility of the proposed method. The proposed method recognized 5 to 20 types of gaze movements with an F-value of 0.96 to 1.0. In addition, the proposed method was available with a limited number of sensors, such as two or three, and robust against disturbance in some usage conditions (e.g., body vibration, facial expression change). This paper provides helpful findings for the design of gaze movement recognition methods for simple hands-free interaction using eyewear devices at a low cost. Full article

Harmonic noises widely exist in industrial fields and always affect the computational accuracy of neural network models. The existing original adaptive zeroing neural network (OAZNN) model can effectively suppress harmonic noises. Nevertheless, the OAZNN model’s convergence rate only stays at the exponential convergence, that is, its convergence speed is usually greatly affected by the initial state. Consequently, to tackle the above issue, this work combines the dynamic characteristics of harmonic signals with prescribed-time convergence activation function, and proposes a prescribed-time convergent adaptive ZNN (PTCAZNN) for solving time-varying matrix inverse problem (TVMIP) under harmonic noises. Owing to the nonlinear activation function used having the ability to reject noises itself and the adaptive term also being able to compensate the influence of noises, the PTCAZNN model can realize double noise suppression. More importantly, the theoretical analysis of PTCAZNN model with prescribed-time convergence and robustness performance is provided. Finally, by varying a series of conditions such as the frequency of single harmonic noise, the frequency of multi-harmonic noise, and the initial value and the dimension of the matrix, the comparative simulation results further confirm the effectiveness and superiority of the PTCAZNN model. Full article

Small targets in long-distance aerial photography have the problems of small size and blurry appearance, and traditional object detection algorithms face great challenges in the field of small-object detection. With the collection of massive data in the information age, traditional object detection algorithms have been gradually replaced by deep learning algorithms and have an advantage. In this paper, the YOLOV3-Tiny backbone network is augmented by using the pyramid structure of image features to achieve multi-level feature fusion prediction. In order to eliminate the loss of spatial feature information and hierarchical information caused by pooling operations in convolution processes and multi-scale operations in multi-layer structures, a spatial attention mechanism based on residual structure is proposed. At the same time, the idea of reinforcement learning is introduced to guide bounding box regression on the basis of the rough positioning of the native boundary regression strategy, and the variable IoU calculation method is used as the evaluation index of the reward function, and the boundary regression model based on the reward mechanism is proposed for fine adjustment. The VisDrone2019 data set was selected as the experimental data support. Experimental results show that the mAP value of the improved small-object detection model is 33.15%, which is 11.07% higher than that of the native network model, and the boundary regression accuracy is improved by 23.74%. Full article