The 50 most-cited articles in Electronics Letters, 2020

This Letter presents a new second‐order discrete map model, which is derived from a simple sampling switch‐based memristor‐capacitor circuit. The memristor‐based map model has infinite unstable and critical stable fixed points, and exhibits chaotic and hyperchaotic behaviours via a period‐doubling bifurcation scenario. These complex dynamical behaviours are confirmed by the phase portraits, iterative sequences and basins of attraction.

Brain complexity and non‐stationary nature of electroencephalography (EEG) signal make considerable challenges for the accurate identification of different motor‐imagery (MI) tasks in brain–computer interface (BCI). In the proposed Letter, a novel framework is proposed for the automated accurate classification of MI tasks. First, raw EEG signals are denoised with multiscale principal component analysis. Secondly, denoised signals are decomposed by empirical wavelet transform into different modes. Thirdly, the two‐dimensional (2D) modelling of modes is introduced to identify the variations of different signals. Fourthly, a single geometrical feature name as, a summation of distance from each point relative to a coordinate centre is extracted from 2D modelling of modes. Finally, the extracted feature vectors are provided to the feedforward neural network and cascade forward neural networks for classification check. The proposed study achieved 95.3% of total classification accuracy with 100% outcome for subject with very small training samples, which is outperforming existing methods…

Today, music is a very important and perhaps inseparable part of people's daily life. There are many genres of music and these genres are different from each other, resulting in people to have different preferences of music. As a result, it is an important and up‐to‐date issue to classify music and to recommend people new music in music listening applications and platforms. Classifying music by their genre is one of the most useful techniques used to solve this problem. There are a number of approaches for music classification and recommendation. One approach is based on the acoustic characteristics of music. In this study, a music genre classification system and music recommendation engine, which focuses on extracting representative features that have been obtained by a novel deep neural network model, have been proposed. Acoustic features extracted from these networks have been utilised for music genre classification and music recommendation on a data set.

Since its inception, multiple‐input multiple‐output (MIMO) has become a magical technology that continues to break new grounds and deliver the needed upgrades in mobile communications. The emerging 5G systems are also being labelled by many as the massive MIMO generation. This somewhat oversimplified view is perhaps a reflection of the great impact MIMO has had on our generation of mobile communication networks. Although the technologies have evolved in the past decades, the principle remains the same—to exploit the diversity of different copies of signals at independent locations for reducing the degree of fading and randomness of wireless channels. Through signal processing and coding, the diversity has been translated successfully into capacity gain and enhancement in other forms of the quality‐of‐service. This article identifies “fluid” antenna as a trending technology that may succeed MIMO and become a reality to transform wireless communications to a new height. Fluid antenna is a radical approach that advocates software‐controlled position‐flexible shape‐flexible antenna. The…

This Letter reports a new no‐equilibrium chaotic system with hidden attractors and coexisting attractors. Bifurcation diagram shows that the proposed system generates chaos through period‐doubling bifurcation with the variation of system parameters, and the hidden chaotic and periodic attractors are visually given by phase portraits. The coexisting chaotic and periodic attractors from different initial conditions are observed in the system. The analogue circuit realisation and microcontroller‐based experimental implementation of the system are given to verify its physical existence.

Abstract Advancement in vision‐based techniques has enabled the autonomous vehicle system (AVS) to understand the driving scene in depth. The capability of autonomous vehicle system to understand the scene, and detecting the specific object depends on the strong feature representation of such objects. However, pothole objects are difficult to identify due to their non‐uniform structure in challenging, and dynamic road environments. Existing approaches have shown limited performance for the precise detection of potholes. The study on the detection of potholes, and intelligent driving behaviour of autonomous vehicle system is little explored in existing articles. Hence, here, an improved prototype model, which is not only truly capable of detecting the potholes but also shows its intelligent driving behaviour when any pothole is detected, is proposed. The prototype is developed using a convolutional neural network with a vision camera to explore, and validates the potential, and autonomy of its driving behaviour in the prepared road environment. The experimental analysis of the…

Autism is a type of neurodevelopment disorder in which individuals often have difficulties in social relationship, communication, expressing and controlling emotions and poor eye contact, among other symptoms. Currently, electroencephalography (EEG) is the most popular tool to investigate the presence of autism biomarkers. Generally, EEG recordings generate huge volume data with dynamic behaviour. In current practice, the massive EEG data are visually analysed by specialist clinicians to identify autism, which is time consuming, costly, subject to human error, and reduces decision‐making reliability. Hence this Letter aims to develop an efficient autism diagnostic system that can automatically identify autism based on time–frequency spectrogram images from EEG signals. Firstly, the raw EEG data is pre‐processed using several techniques, such as re‐referencing, filtering and normalisation. After that, the pre‐processed EEG signals are converted to two‐dimensional images using a short‐time Fourier transform. Then, textural features are extracted, and significant features are selected…

Emotions are the most powerful information source to study the cognition, behaviour, and medical conditions of a person. Accurate identification of emotions helps in the development of affective computing, brain–computer interface, medical diagnosis system, etc. Electroencephalogram (EEG) signals are one such source to capture and study human emotions. In this Letter, a novel time‐order representation based on the S‐transform and convolutional neural network (CNN) is proposed for the identification of human emotions. EEG signals are transformed into time‐order representation (TOR) based on the S‐transform. This TOR is given as an input to CNN to automatically extract and classify the deep features. Emotional states of happiness, fear, sadness, and relax are classified with an accuracy of 94.58%. The superiority of the method is judged by evaluating four performance parameters and comparing it with existing state‐of‐the‐art on the same dataset.

In this Letter, the physical layer security for a novel reconfigurable intelligent surface (RIS)‐assisted non‐orthogonal multiple access (NOMA) system in a multi‐user scenario is investigated, where the authors consider the worst case that the eavesdropper also utilises the advantage of the RISs. More specifically, they derive analytical results for the secrecy outage probability (SOP). From the numerical results, they observe that the use of RISs can improve the secrecy performance compared to traditional NOMA systems. However, for the worst case that the received signals at the eavesdropper come from the RISs and source, increasing the number of intelligent elements on the RIS has a negative impact on the secrecy performance. At high signal‐to‐noise ratios, the system's SOP tends to a constant. Finally, the secrecy performance can be improved through group selection.

Radar sensors have a new growing application area of dynamic hand gesture recognition. Traditionally radar systems are considered to be very large, complex and focused on detecting targets at long ranges. With modern electronics and signal processing it is now possible to create small compact RF sensors that can sense subtle movements over short ranges. For such applications, access to comprehensive databases of signatures is critical to enable the effective training of classification algorithms and to provide a common baseline for benchmarking purposes. This Letter introduces the Dop‐NET radar micro‐Doppler database and data challenge to the radar and machine learning communities. Dop‐NET is a database of radar micro‐Doppler signatures that are shareable and distributed with the purpose of improving micro‐Doppler classification techniques. A continuous wave 24 GHz radar module is used to capture the first contributions to the Dop‐NET database and classification results based on discriminating these hand gestures as shown.

Emotion recognition is actively used in brain–computer interface, health care, security, e‐commerce, education and entertainment applications to increase and control human–machine interaction. Therefore, emotions affect people's lives and decision‐making mechanisms throughout their lives. However, the fact that emotions vary from person to person, being an abstract concept and being dependent on internal and external factors makes the studies in this field difficult. In recent years, studies based on electroencephalography (EEG) signals, which perform emotion analysis in a more robust and reliable way, have gained momentum. In this article, emotion analysis based on EEG signals was performed to predict positive and negative emotions. The study consists of four parts. In the first part, EEG signals were obtained from the GAMEEMO data set. In the second stage, the spectral entropy values of the EEG signals of all channels were calculated and these values were classified by the bidirectional long‐short term memory architecture in the third stage. In the last stage, the performance of…

Cloud clients face high security risks while sharing physical resources with multiple users. A malicious cloud user exploits co‐residency and hypervisor vulnerabilities to steal and hamper sensitive information of victim virtual machine (VM). To address this alarming issue, this Letter proposes a secure resource allocation model by developing a threat detector based on the analysis of co‐located inter‐VM relations and a workload predictor in the background. The anomalous network traffic, response speed, bandwidth usage and illegal inter‐VM links are considered as security breaches indicator that assists in future threat detection beforehand and consequently guides the secure and resource efficient VM allocation. The comparison of proposed model with state‐of‐the‐art: FCFS, Random‐fit and NSGA‐II based resource allocation approaches indicate that it significantly reduce security threats by 77.38% and number of active servers up to 5.39% with improved resource utilisation up to 26.84% over Random‐fit.

The authors report on highly scaled 60 nm gate length graded‐channel AlGaN/GaN high electron mobility transistors (HEMTs) with a record power added efficiency (PAE) of 75% at 2.1 W/mm power density at Vdd = 10 V and the PAE of 65% at 3.0 W/mm power density at 30 GHz at Vdd = 14 V. Under two‐tone power measurement, the graded‐channel AlGaN/GaN HEMTs demonstrated similar power performance with peak PAE >70% at 30 GHz. This novel channel design shows great promise for high‐efficiency millimetre‐wave (mmW) power amplifiers up to 3 W/mm RF power density operation.

Owing to the significantly installed capacity of solar energy during the past decades, the operation and maintenance of the large photovoltaic power station is a big challenge, as the manual inspection is labour‐intensive and expensive. This Letter presents a solution for the intelligent inspection of the hotspot with the unmanned aerial vehicle in the large‐scale photovoltaic plant. First, a traditional image processing method is presented to eliminate the noise and crop the infrared image, which can make the defected feature more obvious. Then, the module is extracted by the line segments detection. Finally, considering the great advance in the realm of the computer vision, a deep‐learning‐based method for automatic hotspot detection is proposed for locating the hotspot with the data augmentation. The deep‐learning‐based model can extract the hotspot feature by the training process in the data set. In the end, the performance of the method proposed is extensively evaluated and the numerical results prove the accuracy and precision of the model.

A novel waveguide step‐twist integrated with a bandpass filter is presented in this Letter. The twist is composed of four equally rotated cavities to achieve polarisation rotation and filtering functionalities simultaneously. Such step‐twist can achieve a significant reduction in size and weight. The design is demonstrated at Ka‐band using waveguide technology and is fabricated using a stereolithography apparatus 3D printing together with metal plating. The device is designed to have a centre frequency of 32 GHz and a bandwidth of 1 GHz. The measured result shows good agreement with simulations, with a measured average insertion loss of 0.84 dB and a return loss better than 15 dB across the passband.

Identification of human emotion involving electroencephalogram (EEG) signals has become an emerging field in health monitoring application as EEG signals can give us a more diverse insight on emotional states. The aim of this study is to develop an efficient framework based on deep learning concept for automatic identification of human emotion from EEG signals. In the proposed framework, the signals are pre‐processing for removing noises by low‐pass filtering and then delta rhythm is extracted. After that, the extracted rhythm signals are converted into the EEG rhythm images by employing the continuous wavelet transform and then deep features are discovered by using a pre‐trained convolutional neural networks model. Afterwards, MobileNetv2 is used for deep feature selection to obtain the most efficient features and finally, long short term memory method is employed for classification of selected features. The proposed methodology is tested on ‘DEAP EEG data set’ (publicly available). This study considers two emotions namely ‘Valence’ and ‘Arousal’ for classification. The…

In this Letter, a recorded single channel of 6.915-Gbit/s white-light visible light communication (VLC) system is reported and experimentally demonstrated. By optimising the bit-loading algorithm onto the direct-current optical OFDM signal and without using an optical blue filter, the high data rate can be achieved. After a free-space propagation distance of 1.5 m, the white-light beam diameter of 14 cm and illuminance of 795 lux are measured. The proposed white-light VLC system can provide both lighting and communication simultaneously with functional propagation distance.

Wilson's disease (WD) is caused by the excessive accumulation of copper in the brain and liver, leading to death if not diagnosed early. WD shows its prevalence as white matter hyperintensity (WMH) in MRI scans. It is challenging and tedious to classify WD against controls when comparing visually, primarily due to subtle differences in WMH. This Letter presents a computer‐aided design‐based automated classification strategy that uses optimised transfer learning (TL) utilising two novel paradigms known as (i) MobileNet and (ii) the Visual Geometric Group‐19 (VGG‐19). Further, the authors benchmark TL systems against a machine learning (ML) paradigm. Using four‐fold augmentation, VGG‐19 is superior to MobileNet demonstrating accuracy and area under the curve (AUC) pairs as 95.46 ± 7.70 % , 0.932 ( p < 0.0001 ) and 86.87 ± 2.23 % , 0.871 ( p < 0.0001 ), respectively. Further, MobileNet and VGG‐19 showed an improvement of 3.4 and 13.5% , respectively, when benchmarked against the ML‐based soft classifier – Random Forest.

Active infrared imaging is one of the promising remote and whole field characterisation techniques for non‐destructive testing and evaluation of various solids irrespective of their electrical and magnetic prosperities. This technique relies on a mapping of thermal response for a predefined incident heat flux over the test object to detect the presence of surface and subsurface anomalies. Due to its fast, non‐contact, safe and quantitative testing capabilities, infrared thermography has gained significant importance in the testing of fibre reinforced polymers. This Letter highlights testing and evaluation of glass fibre‐reinforced polymer (GFRP) specimen for detection of subsurface hidden defects using pulse compression favourable thermal wave imaging techniques (for an imposed digitised chirp as well as a 7‐bit Barker coded modulated heat fluxes over the test specimen). Further depth scanning capabilities of the proposed schemes have been compared using a time‐domain pulse compression based approach. Proposed analytical, as well as simulation studies, have been validated with the…

The authors present the implementation of two amplifiers in a commercial flexible amorphous Indium Gallium Zinc Oxide (a‐IGZO) thin‐film transistor (TFT) technology: a Cherry Hooper (CH) amplifier and a 4‐stage common source amplifier. The CH amplifier is designed as a pre‐amplifier for wireless receivers. It is optimised for a high gain‐bandwidth product (GBW). From a supply voltage of V it provides 19.4 dB gain and has a ‐bandwidth of 5.3 MHz, while consuming 0.2 mW. It has a GBW of 49.35 MHz, which is more than a factor two better than previously reported a‐IGZO TFT amplifiers. The 4‐stage common source amplifier is designed as output buffer, has a very wide range of operating conditions and strong robustness against manufacturing tolerances. From a supply voltage of V it provides 28.9 dB gain, has a ‐bandwidth of 1.2 MHz, and a GBW product of 33.43 MHz, while consuming 14.2 mW. Both circuits can operate from a supply voltage between 2.5 and 10 V.

This Letter proposes demodulation type PLL for phase and frequency estimation of single‐phase system that can reject DC offset. Using results from the adaptive estimation literature, this Letter proposes a linear parametric model‐based initial phase angle estimation approach. Then by using differentiation and integration operation on the estimated initial phase angle, the frequency is estimated. This avoids the use of any low‐pass filter unlike conventional demodulation‐based technique. Moreover, unlike existing demodulation‐based technique, the proposed technique can completely reject DC offset. Comparative experimental results, provided with state‐of‐the‐art DC offset rejection‐based enhanced phase locked‐loop, clearly demonstrate the suitability of the proposed technique.

The unnatural activities of brain due to seizure events are analysed by electroencephalogram (EEG) signals which are captured from the brain. In this work, a methodology is proposed to classify the seizure EEG signals. In the proposed method, a novel sparse spectrum based empirical wavelet transform (SS‐EWT) is applied to decompose the EEG signal into coefficients. From the obtained SS‐EWT coefficients, the cross‐information potential and normalised energy are extracted as features. Then these features are ranked using the RELIEFF method to obtain significant features. After ranking, these features are fed into the k ‐nearest neighbour ( k ‐NN) classifier to classify EEG signals corresponding to different brain activities. In this work, the first classification problem is the classification of the seizure (S), seizure‐free (F), and normal (Z) EEG signals in which obtained classification accuracy (Acc) is . The second classification problem is the classification of S and Z EEG signals in which Acc is achieved by the proposed method.

This Letter proposes a reliable and lightweight key generator based on a novel bit‐self‐test arbiter physically unclonable function (BST‐APUF). The BST‐APUF adds a delay detection circuit into a classical APUF to automatically test the delay deviation that produces each bit of the PUF response and generates a reliability‐flag for each response to indicate its reliability. The key generator collects robust responses and produces a secure key using a cryptographic entropy accumulator. FPGA implementation results show that the overhead and the helper data length of the authors proposed key generator are significantly lower than that of the state‐of‐the‐art schemes when generating a 128‐bit key with a bit error rate of 10 −9 .

Recently, transformer has been applied to the image caption model, in which the convolutional neural network and the transformer encoder act as the image encoder of the model, and the transformer decoder acts as the decoder of the model. However, transformer may suffer from the interference of non‐critical objects of a scene and meet with difficulty to fully capture image information due to its self‐attention mechanism's dense characteristics. In this Letter, in order to address this issue, the authors propose a novel transformer model with decreasing attention gates and attention fusion module. Specifically, they firstly use attention gate to force transformer to overcome the interference of non‐critical objects and capture objects information more efficiently via truncating all the attention weights that smaller than gate threshold. Secondly, through inheriting attentional matrix from the previous layer of each network layer, the attention fusion module enables each network layer to consider other objects without losing the most critical ones. Their method is evaluated using the…

Single switched‐capacitor and series LC resonant converter‐based active voltage balancing circuit are presented in this Letter. This converter is proposed to balance the cell voltage in series‐connected electrochemical energy storage devices namely battery or supercapacitor. This balancing circuit directly transfers the energy from higher capacitive energy storage cells to lower energy storage cells in the string. It realises the maximum energy recovery and zero voltage gap between the cells and overcomes the drawback of switching loss, conduction loss, balancing time duration, and the voltage difference between the cells of conventional switched‐capacitor as well as single LC converter. The details of the balancing circuit operation, theoretical, and mathematical analysis are presented. The experimental result demonstrated that the balancing circuit result where the voltage difference is 451–0 mV in 124 min for two 12 V, 4.5 Ah lead‐acid batteries.

A bandwidth‐enhanced four‐antenna multiple‐input–multiple‐output (MIMO) system based on a substrate‐integrated‐waveguide (SIW) cavity is proposed. By etching two long rectangular slots on the top plane of the cavity, four quarter‐mode sub‐cavities with the same size are constructed and then individually fed by a coaxial port to form a four‐antenna MIMO system. Suitable port isolation is achieved by optimising the length and width of these slots. To enhance bandwidth, a pair of back‐to‐back L‐shaped slots is cut to split each sub‐cavity into three smaller parts. Three resonant modes are generated in these parts and merged into the operating band. A sample with the overall size of 0.58 λ 0 × 0.58 λ 0 × 0.02 λ 0 has been fabricated. Measured bandwidth is enhanced from 80 to 230 MHz and measured isolation is better than 15.0 dB. Owing to covering the band of 3.40–3.60 GHz, the proposed antenna system has potential applications for fifth‐generation communication.

A hollow metallic waveguide‐input resonant tunnelling diode three‐terminal mixer device based on a photonic‐crystal platform for terahertz (THz) communication applications is presented. Coherent detection was successfully demonstrated, with an increase in the sensitivity of >30 dB compared with that of direct detection. Error‐free communications with a data rate >10 Gbit/s at 0.3 THz using the developed device as a coherent THz receiver are achieved.

Biomedical data are being collected for fields like cancer diagnosis and prognosis, brain signals, speech signals, genetic engineering to name a few. These data are very high dimensional these days, which makes it difficult to extract knowledge out of it through machine learning algorithms. In this work, the authors proposed a hybrid feature selection method based on a multi‐attribute decision‐making method PROMETHEE (preference ranking organisation method for enrichment evaluations) and Jaya optimisation algorithm. Their proposed method works in two phases. In the first phase, five filter methods are applied to get the ranking for each feature of the data set. In the second phase, all the five individual ranks are used as input choices for PROMETHEE which gives us a final rank for all the features. Then the top 3% features are selected for training the machine learning model. This technique is applicable for feature reduction in any high‐dimensional biomedical data. Here, they have studied Parkinson's disease data set. The result shows that the proposed method improves the…

Moving towards commercialisation of chipless RFID systems, there are still some pressing issues. In the tag design level, the issues are high data capacity in the order of tens of bits, compact design and the immunity to the interference which can be achieved by a cross‐polar response. It is demanded to yield the highest numbers of bits in a tag ID while the occupied physical space and allocated bandwidth are minimised. Increasing the number of resonators in a conventional multi‐resonator chipless tag causes a significant tag size growth. The destructive coupling effect between resonators in multi‐resonator tags is another critical issue. In this Letter, to combat those issues, a multi‐band resonator is proposed. The comb‐shaped tags contain both co‐polar and cross‐polar Radar cross section (RCS) responses, hence they can be used in high reflective environments. Two samples of 14‐bit and 40‐bit comb‐shaped tags are designed, which have physical sizes of 17 mm × 13 and 30 mm × 45 mm, respectively. The promising performance of the proposed structure, which is beyond the state of art…

This Letter reports a high‐performance film bulk acoustic resonator (FBAR) that can be applied in 5G wireless communication. The FBAR has a back‐etched free‐standing structure using an ultra‐thin single‐crystalline lithium niobate (LiNbO 3 ) as the piezoelectric film. The thin LiNbO 3 was obtained by the smart cut method and FBARs were fabricated by a standard MEMS process. Owing to the superior bulk‐like properties of the single‐crystal thin film, the fabricated FBARs have a resonant frequency of 5.0 GHz, a quality factor above 1800 and an electromechanical coupling coefficient of 2.66%, demonstrating a promising potential of FBAR filters in 5G and future 6G applications.

A novel deep-learning-based method for semantic segmentation of RGB and thermal images is introduced. The proposed method employs a novel neural network design for multi-modal fusion based on multi-resolution patch processing. A novel decoder module is introduced to fuse the RGB and thermal features extracted by separate encoder streams. Experimental results on synthetic and real-world data demonstrate the efficiency of the proposed method compared with state-of-the-art methods.

The sigmoid activation function plays an essential role in implementing neural networks in hardware. However, due to a high degree of non-linearity, the hardware implementation of the activation function itself is a daunting job. In this Letter, the authors proposed an accuracy controlled iterative algorithm for the piecewise linear approximation of the sigmoid function. The proposed algorithm approximates sigmoid activation with an optimised number of linear functions for a given error. The accuracy and hardware complexity of the resulting approximation function are compared with the state-of-the-art literature.

Electroencephalogram (EEG) signals are widely used in diagnosis of epilepsy. Accurate classification of seizure types based on EEG signals can provide vital information for diagnosis and treatment. Since visual inspection and interpretation of seizure types are time consuming and prone to errors, a novel classification method combining wavelet packet decomposition (WPD) and local detrended fluctuation analysis (L‐DFA) is proposed for the computer‐aided diagnostic system. The proposed method is able to classify a wide variety of seizures automatically and accurately. As the first step towards this goal, raw EEG signals are decomposed by WPD according to intrinsic frequency bands of human brain. Then L‐DFA is applied to characterise the dynamical fractal structure of sub‐band signals. Finally, EEG signals are classified by support vector machine based on the combined fractal spectrum features. The experimental results on Temple University Hospital database show that the proposed method achieves a total classification accuracy of 97.80%, outperforming existing methods based on the same…

Low‐dielectric constant (low‐ k ) material is critical for advanced FinFET technology parasitic capacitance reduction to enable low‐power and high‐performance applications. Silicon Oxycarbonnitride (SiOCN) is one of the most promising low‐ k materials for FinFET gate sidewall spacer. The k value of SiOCN can be controlled in the range of 4.1–5.2 by modifying the chemical contents during the deposition process. However, the integration of SiOCN with k value lower than 5.2 for advanced FinFET technology faces substantial challenges associated with the material damage from subsequent manufacturing processes. Here, the authors demonstrate a hybrid low‐ k spacer scheme on a fully integrated 7 nm FinFET technology platform, in which SiOCN with k value of 4.5 was successfully integrated along the sidewalls of the gate electrode as spacer while retaining the structural integrity and dielectric properties. Device characterisation on the hybrid low‐ k spacer scheme ( k = 4.5) demonstrated 12/11% reduction in P/NFET overlap capacitance ( C OV ) and 3% reduction in ring oscillator effective…

Mobile robot path planning is an important part of the mobile robot field. The swarm intelligence optimisation algorithm has certain advantages in solving such multi‐objective optimisation problems, so this Letter proposes to apply the pigeon‐inspired optimisation (PIO) algorithm to the path planning problem. However, the traditional PIO algorithm is suffering from the problems of easy to get into local optimisation, low stability and premature convergence. To overcome its shortcomings, this Letter proposes a logistic beetle algorithm search–PIO (LBAS‐PIO) algorithm. In the optimisation process of the LBAS‐PIO algorithm, the PIO algorithm is initialised by the logistic mapping, making the search space more extensive. Learning from the idea of the beetle algorithm search, each pigeon has its own judgment of the environment and the number of iterations and search time are reduced. Further, the path evaluation function of the algorithm and the execution order of the operators are optimised to make the algorithm smoother and easier to find the global optimal solution. Simulation and…

Discriminative correlation filters (DCFs) have recently achieved competitive performance in visual tracking benchmarks. However, such trackers perform poorly when the target undergoes occlusion, viewpoint variation or other challenging attributes. To tackle these issues, in this Letter, the authors combine the fast DCF trackers with the precise deep learning methods to eliminate the accumulating drift for the vehicle tracking based on unmanned aerial vehicle platform. Specifically, the authors employ the tracking result of the DCF tracker as the input of the boundary regressing network. After judging the existence of the target in the input patch, the proposed network would estimate the boundary of the target vehicle. Furthermore, the output would be updated to the tracking template, aiming at eliminating the accumulation errors and achieving a long‐term tracking. The effectiveness of the proposed algorithm is validated through experimental comparison on widely used tracking benchmark data sets.

A compact dual‐band bandpass filter (BPF) based on parallel coupled lines and shorted stubs has been presented in this Letter. The transmission and reflection coefficients of the proposed dual‐band BPF are straightforwardly derived via the input impedances using odd‐ and even‐mode analysis methods. Four transmission poles in the two passbands and nine deep transmission zeros (TZs) in the stopband can be realised. The centre frequencies and bandwidths of the two passbands can be tuned by controlling the impedance parameters of the coupled lines and shorted stubs. For demonstration, a prototype example of this dual‐band BPF with a small size is experimentally characterised. The measured results show that it has high‐frequency selectivity with multiple TZs at the stopband.

When a wheeled mobile robot is travelling at high longitudinal velocities and turning, it may sideslip. Except for a few researchers, this issue has not received much attention. However, there is a high possibility that sideslip may cause danger, so the authors think this problem needs to be solved urgently. The mechanism of preventing sideslip is simple, which is to reduce the longitudinal velocity of the robot when turning. But, how to slow down is a question worth studying. To this end, they have proposed two schemes, but these two schemes have poor real‐time performance and poor robustness to positioning errors, respectively. In order to solve the above problems, considering that the real‐time and robustness of fuzzy control are very good, they propose a controller by combining robust control and model predictive control in this Letter. The fuzzy control is used to adjust the longitudinal velocity according to the state of the robot, and the model predictive control is used to achieve path tracking. According to the simulation results, the proposed fuzzy model predictive…

This Letter demonstrates a non‐destructive prediction methodology for determining the necessary magnetic field to be imposed in a porous fin satisfying a particular heat generation occurring within an electronic device. For the first time, all possible kinds of heat transfer have been incorporated here, which were otherwise ignored in other published studies of a similar kind. Just observing the surface thermal response, golden section search (GSS) solver, in conjunction with a forward numerical scheme, has been used in this work to determine the strength of the magnetic field. Estimations are done for various levels of additive white Gaussian noise and satisfactory reconstructions are noted for noise level even up to 12%. The numerical method has been convincingly validated with other schemes of the published literature. Sensitivity analysis reveals that the temperature distribution is per se a strong function of the fin porosity and governed by a mutual trade‐off between the heat generation rate and the imposed magnetic field. The results obtained from the present analysis using…

High‐frequency performance of vertical InAs/InGaAs heterostructure nanowire MOSFETs on Si is demonstrated for the first time for a gate‐last configuration. The device architecture allows highly asymmetric capacitances, which increases the power gain. A device with L g = 120 nm demonstrates f T = 120 GHz, f max = 130 GHz and maximum stable gain (MSG) = 14.4 dB at 20 GHz. These metrics demonstrate the state‐of‐the‐art performance of vertical nanowire MOSFETs.

In this work, metal–insulator–semiconductor field effect transistors (MISFET) with gate length of 350 nm were fabricated on hydrogen‐terminated polycrystalline diamond by a self‐aligned process. Aluminium film with thickness of 2 nm was evaporated on the sample and formed self‐oxidised alumina to act as the gate dielectric. The devices show good direct current and radio frequency performances with a maximum frequency of oscillation ( f max ) of 34 GHz and continuous‐wave output power density of 650 mW/mm at 10 GHz.

Electroencephalogram (EEG) measures brainwaves that have been the widely used modality for brain–computer interface (BCI) applications. EEG signal with machine learning has gained substantial success in the BCI. However, the availability of limited training data, appropriate model selection and high false‐positive rates are the challenges that need immediate attention. Therefore, in this Letter, the authors present a mental task classification model based on the notion of transfer learning that addresses the issue of data scarcity, model selection and misclassification ratio. In the framework, the proposed model uses pre‐trained network for the extraction of diverse feature and classify using support vector machine. The authors employed four pre‐trained networks to identify the optimal network for the proposed framework: Vgg16, Vgg19, Resnet18 and Resnet50. The highest classification accuracy of 86.85% (using Resnet50) was achieved using transfer learning. Comparison results showed that convolutional neural network‐based approach outperformed conventional machine learning approaches…

Compact substrate‐integrated waveguide (SIW) dual‐band bandpass filter with closely spaced passbands and wide‐stopband characteristics is proposed for the first time based on inline‐coupled perturbed SIW triple‐mode cavities. The first three resonant modes in the triple‐mode cavities can be flexibly controlled so that two closely spaced passbands can be realised based on the first two resonant modes, and wide‐stopband can be achieved by staggering the third and other higher‐order resonant modes without resorting to extra structures. The external and internal couplings of the dual passbands can be specified and designed flexibly benefitting from the offset feeding ports and coupling windows. A prototype centred at 13 and 14 GHz is synthesised, designed, fabricated, and tested as a demonstration, extending the stopband to 1.97 f 1 with rejection level better than 25 dB.

In this Letter, performance of multiple users in downlink of non‐orthogonal multiple access (NOMA) network considering the general shadowed fading model which was proposed in recent years is studied. In particular, main metrics including the outage probability, ergodic rate related to ordered channels are derived in novel and exact expressions for arbitrary values of the fading parameters. Moreover, numerical simulations are carried out in order to exhibit further insights on how the system performance is affected by the fading parameters.

The authors propose a new non‐destructive inspection method for underground fiberglass‐reinforced plastic mortar (FRPM) pipelines. Their method utilises time‐domain responses of microwave guided‐modes propagating along an FRPM pipe‐wall. Through the time‐domain analyses, they found that a microwave guided‐mode signal propagating along a pipe‐wall is spread out to various waves corresponding to straight and circumferential waves along the pipe‐wall and that the position of an unwanted foreign object on the wall can be detected by examining the path of the wave interrupted by the foreign object. Additionally, they also derived a propagation equation that enables them to calculate a time‐domain response of the spread microwave guided‐mode signals propagating along the pipe‐wall. This method can be applied to pipelines consisting of various types of materials.

A new recurrent neural network is presented for solving linear algebraic systems with finite‐time convergence. The proposed model includes an exponential term in the Zhang neural network dynamical system, which leads to a faster convergence of the error‐monitoring function in comparison to previous methods. Theoretical analysis, as well as simulation results, validate the efficacy of the proposed model.

Tertiary control is the highest level in hierarchical control of microgrids to perform power exchange between the microgrid and external networks. In this Letter, a tertiary control strategy based on predictive function control (PFC) is proposed to manage power flow in DC microgrid (DCMG) clusters. Unlike linear PI control suitable for single‐input and single‐output systems, the PFC strategy is a nonlinear control method and capable of multiple input and multiple output control tasks with multiple objectives optimisation on‐line. By solving the cost function in the finite receding horizon, voltage set points are computed in real‐time to create bus voltage difference for power transfer between interconnected DCMGs. The PFC‐based tertiary control for the DCMG cluster is verified through simulations.

In this Letter, the authors present a study on linear channel estimators and their respective mean square error expressions acknowledging spatially correlated channels and pilot contamination. They also investigate the impact of imperfect channel covariance matrix knowledge.

Electroencephalogram (EEG) is a diagnostic method that provides information about the functioning of the brain. EEG can be used to diagnose the abnormally functioning part of the brain by monitoring electrical activities. An automated system has been proposed to create a computer‐based expert opinion needed in the detection of epilepsy and to capture a more objective view. To this end, the approximation and detail coefficients of EEG signals are calculated by using the wavelet packet transform (WPT). The coefficients were subjected to feature extraction using dispersion entropy and line length methods. The extracted feature vector has been applied as input to the support vector machine (SVM) and k‐nearest neighbour (KNN) classifiers. The proposed method was tested using the public EEG seizure dataset created by the University of Bonn. In this study, the dataset was evaluated in two different ways as binary cases and multiclass cases. Evaluated classification accuracy was 100% for binary classification with SVM. For multiclass classification evaluated accuracy was 99.85% with KNN.…

Active infrared thermography or thermal wave imaging is one of the promising non‐contact, non‐invasive, remote, patient friendly and safe imaging modality for the testing and evaluation of various bio‐materials. Among various thermal wave imaging modalities recently proposed, frequency modulated thermal wave imaging gained its acceptance due to its high depth resolvability for visualising the surface and sub‐surface features of the test sample. This is due to its continuous depth scanning capabilities with the imposed continuous band of thermal waves with significant and equal magnitudes into the test sample with moderate peak power imposed heat flux in a limited span of experimentation time. This Letter highlights a novel analytical approach for examination of the severity of the osteoporosis. Further, the proposed analytical treatment is validated with the results obtained from the commercially available finite‐element based modelling and simulation.