A method for designing sparse large planar arrays in whole field with a minimum number of elements is proposed in this Letter. The method is developed by compressed sensing algorithms and a new composite beam pattern covering both far- and near-field information. The experimental results demonstrate that the sparse planar array achieves a 95.3% element thinning compared to the fully sampled planar array and the side lobe peaks in near-field conditions are at least 3.4 dB lower than those in previous studies.

In this Letter, an attempt has been made to segment the brainstem structure from structural magnetic resonance (sMR) images and differentiate early mild cognitive impairment (EMCI) from normal and Alzheimer's condition using multifractal texture measures. The images considered from public domain database are preprocessed and brainstem is segmented using fuzzy ‘C’ means-based connected component labelling method. Multifractal spectrum (MS) is evaluated for the segmented brainstem structure using the multifractal detrended moving average technique. Seven MS-based texture measures are extracted and statistically analysed using one-way analysis of variance. Classification algorithms namely, naïve Bayes, decision tree and random forest (RF) are employed to distinguish the EMCI condition. Ten-fold cross-validation approach is executed and its performance is evaluated using accuracy, precision, recall and area under the curve. The results indicate that the segmented brainstem structures possess multifractal characteristics. Of the extracted MS-based texture measures, α _max, α ₀, α_R, B and Δα are found to be statistically significant. RF gives the highest accuracy of 92.3% in distinguishing EMCI from other subject groups. Hence, the proposed approach with brainstem MS-based texture measures in combination with RF can be used as an imaging biomarker for the diagnosis of EMCI.

This work reports the first of its kind custom implantable cathodic voltage controlled electrical stimulator (iCVCES) for use with implanted orthopaedic devices. The iCVCES allows researchers to study DC cathodic voltage controlled electrical stimulation (CVCES) in vivo with no percutaneous leads. The iCVCES is a microcontroller-based potentiostat circuit with wireless (bluetooth) communication that controls the DC cathodic stimulation and monitors the environment around the implant. Comprising a single PCB with a coin cell battery, the device is able to hold a constant voltage as large as −1.8 V in a dynamic impedance environment. In vitro tests demonstrate functionality comparable to bench-top systems, and in vivo tests demonstrate operation in live rats, for up to 8 days, with the ability to provide constant stimulation with a maximum variation of 2 mV.

In this work, a 5-GHz current-controlled ring oscillator based integer PLL is implemented with a spur reduction sampler to reduce the reference spurs. The sampler can have taps with each tap sampling the oscillator's control voltage at offsets of the reference phase. The sampler introduces bandstop notches at the frequencies where the spurs appear. The samplers are capable of reducing the spurs from to , a reduction by 50 dB, as evident from simulation results performed in 28 nm technology.

This Letter presents a 300 GHz signal source, realised using an 800 nm transferred-substrate InP double heterojunction bipolar transistor process. The source is based on an active frequency tripler. It delivers −1.6 dBm peak output power at 306 GHz. The DC consumption is only 36 mW, which corresponds to 2% conversion efficiency. The measured tripler achieves more than 30 GHz bandwidth and exhibits very low unwanted harmonics. The chip area of the source is only 0.94 × 0.84 mm².

Power attacks are able to leak the secret key of protected cryptographic circuits as long as the number of sampled power data is sufficient to filter the injected noise. However, in this Letter, a novel power attack is proposed to break protected cryptographic circuits when the number of available power data is inadequate for filtering the injected noise. To achieve this goal, a state-of-the-art machine learning technique: autoencoder is utilised for generating a certain number of new power data that are similar to the original power data to assist the noise filtering. Within the proposed autoencoder neural networks, the input and output layers are set with arrays. Furthermore, two convolution layers are used in the encoder block and decoder block, respectively, to achieve similar features between the input layer and the output layer. Under the assistance of autoencoder, the novel power attack reveals the secret key of a protected cryptographic circuit with 300,000 power data. In contrast, the regular power attacks fail to break the cryptographic circuit under the same number of power data.

In this Letter, a field programmable gate array (FPGA)-based synchronising control system design for spinning disk confocal image scanning microscope is described. Based on the analysis on the condition of laser triggering, the author simplifies the design by transforming the floating-point calculation formula to a very simple form of integer calculation, which can then be implemented by fixed-point calculation on FPGA without trimming any bit, for which there is no computational precision loss. The testing of simulation and experiment shows that the design is very accurate and reliable.

Face biometry is a popular user authentication scheme that is easy to use and tends to be less invasive than other user authentication approaches. Despite the success achieved by face biometrics, face spoofing attacks (or presentation attacks) still pose a challenge to researchers. In practice, fraudsters may deceive a face authentication system by displaying fake copies of an authorised user face, such as photos or videos, and gain unauthorised access to the system. This work proposes a method for detecting unauthorised access attempts using misrepresentations of the identity of an authorised user. The proposed methodology for presentation attack detection relies on the observation of imaging and liveness attributes, such as the detection of liveness using the face deformation energy, and imaging attributes usually found in authentic accesses such as facial and background textures, and steganalysis features. Based on the experimental results, the proposed approach potentially can detect face spoofing attacks at each frame of video sequences with error rates of half total error rate (HTER), and also in full video sequences with , for the CASIA and Nanjing University of Aeronautics and Astronautics databases, respectively.

Recent studies have shown that deep convolutional neural networks (CNNs) significantly boosted the performance of single-image super-resolution (SISR). In this Letter, the authors present a novel dual branches network (DBN) for SISR. Different from traditional CNN, the authors' DBN utilises the benefits of the residual structure and the densely connected structure together. Their key strategy is to divide the input path of the network into dual branches: a residual branch and a dense branch. This dual branches structure reuses valuable features and explores new features effectively. Experimental comparisons demonstrated the high ability of their DBN over the state-of-the-art framework for SISR with alleviating blurs of output images.

Image style transferring is a process of generating an output image in a target style from a given pair of content and target style images. Recently, a simple linear interpolation technique in encoded feature space has been employed in this process to generate output images of intermediate style because controlling the strength of style transferring effect is a key function of an image editing filter. However, this simple technique is effective in generating images of around full style but around zero style hence it cannot smoothly control the style transferring effect from an original content image to a fully stylised image. In this Letter, the authors tackle the missing work on style-strength control from content reconstruction to full style generation. To deal with this problem, they propose to use additional unbiased training data and losses for a style transfer network to learn an unbiased regression between output style strength and style control parameter. Experimental results verified that the proposed method achieved a full range of style-strength control from zero style to full style with no additive complexity in image generating process.

Learning-based image super-resolution is considered as a promising solution to reconstruct a high-resolution image from a low-resolution image. To improve the super-resolution performance dramatically, this Letter focuses on the effect of training dataset on the performance and proposes an image super-resolution scheme based on patch orientation-specified network. In particular, a deep neural network is trained using patches with a specific orientation and angular transformation is combined with the neural network to cope with various orientations in input patches. Experimental results show the suggested network model is superior to existing state-of-the-art super-resolution alternatives.

Recent convolutional neural networks have made significant advancements in the detection of road cracks. However, the lack of accurate crack training data reduces the generalisation ability of the deep model. In this Letter, a semi-automatic pavement crack labelling algorithm is proposed to solve the problem of insufficient training data. First, the modified C–V model is used to obtain the preliminary segmentation results. Second, the direction of the initial segmentation area is calculated by the ellipse fitting method, and the preliminary segmentation results are used as samples for accurate labelling. Finally, a multi-scale feature extraction module is proposed for learning rich deep convolutional features, which allows the acquired crack features under a complex background to be more discriminant. The experimental results were compared with the manual marking method, and this method can achieve accurate marking of crack images with a low amount of interaction, thereby significantly reducing the cost of ground-truth making. The results of the validation and comparison experiments on test data sets indicate that the proposed method can not only effectively identify cracks, but also overcome the interference of many factors in the environment.

In Global Navigation Satellite System (GNSS) community, pseudorange observables, without integer ambiguity, have large measurement noise, while carrier phase observables, with high measurement accuracy, have integer ambiguity, which restricts the direct use either of them for high-accuracy navigation and positioning. In this regard, fusing pseudorange and ambiguity-free time-differenced carrier phase (TDCP) is a popular data processing technique. In this Letter, the least-squares fusion is studied. A theoretical analysis of the steady performance of this fusion is conducted. A set of formulas of the steady performance is derived, based on which simulations are carried out for different pseudorange-carrier-phase precision ratios. Results showed that the steady accuracy of fusion pseudorange is about two orders of magnitude higher than that of the original pseudorange, and is close to that of carrier phase observables. Besides, the convergence speed and the comparison to the Hatch filter are also checked in the simulations.

A low complexity encoding method and encoder architecture are proposed for the low-density parity-check (LDPC) codes in space applications recommended by Consultant Committee for Space Data Systems. This method uses the generator matrix partition and decomposition to obtain a much smaller dense core matrix. Thus, only one quarter of dense block matrices need to be implemented using the traditional circulant encoding structure. Therefore, the encoder can be implemented with lower computation and storage complexity.

A novel electromagnetic bandgap (EBG) structure is proposed to suppress the simultaneous switching noise in high-speed circuits. The proposed EBG pattern is etched on the power plane while the ground plane remains intact. The unit is composed of an S-bridge EBG embedded with a coplanar EBG. The proposed EBG structure possesses an ultrawide bandgap extending from 0.28 to 25 GHz when the suppression depth is −45 dB. A prototype of the proposed EBG power plane is fabricated and tested, and good agreement is achieved between the simulation and the measurement.

The performance of high-frequency hybrid sky-surface wave radar (HFSSWR) is known to suffer from the spread Doppler clutter (SDC). As a major source of the SDC, ionospheric clutter generally possesses high directivity within the main detection range. Classical coherent sidelobe cancellation method provides an efficient way to suppress the highly directional clutter. However, it performs poorly when the directions of a target and the clutter are both in the main lobe. To address this problem, a novel clutter suppression method using oblique projection is proposed, which does not require priori knowledge of the targets, such as directions, Doppler frequencies, strength etc. By combining training data selection based on oblique projection with SDC suppression in the Doppler domain, this method is implemented in the interested range cell for all target directions. Experimental results indicate that the proposed method can achieve better clutter suppression performance based on real data.

The micro-Doppler effect is a useful signature for classifying various human behaviours. However, most micro-Doppler researches assume that only a single moving target exists during the observation. Their works lack in separating micro-motion features from multi-movers. When more than one target is present, their performance will deteriorate heavily. To address this issue, the authors design a new 3D (three-dimensional) model, range–velocity–time points, to separate and describe multi-mover micro-motions measured by the ultra-wideband radar. These 3D points contain the range–velocity–time information simultaneously. By dividing points in the 3D space instead of single Doppler domain, micro-Doppler signatures of each target can be separated effectively. Multi-people motion simulation results verify the effectiveness of the authors' method.

Pulse-agile radar systems are becoming more prevalent as the demand for adaptive and cognitive systems increases. This focus is motivated by the need for interference avoidance and spectrum sharing. Pulse agility within a coherent processing interval (CPI) or intra-CPI adaption has been shown to cause distortion, which will negatively impact the radar's performance. This problem can be framed in the context of image processing such that an ideal range–Doppler image is corrupted by some point spread function. Deconvolution is then applied to remove this distortion and improve radar performance while maintaining the ability to use computationally efficient fast Fourier transform-based processing.

Vertical cell transistor is necessary to drastically reduce the chip size of the dynamic random access memory. This structure has a great advantage in terms of shrinkage, but it also has the disadvantage of increasing the OFF-state current by causing floating body effect (FBE). For the first time, it is demonstrated that a stretched tunnelling diode, which consists of a p⁺ layer next to the n⁺ active layer in the buried body, leads to a drastically suppressed FBE. The OFF-state current is sharply reduced by about seven orders compared with a conventional structure. Furthermore, the decrease in the OFF-state current is at minimum when the length of the stretched p ⁺ region is approximately half the channel length (L _p/L=1/2).

Gold codes are widely used for pseudo-noise (PN) ranging, and non-commensurate sampling is an effective solution to improving the tracking performance of digital code tracking loops. However, the sequence transition density of gold codes is low, which limits the achievable ranging accuracy. Here, the authors present a non-commensurate sampling and double-loop-based regenerative PN code tracking approach. With this approach, the tracking of the original regenerative code is equivalent to that of its clock component, and the sequence transition density is maximised. Thus, the tracking error is apparently reduced and optimal tracking performance is achieved. In the double-loop structure, a unified tracking loop is employed to facilitate performance comparison. Simulation results demonstrate the performance improvement of the proposed approach over the traditional gold code tracking scheme, and the relationship between the non-commensurate sampling parameters and the achievable tracking accuracy. The approach is especially suitable for precision ranging applications, and can be extended to other applications involving accurate signal tracking in digital equipment.

In this Letter, the authors present a closed-form derivation of the probability density function (PDF) for the notch depth (ND) of the dominant mode rejection adaptive beamformer (ABF). Their PDF expression closely approximates the true PDF for the case of single interferer at reasonable levels of interference-to-noise ratio. Their simulation results illustrate the close approximation between proposed distribution function and the ND histogram over a wide range of snapshots for fixed number of sensors.

In this Letter, the authors derive the moment generating function-based BER expressions for binary phase shift keying and M-QAM signal for OFDM systems over –– fading. This fading model is known as the generalised fading model because it can accurately characterise all the well known small-scale fading models given in the literature. In order to validate the derived BER expressions, analytical and simulated BER curves for a variety of modulation formats at different values of fading parameters are also shown in the letter.