Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical Analysis of Tunnel-Injected Sub-300 nm AlGaN Laser Diodes

Electrically-pumped AlGaN-based edge-emitting laser diodes with a buried tunnel junction (TJ) for sub-300 nm emission are designed in this paper. Hole injection is one of the major concerns for the design of ultraviolet (UV) lasers based on this material system. The use of a low-resistive TJ as an intracavity contact within the devices will offer an opportunity to replace highly resistive p-type AlGaN-based cladding and contact layers by their n-doped counterparts. This advanced polarization-engineered interband TJs will lead to improved hole injection and a significantly reduced threshold voltage. The thermal properties of the tunnel-injected devices are thoroughly studied theoretically. For the demonstration of continuous-wave operating lasers, possible improvements in terms of better thermal management of the device are also discussed. Our improved design allows CW-operating AlGaN lasers with a threshold current density of <8 kA/cm2 and maximum optical output power of > 220 mW, yielding a wall-plug efficiency of > 2.8%.
Where Do Pedestrians Look When Crossing? A State of the Art of the Eye-Tracking Studies

It has been widely shown in the literature that analysing eye movements and positions can provide useful information for a better understanding of human perception and cognition. The eye-tracking technology, as a process of measuring where people look, has established itself as a widespread means of studying visual information processing in several domains, including in the study of human walking. Street-crossing can be defined as a particular form of walking. Indeed, several elements have to be considered in the decision-making process, such as the distance headway, traffic density, vehicle speed, etc. It is also a very risky aspect of walking as pedestrians are considered one of the most vulnerable road users. In this article, we present an up-to-date comprehensive review of existing eye-tracking experiments in the literature, from the pedestrian’s point of view, with a view to study the effects of both internal (e.g., age) and external (e.g., road environment) factors on pedestrians’ road crossing gaze behaviour. Furthermore, the current gaps in the literature are then discussed in order to open up some future perspectives in the field, such as the forthcoming introduction of automated vehicles on the roads.
Actively Q-Switched Tm:YAP Laser Constructed Using an Electro-Optic Periodically Poled Lithium Niobate Bragg Modulator

We present an EO Q-switched Tm:YAP laser constructed with a periodically poled Mg-doped congruent lithium niobate (PPMgCLN) Bragg modulator. A 2-mm-thick PPMgCLN modulator was adopted, and the internal stress generated in the electric poling process was released by annealing. The peak stress-induced refractive index variance for e-wave was deduced to be 2.29 × 10−5 at 1064 nm. When the absorbed pump power and repetition rate were 13.4 W and 1 kHz, respectively, the laser had a Q-switched pulse energy of 2 mJ, a pulse duration of 60 ns, and a peak power of 33.3 kW. By varying the reflection of the output coupler, output wavelengths of between approximately 1985 and approximately 1940 nm can be selected, making this laser a potential tool for pumping mid-IR sources and biomedical applications.
Review on Breast Cancer Disease Predictive Modelling using Swarm Intelligence

Breast Cancer (BC) is one among the most critical diseases affecting women across the world mostly between the ages 35-55 and is growing every year in a capricious rate. By identifying the disease in its early stages, we can control the occurrence of BC. The manual approach used by radiologists has failed since they are similar in the appearance of micro-calcifications, and segmentation of breast images are complicated process. Hence there is a need of automated systems for detection of the disease in early-stage which helps to assist radiologists for diagnosing the disease in a precise manner and make necessary decisions for patients’ treatment in the future. For classifying and predicting the disease, swarm intelligence (SI) plays a major role in terms of obtaining an optimal solution and reducing its training time. This paper presents study on review and analyses the performance of breast cancer disease predictive modelling using swarm intelligence. Also, it recommends the solution for forthcoming exertion to predict the location of the tumour in an efficient manner.
Automated robotic breast ultrasound acquisition using ultrasound feedback

Current challenges in automated robotic breast ultrasound (US) acquisitions include keeping acoustic coupling between the breast and the US probe, minimizing tissue deformations and safety. In this paper, we present how an autonomous 3D breast US acquisition can be performed utilizing a 7DOF robot equipped with a linear US transducer. Robotic 3D breast US acquisitions would increase the diagnostic value of the modality since they allow patient specific scans and have a high reproducibility, accuracy and efficiency. Additionally, 3D US acquisitions allow more flexibility in examining the breast and simplify registration with preoperative images like MRI. To overcome the current challenges, the robot follows a reference- based trajectory adjusted by a visual servoing algorithm. The reference trajectory is a patient specific trajectory coming from e.g. an MRI. The visual servoing algorithm commands in-plane rotations and corrects the probe contact based on confidence maps. A safety aware, intrinsically passive framework is utilised to actuate the robot. The approach is illustrated with experiments on a phantom, which show that the robot only needs minor pre-procedural information to consistently image the phantom while relying mainly on US feedback.
Ground Moving Vehicle Detection and Movement Tracking Based on the Neuromorphic Vision Sensor

Moving-objects detection is a critical ability for an autonomous vehicle. Facing the high detection requirements and the slow target-extraction problem of a common camera, this article proposes to utilize neuromorphic vision sensor (DVS) for detecting the moving objects and estimating their movement states. For a better detection work, the DVS image’s noise points are filtered and the CTRV kinematics model is built in advance. In order to distinguish the overlapped or nearby bodies and get the accurate clustering number, this article proposes a 3-D improved $K$ -means method. As the clustering centers can be influenced by the movement easily, the moving objects’ clustering centers appear unstable, so the movement estimation also fluctuates. In order to obtain stable movement estimation, this article proposes a strong tracking center differential external Kalman filter (SCDEKF) to track the moving objects, and the method has higher accuracy and less computational load. In order to verify the advantages of proposed methods, a simulation environment was established in the Gazebo, and the common cameras were also added in simulation for comparison with the DVS sensor. The simulation results show that the 3-D improved $K$ -means method with DVS can cluster the moving objects accurately, and the SCDEKF can provide more accurate movement estimation than the traditional EKF method. Finally, two experiments were conducted to prove the methods’ superiority. The main contribution is to solve the exploitation problems faced by the short-term borne sensor and promote its application in transportation.
The Examination of the Corporate Organisation and Implementation of Industry 4.0 in a High Value German Manufacturing Firm

This paper examines the corporate organisational aspects of the implementation of Industry 4.0. Industry 4.0 builds on new technologies and appears as a disruptive innovation to manufacturing firms. Although we do have a good understanding of the technical components, the implementation of the management and organisational aspects of Industry 4.0 is under-researched. It is challenging to find qualitative empirical evidence which provides comprehensive insights about real implementation cases. Based on a case study in a German high value manufacturing firm, we explore the corporate organisation and implementation of Industry 4.0. By using the framework of Complex Adaptive System (CAS), we have identified three key factors which facilitate the implementation of Industry 4.0 namely 1. ) Organisational structure changes such as the foundation of a central department for digital transformation, 2. ) The election of a Chief Digital Officer as a personnel change, and 3. ) Corporate opening up towards cooperating with partners as a cultural change. We have furthermore found that Lean Management is an important enabler that ensures readiness for the adoption of Industry 4.0.
Hardware-Friendly Laplacian-Based Multi-Focus Image Fusion in DCT Domain for Visual Sensor Network

Visual sensor network (VSN) requires a multi-focus image or video frame fusion technique involving focus measure computation in the DCT-domain to generate an all-in-focus image. Such techniques are implemented on resource-constrained on-board systems requiring hardware-friendly implementations. In this article, we first show that components of the Laplacian matrix are related to the discrete cosine transform (DCT) basis. The relation is that the eigenvalues of the Laplacian with proper boundary condition form the diagonal elements of the diagonal matrix generated by the DCT operation on the Laplacian. Exploiting this relation, we propose a focus measure which works on the DCT coefficients reflecting the spatial-domain Laplacian operation. Certain simplifications allow our focus measure computation through hardware-friendly integer multiplication and summation, where matrix multiplication involves just N scalar multiplications for an $N\times N~2\text{D}$ signal. Finally, we propose an approach which suitably fuses multi-focus images or video frames in DCT based image or video coding framework through detection of properly focused area and neighborhood consistency analysis. We show that our proposed approach is hardware-friendly, computationally simple, and is fast enough for VSN. Through experimental results, we show that our approach outperforms the relevant state-of-the-art in multi-focus image fusion for VSN both quantitatively and subjectively. We also show that our approach is effective in comparison to the state-of-the-art and a few latest generic multi-focus image fusion techniques in terms of quantitative and subjective evaluations.
X-Band Microwave Absorption in Nd³⁺-Substituted Mg–Cd Spinel Ferrites Synthesized Under Microwave Sintering

The electromagnetic parameters and microwave-absorbing properties of Nd<sup>3+</sup>-substituted Mg–Cd spinel ferrites with the chemical formula Mg<sub><italic>x</italic></sub>Cd<sub>1–<italic>x</italic></sub>Nd<sub>0.03</sub>Fe<sub>1.97</sub>O<sub>4</sub> (where <inline-formula> <tex-math notation="LaTeX">$x = 0$ </tex-math></inline-formula>, 0.2, 0.4, 0.6, 0.8, and 1.0) were investigated using vector network analyzer (VNA) in conjunction with the material measurement software. The permittivity of all the ferrites follows the usual dispersion behavior in the microwave frequency range. The behavior of ac conductivity and dielectric losses with frequency for various Mg content is quite similar. The variations in permeability with frequency for compositions with <inline-formula> <tex-math notation="LaTeX">$x = 0.4$ </tex-math></inline-formula>, 0.6, and 1 in the frequency range of 10–12.4 GHz obeys Snoek’s law. The peaks observed in the magnetic loss tangent spectra may be due to spin rotation created in the ferrites. With an increase in magnesium content, the reflection loss peak gets deepened, signifying the increased improvement in the absorption properties. Thus, it can be stated that Mg-substituted Cd–Nd ferrites have better performance when compared with pure Cd–Nd ferrite, proposing it to be a suitable material for electromagnetic shielding applications in the <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>-band.
A 12-bit 100-kS/s SAR ADC for IoT Applications

This paper presents a low-power 12-bit successive-approximation register (SAR) analog-to-digital converter (ADC) for IoT applications. The SAR ADC uses an adaptive sampler to increase the input tracking time and reduce the probability of metastability. A cyclic loop delay control circuit is proposed to optimize the total conversion time for this 12-bit SAR ADC. Furthermore, the capacitor swapping scheme is applied to maintain better ADC linearity with a smaller total capacitance and relax the ADC input driving capability. The prototype ADC was fabricated in a 180-nm CMOS technology. It consumes a total power of $1.15 \mu \mathrm{W}$ from a 0.7-V supply at 100-kS/s. With the capacitor swapping scheme, the measured SNDR and SFDR are 63.7 and 84 dB, respectively. With the MSB weight correction, the measured ENOB is 10.8 bits, equivalent to a peak figure-of-merit of 6.6 fJ/conversion-step.
A Mode-Localized Resonant Accelerometer With Self-Temperature Drift Suppression

This paper theoretically and experimentally demonstrates the temperature drift suppression capability of a mode-localized accelerometer based on three degree of freedom weakly coupled resonators. The theoretical model of the temperature characteristics of the amplitude ratio and frequency of the mode-localized accelerometer under the influence of Young’s modulus were established. The effect of the temperature on the input conversion mechanism and weakly coupled resonators are both included in the model. The simulation results show that thermal stress is the main factor affecting the output metric of the mode-localized accelerometer with mechanical coupling beam and, consequently, a stress relief structure is proposed to suppress the effect of stress on the output metric. Compared with the mode-localized accelerometer without stress relief structure, the temperature coefficients of sensitivity and two output metrics of the accelerometer with the stress relief structure are both suppressed by one order of magnitude via the simulation and experiment verification from 30°C to 60°C. And the temperature suppression coefficient of amplitude ratio is improved by 43.9 times than that of the frequency which shows that the mode-localized accelerometer based on amplitude ratio has an excellent temperature drift suppression capability.
Ergodic Capacity and Outage Performance Analysis of Uplink Full-Duplex Cooperative NOMA System

We propose an uplink full-duplex (FD) cooperative non-orthogonal multiple access (NOMA) system, where a dedicated Full-duplex (FD) relay is used to help two uplink users transmitting information to the base station. A novel FD relay transmission mode is proposed, in which the FD relay utilizes successive interference cancellation (SIC) and self-interference cancellation to decode two users symbols at the relay receiving antenna, and the relay transmitting antenna transmits superimposed signal to the base station using superposition coding. In this paper, we also consider a more realistic scenario where self-interference cancellation is imperfect. Hence, the residual-self interference exists. Moreover, the ergodic sum rate and the outage probability of the proposed system are investigated and the accurate analytical expressions are derived. Simulation results validate that, compared with the uplink half-duplex (HD) cooperative NOMA system and the uplink HD cooperative orthogonal multiple access (OMA) system, our proposed system obtains better performance of the ergodic sum rate and outage probability in the main signal to noise ratio (SNR) regime.
Non-Locally up-Down Convolutional Attention Network for Remote Sensing Image Super-Resolution

Recently, single image super-resolution (SISR) has been widely applied in the field of remote sensing image processing and obtained remarkable performance. However, existing CNN-based remote sensing image super-resolution methods are unable to exploit shallow visual characteristics at global receptive fields, which results in the limited perceptual capability of these models. Furthermore, the low-resolution inputs and features contain abundant low-frequency information, which are weighed in channels and space equally, hence limiting the representational ability of CNNs. To solve these problems, we propose a non-locally up-down convolutional attention network (NLASR) for remote sensing image super-resolution. First, a non-local features enhancement module (NLEB) is constructed to obtain the spatial context information of high-dimensional feature maps, which allows our network to utilize global information to enhance low-level similar structured texture information with effect, overcoming the defects of deficiency perceptual ability of shallow convolutional layers. Second, an enhanced up-sampling channel-wise attention (EUCA) module and enhanced down-sampling spatial-wise attention (EDSA) module are proposed to weight the features at multiple scales. By integrating the channel-wise and multi-scale spatial information, the attention modules are able to compute the response values from the multi-scale regions of each neuron and then establish the accurate mapping from low to high resolution space. Extensive experiments on NWPU-RESISC45 and UCMerced-LandUse datasets show that the proposed method can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.
Joint Component Design for the JSCC System Based on DP-LDPC Codes

The joint base matrix <inline-formula> <tex-math notation="LaTeX">${{{{B}}_{{\mathrm{J}}}}}$ </tex-math></inline-formula> of the joint source-channel coding (JSCC) system based on double protograph low-density parity-check (DP-LDPC) codes consists of four components, namely, the source code <inline-formula> <tex-math notation="LaTeX">${{{{B}}_{{\mathrm{s}}}}}$ </tex-math></inline-formula>, the channel code <inline-formula> <tex-math notation="LaTeX">${{{{B}}_{{\mathrm{c}}}}}$ </tex-math></inline-formula>, the type-1 connection edge <inline-formula> <tex-math notation="LaTeX">${{{{B}}_{{ \textit {L1}}}}}$ </tex-math></inline-formula> and the type-2 connection edge <inline-formula> <tex-math notation="LaTeX">${{{{B}}_{{ \textit {L2}}}}}$ </tex-math></inline-formula>, each having a non-negligible influence on the system performance. Different from the traditional component-specific design approach, we propose a joint design and optimization algorithm based on the idea of multi-objective differential evolution (MODE). Specifically, we consider the optimization of the DP-LDPC JSCC system through joint design of three components <inline-formula> <tex-math notation="LaTeX">${{{{B}}_{{\mathrm{s}}}}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${{{{B}}_{{\mathrm{c}}}}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${{{{B}}_{{ \textit {L1}}}}}$ </tex-math></inline-formula> and all four components <inline-formula> <tex-math notation="LaTeX">${{{{B}}_{{\mathrm{s}}}}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${{{{B}}_{{\mathrm{c}}}}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${{{\mathbf{B}}_{{ \textit {L1}}}}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${{{{B}}_{{ \textit {L2}}}}}$ </tex-math></inline-formula>, respectively. The proposed algorithm has low search complexity due to the reduction in size and element value of base matrices. The joint protograph extrinsic information transfer (JPEXIT) analyses and the simulation results demonstrate that the resulting JSCC system is free from a high error floor, requires fewer number of iterations for reaching the same bit error rate (BER) and achieves significant coding gains as compared to the state-of-the-art. Our DP-LDPC JSCC system is also shown to outperform its separation-based counterpart by a wide margin.
Forecasting Sales of Truck Components: A Machine Learning Approach

In this paper, forecasting sales model for truck components using machine learning algorithms is proposed. The forecasting model helps companions (i.e. Volvo Trucks) in the activity of trade and business. It also plays a major role for firms in decision-making operations in the areas corresponding to sales, production, purchasing, finance, and accounting. In order to achieve good forecasting sales mode, firstly, a normalization approach is performed on the time-series data to reduce and eliminate the data redundancy. After that, feature extraction and selection techniques are employed on the normalized data. Finally, different machine learning methods such as Support Vector Machine Regression, Ridge Regression, Gradient Boosting Regression and Random Forest Regression have been applied to the features of the normalized time-series data. Results depict that ridge regression method gives the most promising forecasting sale results of truck components compared to the other machine learning methods.
Geometric Pretraining for Monocular Depth Estimation

ImageNet-pretrained networks have been widely used in transfer learning for monocular depth estimation. These pretrained networks are trained with classification losses for which only semantic information is exploited while spatial information is ignored. However, both semantic and spatial information is important for per-pixel depth estimation. In this paper, we design a novel self-supervised geometric pretraining task that is tailored for monocular depth estimation using uncalibrated videos. The designed task decouples the structure information from input videos by a simple yet effective conditional autoencoder-decoder structure. Using almost unlimited videos from the internet, networks are pretrained to capture a variety of structures of the scene and can be easily transferred to depth estimation tasks using calibrated images. Extensive experiments are used to demonstrate that the proposed geometric-pretrained networks perform better than ImageNet-pretrained networks in terms of accuracy, few-shot learning and generalization ability. Using existing learning methods, geometric-transferred networks achieve new state-of-the-art results by a large margin. The pretrained networks will be open source soon1 .
Source Independence of Force Ripple in Linear Induction Machines

In this article, it is shown that in a linear induction machine (LIM), the force ripple that arises due to the magnetic asymmetry does not depend on whether the LIM is being fed from a voltage source or a current source. When the LIM is fed from a three-phase balanced voltage source, the primary voltage is balanced but the LIM draws an unbalanced current from the supply. When the LIM is fed from a three-phase balanced current source, the primary current is balanced but an unbalanced voltage appears across the LIM terminals. In both these cases, the unbalance creates a ripple in the propulsive force of the LIM. This force ripple increases with the increase in the speed of the LIM. This article shows that at any given speed, the percentage of the ripple component in the propulsive force remains constant irrespective of all operating conditions. The variation of the force ripple with the speed can be represented by a curve, which is unique for one LIM. This curve neither depends on the value of the primary voltage or primary current, nor does it depend on the nature of the energy source from which the LIM is being supplied. The force ripple arises from the flux distortion due to end effects and can, therefore, be instrumental in estimating the amount of end effects existing in an LIM.