Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Skin-Disease Classification Based on Customized Loss Function Combined With Balanced Mini-Batch Logic and Real-Time Image Augmentation

Skin cancer is one of the most common cancers in the world. However, the disease is curable if detected in the beginning stage. Early detection of malignant lesions through accurate techniques and innovative technologies has a significant impact on reducing skin cancer mortality rates. Recently, artificial intelligence has come to the forefront to facilitate skin cancer diagnosis based on medical images. Many deep learning models have been studied and developed, but the imbalance of performance among classes in the multi-class classification is still a challenging problem. This study proposes a hybrid method for handling class imbalance of skin-disease classification. This method combines the data level method of balanced mini-batch logic followed by real-time image augmentation with the algorithm level method of designing new loss function. The training dataset includes 24,530 dermoscopic images of seven skin disease categories, which is by far the largest dataset of skin cancer. The performance metrics of six proposed methods are evaluated on a test dataset of 2,453 images. Our proposed EfficientNetB4-CLF model achieves the highest accuracy of 89.97% and also the highest mean recall of 86.13% with the smallest recalls’ standard deviations of 7.60%. Compared to the original methods, our proposed solution not only surpasses 4.65% (86.13% vs 81.48%) of mean recalls but also reduces 4.24% of the recalls’ standard deviations (from ±11.84% to ±7.60%). This result indicates that our hybrid method is highly effective in training the Deep CNN network on the skin-disease imbalanced dataset. It addresses the problem of slow learning of the minority classes in the networks by combining the data level method of balanced mini-batch logic followed by the real-time image augmentation with the algorithm level method of the newly designed loss function.
Efficiency Analysis of Machine Learning Intelligent Investment Based on K-Means Algorithm

With the rapid development of technologies such as big data, intelligent data analysis and cloud computing, the application of Internet financial technology has become more and more extensive, and with the advent of the era of large asset management in the domestic wealth management industry, in order to improve the efficiency of financial services, traditional finance is needed. The products and services provided by the industry have been innovated, resulting in smart investment. Compared with traditional investment, smart investment as a new business model has the advantages of low threshold, low cost and high efficiency. However, as far as its nature is concerned, smart investment must first play the role of an investment adviser. Therefore, for enterprises or individuals who invest, the investment efficiency of smart investment is the most important. At present, the research on the efficiency analysis of smart investment, due to the improper selection of algorithm models or the lack of deep data mining, leads to the analysis of the investment efficiency of smart investment products is inconsistent with or even deviated from the actual situation. In view of these problems, this paper selects China Merchants Bank’s Capricorn Intelligence as the research object, and analyzes the investment efficiency of smart investment based on K-means cluster analysis and data mining technology. The results show that Capricorn has a certain randomness in the selection process of the fund, and chooses to reduce the rate of return in order to control the risk. The investment portfolio formulated for the customer has obvious timing. The results show that the machine learning based on K-means algorithm makes a concrete analysis of the investment efficiency of Capricorn Smart Investment, this method can also be used for the efficiency analysis of other smart investment products.
Molecular mechanisms of lipid-rich myelin membrane sheet formation

Myelin is a membrane of vital importance. In the vertebrate nervous system, it promotes rapid conduction of nerve impulses via insulating the axons. To act as an insulator, myelin needs to assemble into a stable, lipid-rich structure. While the biochemical composition of myelin is well described, mechanisms regulating this unique molecular composition are poorly understood. In this study, we show that oligodendrocytes employ a molecular sieve to filter out membrane proteins from compact myelin domain. Myelin basic protein (MBP) forms a size barrier and controls the entry of proteins into the compact myelin based on the bulkiness of their cytoplasmic domains. In fact, only proteins with cytosolic domain of less than 30 amino acids cross the permeability barrier. Mechanistically, we show that after binding to the inner leaflet of the myelin bilayer, MBP self-associates and phase separates. Self-assembly requires hydrophobic interactions between the phenylalanine residues. Replacing phenylalanine residues with serines abolished the self-association between the MBP molecules without perturbing membrane binding capabilities. We further show that during development, molecular self-assembly of the MBP molecules is required for the extrusion of bulky proteins from the compacted myelin membrane sheets. This system might have evolved in oligodendrocytes in order to generate an anisotropic membrane organization that facilitates the assembly of highly insulating lipid-rich membranes.
A Hybrid Electric Circuit Battery Model Capturing Dynamic Battery Characteristics

An accurate and dynamic battery model that could be simulated in a circuit designing environment is critical for the designing of energy efficient systems. A variety of battery models have been proposed but they leave room for improvement as they do not account for all the nonlinear dynamic battery characteristics in model with sufficient accuracy. For energy aware circuit designing, we need an equivalent circuit model that can be co-simulated in circuit simulation environment and is capable of predicting the dynamic battery characteristics with higher accuracy. The proposed model is a hybrid model composed of diffusion model and combined electric circuit-based model, which accounts for nonlinearities of rate capacity effect, recovery effect, capacity fading, storage runtime and open circuit voltage, current- and temperature- dependency to transient response. This new hybrid model is simulated in MATLAB-Simulink and the results are validated through experimental observations for Lithium ion, Lead Acid and Nickel Metal Hydride batteries.
A 42-GHz TIA in 28-nm CMOS With Less Than 1.8% THD for Optical Coherent Receivers

This letter shows a fully differential linear transimpedance amplifier designed for the emerging coherent optical communications for high data rate transmissions. The purpose of this letter is to present a possible solution in 28-nm CMOS technology instead of the most frequently used BiCMOS. The idea is to take advantage of the linearity of CMOS to partially offset its inherently lower transconductance over current ratio, which typically limits the bandwidth. The proposed TIA presents a −3-dB bandwidth equal to 42 GHz and its transimpedance gain can be programmable from 78 to 36 dB $\Omega $ with four variable gain stages. At maximum gain, the measured average input noise is equal to 18 pA/√Hz whereas the maximum total harmonic distortion for an output voltage swing of 500-mV peak-to-peak differential is always lower than 1.8% over the entire gain range. The complete transimpedance amplifier including the bias and dc offset cancellation circuits consumes 319 mW from a 2.4-V supply voltage.
Ecological Cooperative Adaptive Cruise Control for a Heterogeneous Platoon of Heavy-Duty Vehicles With Time Delays

To deal with energy crisis and environmental issues, higher fuel economy standards and more stringent limitations on greenhouse gas emissions for ground vehicles have been made. Ecological cooperative adaptive cruise control (Eco-CACC) has been considered as an effective solution to decrease the fuel consumption and greenhouse gas emissions of a platoon of vehicles, and this article proposes an Eco-CACC strategy for a heterogeneous platoon of heavy-duty vehicles with time delays. The proposed Eco-CACC strategy consists of distributed control protocols for the following vehicles and a new model predictive controller for the leading one. Firstly, after the distributed control protocols are designed based on the neighboring vehicle information, the sufficient conditions that guarantee the internal stability and string stability are derived, and the upper bound of the time delays under controller parameters is also obtained. Secondly, assuming the vehicle platoon as a rigid body with followers staying at desired positions, the new model predictive controller is designed in order to further improve the overall fuel economy of vehicle platoon. Simulations are conducted to validate the sufficient conditions of internal stability and string stability, and to explore the fuel saving performance of the proposed control strategy. The simulation results demonstrate that, compared with the benchmark, the proposed Eco-CACC strategy can significantly improve the fuel economy of heterogeneous platoon.
A Review of Emerging Devices Beyond MOSFET for High Performance Computing

The Metal Oxide Semiconductor Field Effect Transistor (MOSFET) device dimensions are continuously shrinking to achieve higher performance, lower cost, and to satisfy the low power demand of high throughput applications. To continue with Moore's law, promising Nanoscale devices with less effects of short channel needs to be fabricated. The emerging devices must be compatible with the Complementary Metal Oxide Semiconductor (CMOS) fabrication technology. The device design community is in search for perfect alternatives of conventional MOSFET. This paper reviews few emerging devices for future high speed, small size and low power which can replace conventional MOSFET.
Dynamic Modeling and Motion Control of a Cable-Driven Robotic Exoskeleton With Pneumatic Artificial Muscle Actuators

This paper presents the design, dynamic modeling and motion control of a novel cable-driven upper limb robotic exoskeleton for a rehabilitation exercising. The proposed four degree-of-freedom robotic exoskeleton, actuated by pneumatic artificial muscle actuators, is characterized by a safe, compact, and lightweight structure, complying with the motion of an upper limb as close as possible. In order to perform a passive rehabilitation exercise, the dynamic models were developed by the Lagrange formulation in terms of quasi coordinates combined with the virtual work principle, and then the adaptive fuzzy sliding mode control was designed for the rehabilitation trajectory control. Finally, rehabilitation experiments were conducted to validate the prototype of upper limb robotic exoskeleton and the controller design.
Asymptotic Performance of Discrete-Valued Vector Reconstruction via Box-Constrained Optimization With Sum of $\ell _{1}$ Regularizers

In this paper, we analyze the asymptotic performance of convex optimization-based discrete-valued vector reconstruction from linear measurements. We firstly propose a box-constrained version of the conventional sum of absolute values (SOAV) optimization, which uses a weighted sum of $\ell _{1}$ regularizers as a regularizer for the discrete-valued vector. We then derive the asymptotic symbol error rate (SER) performance of the box-constrained SOAV (Box-SOAV) optimization theoretically by using the convex Gaussian min-max theorem (CGMT). We also derive the asymptotic distribution of the estimate obtained by the Box-SOAV optimization. On the basis of the asymptotic results, we can obtain the optimal parameters of the Box-SOAV optimization in terms of the asymptotic SER. Moreover, we can also optimize the quantizer to obtain the final estimate of the unknown discrete-valued vector. Simulation results show that the empirical SER performance of Box-SOAV and the conventional SOAV is very close to the theoretical result for Box-SOAV when the problem size is sufficiently large. We also show that we can obtain better SER performance by using the proposed asymptotically optimal parameters and quantizers compared to the case with some fixed parameter and a naive quantizer.
Detection of Railway Leakage Cable Jigs Based on SSD

The detection of railway leakage cable jigs is very important for ensuring reliability of railway communication system. However, the detection of jigs mainly relies on manual way at present, which is waste of manpower and inefficiency. With the rapid development of deep learning, SSD algorithm is well known for its high speed and accuracy. This paper mainly designs and realizes an intelligent detection system of railway cable leakage jigs based on SSD model. Experimental results show that, the average accuracy of mAP reaches 88.13%, which provides an efficient solution for intelligent detection of railway cable leakage jigs.
Implementation of High Resolution Digital Pulse Width Modulator Based on FPGA

Conventional pulse width modulators (PWM) for radar detection application are analogue with complex architecture or digital with a low degree of accuracy. PWMs implemented with simple architecture and high resolution are essential requirements of modern embedded system for performing diversified control tasks. The previously reported PWMs are not ideal in terms of simple architecture, and high resolution. This work proposed four digital pulse width modulators that cater to these two major design concerns. Mixed-mode clock manager(MMCM) block and digital programmable circuit are employed as fundamental resources to construct the field programmable gate array(FPGA) based DPWM. Corresponding solutions together with simple architecture and high resolution DPWMs based on Flip flop(FF), counter, and carry chain utilizing ZYN Q - 7020 FPGA are discussed and presented respectively. At the same time, experimental results indicate a high resolution of 20.03ps achieved by the MMCM based DPWM, while the carry chain based DPWM leads to a 592ps resolution. The proposed architectures are validated using ALINX AX7020 development board.
GT-GAN: A General Transductive Zero-Shot Learning Method Based on GAN

Most zero-shot learning (ZSL) methods based on generative adversarial networks (GANs) utilize random noise and semantic descriptions to synthesize image features of unseen classes, which alleviates the problem of training data imbalance between seen and unseen classes. However, these methods usually only learn the distributions of seen classes in the training stage, ignoring the unseen ones. Due to the different distributions of seen and unseen samples, i.e., image features, these methods cannot generate unseen features of sufficient quality, so the performances are also limited, especially for the generalized zero-shot learning (GZSL) setting. In this article, we propose a general transductive method based on GANs, called GT-GAN, which can improve the quality of generated unseen image features and therefore benefit the classification. A new loss function is introduced to make the relative positions between each unseen image and its $k$ nearest neighbors in the feature space as consistent as possible with their relative positions in the semantic space; this loss function may be easily applied in most existing GAN-based models. Experimental results on five benchmark datasets show a significant improvement in accuracy compared with that of original models, especially in the GZSL setting.
Robust Distortion Estimation of Fisheye Cameras under Stereographic Projection Model

The imaging geometry of a real imaging system always deviates from the ideal projection models. This deviation is treated as distortion, which is always estimated along with other parameters when calibrating a camera. Although the perspective projection model is chosen in most existing distortion estimation methods, it can’t be used to describe a fish-eye imaging system. In this paper, we proposed a method to estimate the distortion of the fish-eye cameras in which the stereographic projection model is being used. Images of spheres and sets of parallel lines are utilized to estimate the distortion parameters. The method is validated on synthetic, simulated, and real images. Experimental results show that our method is robust to noise, and a subpixel accuracy is achieved. By applying estimated parameters, undistorted fisheye images can be treated as a stereographic projection of the world, which will be highly convenient for computer vision tasks.
Concealed Threat Detection based on Multi-view Millimeter Wave Imaging for Human Body

Near-range millimeter wave (MMW) has attracted much more attentions for its great potential in security check. In this paper, deep-learning based concealed threat detection is researched by MMW human body imaging systems. To improve the detection performance for small objects in MMW images in noises and textures, we design a voting classifier based on multi-view images in a scan. Faster region with conventional neural network (Faster-RCNN) and online hard example mining (OHEM) architectures are utilized to detect objects in each image, where the results are combined and voted for a more reliable detection in the scan. The proposed method is validated on our dataset of 700k MMW human body images, which surpassed the state-of-the-art object detectors both in precision and recall.
Walking Control of a Biped Robot on Static and Rotating Platforms Based on Hybrid Reinforcement Learning

In this paper, we proposed a novel Hybrid Reinforcement Learning framework to maintain the stability of a biped robot (NAO) while it is walking on static and dynamic platforms. The reinforcement learning framework consists of the Model-based off-line Estimator, the Actor Network Pre-training scheme, and the Mode-free on-line optimizer. We proposed the Hierarchical Gaussian Processes as the Mode-based Estimator to predict a rough model of the system and to obtain the initial control input. Then, the initial control input is employed to pre-train the Actor Network by using the initial control input. Finally, a model-free optimizer based on Deep Deterministic Policy Gradient framework is introduced to fine tune the Actor Network and to generate the best actions. The proposed reinforcement learning framework not only successfully avoids the distribution mismatch problem while combining model-based scheme with model-free structure, but also improves the sample efficiency for the on-line learning procedure. Simulation results show that the proposed Hybrid Reinforcement Learning mechanism enables the NAO robot to maintain balance while walking on static and dynamic platforms. The robustness of the learned controllers in adapting to platforms with different angles, different magnitudes, and different frequencies is tested.
Design of a Rotary-Linear Motor With Unipolar SPM and Voice Coil Structure

In this article, a rotary-linear motor that achieves rotary and linear motions with a simple one-air-gap structure, low rotary ripple, and smooth linear force is proposed. The proposed motor combines a five-phase unipolar surface-mounted permanent-magnet (USPM) and a voice coil structure to simplify the motor structure, which motor can be named as rotary-linear surface-mounted permanent-magnet voice coil motor (RL-SVCM). And because of the characteristics of the rotary five-phase armature circuit and linear voice coil circuit, the proposed motor can provide low rotary ripple and smooth linear force. For designing the motor, the magnetic equivalent circuit (MEC) method is used to study the relationship between the linear force performance and motor structure parameters, and the effect of the flux bridges on the linear force and rotary torque. Due to uncertain variables in MEC, the FEM method is used to analyze the precise output result of rotary torque and linear force, and non-interaction between rotary and linear motions.
Computational Fluid Dynamics Study for Drag Reduction of an Airborne Surveillance Gimbal

This article describes the drag minimization of an existing gimbal assembly mounted to an airplane, scanning a sight for surveillance purposes. The aerodynamics problem has been numerically investigated using a Commercial Computational Fluid Dynamics software Fluent 19.2 by solving Unsteady Reynolds-Averaged Navier Stokes equations. For the existing surveillance system, the given flight operational conditions were analyzed based on International Standard Atmosphere where the flow obtained is incompressible turbulent flow. A validation and verification study on a three-dimensional sphere simulation was conducted at two Reynolds numbers of 1×106 and 1.5644×106 using two different turbulence models, the SST k-$\omega$ and the realizable k-$\varepsilon$. The drag and lift forces and flow separation angle were compared with literature. Based on the flow simulation setup of the sphere, the existing gimbal was simulated for Reynolds number of 1.5644×106 comparing both turbulence models. Large zone of vorticity, low-pressure wake, separation and vortex shedding were clearly observed introducing huge drag. Two force design changes were suggested which are changing the gimbal orientation and optimizing the gimbal geometry. The simulation results described in this study have illustrated the capability of both turbulence models. The results obtained agreed really well when compared computationally and experimentally. Improvements of almost 60% were obtained by the two suggested solutions where the optimized gimbal showed higher improvement percentage.
Detecting DNS Tunnels Using Session Behavior and Random Forest Method

DNS server is one of the most important Internet infrastructures in modern society. DNS Tunnel technology is firstly used to get free WiFi, more and more security incidents using DNS protocol to transmit information or illegal commands in recently advanced persistent threat attacks. Most security products such as firewalls, intrusion detection systems and intrusion prevention systems rarely detect DNS communication, which provides naturally command and control communication channel for attackers. The traditional detection methods only focus on the network communication feature for DNS tunnel tools, such as iodine, dnscat2 and dns2tcp. Then many machine learning detection methods are introduced to automatically learn the abstract features and classify the illegal communication method, but the detection result is not still satisfied with high accurate and low false positive. In order to solve this issue, the paper proposed an automatically detecting methods based on session behaviors and random forest algorithm. The experiments proved the method could achieve high accuracy (99.79%) with recall (98.67%), which is higher than other general algorithms.