Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Power CMOS VCO Using an 8-shaped Transformer

This letter designs an LC-type CMOS NP-crosscoupled voltage-controlled oscillator (VCO) using an S-shape transformer used to boost the P-FET and N-FET source voltage swings for low power dissipation. The transformer uses two 3-turn coils twisted in series as the primary and the layout method reduces the number of crossing metal lines and parasitic capacitance. The one-turn 8-shaped secondary interleaves with the primary to get high-coupling coefficient. The transformer topology enables symmetric transformer layout and the two lobes of the 8-shaped primary and secondary inductors radiate far-field magnetic fields to suppress the magnetic field radiation. The die area of the VCO in the TSMC 0.1S$\mu$m CMOS process is 0.762 × 0.S55 mm2. The measured phase noise of the VCO at 2. 7S GHz.
Detecting Kernel Rootkits in a Virtualized Infrastructure with Low-Level Architectural Features

Security exploits and ensuant malware pose an increasing challenge to the cloud computing environments as the variety and complexity of malware continue to increase. Kernel rootkits are more formidable than other malware for their stealthiness and high privilege. A variety of software-based detection mechanisms have been explored to defeat kernel rootkits. However, existing methods suffer from their complexity. In this paper, we introduce HKRD, a system that utilizes low-level architectural features in the hypervisor to detect and identify malicious behaviors of kernel rootkits in a VM. By combining architectural features with machine learning on the Xen hypervisor, our implemented prototype shows its capacity to detect kernel rootkits with high accuracy and moderate performance cost.
Simplified derivation of the modified Chernoff upper bound

Modified forms of the Chernoff bound were first presented in 1982, offering improvements on the Chernoff bound for systems corrupted by additive interference and independent zero-mean Gaussian noise. Both upper and lower bounds were developed, with the upper bound generally being significantly tighter than the Chernoff bound. However, despite this it has not been as widely adopted in practice as one might expect given its accuracy and inherent simplicity. It is conjectured that this is due, at least in part, to the relatively obtuse, nonintuitive character of the derivation, involving analytic continuation and contour integration in the complex plane. A new, direct derivation is presented that enables the essential underlying simplicity and ease of application to be readily appreciated. To highlight this simplicity details are provided of example applications, as well as a prescriptive demonstration of the formulation of the MCB from first principles. As validation of the use of this technique comparisons are made of the performance and complexity of the MCB with other commonly used error rate calculation techniques.
A new method for the identification of non-Gaussian line profiles in elliptical galaxies

A new parameterization for the line profiles of elliptical galaxies, the Gauss-Hermite series, is proposed. This approach expands the line profile as a sum of orthogonal functions which minimizes the correlations between the errors in the parameters of the fit. This method also make use of the fact that Gaussians provide good low-order fits to observed line profiles. The method yields measurements of the line strength, mean radial velocity, and the velocity dispersion as well as two extra parameters, h3 and h4, that measure asymmetric and symmetric deviations of the line profiles from a Gaussian, respectively. The new method was used to derive profiles for three elliptical galaxies which all have asymmetric line profiles on the major axis with symmetric deviations from a Gaussian. Results confirm that elliptical galaxies have complex structures due to their complex formation history.
Multi-Armed Bandit and Repeated Auction Approaches for User Admission and Channel Allocation in 5G HetNets

Heterogeneous network and cognitive radio technology were introduced as part of 5G architecture to improve spectrum utilization and user capacity. Admitting secondary users in the wireless network is a challenge in the deployment of 5G. This paper presents a learning algorithm based on the multi-armed bandit technique to intelligently associate secondary users to the secondary base stations available in the network. Availing spectrum to secondary base stations and their associated users is done through a repeated auction game. The multi-armed bandit algorithm applied to the problem of user association enables secondary users oblivious about the channel environment to achieve higher data rates through association with the adequate secondary base station. Simulation results show a noticeably superior performance of the proposed multi-armed bandit algorithm compared to the matching game and random selection.
A Deep Subdomain Adaptation Network With Attention Mechanism for Malware Variant Traffic Identification at an IoT Edge Gateway

The prevailing of malware variants in ubiquitous Internet of Things (IoT) devices causes enormous losses. Accurate and timely identification of malware variant traffic at an IoT edge gateway can effectively reduce the loss. TransNet, the state-of-the-art technology for malware variant traffic detection, considers only global domain adaptation and ignores the alignment of distributions between different subdomains, which fails to capture the fine-grained information of classification targets. Besides, TransNet converges very slowly, which may use up precious resources in IoT devices. This article proposes a deep subdomain adaptation network with attention mechanism (DSAN-AT) to accurately and efficiently identify malware variant traffic at an IoT edge gateway. DSAN-AT utilizes local maximum mean discrepancy (LMMD) to align the traffic feature distributions of subdomains in the source and target domains. It also exploits channel and spatial attention mechanisms to accelerate learning traffic features between different subdomains to save precious computing resources at the IoT edge gateway. Our experimental study demonstrates that DSAN-AT achieves an average accuracy of 97.15% (96.37% for TransNet) and converges fast without using a large target domain training data set. DSAN-AT has strong practicality for identifying malware variant traffic at an edge IoT gateway.
Seamless and Intelligent Resource Allocation in 6G Maritime Networks Framework via Deep Reinforcement Learning

Sixth-generation (6G) communication networks will fulfill users’ requests for high data speeds and low latency without causing network outages throughout the world. However, marine communication in deep-sea waters is expanding as maritime traffic grows. To serve mission-critical applications, a growing number of maritime end-users require high throughput and low latency. Although deep-sea satellite connections will enable 6G networks, however due to limited service capacity owing to the long propagation delay between end-user and satellites. We propose unmanned aerial vehicles (UAVs) as aerial backhauling and a relay medium in the marine communication network assisted by satellites and coastal base stations (BSs). The power allocation strategy of multi-satellites for a 6G network is investigated in this work. The power allocation problem is non-convex, hence deep reinforcement learning (DRL) is used to solve it instead of the conventional optimization technique. Our research aims to optimize the network’s total capacity in the case when the satellites are randomly and densely dispersed. We provide a deep neural network and a method for mapping wireless power allocation for multi-satellites. The proposed technique can achieve a better overall capacity in comparison to the water-filling and the Q-learning method. The simulation results further demonstrate that the suggested technique offers a notable increase in stability and convergence speed.
The Frequency–Temperature Analysis of Vibrations of Quartz Crystal Plates of a Novel Orientation with the First-Order Mindlin Plate Equations

Quartz crystal is an anisotropic material and its physical properties will change with orientation. For vibrations of quartz crystal plates, the temperature is one of the most important factors affecting the mode-coupling and characteristics of typical modes. In some applications of piezoelectric devices, it is hoped that the frequency of the resonator will remain constant in the operating range of temperature. With the incremental thermal field theory, vibrations of the quartz crystal plate of a novel orientation in the thermal field are analyzed, and the frequency stability is examined and evaluated in comparison with known orientations for possible resonator applications.
Profile face recognition based on elements by normalizing global and local features

Despite recent advances in face recognition using deep learning, pose changes are still one of the challenging problems. In this paper, we presented a method to normalize the image in the feature space by capturing local consistency during training. we used facial elements such as eyes, lips, nose, and hairstyle in addition to its general state for recognition. For this purpose, all the elements were detected and localized by semantic segmentation and then normalized. Specifically, an Attention-based convolutional neural network is introduced to integrate local features with their long-range dependencies yielding better feature representations and hence generate representation that preserves identities better, especially for larger pose angles. We used the face/head segmentation dataset for training and the FERET dataset for testing, and the results were good for images with the most difficult angular conditions. The performance of this model on the +90° images of the FERET dataset for 980 samples was 49.6%compared to VGGFace which was only 2.6% accurate.
Channel Characterization at Sub-THz Band with Measurements and Ray Tracing in Indoor Case

Ultra-wide bandwidth (BW) available at terahertz (THz) and sub-THz frequencies makes these bands interesting for wireless communication systems. A large BW at the sub-THz frequency band offers a promising solution for applications with short-range and high data rate requirements. Since the provision of high-speed computational systems, deterministic channel models have been extensively used for indoor field prediction and radio channel characterization, especially for high frequencies i.e., millimeter wave (mmWave) and sub-THz frequencies. We have developed an in-house built ray tracing (RT) tool, capable of characterizing a radio channel with good accuracy and precision. The aim of this paper is to validate the power angular spectrum (PAS) acquired through our RT tool with the measurement data at sub-THz frequencies of 90, 95, and 100 GHz, in both line-of-sight (LOS) and non-LOS (NLOS) University corridor environment. A photonics-based measurement setup with high accuracy is utilized for generating the sub-THz signal, and for acquiring the measurement data. In addition to the PAS, other channel metrics considered for the analysis through RT simulations are root mean square delay spread (RMS-DS) and RMS angular spread (RMS-AS) in an azimuth plane. The results presented in this paper show that our in-house built RT is capable of providing considerably good agreement between the measured and simulated PAS in both LOS and NLOS states.
Research on Intention Perception Algorithm of Exoskeleton Based on Multi-sensor Information Fusion

As a human-robot coupling system, human-robot interaction plays an important role in the power-assisted exoskeleton robot. Accurately identifying and perceiving the wearer’s motion intention is one of the research difficulties of exoskeleton robots. Aiming at the problem that the wearer’s motion intention is difficult to perceive during human-computer interaction, this paper uses the harmony search algorithm to optimize the one-versus-one support vector machines, and combines with the finite state machine to propose a new intention perception algorithm. The experimental results show that the algorithm can quickly and accurately perceive the wearer’s movement intention, and can identify the abnormal movement state transition in time to ensure the security of the system.
Global Dust Budgets of the Magellanic Clouds

Abstract Within galaxies, gas and dust are constantly exchanged between stars and the interstellar medium (ISM). The life-cycle of gas and dust is the key to the evolution of galaxies. Despite its importance, it is has been very difficult to trace the life-cycle of gas and dust via observations. The Spitzer Space Telescope and Herschel Space Observatory have provided a great opportunity to study the life-cycle of the gas and dust in very nearby galaxies, the Magellanic Clouds. AGB stars are more important contributors to the dust budget in the Large Magellanic Cloud (LMC), while in the Small Magellanic Cloud (SMC), SNe are dominant. However, it seems that the current estimates of the total dust production from AGB stars is insufficient to account for dust present in the ISM. Other dust sources are needed, and supernovae are promising sources. Alternatively the time scale of dust lifetime itself needs some revisions, potentially because they could be unevenly distributed in the ISM or clumps.
Recognition of Human-object Interaction in Video through a Two-stream Network Integrating Multiple Features

To understand a scene, a machine not only has to learn to recognize individual object instances, but also pair them up and recognize visual relationships between them. Human-object interaction (HOI) recognition is one of the fundamental tasks in understanding the visual world. In this paper, we address the task of understanding and recognizing HOI in videos, and represent HOI as the doublet of . This paper proposes a two-stream network model that fuses multiple features. Since appearance information (the appearance features of instances), spatial information (the distance between each key part of the human body and the interacting object), and motion information (optical flow) in the video are all essential cues for recognizing HOI, our model uses two streams to fuse the information to complete the HOI recognition task. We valid the effectiveness of the model by conducting experiments on two recently proposed public video datasets (Charades and CAD-120 datasets), and perform ablation experiments to show the effect of components.
Semi-supervised Fingerprint Construction and Localization System For Large Indoor Area

Many applications of location-based indoor navigation services require precise location information of a user. While Global Positioning System (GPS) loses reliability indoors, fingerprints-based localization technology (FBLT) embodies superiority regarding accuracy and robustness. In a Bluetooth-based fingerprint localization system, a radio map is constructed offline and used as a reference for subsequent real-time localization tasks. However, the quality of the fingerprint radio map could be problematic when it comes to a large, broad space with low beacon density. Data collection in such a space could be exhausting as well. Another main issue is that different mobile devices receive heterogeneous signal strength at the same location. In this article, we propose a highly practical localization system with a semi-supervised learning fingerprints construction method that provides an efficient solution for a large-scale localization system in a complex indoor environment. We also conducted a series of experiments to evaluate the performance of this system.
Emotion Recognition Classification Using an EEG- based Brain Computer Interface System based on Different Machine Learning Models

Brain Computer Interface (BCI) is a communication channel between brain and machine which helps to control the instruction over the machine. The BCI helps in both the situations where patients are not able to move a bit and utter a word. There are many devices made that are being used by the people with disabilities and helping them in doing their work. Brain computer interfaces have been made useful for the patients with mental disorders like strokes and also for the other physical disorders. BCI first acquires and analyzes the brain Electroencephalography (EEG) signals, translating them into the desired actions that are needed to fulfill the command given by the brain. The recent advancement in the brain computer interface technology is exciting for the scientists, engineers and clinical persons which further enlighten the rapid development and growing research. The classification and detection on BCI systems for the patients have been made. This study proposes a novel stochastic model that classifies the emotions as: positive, negative and neutral with the accuracy of 96% using the ensemble model (Multiclass Logistic Regression, Light Gradient Boosting Machine (LGBM), Random Forest Classifier and Decision Tree Classifier).
Submanifolds with parallel mean curvature vector of a Euclidean space or a sphere

Liebmann [11] and Sίiss [19] proved that the only convex hypersurface with constant mean curvature in a Euclidean space is a sphere. To prove this theorem, we need integral formulas of Minkowski in which the so-called position vector plays a very important role. Recently various attempts have been done to generalize this theorem of Liebmann and Suss to the case of hypersurfaces in a Riemannian manifold. See, for example, Hsiung [3], Katsurada [4, 5], Koyanagi [10], Otsuki [17], Tani [20, 26], Yano [21, 22, 26]. In these papers, authors assume the existence of a vector field in the Riemannian manifold or that of a vector field along the hypersurface which plays the role of the position vector in the case of hypersurfaces in a Euclidean space and prove that, under certain conditions, the hypersurface under consideration is umbilical. When the ambient Riemannian manifold is a general one, it is almost impossible to give conditions under which the hypersurface is isometric to a sphere, but when the ambient Riemannian manifold admits a scalar function v such that ΫΫv=f(v)g, where g is the Riemannian metric and V the Riemannian connection, we can give conditions under which the hypersurface under consideration is isometric to a sphere. (See Yano [22]). The attempts have recently been started to generalize the above results to the cases of general submanifolds in a Riemannian manifold by Katsurada [6, 7, 8], Kόjyό [7] and Nagai [8, 12]. They assume that the ambient Riemannian manifold admits a conformal Killing vector field and that this vector field is contained in the linear space spanned by the mean curvature vector of the submanifold and the tangent space of the submanifold. The present author [24] studied similar problems under conditions a little bit weaker than those of Katsurada, Kδjyδ and Nagai. The main purpose of the present paper is to determine all the submanifolds in a Euclidean space or in a sphere which satisfy the conditions imposed by the present author in [24].
Open-Circuit Fault-Tolerant Vector Control for Five-Phase SPMSM Based on Five-Phase Six-Leg Inverter

Five-phase permanent-magnet synchronous motor (PMSM) has the ability of fault-tolerant operation, which is beneficial for high reliability occasions. A fault-tolerant current vector control strategy based on five-phase six-leg inverter is proposed for open-circuit fault in this article. It deals with single-, double-, and triple-phase fault conditions. First, the overall control scheme is established based on the mathematical model of five-phase surface-mounted PMSM. To release more control degrees of freedom, the constraint for the sum of fault-tolerant phase currents is removed, and the control variates method is used to calculate the optimal fault-tolerant currents. Furthermore, to achieve the current tracking, the corresponding reduced-order current transformation matrices are derived, and the corresponding main power topology, the five-phase six-leg voltage source inverter (VSI), is established. The unbalanced operation of the topology is investigated, the equivalence to the full-bridge VSI is proved, and the voltage utilization and voltage unbalance of five phase half-bridge, full-bridge, and six-leg inverter are compared. Then, the corresponding carrier-based PWM control strategy based on zero-sequence injection has been proposed, which is equivalent with the three-dimensional space vector PWM algorithm and is computationally efficient. The experimental results confirm the effectiveness of the proposed fault-tolerant control strategy.
Performance Comparison between Simple and Adam—Eve-Like Genetic Algorithms in Optimal PMU Placement Problem

In this research, the Adam-Eve algorithm is investigated, and its performance is compared with the simple genetic algorithm. The simple genetic algorithm is the simplest type of genetic algorithm in which the population size is constant, and the probability of mutation and crossover in it does not change over different generations. Adam-Eve algorithm is one of the adaptive algorithms that the number of population and rates of mutation and crossover in it are variable. In this research, both algorithms are implemented, and they are used to solve the phasor measurement unit placement problem in the power system. The simulation results show that the probability of achieving global optimal in the Adam-Eve algorithm is higher, although the convergence speed of this algorithm decreases.
A Brief review on Network Identity-based Moving Target Defense

In recent years, a new proactive defense approach called Moving Target Defense (MTD), which strives to change the asymmetrical condition between the defender and adversaries due to the static nature of the system, has emerged. MTD proactively changes a specific aspect of the system randomly and dynamically to make the system unpredictable for adversaries. This paper presents a dynamic network classification extension based on the more recent MTD works and provides a brief review of seven common attributes of network-identity-based MTD.
Applying Deep Knowledge Tracing Model for University Students’ Programming Learning

Programming ability has become one of the most critical competencies. In the past, it was hard for teachers to find and understand the problems that students face in the coding process. Nowadays, we could apply data mining methods focused on the educational domain. Moreover, through deep knowledge tracing, we can model students' learning trajectories, understand their current knowledge level, and help students overcome their weaknesses. This study was conducted at a national university in northern Taiwan. A total of 20 graduate students participated in the experiment for 16 weeks. This study combines deep knowledge tracing to develop a program learning system. The system supports the predictions based on the data accumulated from students' learning processes. The system dashboard can immediately help students and teachers understand students' learning behavior and mastery of various knowledge points and provide corresponding learning suggestions. The results show that students' program ability has been significantly improved in this study. Deep knowledge tracing can effectively be used in programming classes to evaluate students' abilities according to their different knowledge points.