Miracle-dz/UniFAR-FTUs huggingface dataset:
Model of Autonomous Vehicle with Function to Identify Road Signs

Autonomous vehicles are becoming part of real life. They include camera systems with object recognition. The use of recognition systems has a much wider application. The aim of the work is to create a functional model of an autonomous vehicle with traffic sign recognition. The theoretical part of the work describes the current state of the solution of autonomous vehicles, their development and problems inhibit their application in practical life. In the next part we deal with algorithms that evaluate the image obtained by the camera. The main topic of the work is a description of the program that drives the vehicle based on the camera captured and processed image. The program is trained to recognize standard traffic signs. The vehicle responds to the recognized signs in a predefined way.
A Comprehensive Analysis of Twitter Data: A Case Study of Tourism in Indonesia

This paper extensively analyzes Twitter data based on a study of tourism in Indonesia. Some keywords “candi borobudur”,”danau toba”, “gunung bromo”,”pantai kuta”, and “raja ampat” are applied. Some techniques are applied in order to analyze obtained data. Based on word cloud technique, those tourism destination keywords contain top 10 popular word such as “bali”,”Indonesia”, “banget”,”jazz”, “gue”,”papua barat”, “liburan”,”sih”, “daerah”,”orang”. Besides, machine learning approaches are also applied to these Twitter data, and the result shows that some noisy or ambiguous words affect the accuracy of the machine learning model.
Fault Detection for Photovoltaic Systems Using Fuzzy C-Means Clustering

This work aims at developing a fault detection system for photovoltaic systems (PVSs) based on the Fuzzy C-Means (FCM) clustering technique. A simulator is developed based on a mathematical model of the PVS by considering three groups of faults: shading, short circuit, and junction box to investigate the fault effects in photovoltaic systems. Several tests are carried out and indicate that the FCM is able to detect these faults and separate the data of a faulty PVS from that of a healthy one based on uncertain data obtained from different weather conditions.
Learning Physically Meaningful Representations of Energy Systems with Variational Autoencoders

Given the growing number of volatile energy producers and consumers and the limitations of traditional static load prediction models, we have analyzed the ability of neural networks to predict the loads of an electrical transformer and to understand the physical relationships in the electric grid. To do this, we use a variational autoencoder to learn the load behavior of a neighborhood with three houses. Since a variational autoencoder learns a latent representation, we analyzed the possibility of learning physical relationships of the electric grid. By adapting the prediction model to learn a physical variable, namely phase shift, we show that a variational autoencoder can learn physical relations. Our results show a significant improvement in terms of the correlation of the latent and physical variables by integrating prior knowledge in the form of the corresponding power values as the training objective.
Thermodynamics of the BCS-BEC crossover

We present a self-consistent theory for the thermodynamics of the BCS-BEC crossover in the normal and superfluid phase which is both conserving and gapless. It is based on the variational many-body formalism developed by Luttinger and Ward and by DeDominicis and Martin. Truncating the exact functional for the entropy to that obtained within a ladder approximation, the resulting self-consistent integral equations for the normal and anomalous Green functions are solved numerically for arbitrary coupling. The critical temperature, the equation of state, and the entropy are determined as a function of the dimensionless parameter 1/k{sub F}a, which controls the crossover from the BCS regime of extended pairs to the BEC regime of tightly bound molecules. The tightly bound pairs turn out to be described by a Popov-type approximation for a dilute, repulsive Bose gas. Even though our approximation does not capture the critical behavior near the continuous superfluid transition, our results provide a consistent picture for the complete crossover thermodynamics which compares well with recent numerical and field-theoretic approaches at the unitarity point.
Study of the Effect of Transformer Windings Coupling Coefficient of Flyback Resonant Converter for Wireless Energy Transfer on its Output Characteristics

The article analyzes the influence of the coupling coefficient of the transformer windings on the output characteristics of the resonant flyback converter intended for use in applications for the implementation of contactless power transmission. Expressions for the theoretical calculation of the dependences of the output characteristics of the converter on the coupling coefficient have been obtained. The coupling coefficient has been calculated for the L-shaped transformer replacement circuit of the flyback resonant converter. A simulation experiment of the wireless capacitor charging using the flyback resonant converter has been carried out. Its results coincide with the theoretical ones. It has been established that during the flyback converter design. , it is most appropriate to use an L-shaped transformer replacement circuit. , which causes the difference between theoretical and experimental results within 2%.
Performance Analysis in Eddy Current Brake at Different Temperatures Using The Finite Element Method

The vehicle brake is one of the important parts. Brakes are components that function to convert energy from mechanical energy into heat. The brake part generally generates heat when the braking process is active. The heat generated needs to release. One type of braking system with non-contact is the Eddy Current Brake (ECB) and regenerative braking. ECB is an electric braking system with the principle of eddy current. Overheating in Eddy's current brakes will decrease braking performance because of changes in properties. This paper will discuss how temperature affects braking performance. This research used the finite element method (FEM) as the method of the analysis process. The modelling simulates the braking process with changing conductor temperature ranges and analyses the resulting braking torque. An increase in temperature will result in a decrease in braking torque of up to 10%. Therefore, maintaining the temperature on the ECB will affect the braking performance in optimal conditions.
20 GHz LNA and 29 GHz PA on SiGe BiCMOS technology for SatCom phased array systems

This paper presents a K-band single-ended Low-Noise Amplifier (LNA) and a Ka-band single-ended Power Amplifier (PA) for a SatCom phased-array transceiver. The two circuits have been developed in 130-nm SiGe:C BiCMOS technology and fully characterized. In particular, the PA achieves 28.5 dB peak small-signal gain, 38.8 % peak power added efficiency (PAE), and an OIP3 better than 23.7 dBm within the entire operating frequency band. The maximum power consumption is 76 mW with a 2 V supply voltage, and it occupies an area of 1234 μm × 758 μm, pads included. The LNA achieves 33 dB peak power gain, 2.3 dB minimum noise figure, and -20 dBm IIP3 at 20 GHz. The NF, in the entire frequency band of interest, is less than 2.6 dB. It exhibits a power consumption of 20.7 mW with a supply voltage of 2.4 V and occupies an area of 1000 μm × 800 μm pads included.
Research on Key Technologies of Human Upper Limb Bone Diagnosis based on Convolutional Neural Network

Fracture is a common disease in medical clinic, which is often diagnosed based on medical images. In order to reduce the reading time of experts and reduce the misdiagnosis rate, a medical auxiliary detection system for upper limb bone abnormality detection is proposed in this paper. The convolution neural network method is used to detect and diagnose the upper limb bone. In this paper, the open mura data set is used in the data set, and the adaptive contrast restriction algorithm is used to process the bone image, so that the bone edge is more obvious and the focus position is more clearly visible. Then, the upper limb bone injury data set and the upper limb bone position classification data set are constructed respectively. For medical image recognition, this paper proposes an IDNet network based on perception and Densenet: in the task of upper limb bone injury classification, the accuracy of IDNet reaches 0.92; In the classification task of upper limb bone parts, the accuracy of IDNet reached 0.99.
A Lightweight Scheme for Mitigating RPL Version Number Attacks in IoT Networks

Internet of Things (IoT) systems incorporate a multitude of resource-limited devices typically interconnected over Low Power and Lossy Networks (LLNs). Robust IP-based network routing among such constrained IoT devices can be effectively realized using the IPv6 Routing Protocol for LLN (RPL) which is an IETF-standardized protocol. The RPL design features a topology maintenance mechanism based on a version numbering system. However, such a design property makes it easy to initiate Version Number (VN) attacks targeting the stability, lifetime, and performance of RPL networks. Thus the wide deployment of RPL-based IoT networks would be hindered significantly unless internal routing attacks such as the VN attacks are efficiently addressed. In this research work, a lightweight and effective detection and mitigation solution against RPL VN attacks is introduced. With simple modifications to the RPL functionality, a collaborative and distributed security scheme is incorporated into the protocol design (referred to as CDRPL). As the experimental results indicated, it provides a secure and scalable solution enhancing the resilience of the protocol against simple and composite VN attacks in different experimental setups. CDRPL guaranteed fast and accurate attack detection as well as quick topology convergence upon any attack attempt. It also efficiently maintained network stability, control traffic overhead, QoS performance, and energy consumption during different scenarios of the VN attack. Compared to other similar approaches, CDRPL yields better performance results with lightweight node-local processing, no additional entities, and less communication overhead.
Exploring Instruction Fusion Opportunities in General Purpose Processors

The Complex Instruction Set Computer (CISC) paradigm has led to the introduction of instruction cracking in which an architectural instruction is divided into multiple microarchitectural instructions ($\mu$-ops). However, the dual concept, instruction fusion is also prevalent in modern microarchitectures to maximize resource utilization. In essence, some architectural instructions are too complex to be executed as a unit, so they should be cracked, while others are too simple to waste resources on executing them as a unit, so they should be fused with others. In this paper, we focus on instruction fusion and explore opportunities for fusing additional instructions in a high-performance general purpose pipeline. We show that enabling fusion for common RISC-V idioms improves performance by 7%. Then, we determine experimentally that enabling fusion only for memory instructions achieves 86% of the potential of fusion in this particular case. Finally, we propose the Helios microarchitecture, able to fuse non-consecutive and noncontiguous memory instructions, and discuss microarchitectural changes required to do so efficiently while preserving correctness. Helios allows to fuse an additional 5.5% of dynamic instructions, yielding a 14.2% performance uplift over no fusion (8.2% over baseline fusion).
Low-Rank Tensor Completion-Based Channel Database for Cell-Free Massive MIMO Networks

In Cell-Free Massive Multiple-Input Multiple-Output (CF Massive MIMO) systems, channel estimation is one of the key technologies because important components such as linear precoding schemes (e.g., conjugate beamforming and zero-forcing) and transmission power control schemes are based on channel coefficients between access points and users. In this paper, we propose low-rank tensor completion-based channel database for large scale fading coefficients that include path loss and shadowing effects. In the proposed channel database, the large scale fading coefficients are formulated as a low-rank third-order tensor, which is referred to as channel tensor, and these coefficients are stored by means of factor matrices and a core tensor, which are estimated from partially observed coefficients using a low-rank tensor completion technique. The large scale fading coefficients for a newly generated user are obtained from a simple calculation with the core tensor and the factor matrices. The proposed channel database is scalable because the number of elements in the core tensor and the factor matrices can be reduced significantly as compared with the number of elements in the channel tensor. In this paper, we present a basic idea of the proposed channel database and evaluate its basic performance with simulation experiments.
Zulu Compound Nouns—Towards A Preliminary Analysis

This paper is an attempt to explain the compounding of various types of ‘words’ to form compounds of specific name-reference. Ideas are given about the difference between morphological and phonological vowel deletion and what these signify, and about how the semantic import of the compounds can be derived from the two lexical items used in the compound. The structural differences between personal names and other compound nouns are discussed. Some suggestions are put forward on the categorization of compound nouns. There is some discussion on the relationships between underlying structures and their surface realizations.
Symmetry-enforced heavy-fermion physics in the quadruple-perovskite CaCu3Ir4O12

Heavy-fermion materials are mostly rare-earth or actinide intermetallics with very few exceptions in d-electron systems. The physical mechanism for these d-electron heavy fermion systems remains unclear. Here by studying the quadruple-perovskite CaCu3Ir4O12, we propose a symmetry-based mechanism that may enforce heavy-fermion physics in d-electron systems. We show that electron hoppings between neighboring Cu 3d-orbitals are strictly prohibited by the crystal symmetry, so that Cu 3d-electrons can only become delocalized through hybridization with other more itinerant bands, resembling that in typical heavy-fermion rare-earth intermetallics. This provides a useful way to enforce heavy-fermion physics in d-electron systems and may help future design of new heavy-fermion materials.
TM-Mode Dielectric Resonator Filter and Diplexer With Transmission Zeros Using Extracted-Zero Technique

In this paper, a TM-mode dielectric resonator (DR) filter panel is discussed, which was designed as a concept study for the implementation within UMTS base stations for Asia marked. To avoid interference with other communication bands, stronger blocking of signals in close vicinity of the lower side of the RX passband was required. To meet the stringent specification masks, the implementation of transmission zeros (TZs) is required. Within this design study, the TZs have been realized applying the extracted-zero (EZ) technique by implementing two offline resonators which are directly coupled to the first/last resonator of the filter, respectively. To the best of our knowledge, this is the first time to report the EZ technique for the application with TM-mode DR. Furthermore, the same type of ceramic rod was applied for the design of TX, RX, and EZ resonators by suitable dimensioning of their enclosure. The design is discussed and measurement performance of a filter panel prototype is presented.
A framework for multi-stage ML-based electricity demand forecasting

This paper proposes a novel framework for energy utility companies to anticipate their customers' energy usage based on their historical consumption data. The proposed framework comprises three major stages: (i) it detects and removes anomalies in consumers' energy consumption data by employing the isolation forest (iForest); (ii) it forms clusters of distinct consumer groups based on similarities in their consumption behavior via the k-means clustering algorithm; and (iii) it predicts electricity consumption by using deep learning algorithms. To this end, two different deep learning algorithms are designed: a long short-term memory (LSTM) network and the combination of convolutional neural network (CNN) and LSTM (referred to as CNN-LSTM) with multiple inputs. Since the latter is a combination of CNN and LSTM models, we apply a 2-D discrete wavelet transform (DWT) based feature extraction to the Gramian angular field (GAF) transformation of the time series to improve the accuracy of predictions. Various evaluation metrics are utilized for 1-hour- and 24-hours-ahead predictions with two different sliding-window sizes, i.e., 24 hours and 36 hours. The results demonstrate that the CNN-LSTM performs significantly better in predicting 24-hours-ahead electricity consumption.
AU-Scale Synchrotron Jets and Superluminal Ejecta in GRS 1915+105

Radio imaging of the microquasar GRS 1915+105 with the Very Long Baseline Array (VLBA) over a range of wavelengths (13, 3.6, 2.0, and 0.7 cm), in different states of the black hole binary, always resolves the nucleus as a compact jet of length ~10λcm AU. The nucleus is best imaged at the shorter wavelengths, on scales of 2.5-7 AU (0.2-0.6 mas resolution). The brightness temperature of the core is TB ≥ 109 K, and its properties are better fitted by a conically expanding synchrotron jet model rather than a thermal jet. The nuclear jet varies in ~30 minutes during minor X-ray/radio outbursts and reestablishes within ~18 hr of a major outburst, indicating the robustness of the X-ray/radio (or disk/jet) system to disruption. At lower resolution (80-240 AU), more extended ejecta are imaged at ~500 AU separation from the stationary core. Time-lapse images clearly detect the superluminal motion of the ejecta in a few hours. The measured velocity is 1.5c ± 0.1c (D/12 kpc) for the approaching component and is consistent with ballistic motion of the ejecta from 500 AU outward, perhaps even since birth. The axis of the ejecta differs by ≤12° clockwise from the axis of the AU-scale jet, measured in the same observation. Both axes are stable in time (±5°), the AU scale for 2 yr and the large scale for over 4 yr. Astrometry over 2 yr relative to an extragalactic reference locates the black hole to ±1.5 mas, and its secular parallax due to Galactic rotation is 5.8 ± 1.5 mas yr-1, consistent with a distance of 12 kpc. Finally, a limit of ≤100 km s-1 is placed on its proper motion with respect to its neighborhood. Some accreting black holes of stellar mass (e.g., Cyg X-1, 1E 1740-2942, GRS 1758-258, GX 339-4) and supermassive black holes at the center of galaxies (e.g., Sgr A*) lack evidence of large flares and discrete transient ejecta but have compact radio cores with steady, flat-spectrum "plateau" states, like GRS 1915+105. To the present day GRS 1915+105 is the only system where both AU-scale steady jets and large-scale superluminal ejections have been unambiguously observed. Our observations suggest that the unresolved flat-spectrum radio cores of accreting black holes are compact quasi-continuous synchrotron jets.
A Model for Malware Detection Method based on API call Sequence Clustering

In recent years, the extensive development of malware industry has brought many threats to the development of information technology. How to effectively detect malware has attracted much attention. The sequence of API calls has been proved to be effective in malware detection. There are obvious differences between the API call sequences of malware and good software. Based on this difference, in this paper, we introduce a Windows platform malware detection method based on deep neural network, which learns the API sequences. We use word embedding to understand the context relationship between API functions in malware call sequence. At the same time, we separate single functions with similar context characteristics into groups, and then convert the API call sequence into the corresponding group sequence. Finally, the group sequence is embedded and sent into the deep neural network to train the binary classifier to detect malware.
Towards a Hybrid UHF RFID and NFC Platform for the Security of Medical Data from a Point of Care

In recent years, body-worn RFID and NFC (near field communication) devices have become one of the principal technologies concurring to the rise of healthcare internet of thing (H-IoT) systems. Similarly, points of care (PoCs) moved increasingly closer to patients to reduce the costs while supporting precision medicine and improving chronic illness management, thanks to timely and frequent feedback from the patients themselves. A typical PoC involves medical sensing devices capable of sampling human health, personal equipment with communications and computing capabilities (smartphone or tablet) and a secure software environment for data transmission to medical centers. Hybrid platforms simultaneously employing NFC and ultra-high frequency (UHF) RFID could be successfully developed for the first sensing layer. An application example of the proposed hybrid system for the monitoring of acute myocardial infarction (AMI) survivors details how the combined use of NFC and UHF-RFID in the same PoC can support the multifaceted need of AMI survivors while protecting the sensitive data on the patient’s health.
Optical and electrical spin injection and spin transport in hybrid Fe/GaAs devices

We discuss methods for imaging the nonequilibrium spin polarization of electrons in Fe/GaAs spin transport devices. Both optically and electrically injected spin distributions are studied by scanning magneto-optical Kerr rotation microscopy. Related methods are used to demonstrate electrical spin detection of optically injected spin-polarized currents. Dynamical properties of spin transport are inferred from studies based on the Hanle effect, and the influence of strain on spin transport data in these devices is discussed.
Highly Linear Concurrent Tri-Band GaN LNA MMIC for 0.8/1.8/3.6 GHz Applications

Rugged and highly linear GaN HEMT LNAs are interesting in the receiver path for many applications. This paper presents a novel concept of a multiband GaN LNA MMIC chip for the frequencies of 0.8 GHz, 1.8 GHz and 3.6 GHz, realized in coplanar technology, with 0.15 μm-gate AlGaN/GaN HEMTs. The MMIC has a dimension of 4.35×2.15mm2 and provides a small-signal gain of 29 dB at 0.8GHz, and 34 dB at 1.8 GHz and 3.6 GHz with a noise Figure of 4 dB, 2.7 dB and 2.2 dB, respectively. Third-order output intercept point is determined to be 30dBm at 0.8 GHz, 34dBm at 1.8 GHz and 36 dBm at 3.6 GHz, proving that the LNA is well suited for highly linear multiband receiver applications.