Miracle-dz/UniFAR-FTUs huggingface dataset:
Embodying Current division transconductance reduction in M-CDTA and its application as low frequency filter

This research paper brings forward an enhancement to existing Modified-Current Differencing Trans-Conductance Amplifier (M-CDTA) which works on subthreshold region in order to provide low gm and make it more suitable to low frequency filter application. Here, current division gm reduction technique is embodied in M-CDTA to provide even lower gm. The proposed block is termed as New Modified-Current Differencing Trans-Conductance Amplifier (NM-CDTA) in context of this paper. The functional verification of the proposed block is done using Net-listing in ORCAD PSpice A/D simulation tool. A multiple input single output filter is also included in paper as an application of NM-CDTA and its operation is verified through simulations.
Early Detection Method for Subclinical Mastitis in Auto Milking Systems Using Machine Learning

Bovine mastitis is an inflammation of the udder or mammary gland and dairy farmers must control its occurrence to prevent economic losses. The introduction of auto milking systems makes management of farms and udder health more efficient and auto detection systems for common diseases in dairy farms, which are implemented auto milking systems and detect the diseases based on some measurements while milking, are needed. In this study, we propose a novel model for subclinical mastitis detection. Our dataset was collected from dairy farms in Japan and labeled using risk values calculated by a commercially available milk analyzer based on lactate dehydrogenase (LDH) in order to train our model. Several measurements that can be obtained from any auto milking system, such as electrical conductivity in milk, were used as time series features. The models were trained using machine learning (a support vector machine or random forest) and their performances were compared. Our model detects the onset of subclinical mastitis with an accuracy of 81% in terms of sensitivity and 46% precision. In addition, some cases of subclinical mastitis can be detected earlier than when using an alert system based on LDH. Our model can be expected to be improved and utilized in real dairy farms.
Kilovolt Tri-Gate GaN Junction HEMTs with High Thermal Stability

The lack of reports on the high-temperature performance of tri-gate GaN power HEMTs has raised serious concern on their competitiveness. This work demonstrates the first normally-off tri-gate GaN HEMT that can block kilovolts at 150 °C and zero gate bias. The key enabling device design is a new type of tri-gate, the junction tri-gate, which comprises p-n junction wrapping around AlGaN/GaN fins in the gate region. At 150 °C, the GaN tri-gate junction HEMT (Tri-JHEMT) retains strong gate control and stable threshold voltage, while the traditional tri-gate metal-insulator-semiconductor (MIS) HEMTs fabricated on the same wafer shows a large threshold voltage shift and inferior capabilities of voltage blocking. These results show the excellent thermal stability of GaN Tri-JHEMTs and their great potentials for power electronics applications.
Experimenting Joint Vehicular Communications and Sensing with Optimized 5G NR Waveform

This article contributes to the experimentation of joint vehicular communications and radio-based sensing using a fifth-generation (5G) New Radio (NR) waveform. Firstly, simulations are carried out to observe the effect of using default (communication-purpose) 5G NR waveforms for sensing, and they indicate high side-lobes in the range profile due to the existence of unused communication subcarriers within the frames of the 5G NR waveform. These can be filled with optimized frequency-domain symbols to minimize the side-lobes. As the main result, these observations are validated through over-the-air measurements with practical 5G NR waveforms, operating at the mm-wave frequency of 27.7 GHz. For this, an outdoor environment is mapped with both the default 5G NR waveform and the optimized waveform, and the latter showcases considerable improvement in the mapping image due to side-lobe suppression.
A Security Authentication Method between the Charging Pile and Battery Management System

Controller area network (CAN) bus is used to communicate between the charging pile and battery management system (BMS). To improve the system security in charging, we propose an authentication method based on network to replace the traditional localized methods that have high demand on the computing power of the hardware. The complete authentication method includes two parts: position determination and identity authentication. We calculate the distance of the charging pile and BMS by their GPS data. If it is less than a threshold value, the position determination will be processed. Then, the identity authentication is started. The server produces a piece of 8-byte random verification code (RVC) and encrypts it. The RVC and its ciphertext are sent to the charging pile and BMS separately. BMS decrypts the ciphertext and transmits it to the charging pile via CAN bus. If the plaintext is consistent with the original RVC, the authentication will be successful. Experimental results demonstrate that the proposed method can make real-time work on existing hardware. The security is improved and the efficiency is also considered. Besides, the method has a good performance in precision. The research can provide a reference for the security of electric vehicles charging.
Open Defect Detection in Assembled Circuit Boards With Built-In Relaxation Oscillators

In this article, we propose two kinds of electrical interconnect test methods for production tests and field ones of assembled circuit boards, which are performed prior to and after shipping to market, respectively. For these tests, we also propose a built-in test circuit. The methods we followed are based on the oscillation frequency of a relaxation oscillator (ROsc) embedded in integrated circuits (ICs). The frequency is measured as the number of pulse signals within a specified test time. Using the test methods, open defects at the interconnects between IC pins and a printed circuit board (PCB) are detected that are modeled as a resistor, a capacitor, and an open-circuit fault. We examined the detectability of open defects using the process variations afforded by SPICE simulation. The simulation results show that open defects were detected, modeled as a resistor of 45.8 <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> or above, along with open defects modeled as a capacitor and an open-circuit fault using the production test method. In addition, a resistance increase of 1.2 <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> at defect-free interconnects occurred after shipping to market was detected using the field test method. We also prototyped ICs with embedded ROscs and built an experimental circuit made of the ICs on PCBs. Moreover, we experimentally examined whether open defects could be detected. The results show that with the production test method, open defects modeled as a resistor of 10 <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> or above and modeled as a capacitor and an open-circuit fault can be detected.
Harvest-Then-Transmit-Based TDMA Protocol with Statistical Channel State Information for Wireless Powered Sensor Networks

Wireless power transfer (WPT) is a promising solution for wireless sensor networks in IoT eras. However, due to the limitation of the spectrum, we need to address the research challenge of scheduling for both the power transfer and data transmission to maximize the spectrum efficiency. In this paper, we investigate harvest-then-transmit-based time division multiple access (TDMA) protocol with statistical channel state information. We show that the proposed protocol has the optimal time slot length to minimize the data transmission delay. Numerical results show that the proposed protocol achieves high spectrum efficiency and improve the battery charging ratio, compared with the existing harvest-then-transmit-based protocol.
Global Urban Detection Based on High-Resolution and Multi-temporal Sentinel-l Big Data

to achieve global urban detection from the high-resolution and Multi-temporal Sentinel-l image on Google earth engine platform. Relative to the massive amount of SAR image collected, the completeness of training dataset hardly can be ensured when the sampling is limited. Therefore, different from other existing works, the building of training dataset is also taken into consideration of designing the deep learning framework, and some dynamic programming and transfer learning strategies are adopted to improve its classification ability in this cloud-based platform. Taking Mumbai, Beijing and Stockholm as example, experiment results on real data illustrate the feasibility of the proposed method.
Artificial Intelligence Technologies for COVID-19-Like Epidemics: Methods and Challenges

The outbreak of coronavirus COVID-19 not only brings great disaster to the people of the world, but also brings heavy burden to the medical and health network system. Massive network data traffic and resource optimization requests make traditional network architectures unable to calmly deal with the impact of COVID-19. Artificial intelligence (AI) can effectively raise the upper limit of the medical and health network, as evidenced by the ever-increasing restorative clinical data. In addition, the development of next-generation network (NGN) technologies based on machine learning (ML) has created unlimited possibilities for the emergence of emerging medical methods. In order to reflect the effective results of the current application of AI technologies in the fight against the COVID-19 epidemic and provide a reliable guarantee for subsequent diagnosis and treatment of COVID-19 epidemics, a series of AI technologies which can be used in the diagnosis and treatment of COVID-19 are systematically summarized and analyzed. Based on various AI technologies and methods, we try to propose an AI-based medical network architecture. The architecture uses AI technologies to quickly and effectively realize the monitoring, diagnosis and treatment of patients. Finally, we rationally analyzed the technical challenges and practical problems that may be faced in implementing the architecture. The purpose of this article is to inspire scholars and medical researchers to carry out the latest research in response to the COVID-19 epidemic and make breakthrough medical technology progress.
MAD-Malicious Activity Detection Framework in Federated Cloud Computing

In federation of cloud, multiple cloud service providers share their resources based on certain assumptions and trust. Most of the times, identity of a user and permission to give access are shared using service access requirements. In intercloud security, building mutual trust relationship between multiple cloud federated identities is the main goal. Securing the components involved in cloud federation can be helpful in building mutual trust relationship between federated identities. Intrusion Detection and Prevention techniques helped a lot in detecting the malicious activities performed by the intruders. In this domain of securing data, a lot of research is being done. Recently, artificial intelligence and machine learning have greatly attracted the attention of researchers to integrate the concepts of network security with artificial intelligence. In this research, we have studied artificial intelligence techniques and finalized artificial neural network (ANN) model to detect the intrusions based on anomalies. In previous studies, it was discussed that major challenges in anomaly based intrusion detection systems is to lessen the false positive rate (FPR). So, in this research the main emphasis is done on reducing the failure rate of FPR of the intrusions done to the system while maintaining the overall perfromance and accuracy of the proposed model.
Reducing the Influence of the Contact Area on a Soft Capacitive Force Sensor

In this study, the influence of the contact area on measurements by a soft capacitive force sensor was analyzed. Based on the results, a method for selecting a dielectric material to reduce the contact area influence on the sensor is proposed. First, the soft capacitive force sensor was approximated as a discrete model, which was used to analyze the relationship between the capacitance of the sensor, applied force, and contact area. This relationship was used to derive a stress-strain curve for a dielectric that can remove the contact area influence, which was defined as the ideal curve. The proposed method uses the ideal curve to evaluate dielectrics for whether they can decrease the contact area influence. Experiments confirmed that the proposed method can be used to fabricate a soft force sensor with a small contact area influence.
An Efficient All-Digital Timing Skew Estimation Method for Time-Interleaved ADCs

Timing skew mismatch in time-interleaved analog-to-digital converters (TIADCs) greatly degrade the dynamic performance of the TIADC, which can be improved effectively by digital estimation and correction methods. In this paper, an efficient all-digital timing skew estimation method for M-channel TIADCs is proposed. Through Hilbert filtering and inverse trigonometric calculation, timing skew mismatch can be estimated at one time by injecting a known test sinusoidal signal. Compared with the previous foreground method, the proposed technique has a higher computational efficiency and looser limitations. Simulation results show that, for a four-channel 10-bit TIADC model, with ${f}_{in}/ {f}_{s}=0.347$, signal to noise and distortion ratio (SNDR) and spurious free dynamic range (SFDR) are improved from 29.5dB and 31.1dB to 61.9dB and 85.8dB, respectively.
Travel Time Prediction and Route Performance Analysis in BRTS based on Sparse GPS Data

A Bus Rapid Transit System (BRTS) with earmarked lanes potentially provides efficient public transportation, and helps in controlling urban traffic congestion. While travel time prediction (TTP) is essential in a BRTS, existing algorithms generally assume GPS logs available at short uniform intervals. However, those are rarely evaluated on BRTS in emerging economies, where logged GPS data could be available at sparse nonuniform intervals. To fill the gap, we study the efficacy of certain well known ML models, namely, Random Forests (RF), Light Gradient Boosting (LGB), and Extreme Gradient Boosting (XGBoost, XGB) in utilizing historical data. Performance of those ensemble learning methods is compared with that of a conventional travel time prediction (CTTP) method, which uses historical averaging. It was found that XGB was superior to other methods at hand, and the prediction error by approximately 60% compared to the CTTP method. Alongside improving the experience of commuters, the proposed XGB-based TTP method also improves the estimation of intersection crossing time (ICT), which potentially leads to efficient traffic policy making.
Impedance Modeling and Stability Analysis of Three-Phase Four-Leg Grid-Connected Inverter Considering Zero-Sequence

With the increasing applications of grid-connected voltage source converters, it is important to analyze the impedance characteristic of the grid-connected system to implement the stability analysis of the grid connection operation. Compared with the three-phase three-wire system, an additional zero-sequence path exists in the three-phase four-wire system. Therefore, it is necessary to consider the influence of the zero sequence when analyzing the stability of the system. In this paper, an impedance model including positive-sequence, negative-sequence and zero-sequence impedance of the three-phase four-leg grid-connected inverter is established. Through impedance characteristic analysis, the relationships between zero-sequence impedance and positive-sequence, negative-sequence impedance can be found which are decupled. Based on the developed zero-sequence impedance model, the main factors affecting the zero-sequence impedance model are revealed. Then, the system stability consisting of the grid and three-phase four-wire inverter is investigated, and the experimental results validate the theoretical analysis.
State of Charge Estimation for Lithium-Ion Battery Based on NARX Recurrent Neural Network and Moving Window Method

An accurate state of charge (SOC) estimation depends on an accurate battery model. The influence of nonlinear and unstable interference factors makes the accurate SOC estimation difficult. To obtain an accurate battery model, a method based on the NARX (nonlinear autoregressive network with exogenous inputs) recurrent neural network and moving window method is proposed. This paper improves the accuracy, modelling speed and robustness of SOC estimation from the following three aspects. First, to overcome the excessive reliance on the amount of data in the model training process, the NARX recurrent neural network is used to establish the battery model. NARX (nonlinear autoregressive with external input) recurrent neural network with the delay and feedback functions can keep the input and output of a previous moment and add it to the calculation of the next moment. Therefore, better estimation results are achieved using a small amount of data; second, the moving window method is used against the gradient explosion and the gradient vanishing that may occur in the NARX model training process. Third, by comparing it with other methods under different working conditions and different temperatures, the validity of the proposed model is verified. The results indicate that the proposed model has a higher accuracy and speed of the SOC estimation. The RMSE performance of the proposed model is reduced by approximately 65%, and the execution time is shortened by approximately 50%.
Reliability Enhancement by PDCP Duplication and Combining for Next Generation Networks

User equipment (UE), in general, has access to multiple radio access technologies (RATS). Besides, secondary base-stations can also aid the UE’s communication. Thus data can reach the UE from the primary base-station via multiple secondary base-stations. Taking advantage of the benefits of using such multiple paths, we propose in this paper a cross-layer scheme that improves block error rate (BLER) and throughput. We propose data duplication and integration at the Packet Data Convergence Protocol (PDCP) sublayer of layer 2 for downlink operations of 4G LTE / 5G NR. At the PDCP of ENodeB (gnodeB), the PDCP protocol data unit (PDU) is duplicated and sent to the UE via various paths. All these PDUs have the same sequence number (SN). Data from various networks are aptly linked using the PDCP assigned SN at EnodeB to obtain a composite and improved version of the PDU at UE. Although the cross-layer technique involves new procedures when combining data at PDPCP, the average delay for a PDCP-based aggregation scheme is very small compared with the standalone scheme because of the significant improvement in the block error rate (BLER) provided by the scheme over the BLER of the traditional standalone system. Finally, the BLER improvement results in throughput enhancement.
Artificial Intelligence In Interferometric Synthetic Aperture Radar Phase Unwrapping: A Review

Interferometric synthetic aperture radar (InSAR) is a radar technique widely used in geodesy and remote sensing applications, e.g., topography reconstruction and subsidence estimation. Phase unwrapping (PU) is one of the key procedures of InSAR signal processing. Artificial intelligence (AI) techniques have proven to be potentially powerful in many fields and have been introduced into the PU domain, achieving superior performance. In this article, we provide a comprehensive overview of AI-based PU techniques in InSAR. We survey the AI-based single-baseline (SB) PU methods and then review the AI techniques related to multibaseline (MB) PU. In addition, we show several experimental examples of these methods, from both simulated and real InSAR data sets, which gives readers an overview of AI-based PU processing's potential and limitations. It is our hope that this article will provide researchers with guidelines and inspiration to further enhance the development of AI-based PU.
CNN-Bi-LSTM Based Household Energy Consumption Prediction

Driven by technological advances, there is a increase in electricity-based equipments and this leads to excessive energy consumption (EC) and demand for power every day. To enhance power management and collaboration between electricity used in a building and the smart grid, the EC must be predicted. Forecasting techniques used for prediction of the energy accurately are limited due to challenges like dynamic behaviour of residents and climatic condition. So, to conquer such challenges we proposed a deep learning based methodology. The proposed methodology uses hybrid model consisting of CNN and Bi-LSTM for predicting EC. The performance of the proposed methodology is tested using publically available real dataset. Test results shows that the proposed methodology are able to predict the consumption with very small error. The proposed methodology helps in management for producing optimum amount of power.
SUICIDE RATES INDIFFERENT COUNTRIES

numerous influences, including the individual’s personality, biology, culture and social environment, as well as the macroeconomic and political context. In 2000 there were over 800 000 suicides worldwide. The World Health Organization estimates that more people die each year from suicide than in all the world’s armed conflicts. The rate of suicide has generally been increasing over the past 50 years and there has been a change in the age risk profile: those over 45 years old represented 60% of the total in 1950 but only 45% by 1998. These rate changes are mainly due to fluctuations in the number of male suicides (World Health Organization, 2002).
Domain Shadowing: Leveraging Content Delivery Networks for Robust Blocking-Resistant Communications

We debut domain shadowing, a novel censorship evasion technique leveraging content delivery networks (CDNs). Domain shadowing exploits the fact that CDNs allow their customers to claim arbitrary domains as the back-end. By setting the front-end of a CDN service as an allowed domain and the back-end a blocked one, a censored user can access resources of the blocked domain with all “indicators”, including the connecting URL, the SNI of the TLS connection, and the Host header of the HTTP(S) request, appear to belong to the allowed domain. Furthermore, we demonstrate that domain shadowing can be proliferated by domain fronting, a censorship evasion technique popularly used a few years ago, making it even more difficult to block. Compared with existing censorship evasion solutions, domain shadowing is lightweight, incurs negligible overhead, and does not require dedicated third-party support. As a proof of concept, we implemented domain shadowing as a Firefox browser extension and demonstrated its capability in circumventing censorship within a heavily censored country known by its strict censorship policies and advanced technologies.
Interference Aware Multipath Routing in Multi-rate Wireless Sensor Networks

In wireless sensor networks, sensor nodes have a short transmission range and data is transferred from source to destination node using the multi-hop transmission. Sensor nodes are powered by battery and the link qualities are different, and the routing protocol in the wireless sensor network is one of the important technical issues. Multipath routing was proposed to reduce the data congestion and increase data throughput. In the multipath routing, however, each path can be interfered by the other path, and it can aggravate network performance. In this paper, we propose the multipath routing scheme for multi-rate wireless sensor networks. The multipath routing selects transmission paths to minimize transmission delay and path interference.