Miracle-dz/UniFAR-FTUs huggingface dataset:
An Intelligent Fault Diagnosis Method Based on Domain Adaptation and Its Application for Bearings Under Polytropic Working Conditions

In engineering practice, mechanical equipment is usually in polytropic working conditions, where the data distribution of training set and test set is inconsistent, resulting in insufficient generalization ability of the intelligent diagnosis model. Simultaneously, different tasks often need to be modeled separately. Domain adaptation, as one of the research contents of transfer learning, has certain advantages in solving the problem of inconsistent feature distribution. This article designs and establishes a domain adaptation framework based on multiscale mixed domain feature (DA-MMDF) for cross-domain intelligent fault diagnosis of rolling bearings under polytropic working conditions. The proposed method first uses the MMDF extractor to obtain features from the collected data, which constructs a complete feature space through variational mode decomposition (VMD) and mixed domain feature extraction to fully mine the state information and intrinsic attributes of the vibration signal. Second, the dimensionality reduction and optimization of features are achieved through extreme gradient promotion, and meaningful and sensitive features are selected according to the importance of features to eliminate redundant information. The optimized important features are combined with the manifold embedded distribution alignment method to realize the distribution alignment of data in different fields and cross-domain diagnosis. In order to verify the effectiveness of the proposed approach, the rolling bearing data sets gathered from the laboratories are employed and analyzed. The analysis result confirms that DA-MMDF is able to achieve effective transfer diagnosis between polytropic working conditions. Compared with traditional intelligent fault diagnosis methods and DA methods, the method proposed in this article achieved the state-of-the-art performances.
Evaluation of Cu/Graphene Integration Schemes for Its Application on CMOS BEOL Interconnect

With CMOS technology feature size continually scaling down, EM reliability issue of Cu interconnect is becoming more and more severe. As a 2D material, Graphene can sustain much higher current density, which may have potential application value to CMOS BEOL interconnect. And introduction of Graphene as capping layer on Cu metal line will be a much more effective way to improve the Cu interconnect EM performance. In this work, different Cu/Graphene integration schemes were implemented and evaluated based on 200mm Cu BEOL, including transfer based scheme and self-aligned scheme, and advantages and drawbacks of these schemes were analyzed. Further work is still ongoing for optimizing the integration scheme and realizing process integration of the Cu/Graphene interconnect on 200mm Cu BEOL.
Emotional Space by Combining Recognition and Unification Tasks Using Multimodal DNN

To emulate human emotions in agents, the mathematical representation of emotion (an emotional space) is essential for human-computer interaction. In this study, we aim at the acquisition of a modality-independent emotional space. We propose a method of acquiring an emotional space by integrating multimodalities on a DNN and combining the emotion recognition task and the unification task of an emotional space of each modality. Through the experiments with audio-visual data in various dimensions of the emotional space, we confirmed that the proposed method could acquire a modality-independent emotional space with recognition performance as well as that without the unification task.
Ordinal Preferences Driven Reputation Measurement for Online Services with User Incentive

A core source of raw information used as inputs to the reputation systems of online services is the feedback ratings provided by users. However, it is impossible that all users rate services with the same criteria and so ratings of different users are incommensurable. Meanwhile, users are not necessarily willing to provide honest feedbacks. Thus, aggregating dishonest cardinal ratings into reputation will potentially lead to unreliable and misleading reputation. In this paper, we propose a reputation model that aggregates ordinal user preferences rather than cardinal ratings for online services with user incentive. A distance metric is defined to measure the discrepancy between ordinal preferences. Then an optimal reputation model with the attributes of incentive compatible and individually rational is proposed. We design a B&B algorithm to solve the optimization problem so that a reputation vector that maximizes the total value of all users can be found efficiently. A comprehensive experimental study and performance analysis are conducted to evaluate the effectiveness and efficiency of the proposed method.
Theoretical Analysis of Illumination-Light VN-CSK Based Equal-Weight (2, 2) VSS with MPOMS Balancer

On an indoor illumination visible light (IVL) communication channel, the new data sharing system based on the Visual Secret Sharing (VSS) scheme has been proposed. In this paper, the theoretical formulas based on the positional relationship between the receiver and illumination apparatuses in the proposed system are derived. As a result, the relationship between the receiver position and the error rate in the indoor model under (2, 2) VSS conditions is clarified. Moreover, it is found that the error rate becomes worse as the distance between each LED and the receiver increases.
Loss Evaluation based on Experiment on Compact and High-speed IPMSM Using Strong Magnet and Low-iron-loss Material

Interior permanent magnet synchronous motors are widely used as vehicle traction motors because of their compactness and high efficiency. These motors have been downsized by making them rotate at higher speeds, but further improvements in power density are required in the future. The present study attempts to clarify the motor structure that is suitable for compactness and high efficiency. We compare the characteristics of a small-diameter single-layered V-shaped magnet prototype (1V-80) and a small-diameter double-layered prototype that uses strong magnets and a low-iron-loss material (2D-2-80-EN-M) with a conventional single-layered V-shaped prototype (1V) by actual machine testing. The results showed that the 2D-2-80-EN-M model can reduce the loss by more than 40% for the 1V model when the inverter carrier frequency is 20 kHz.
A 2-stage with 3-stack Comparator with Common Mode Range Extension Technique for Mobile DRAM Interface

As DRAM interface speed requirement is substantially increased, receiver design especially for widespread mobile applications such as LPDDR4/4X, faces more and more challenges due to reduced VDDQ power supply level as well as supporting terminated and un-terminated operation mode. A 2-stage with 3-stack StrongArm Comparator is proposed in LPDDR4/4X DRAM Receiver, for enhancement headroom margin to achieve over 4266Mbps data rate. Common mode range extension technique(CMRET) is proposed to achieve RX comparator re-generation delay reduction for 4266Mbps data rate with wide input range.
Early Fault Detection of Rolling Elememt Bearings Based on Visibility Graph Modeling of Vibration Signals

In this paper, the relevant information hidden in the frequency components of the vibration signal in the rolling element bearing (REB) is an important indicator of the REB status. In order to make better use of this information, visibility graph is introduced into REB failure analysis. The original signal is firstly converted into the time-frequency domain via short-time Fourier transform (STFT), and then the periodogram method is adopted to extract the power spectrum. Afterwards, visibility graph is utilized to model the power spectrum to extract relevant information between each frequency component. Next, anomaly score will be obtained based on specific distance metric. Finally, faults will be detected through a retrospective analysis method. The theory crafting and experiments implemented in a REB fault scenario have proved the feasibility and superiority of this method.
Deep Greedy Network: A Tool for Medical Diagnosis on Exiguous Dataset of COVID-19

The extensive outbreak of COVID-19 has created a worldwide health crisis. Transmission of this disease occurs among people through droplets which causes severe respiratory distress and in turn can also lead to fatal death. At the pinnacle of this pandemic, scientists endeavor to discover the medication for the COVID-19 victims. Artificial Intelligence algorithms, especially, deep learning, on the other hand, is used for the diagnosis of the COVID-19 patients but this requires an enormous radiographic data set to effectively provide an optimized outcome for a particular scenario. This work presents a new technique called ‘Deep Greedy Network’ which will work efficiently with a finite number of datasets. In spite of peculiarity caused due to limited dataset, the anomaly of overfitting and underfitting could be effectively overcome using the proposed algorithm. This, in turn, is simultaneously going to be both cost-effective and efficient. The proposed architecture ensures the efficacious result after the proper judgement of the trained model on the given X-ray datasets of COVID-19 cases.
IoT Applications in Smart Agriculture: Issues and Challenges

The rapid development of Internet of Things (IoT) technologies created tsunamis almost in every industry across the world and particularly in agriculture. This massive changes are shaking the existing agriculture methods and creating new wave of opportunities. Due to the increase of world population by 30%, agriculture products will have a very high demand by 2050. Human resources for agriculture development is becoming less due to migration of young people to big cities and land use for agriculture cultivation is being used for rapid development. As a result, most of the agriculture activities need to be automated to fulfill the food demand. IoT and related technologies will be the potential solution to solve the above agricultural and food demand issues. This paper will explore the latest trends in IoT agriculture applications and highlight the issues and challenges particularly in network and open source software for smart agriculture.
Proof-of-concept demonstration of a 22-GHz vehicular communication system over an urban roadway testbed

This report presents the proof-of-concept demonstration of a 22-GHz based vehicular communication system over an urban roadway testbed. The testbed consists of three different cell sites along main roads and a vehicle with a terminal equipment. Each cell site has base station equipment including a baseband modem and two sets of RF transceiver and antenna designed and implemented in the 22 GHz band. The vehicle has the terminal equipment that is comprised of a baseband modem and six sets of RF transceiver and antenna. Functionality and performance have been checked and tested over the testbed. Upon the functionality check, cell searches and beam switching have been proved out to function properly. Downlink data rates of 1.9 – 2.5Gbps have been achieved by system optimization during the performance test while the vehicle ran through the urban roadway.
A Low Noise Precision Operational Amplifier

A low noise precision operational amplifier is presented in this paper, which has the advantages of low offset and low noise for high precision, high resolution and low error applications. The design was fabricated with a 40V bipolar process. The input stage of the circuit is a resistive load differential pair with an input bias current compensation structure. The second stage is a common-emitter structure in the middle. The output stage is complementary push-pull structure. When power supply is ±15V, measured results show that slew rate of the OPA is 0.3V/μs, it gets an open loop gain of 132dB with a supply current of 1.7mA and an input bias current of 2nA, the equivalent input noise is only 8nV/ √ Hz, and the area is about 2.3mm×1.6mm.
Loop2Recursion: Compiler-Assisted Wear Leveling for Non-Volatile Memory

Non-Volatile Memory (NVM) technologies, such as Phase Change Memory (PCM), herald the next generation of main memory as they offer superior features compared with DRAM. Unfortunately, NVM's limited write endurance hinders its adoption as its lifetime can be extremely short under skew writes. This paper observes that the loops in programs are one of the primary causes of uneven writes as they introduce the hot data and cause a large number of stack frames to be allocated to the same locations. To alleviate this problem, we present Loop2Recursion, a compile-time wear leveling technique for transforming loops into recursions automatically. Our approach is flexible as it can avoid a substantial memory overhead by limiting the depth of recursion. Experimental results demonstrate that Loop2Recursion can significantly improve the wear leveling over stack area compared to the state-of-the-art methods, while incurring only negligible performance overhead.
Application of Fuzzy Answer set Programming in Hybrid Deep Neural-Symbolic Architecture

Hybrid deep neural-symbolic architecture for event-detection employs a deep neural network at the back-end to perform low-level reasoning and a symbolic logical module to perform high-level cognitive reasoning. The currently known hybrid architectures use classical Answer Set Programming(ASP), which is unable to perform fuzzy reasoning with uncertainty. Moreover these systems don’t extract new rules from the available data. On the other hand, there are neuro-fuzzy systems that can extract fuzzy rules from data by means of Gaussian Restricted Boltzman Machines (GRBM). Both the aspects should be merged together to achieve human-like intelligent reasoning and learning from environment. But the success of such an integration depends upon the chosen logical system, that can support fuzzy reasoning with uncertainty, as well as, can support the extracted knowledge from GRBM. This work investigates the feasibility of using interval-valued fuzzy logic programming for this purpose. This work focuses on the theoretical aspects from logic programming perspective.
FAGR: An Efficient File-aware Graph Recovery Scheme for Erasure Coded Cloud Storage Systems

With the explosive growth of data in cloud storage systems, Erasure Codes (ECs) have become a typical data redundancy technology because of its low storage cost and high reliability. However, due to a large amount of complex computations and transmissions among massive data and parities, the recovery of lost data in erasure coded storage systems incurs high I/O latency. Although several fast recovery approaches devote to mitigating the recovery time from the application level or device level, the performance of file level recovery is still restricted. It is because a part of the complicated relationships among data, parity and files are ignored in the design of recovery process. To address the above problems, we propose a novel File-aware Graph Recovery (FAGR) scheme, to improve the file level recovery performance during the reconstruction process. The key idea of FAGR is establishing a graph with the mappings among files, blocks, stripes, parities, nodes and the access frequencies of files, and guides the recovery process from file point of view. A corresponding model is established to analyze the cost efficiency of recovery process, which guarantees that FAGR reconstructs the popular files in advance to accelerate the recovery. To demonstrate the effectiveness of FAGR, we conduct several numerical analysis and experiments in clusters. The results show that, compared to typical fast recovery methods, FAGR reduces the average response time of files by up to 81.63 % and improves the throughput by up to 4.44 ×.
Feature Extraction and Classification of Phonocardiograms using Convolutional Neural Networks

Heart auscultation is a primary and cost-effective form of clinical examination of the patient. Phonocardiogram (PCG) is a high-fidelity recording that captures the heart auscultation sound. PCG signal is used as a diagnostic test for evaluating the status of the heart and it helps in identifying related diseases.Automating this process would lead to a quicker examination of patients, especially in an environment where the doctor (specialist) to patient ratio is low. This research paper delves into an approach for extracting vital features from a Phonocardiogram and then classifying it into normal and abnormal classes using Deep Learning techniques. Our contributions include (a) Using class weights [1] a heavy class imbalance in the provided medical dataset, (b) Data transformation from an auditory perspective to a visual one (Spectrograms), (c) Using Deep Convolutional Neural Networks to extract features from the spectrogram and (d) Using the extracted features to classify the PCG signals in terms of quality (good vs. bad) and abnormality (normal vs. abnormal). The proposed algorithm achieved the overall score of 91.45% (91.86% sensitivity and 91.04% specificity) and 86.57% (89.78% sensitivity and 83.37% specificity) on train and test data respectively.
Towards a novel conceptualization of Cyber Resilience

The article revises the different views and conceptualizations for cyber resilience and proposes a novel holistic definition able to accommodate in a unique coherent vision the existing multiple facets. The resulting integrated concept has been formalisied defining a new comprehensive domain-based ontology for Cyber Resilience to be used as a knowledge base for an effective data driven multi-criteria decision making for resilience.
A Low-Profile Wideband Antenna With Monopolelike Radiation Characteristics for 4G/5G Indoor Micro Base Station Application

A wideband, low-profile, vertically polarized antenna with monopolelike radiation characteristics is proposed for 4G/5G indoor base station applications. The design of this antenna is based on the traditional monocone antenna with internal matching and reactive load. The structure of monocone is realized by crossed substrate, which reduces the antenna weight and improves the design flexibility simultaneously. Then, reactive loading of capacitively coupled triangle patches and shorting pins are used to reduce the height of antenna profile to 0. 08λmin, where λmin is the free-space wavelength at the lowest operation frequency. An interdigital capacitor is etched near the antenna's input to adjust the impedance matching. The proposed antenna demonstrates consistent monopolelike, omnidirectional radiation patterns within the operating band. A measured impedance bandwidth for voltage standing-wave ratio (VSWR) < 2.2 of about 81% ranging from 1.66 to 3.95 GHz is achieved, which covers 1.7–2.7 GHz 4G Long-Term Evolution band and 3.3–3.8 GHz 5G N78 band simultaneously. The proposed antenna is tailored for the ceiling micro base station in indoor distributed system.
Development of a Vehicle Routing System for Delivery Services

With the increasing ubiquity of e-commerce also arises the need for faster and more efficient methods to deliver goods from one place to another. Such systems rely heavily on vehicle routing systems, which couriers usually follow to determine their route for the day. In some countries, vehicle routing systems are managed manually, which either depends on the couriers themselves or on another person called a dispatcher, which is prone to errors and may not be very efficient. Hence, this study proposes a two-phase heuristic algorithm that will serve as the "dispatcher" for couriers to efficiently service all the customers within their area. A pairing of clustering and routing algorithms that solve the travelling salesman problem the best was found. Results show that using k-means as the clustering algorithm and a genetic algorithm with some modifications yield the best route recommendations in terms of total cost and processing time. It also is more efficient than the manual method with a savings of 16.5%.
Thermal and Electrical Performance Study of Cold-Finger ³He Cryostat in Precision Measurement of Quantum Hall Resistance and Single-Electron Current

The performance of our newly constructed cold-finger cryostat is investigated for quantum Hall resistance (QHR) and single-electron pump (SEP) devices. The cryostat features a vacuum-sealed sample chamber (SC) attached to a 0.3 K plate inside the inner vacuum can. The SC is designed to accommodate both the SEP and the QHR devices with a shielding partition between them. The measured QHR in the chamber approaches the nominal value for filling factor 2 within 20 parts in 109. The white-noise level of the SEP module, as obtained from the Allan deviation data, is 4.5 fA/ $\sqrt {\mathrm{ Hz}}$ . We also investigated the thermometry of the SC either under vacuum or filled with 3He gas. We calculated the thermal conductivity of the different heat conduction channels in the sample holder and found the dominant channel to be through the chip carriers not signal lines.
AetEC: Adaptive error-tolerant Erasure Coding Scheme Within SSDs

Flash memory-based SSDs are popular across a wide range of data storage markets, while the flash memory as a storage medium is becoming increasingly unreliable. To improve reliability, modern SSDs employ many in-device reliability enhancement techniques, such as ECC and RAID. However, existing intra-SSD reliability techniques do not consider both the multilevel reliability problem of flash memory and the imbalance of wear between blocks. Therefore either too much redundancy is provided for low wear blocks, which increases the write amplification, or multiple errors cannot be tolerated for high wear blocks and chips. We proposed AetEC, an adaptive error-tolerant erasure coding scheme within SSDs. AetEC adaptively applies RS (Reed Solomon) erasure codes with different error correction capabilities according to the number of high-wear blocks in the superblock. To tolerate chip-level failures of high-wear blocks, AetEC maximizes the error tolerance of RS codes. To improve performance, AetEC applies the physical address-based stripe organization and implements a FPGA-based RS encoder and decoder. Furthermore, AetEC proposes a double-linked list write cache to both improve write performance and ensure the consistency of user data and check data. We model and analyze the reliability of AetEC on SSDsim. Experiment results show that, compared with the fixed RAID4, the average response time of AetEC is reduced by up to 32.8%, and write amplification is decreased by up to 37.8%. Compared with the latest wear-aware RAID, AetEC reduces the uncorrectable page error rate (UPER) by two orders of magnitude with 5.9% additional response time.
An Ultra-low-power High-precision Dynamic Gesture Recognition Coprocessor Based On RISC-V Architecture

Contactless Dynamic gesture recognition (DGR), which has the extreme advantages of simple action, wide and strong application, faces challenges such as real-time, long-time work, low power, high complex algorithm, and more. In this article, we propose an ultra-low-power high-precision dynamic gesture recognition coprocessor based on the RISC-V architecture (UHDA). UHDA integrated on RISC-V ecological environment is configured and controlled by extended instruction set architecture (ISA). And UHDA has system feedback to adjust the background and human behavior. We adopt a low-complexity high-precision algorithm based on YCrCb and real-time online test the usual applied four gestures. The algorithm of detection gets an accuracy of 87%~100%. The final result shows that the recognition power of the UHDA with clock-gating in 28nm technology is 33.32 µw/frame. When UHDA runs at long-term standby mode, it saves more than 39.15% power than recognition power.