Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Predictive Maintenance Model for Rolling Components of a Machine based on Speed and Vibration

Machines have come a long way, from the industrial revolution to a modern-day industry 4.0. In this massive transition, one thing that has never changed within a machine is the moving part. Most industries use rotating machine with different load capacity and speed. These machines run at variable load and variable speed creating vibration bootstrap thus causing machine failure due to an increase in vibrations. Most of the researcher used vibration for fault detection in bearings but sometimes it caused by miss alignment in a shaft due to a fraction of overloading the machine. In this paper, we address it to solve those problems by using two parameters speed and vibration. To verify our approach, we use three different kinds of machine learning algorithms: Support Vector Machine (SVM), Naïve Bays, and Random Forest. By using these machine learning algorithms, we tried to find out the relationship between machine failure due to speed and vibration by predicting good and faulty bearings. After applying these models, we have seen that the SVM has 78% accuracy as compared to Naïve Bays, and Random Forest.
Analysis of cold chain development based on ISM model under the situation of (COVID-19)

Affected by the Coronavirus disease (COVID-19), the safety of cold chain logistics has received unprecedented attention. Under the situation that major economies are under impact, how the cold chain industry can further develop has attracted widespread attention in the academic community. In order to make up for the imperfect deficiencies in the index system of cold chain logistics influencing factors in the existing literature, while considering that existing studies rarely pay attention to the impact of the epidemic on the cold chain industry. Based on the ISM model, this article compares and analyzes factors such as the lack of construction of a cold chain logistics standardization system. Research shows that product quality and safety and information system construction are factors at level one, so they are the most critical factors affecting the development of cold chain logistics. Finally, this article enriches the research of cold chain logistics related fields, provides suggestions for the development of the industry, and has theoretical and practical guiding significance.
SMUL-FFT: A Streaming Multiplierless Fast Fourier Transform

Beamspace processing is an emerging paradigm to reduce hardware complexity in all-digital millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) basestations. This approach exploits sparsity of mmWave channels but requires spatial discrete Fourier transforms (DFTs) across the antenna array, which must be performed at the baseband sampling rate. To mitigate the resulting DFT hardware implementation bottleneck, we propose a fully-unrolled Streaming MUltiplierLess (SMUL) fast Fourier Transform (FFT) engine that performs one transform per clock cycle. The proposed SMUL-FFT architecture avoids hardware multipliers by restricting the twiddle factors to a sum-of-powers-of-two, resulting in substantial power and area savings. Compared to state-of-the-art FFTs, our SMUL-FFT ASIC designs in 65nm CMOS demonstrate more than 45% and 17% improvements in energy-efficiency and area-efficiency, respectively, without noticeably increasing the error-rate in mmWave massive MIMO systems.
High-Performance Electro-Optic Manipulation by Plasmonic Light Absorber With Nano-Cavity Field Confinement

Narrowband light absorber with multi-band plasmonic resonances is numerically investigated for high-performance electrical manipulation via introducing the electro-optic (EO) medium in the slit array based grating structure. A maximal absorption efficiency of 99.5% is achieved. The spectral shift sensitivity reaches 0.99 nm/V. Besides, the large spectral intensity change is also obtained due to the use of sharp resonances, which therefore ensures the high signal-to-noise ratio for the manipulation process. It is observed that the strong surface plasmon resonance and the localized optical cavity modes can introduce the differential responses for the multiple modes under the optical adjusting process and also for the EO manipulation process. These features not only contribute to produce the new EO modulator platforms based on the light absorbers but also pave new ways for the cavity-enhanced high-performance EO operation. Moreover, the absorption properties can be well maintained in a wide range of the structural parameters, indicating the high tolerance of the fabrication process. The findings can pave new insights into the high-performance manipulations via the narrowband absorbers with strong electromagnetic field enhancement and hold wide applications in dynamic switching, filtering, displaying, etc.
Deep Iris Feature Extraction

Iris recognition refers to the automated process of individual recognition based on the patterns in their irises. Due to its uniqueness, it is a common modality used in biometric recognition. With a technique pioneered by Daugman, it was shown that it enables recognition with very low false match rates. However, existing approaches still offer room for improvement in terms of accuracy. To address this, we adapt the pipeline defined by Daugman using convolutional neural networks to function as feature extractors and train the convolutional neural networks on a part of CASIA-Iris-Thousand dataset for closed set prediction. Trained models are then used for feature extraction, enabling us to perform open set recognition. With DenseNet-201 we achieve 97.3% recognition accuracy in closed set recognition and 98.5% recognition accuracy in open set recognition, achieving state-of-the-art results.
Fully Automatic Model Based on SE-ResNet for Bone Age Assessment

Bone age assessment (BAA) based on hand X-ray imaging is a common clinical practice for investigating disorders and predicting the adult height of a child. However, the traditional manual method is time consuming and prone to obverse variability. There is an urgent need for a fully automatic framework based on deep learning with high performance and efficiency. We propose an end-to-end BAA model based on lossless image compression and a squeeze-and-excitation deep residual network (SE-ResNet). First, we apply the compression module to compress the raw image without losing important features. Second, the SE-ResNet-based model extracts features of the compressed images. Furthermore, the regression model with improved loss function predicts bone age. The experiments on a public dataset reveal that our method outperforms the baseline models. In conclusion, the presented method is a fully automatic and effective solution to process hand X-ray images for BAAs.
Reinforcement Learning for Random Access in Multi-cell Networks

In this paper, our goal is to maximize the system throughput in a time-slotted uplink multi-cell random access communication system. To this end, we propose a two-stage reinforcement learning (RL)-based algorithm based on the exponential-weight algorithm for exploration and exploitation (EXP3). In each macro-time slot that consists of multiple time slots, users run the RL-based algorithm to choose the associated access point (AP). Then, a transmission policy determines the sub-time slot that user will transmit data in each time slot. Another RL-based learning algorithm is used to obtain an optimal transmission policy. To show that our method is efficient, we compare our proposed algorithm with the $\epsilon$-greedy algorithm in two different scenarios. The simulation results show that the average system throughput of our algorithm outperforms that of $\epsilon$-greedy exploration.
Differential Read/Write 7T SRAM With Bit-Interleaved Structure for Near-Threshold Operation

Near-threshold voltage (<inline-formula> <tex-math notation="LaTeX">$V_{th}$ </tex-math></inline-formula>) operation is an effective method for lowering energy consumption. However, it increases the impact of <inline-formula> <tex-math notation="LaTeX">$V_{th}$ </tex-math></inline-formula> variation significantly, which makes it difficult for previously proposed static random access memory (SRAM) bitcells to achieve high read stability and write ability yields. To achieve these in the near-<inline-formula> <tex-math notation="LaTeX">$V_{th}$ </tex-math></inline-formula> region, a differential 7T SRAM bitcell is proposed in which an additional row-based control signal and an nMOS transistor between the pull-up and pull-down transistors is adopted on one side of the cross-coupled inverter. In addition, the proposed SRAM bitcell can use a bit-interleaved structure without the half-select issue. Compared to differential 10T and 12T SRAM, the proposed differential 7T SRAM achieves 5% and 6% higher SRAM operating frequency and 70% and 23% lower operation energy consumption with a 33% and 49% smaller bitcell area, respectively.
Fuzzy C-Means Clustering-Based mMIMO-NOMA Downlink Communication for 6G Ultra-Massive Interconnectivity

Cooperation between massive multiple input multiple output (mMIMO) and non-orthogonal multiple access (NOMA) can significantly boost the future sixth-generation (6G) network capacity supporting ultra-massive interconnectivity. Co-operative mMIMO-NOMA has been recently considered in 6G high frequency spectrum such as Millimeter Wave and Terahertz because of its enhanced spectral efficiency property. Moreover, hybrid precoding is used importantly in such communications to reduce the overhead of high power consumption and increase the hardware cost performance. In this paper, we present a sub-array mMIMO-NOMA based downlink network architecture for 6G ultra-massive interconnectivity. A fully-connected hybrid precoding scheme is also considered incorporating with the system which supports equal performance with reduced number of RF chain than a fully-connected digital precoder. In addition, fuzzy c-means clustering algorithm is proposed for huddling the users of the mMIMO-NOMA based communication networks. The algorithm is performed on the received signal strength indicator data set, and the resulting clusters can be used to maximize the power and energy efficiency of the network.
A Survey of Mobility Management as a Service in Real-Time Inter/Intra Slice Control

In-network softwarization, Network Slicing provides scalability and flexibility through various services such as Quality of Service (QoS) and Quality of Experience (QoE) to cover the network demands. For the QoS, a set of policies must be considered in real-time, accompanied by a group of functions and services to guarantee the end-user needs based on network demand. On the other hand, for the QoE, the service’s performance needs to be improved to bring an efficient service to cover the demands of the end-user. The 3G Partnership Project (3GPP) defined the slice as a component of resources used to process a set of packets. These resources need to be flexible, which means the resources can be scaled up or down based on the demand. This survey discusses softwarization and virtualization techniques, considering how to implement the slices for future networks. Specifically, we discuss current advances concerning the functionality and architecture of the 5G network. Therefore, the paper critically evaluates recent research and systems related to mobility management as a service in real-time inter/intra slice control by considering the strengths and limitations of these contributions to identify the research gaps and possible research directions for emerging research and development opportunities. Moreover, we extend our review by considering the slice types and their numbers based on the 3GPP Technical Specification (3GPP TS). The study presented in this paper identifies open issues and research directions that reveal that mobility management at a service level with inter/intra slice management techniques has strong potential in future networks and requires further investigation from the research community to exploit its benefits fully.
CLD: An Accurate, Cost-Effective and Scalable Run-Time Cache Leakage Detector

Cache logical side channel attacks pose a significant threat to the security of modern computer systems. This is a result of exploitation of cache information leakages arising from cache contention. Detection of such leakages can be inferred from cache behavior and processes’ access patterns during run time. To achieve this, a detection template that uses available information on cache outputs and process accesses at run-time is required. In this work, such template is proposed and implemented as a hardware monitor called Cache Leakage Detector (CLD). CLD is a high-accuracy, cost-effective and scalable run-time cache information leakage detector. CLD uses cache signals and process IDs to detect exploitable cache access patterns. It does so by identifying potential information leakage patterns. Accuracy of CLD is evaluated by using several benchmarks and injecting attacks into a 128-bit key AES algorithm. The experiments demonstrate that CLD has far higher detection accuracy (0.7964 vs 0.3195) and lower percentage of false positive detections (1.2% vs 30.6%) compared to a state-of-the-art hardware detector. Moreover, CLD introduces a very low area overhead of 0.002% to the total area of the cache. Experimental result section reports the above claims in detail.
A Matching Optimization Method of Distribution Power Internet of Things Based on Network Looseness

Power line carrier communication (PLC) is one of the important technologies to construct and realize ubiquitous power Internet of Things, while the degree of network matching severely affects the communication quality of the network. Combined with the actual topology of a medium voltage (MV) distribution network, the transmission line theory is used to derive the impedance matching relationship between the two slave nodes in the master-slave network mode. Based on this, a matching optimization method for the carrier communication of MV distribution Internet of Things based on network looseness is proposed. Firstly, a large complex distribution communication network is selected and divided into several small impedance-matching networks according to the network looseness on the trunk lines. Then, in each region, the particle swarm algorithm is applied for matching coordination to avoid power wasting and impedance mismatching among multiple communication nodes. The coordinated matching method has fast optimization speed and strong portability, and its feasibility and effectiveness are verified by theoretical derivation, simulation and laboratory test results, which provides an important theoretical basis for the planning, design and popularization of PLC technology in the ubiquitous power Internet of Things.
A Multi-Sensor Data Fusion Method for Intelligent Aging Condition Identification of Viscoelastic Sandwich Structure

Viscoelastic sandwich structure is playing an important role in mechanical equipment. The aging condition identification of viscoelastic sandwich structure is essential for monitoring structural health condition and guaranteeing the safe operation of mechanical equipment. The development of a reliable intelligent aging condition identification method is an ongoing attempt. By analyzing vibration response signals, a multi-sensor data fusion method using multi-scale permutation entropy (MPE) and ensemble-based incremental support vector machine (EISVM) is proposed in this paper. In this method, for obtaining complementary information, multiple sensors are mounted on the different locations of viscoelastic sandwich structure. For extracting effective feature information, MPE is introduced to reflect the structural aging condition change, and multiple feature sets of multiple sensors are obtained. With distance evaluation technique, multiple sensitive feature sets are respectively selected from the multiple original feature sets to discard irrelevant or redundant features. Based on ensemble learning and incremental learning, EISVM is developed to fuse all sensitive feature sets from different sensors for reliable intelligent classification. The proposed method is verified with a well-designed viscoelastic sandwich structure in which the viscoelastic layer subjected to accelerated aging. The testing results show the outstanding performance of the proposed method.
Distributed Supervised Discrete Hashing With Relaxation

The data-dependent hash methods are becoming more and more attractive because they perform well in fast retrieval and storing high-dimensional data. Most existing supervised hashes are centralized, such as supervised discrete hashing (SDH) and supervised discrete hashing with relaxation (SDHR). The SDH algorithm determines the regression target by using ordinary least squares regression and the traditional zero-one matrix encoding of class label information. And SDHR is a constraint to the regression target matrix so that each example is correctly classified and satisfies a larger margin, so as to achieve the purpose of optimizing the regression target. In real environment, a large amount of data will be distributed in different nodes. Therefore, the centralized hash method has great limitations. In this article, we propose distributed supervised discrete hashing algorithm with relaxation (DSDHR) based on SDHR. The SDHR algorithm is introduced into the distributed network. In this framework, all nodes share a centralized hash learning model. At the same time, in order to ensure that the distributed hash algorithm is updated in parallel on multiple nodes, consistency constraints are introduced. In each node, alternative iterative methods are used to obtain the binary hash code, regression target and hash function. Experiments show that DSDHR has certain competitive advantages over some centralized hashing algorithms and some distributed hashing algorithms.
Reverse Engineering the Hamming Code with Automatic Graph Learning

Probabilistic graphical models are used extensively across the information theory, artificial intelligence and machine learning disciplines. In this paper, we work towards realizing a generalized graph-based framework for automated learning in intelligent systems. The proposed automatic graph learning framework employs factor graphs, i.e. Tanner graphs from coding theory, to represent an arbitrary stochastic system of variables and factorized realizations of their joint probability density function. We develop algorithms that are capable of learning statistical relationships between system variables, which involves constructing an appropriate factor graph representation and generating estimates of its component probability density functions, from training data. In this paper, automatic graph learning is used to reverse engineer the Hamming code, based on training data comprised of input-output codeword pairs. We show that automatic graph learning is capable of replicating known decoder performance with an order of magnitude less training data than a multi-layer dense neural network.
Nonlinear Characteristic of Clamp Loosing in Aero-Engine Pipeline System

Long service time and excessive vibration of aero-engine will cause failure of clamps, which usually fix pipelines on the outside of an aero-engine. In this paper, the nonlinear vibration characteristics caused by loosening clamps in aero-engine pipeline are determined. According to the real parameters of an aero-engine clamp-pipeline test rig, the clamp is simplified into a nonlinear spring-damping. Thus, a clamp-pipeline nonlinear model based on cubic stiffness is established. The multi-scale method is used to obtain the analytical solution of the nonlinear model. Based on the real test data, the model parameters of the clamp-pipeline nonlinear system are identified, and then the nonlinear vibration characteristics of the system are obtained. Finally, using the vibration transmissibility method, the influence of the severity of clamp loosening on the nonlinear vibration characteristics of the system is obtained.
A Neural Networks Based Method for Multivariate Time-Series Forecasting

In recent years, more and more deep neural network methods have been used in the forecasting research of multivariate time series. Comparing to the traditional methods such as autoregressive models, methods based on neural networks have achieved superior results. However, the sequence data comes from all aspects, and the data contains long-term and short-term models, which is not a small challenge for data prediction research. We builds a model based on a deep neural network, and uses a convolutional network and a recurrent network in the long-term sequence data input and short-term sequence data input of the model. Convolutional networks are mainly used to extract short-term patterns in sequence data, and recurrent networks are mainly used to extract long-term patterns in sequence data. In addition, in order to solve the problem that the output of the neural network is not sensitive to the change of the input scale, we also added an autoregressive network to the model. When constructing the input of the model, we use the corresponding period characteristics to construct the two input of the model for different datasets: long-term historical data and short-term historical data. Our model has achieved better results than traditional prediction models on four public data sets: out of 32 indicators in 8 methods, 24 optimal results have been achieved.
Copyright Protection of 3D Images Using Digital Watermarking

The Depth-Image-Based-Rendering (DIBR) method has been developed as an effective method to represent 3D images. In this paper, we describe a digital watermarking that has been developed to enforce copyright protection in DIBR 3D images. The algorithm is blind and robust and has been developed in such a way that watermarking is done by applying the rendering operation at the sender’s side as well as at the receiver’s side. This is in contrast to published algorithms where no use was made of the rendering process at the sender’s side. The performance of the proposed algorithm was extensively evaluated with respect to robustness against Gaussian noise. The experimental results show that the algorithm achieves the preset goals of high robustness.
Behavioral Model of Dot-Product Engine Implemented with 1T1R Memristor Crossbar Including Assessment

Memristor is an emerging electrical device that enables non-volatile storage and in-memory computing. The memristive crossbar with high memory density and low energy consumption has drawn much attention for the implementation of dot-product engines, which can be deployed in power-hungry applications with intensive multiply-accumulate operations. However, simulating the crossbar containing a group of memristors based on the device-level modeling is time consuming. In this paper, we propose a model to simulate the memristive crossbar with high flexibility and automation at the behavioral level to perform the vector-matrix multiplication. This system-level model captures the non-linearity of memristors aiming for fast and accurate simulation. With the significantly reduced simulation time, this model enables simulating the systems containing memristive crossbar with large scale like neural networks in a more practical way. Moreover, this model can be exploited to analyze the effects of variations, which provides a condition and contributes to revealing potential computational errors. A multilayer perceptron detecting breast cancer is simulated based on this model to assess the classification accuracy with the presence of variabilities.