Miracle-dz/UniFAR-FTUs huggingface dataset:
Pitfalls of InfiniBand with On-Demand Paging

InfiniBand is a popular high-performance interconnect and offers Remote Direct Memory Access (RDMA), which enables low-latency communication based on kernel bypassing. Although the conventional RDMA technology necessitates manual physical memory management, an emerging extension, On-Demand Paging (ODP), implements automatic memory management based on RDMA-triggered page faults, which benefits productivity. Although the existing studies said the overhead of a page fault of ODP to be small enough, an in-depth investigation in various network situations including retransmission and timeout is missing. In this work, we conduct a comprehensive analysis of the actual behaviors of ODP on different devices and reveal two awful performance pitfalls, which incur longer latencies 3–4 orders of magnitude than a common-case page fault does. We also experimentally demonstrate that the revealed pitfalls are harmful to existing software systems. This paper presents our experimental analysis and lessons learned therefrom.
A New Hybrid Dual Active Bridge Modular Multilevel Based DC–DC Converter for HVDC Networks

Multi-terminal high voltage DC transmission currently represents a leading technology in long-distance power transmission systems. Among the main technical challenges facing such technology, DC fault isolation, permitting different grounding schemes, providing interoperability, and high DC voltage stepping between different HVDC networks, and allowing high-speed power reversal without power interruption especially when connecting the pre-existing voltage source converters (VSC) and line commutated converters (LCC)-based HVDC networks. This paper introduces a new modular multilevel converter (MMC) based front-to-front DC-DC converter to interconnect two different types (LCC/VSC) of HVDC networks. The proposed topology comprises a voltage source MMC (VS-MMC) and a current source MMC (CS-MMC), while both are coupled via an AC link including the isolating transformer. The proposed topology can successfully provide an uninterruptible bi-directional power flow, high DC voltage stepping with a DC fault blocking capability, and low number of semiconductors due to the usage of only half-bridge SMs. The system design is provided with a detailed mathematical analysis. Furthermore, two active power control methodologies are proposed and compared. The first control technique is simpler and entails lower passive elements, while the second technique ensures a zero reactive power over the full range of active power flow. Furthermore, Losses analysis and comparison are provided between the two proposed control techniques. Finally, Control-Hardware-in-the-Loop (CHiL) test validation is employed to confirm the validity of the proposed system under healthy as well as different fault scenarios.
Low-cost Inkjet Printed Paper Poster FSS for 5G Applications

Low-cost inkjet printing of a Frequency Selective Surface (FSS) paper poster to reduce the drop of 5G signals within an indoor environment is introduced in this paper. An array of square loop elements is employed for this application. This low-cost FSS operates at a central frequency of 25.2 GHz while covering the 24 GHz band used in 5G systems. CST Microwave Studio™ was employed to simulate the FSS. The array structure was fabricated using a household Inkjet Printer on a photo paper using nanoparticle silver ink filled in refillable ink cartridges. Simulation transmission responses compared well with measurements. Inkjet printing could print tracks with fine details required for the mm-wave applications and offered reliable conductivity for the filtering performance. Printing of low-cost posters can help in improving the strength of 5G signals.
A Heterogeneous Hybrid Storage Method Based on Ceph Erasure Code

In order to achieve the goal of reducing storage redundancy, Ceph provides support for erasure code technology, which can more effectively use storage space. However, when Ceph selects storage nodes, it only uses storage capacity as the only indicator, without considering the resource load status and the heterogeneous situation of nodes, which makes a large number of read and write tasks allocated to storage nodes with lower performance. The above causes the problem of low reading and writing efficiency in Ceph erasure code mode. Based on this problem, we propose a heterogeneous hybrid storage method based on Ceph erasure code. Under the premise of comprehensively considering node CPU utilization, memory utilization and other performance indicators, Using TOPSIS method to implement multi-attribute decision-making and assigning Ceph erasure code tasks to the storage node with best performance. Experiments show that the method can effectively improve the read and write performance of large objects in Ceph erasure code mode.
CLOCK-RWRF: A Read-Write-Relative-Frequency Page Replacement Algorithm for PCM and DRAM of Hybrid Memory

Recently, with development of data-intensive applications, the traditional DRAM-based memory cannot satisfy the capacity requirement. Phase Change Memory (PCM), which is one of the non-volatile memories, has potentials in high density and low leakage power, and has been emerged as a promising candidate to replace traditional DRAM-based memory. However, owing to the weakness in write endurance and write latency, integrating PCM in hybrid memory faces the enormous challenges. To achieve the most performance improvement and maximize the life span of PCM, we prefer to allocate the excessive write operations to DRAM and read operations to PCM. In this paper, we propose a novel page management policy called CLOCK-RWRF that aggressively decreases write operations of PCM and at the same time maintains the high hit ratio. CLOCK-RWRF uses the relative frequency about read and write operation in a page to judge whether the page is write-intensive or not and push write-intensive page into DRAM. Simulation results show that compared to other algorithms, CLOCK-RWRF can significantly reduce write count on PCM and energy consumption on hybrid memory and has a high hit ratio as similar as CLOCK algorithm.
Creativity First, Science Follows: Lessons in Digital Signal Processing Education

Digital signal processing (DSP) education has traditionally employed more demanding mathematics than most topics found among courses in electrical/electronic/computer engineering. In some cases, the technical challenges posed by some courses have made it difficult for students to complete those courses successfully. Here, we advocate for creativity to be nurtured in the first place, after which the science will flow naturally. To foster creativity, our pedagogical approach includes a variety of solutions incorporating exploratory exercises, open-ended multidisciplinary coursework, blended lecture-laboratory sessions, and a colorful working environment. We firmly believe that creativity is the way forward. Student feedback supports our approach.
Prediction of Air Quality in Major Cities of China by Deep Learning

With global industrialization, air pollution is becoming a critical issue that threatens human health. The World Health Organization (WHO) estimated that air pollution kills several million people worldwide each year. Researchers from various areas and governments and enterprises have invested many resources in investigating and reducing air pollution. Air Quality Index (AQI) is one of the essential indexes indicating air quality or the level of air pollution. A new dataset, including hourly AQI information recorded by 1,615 observation sites covering China from 2015 to 2019, is constructed. Several methods, including linear model and state-of-art techniques, such as Back Propagation Neural Network (BPNN), Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bi-directional Long Short-Term Memory (BiLSTM), are adopted to forecast hourly AQI. The performance of these techniques is evaluated, and experiments show that the BiLSTM gives the best performance.
Multi-user efficient computing task offloading and resource optimization

Aiming at the unload of computing tasks under multi-user wireless interference, we propose a joint optimization scheme of unload, computing and communication resources based on system utility. Based on the efficient offload theory, a greedy algorithm is designed which can make full use of the limited computing and communication resources. Simulation results show that the proposed scheme can effectively improve the system utility and reduce the delay and energy consumption of computing task execution. When the number of users is large, the scheme can still maintain good system performance under resource constrained conditions and improve the speed of finding the optimal unloading strategy.
A Novel LT Encoding Algorithm with Low Error Floor

In this paper, an improved encoding scheme for LT codes is designed to reduce the error floor. The main idea of the proposed LT code is to classify the information nodes according to their degrees. Then the low-degree information nodes will be forced to frequently connect to check nodes, which enables all information nodes to obtain enough degrees to be restored successfully. Besides, the information degree distribution and convergence behavior of the proposed scheme are analyzed. Simulation results show that the proposed LT code can significantly reduce the error floor, and outperform the other two improved schemes, particularly for short length codes.
The Impact of System Distortions and Noise on HV Backscatter and Its Relevance to Above-Ground Biomass Estimation from Spaceborne P-Band SAR Data

This article analyses the effects of system distortions (crosstalk and channel imbalance), Faraday rotation and system noise on estimates of the cross-polarized backscattering coefficient, <inline-formula><tex-math notation="LaTeX">${\boldsymbol{\sigma }}_{{{\bf hv}}}^0$</tex-math></inline-formula>, by a spaceborne synthetic aperture radar. Modeling the unknown system errors and noise by a joint complex Gaussian distribution allows analytic first-order approximations to the mean and variance of the error in <inline-formula><tex-math notation="LaTeX">${\boldsymbol{\sigma }}_{{{\bf hv}}}^0$</tex-math></inline-formula> to be derived that do not depend on the SAR operating frequency. Simulation shows these approximations to be very accurate, given the statistical model and the expected magnitudes of system errors and noise for the P-band instrument to be carried by the European Space Agency BIOMASS mission. Simulation further shows that the <inline-formula><tex-math notation="LaTeX">${\boldsymbol{\sigma }}_{{{\bf hv}}}^0$</tex-math></inline-formula> errors are Gaussian distributed, so their exceedance probabilities can be calculated from just the analytic expressions for the mean and variance of the errors. Exceedance probabilities for above-ground biomass (AGB) can then be calculated under a power law relation between <inline-formula><tex-math notation="LaTeX">${\boldsymbol{\sigma }}_{{{\bf hv}}}^0{{\rm{\sigma }}_{{\rm{hv}}}}$</tex-math></inline-formula> and AGB that is consistent with P-band observations. This allows tradeoff curves between crosstalk and channel imbalance (shown to be segments of hyperbolas) to be calculated, along which the relative error in AGB is within a given percentage of its true value, from which limits on the permissible size of the errors can be determined if BIOMASS mission requirements are to be met.
8 X 4 mm-Wave 3D Printed MIMO Antenna for 5G Wireless Communication

A 3D printed 8 X 4 mm-wave multiple input multiple output (MIMO) antenna is presented in this paper. The proposed MIMO has an overall dimensions of 106.3 mm X 75.7 mm and it operates at 28 GHz mm-wave band. The proposed MIMO consists of 32 single elements where, each of the MIMO element consists of two structures. The first structure of each element is a feeding layer and the second is a radiating structure which is the 3D printed antenna. The radiating structure consists of a slot antenna surrounded by cavity and corrugations that improve the gain of the antenna. Furthermore, the proposed MIMO has 16 elements radiate in the boresight direction and 16 elements with side wall that radiates in different directions covers the elevation plane. Hence, the proposed MIMO provides beam switching functionalities up to ∓30° in the elevation by switching between different element that have different wall heights.
Comic style transfer based on generative confrontation network

Style transfer refers to extracting styles from a specified style image template, and mapping the extracted style features to the content image without destroying the image content. At present, the use of deep learning methods for style transfer is one of the hotspots in the field of image research. This paper implements an image style transfer architecture based on cyclic consistent generative adversarial network, i. e. , cyclic consistent confrontation network, and uses densely connected convolutional network to deepen the number of network layers. Test results show that stylized image with the proposed method is more obvious than that of the original cyclic consistent generative adversarial network, and the image quality is improved apparently.
Bayesian Approaches to Multipath-Enhanced Device-Free Localization

Device-free localization systems infer presence and location of moving users by measuring user-induced perturbations in the signal power between wireless network nodes. Thereby, users not only induce perturbations to the power of the line-of-sight, but also to the power of reflected and scattered signals which are observed in the received signal as multipath components. Since the propagation paths of multipath components differ inherently from the line-of-sight path, these propagation paths can be considered as additional network links. This extended network determines the multipath-enhanced device-free localization system. Based on empirical models that relate perturbations in the power of multipath components to the user location, the localization problem can be solved by nonlinear Bayesian filtering. In this work, we therefore investigate the point mass filter and the particle filter as possible solutions. Using simulations, we demonstrate the applicability of these filter solutions for multipath-enhanced device-free localization. The overall localization performance is comparable for both filters. Further, the simulation results indicate an improvement of the localization performance of multipath-enhanced device-free localization compared to device-free localization.
Performance Characterization of .NET Benchmarks

Managed language frameworks are pervasive today, especially in modern datacenters. NET is one such framework that is used widely in Microsoft Azure but has not been well-studied. Applications built on these frameworks have different characteristics compared to traditional SPEC-like programs due to the presence of a managed runtime. This affects the tradeoffs associated with designing hardware for such applications. Our goal is to study hardware performance bottlenecks in . NET applications. To find suitable benchmarks, we use Principal Component Analysis (PCA) to find redundancies in a set of open-source . NET and ASP.NET benchmarks and use hierarchical clustering to create representative subsets. We perform microarchitecture and application-level characterization of these subsets and show that they are significantly different from SPEC CPU17 benchmarks in branch and memory behavior, and hence merit consideration in architecture research. In-depth analysis using the Top-Down methodology reveals that . NET benchmarks are significantly more frontend bound. We also analyze the effect of managed runtime events such as JIT (Just-in-Time) compilation and GC (Garbage Collection). Among other findings, GC improves cache performance significantly and JITing could benefit from aggressive prefetching and transformation of hardware microarchitectural state to prevent frequent cold starts. As computing increasingly moves to the cloud and managed languages grow even more in popularity, it is important to consider . NET-like benchmarks in architecture studies.
A Legged Robot With Thigh Bi-Articular Muscle-Tendon Complex

Recently, the development of agile-legged robots that mimic the structures and functions of biological muscles and tendons of quadruped mammals has attracted significant attention. The driving force of muscles and the elastic force of muscles and tendons can help improve the performances of these robots. Thus far, most of these robots have been developed based on the muscle-tendon complex of the lower legs. However, some mammals have a larger muscle-tendon complex on the upper legs, which contributes significantly to agile movements. In this study, a legged robot that mimics the functions of the bi-articular muscle-tendon complex of the thigh was developed, and its effects on static and dynamic movements were verified through simulations and experiments. While maintaining a static posture, the transfer of torque between two joints in the thigh mechanism reduced the power consumption by the robot’s motors by half, used for balancing the gravity of the robot. For a dynamic vertical jump, the motion trajectory generation for the robot was performed using non-linear optimization to maximize the jump height. As a result, the jump height was significantly enhanced by the effect of the thigh mechanism, and the height was more than twice the leg length.
Thermal-Aware Fixed-Outline Floorplanning Using Analytical Models With Thermal-Force Modulation

High temperature or temperature nonuniformity has become a serious threat to performance and reliability of high-performance integrated circuits (ICs), which makes the thermal effect turn into a nonignorable issue in the circuit design or the physical design. In order to estimate temperature accurately, the locations of modules have to be determined in advance, which makes an efficient and effective thermal-aware floorplanning play a more important role. Hence, this article proposes a differentiable nonlinear placement model that can optimize temperature and minimize wirelength at the same time without needing to construct a congestion map. In addition, to avoid inducing longer wirelength while optimizing temperature, we propose some techniques, such as thermal-aware clustering, shrink of hot modules, or thermal-force modulation in the multilevel framework. The experimental results demonstrate that temperature and wirelength are greatly improved by our method compared to Corblivar. More importantly, our runtime is quite fast and the fixed-outline constraint can also be satisfied.
Extraction Method of Judicial Language Entities Based On Regular Expression

With the coming of the era of rule of law and intelligence, natural language processing technology plays a pivotal role. At present, a large number of unstructured judicial texts rely on manual processing and archiving. In order to make better use of them and achieve professional application, this paper proposes the goal of analyzing the structure of judgments, extracting the judicial language entities, and describing cases in the form of entity circulation map. As the text carrier of unstructured public events, the judicial document is of better standard format, finely crafted and easy processing, and becomes the research object of this paper. Through the survey of the development of named entity recognition technology, testing and contrasting the use of extraction tool, GATE, as well as considering the cost and effectiveness in the judicial field, this paper put forward a rule-based regular expression method for entity recognition. The scrapy crawler framework is used to obtain judgments classified from China Judgments Online website, so as to realize the task of analyzing the structure of judgments and extracting the judicial language entities.
Optimisation of Artefact Removal Algorithm for Microwave Imaging of the Axillary Region Using Experimental Prototype Signals

Microwave Imaging (MWI) has the potential to aid breast cancer staging through the detection of Axillary Lymph Nodes (ALNs). This type of system can present some challenges, mainly due to the irregular axillary surface. The optimisation of the artefact removal algorithm to successfully remove the surface reflections is of great importance. In this paper, we propose using Singular Value Decomposition (SVD) as an artefact removal algorithm and study the effect of choosing different subsets of antenna positions for artefact removal on imaging results using experimental signals. We show that different subsets of antenna positions affect the results and in some cases prevent the targets detection. Our analysis allowed us to find an optimal combination of parameters which results in Signal-to-Clutter Ratio higher than 2.77 dB and Location Error lower than 14.9 mm for three different experimental tests. These results are relevant for the development of dedicated algorithms for ALN-MWI application.
Training Convolutional Neural Networks (CNN) for Counterfeit IC Detection by the Use of Simulated X-Ray Images

X-ray based non-destructive testing coupled with a deep learning approach in identifying counterfeit ICs is becoming state of the art. The features of the component under test such as copper lead frame and silicon chip are revealed by X-ray radiography, that forms the feature space to classify authentic or counterfeit component. The complexity in using deep learning algorithm is the dependence of convolutional neural networks performance on the large quantity of dataset for training. Obtaining these datasets consume an immense amount of time and work effort, which is prone to suffer from a change in the dimension of the base structure and material characteristics of the component under test. The aim is to construct a virtual X-ray simulation tool to generate synthetic radiographic images of the component under test and validate its effectiveness as training data for the convolutional neural network in identifying counterfeit component. The principle of perspective projection is used to compute the ray path length traversed through an object, and the interaction of a photon with the material is modeled using X-ray attenuation law. The tool is designed to import STL file with the possibility to assign material; for this purpose, the CAD model of the component under test and some counterfeits are developed through re-engineering steps. In total 2000 synthetic X-ray images of the CUT and 1500 X-ray images of counterfeit components are generated to train the CNN. An Image classification algorithm is constructed with VGG16 CNN model, trained with synthetic images and maximum prediction accuracy of 99.60% is attained.
Trading Decision Making Based on Hybrid Neural Network

Now, with the dominance of electronic stock trading, it is possible to find and make profit from the price difference in real time. Machine learning has been applied in stock trading for years by companies. Yet as the rising of deep learning, price forecasting models become more accurate, which create more opportunities to gain higher profits. In this work, a novel hybrid neural network is proposed to deal with the stock trading decision making challenge. After properly training with labeled stock trading data, the hybrid neural network model proposed in this paper has been proved to be able to assist stock trading decisions better and achieve higher profits. The proposed hybrid neural network is evaluated on the stock trading data of Jane Street data set provided by the Kaggle competition. It is shown in the experiments that the proposed hybrid neural network outperforms other neural networks. Our neural network achieves as high profit value as 11417, which reveals the efficiency of the proposed method.