Miracle-dz/UniFAR-FTUs huggingface dataset:
HLS: A Packet Scheduler for Hierarchical Fairness

Hierarchical link sharing addresses the demand for fine-grain traffic control at multiple levels of aggregation. At present, packet schedulers that can support hierarchical link sharing are not suitable for an implementation at line rates, whereas deployed schedulers perform poorly at distributing excess capacity to classes that need additional bandwidth. We present HLS, a packet scheduler that ensures a hierarchical max-min fair allocation of the link bandwidth. HLS supports minimum rate guarantees and isolation between classes. Since it is realized as a non-hierarchical round-robin scheduler, it is suitable to operate at high rates. We implement HLS in the Linux kernel and evaluate it with respect to achieved rate allocations and overhead. We compare the results with those obtained for CBQ and HTB, the existing scheduling algorithms in Linux for hierarchical link sharing. We show that the overhead of HLS is comparable to that of other classful packet schedulers.
Perceptual improvements for Super-Resolution of Satellite Imagery

Super-resolution of satellite imagery poses unique challenges. We propose a hybrid method comprising two existing deep network super-resolution approaches, namely a feedforward network called DeepSUM, and ESRGAN, a GAN-based approach, to super-resolve multiple low-resolution images by a factor of four to obtain a single high-resolution image. We also introduce a novel loss function, called variation loss, to better define edges and textures to create a sharper, perceptually better output. Using our hybrid, we inherit some of the advantages of both deep learning approaches, resulting in super-resolved images that better show boundaries, textures, and details.
Prediction of pedestrian trajectory based on long short-term memory of data

Recently, development of navigation robots and autonomous cars are rapidly progressing. When such robots become popular in our daily life, their collisions with humans should be avoided for safety. For that purpose, we predict pedestrian trajectories with LSTM (long short-term memory) networks and conventional neural networks, and we compare their results. In order to predict sequential data, we use the following two methods: (I) predicting n steps of data with n models, and (II) predicting n steps of data with a model by applying one-step prediction several times. By examining these two methods, it was found that the performances of the method I with the LSTM network and the conventional neural network are comparable, and the performance of the method II with the LSTM network is significantly better than that with the conventional neural network.
An electrical modeling and fuzzy logic control of a fuel cell generation system

The fuel cell generation system consists of a stack, a reformer, and converters. The stack generates DC power by electrochemical reaction. For system design and analysis, it is necessary to obtain electrical models. Simplified electrical models of a fuel cell generation system for system control are proposed. Then using the electrical models, system performance of a fuel cell generation system in which power is boosted by step-up choppers is analyzed. A fuzzy controller is designed for improved system performance. Simulation and experimental results confirmed the high performance capability of the designed system.
Efficient Tensor Slicing for Multicore NPUs using Memory Burst Modeling

Although code generation for Convolution Neural Network (CNN) models has been extensively studied, performing efficient data slicing and parallelization for highly-constrained Multicore Neural Processor Units (NPUs) is still a challenging problem. Given the size of convolutions' in-put/output tensors and the small footprint of NPU on-chip memories, minimizing memory transactions while maximizing parallelism and MAC utilization are central to any effective solution. This paper proposes a TensorFlow XLA/LLVM compiler optimization pass for Multicore NPUs, called Tensor Slicing Optimization (TSO), which: (a) maximizes convolution parallelism and memory usage across NPU cores; and (b) reduces data transfers between host and NPU on-chip memories by using DRAM memory burst time estimates to guide tensor slicing. To evaluate the proposed approach, a set of experiments was performed using the NeuroMorphic Processor (NMP), a multicore NPU containing 32 RISC-V cores extended with novel CNN instructions. Experimental results show that TSO is capable of identifying the best tensor slicing that minimizes execution time for a set of CNN models. Speed-ups of up to 21.7% result when comparing the TSO burst-based technique to a no-burst data slicing approach.
A review on VFD Control and Energy Management System of Induction Motor for Electric Vehicle

This growing pattern of energy insufficiency will, of course, intensify in the future. According to the literature, variable frequency drives can save a lot of energy by changing the speed of the electrical motor mechanism (VFDs). The aim of these studies and analyses is to improve the efficiency of the electrical device system by using VFD control techniques. This paper outlines the principle of control mechanisms for induction motor drives, as well as their benefits and drawbacks. This paper is intended to serve as a one-stop shop for researchers interested in the topic. However, there appears to be no comprehensive review of Variable Frequency Drive (VFD) techniques for induction motor (IM) drives to resolve the need for a detailed insight into IM drives. Efficient control policies for induction motor drives provide a great opportunity for energy savings. As a result, research into the optimal operation of induction motor drives is escalating.
Implementation of DDS Cloud Platform for Real-time Data Acquisition of Sensors

The Industry 4.0 (I4.0) is a multi-disciplinary engineering by combing the IoT, big data, and cloud computing to cope well with the dynamic changing industry. In this paper, the DDS (Data distribution service) communication protocol is employed to implement a cloud platform for data acquisition of various sensors including accelerometer, sound, temperature, brightness, and humility, etc. The sensor signals acquired by the ESP32 microcontroller (MCU) are collected by the Raspberry Pi serving as the edge device and then published with the DDS application to the cloud and stored to the MySQL database. With the Django web server, the sensor signals acquired are real-time shown on the webpage. It is expected that the proposed DDS cloud platform can be applied to a legacy machine to enable sensing and communication abilities such that the development of smart machine is achievable.
Development of Many-Angular Pin Type Load Cell for a Overload Limiter of a Movable Crane

An overload limiter is used to prevent its overturning accident during an operating of a movable crane. Recently the indirectly measuring method, which measures hoisting load and overturning moment of overload limiter, demands instead of the existing method, which measures only hoisting load. The indirectly measuring method is how to conduct the hoisting load and overturning moment as measuring the load of hydraulic cylinder for a luffing driving of boom. So we need to develop the many-angular pin type load cell with the measuring angle of ±10 degree instead of the existing load cell with the measuring angle of ±2 degree. In this study we conducted the finite element analysis in order to evaluate the effect of the aspect ratio of measuring cross section on the measuring limit of the load cell to develop the many-angular pin type load cell. For this investigation, the aspect ratio of measuring cross section and load applying angle were adopted as design parameters and the stresses of measuring part were evaluated for each parameter.
Advances on Polarization Modulator Technology and Methodology for Future Improvements

In this work we present the latest improvements on the fabrication of a non-crystal achromatic half-wave plate working in the sub-THz frequency range (1). This is achieved by hot-pressing polypropylene layers with photo- lithographed copper structures. We will discuss the performance of the first prototypes and a few techniques for the improvement of future designs. General considerations at the root of achromatic designs will also be touched upon which are valid for both optical and waveguide phase modulators.
Design and Implementation of Painting Classification System Based on Double Attention Network

In recent years, deep learning has shown excellent performance in the direction of image recognition and classification. The realization of the image classification function mostly relies on the semantic information of the image. Unlike image classification, painting needs to be classified according to painting style. At present, most of the style definitions of existing paintings are still manually marked and defined by a large number of artists. Therefore, in view of the above problems, We propose a system to classify paintings according to style characteristics. We can pre-train the picture through the VGG19 network to extract high-dimensional features in the picture. then, we update weight parameters by adding spatial attention and channel attention in this system. Therefore, we get the classification result. The system is mainly for the learning and classification of western oil paintings, which realizes the recognition of different painting styles of many painters in the training sample database.
Design and Experiment of Semi-Autonomous Operation Platform Assisted by Teleoperation

In view of the shortage of manpower in complex and dangerous environment, a system configuration of semiautonomous teleoperation equipment which contains a master terminal, a slaver terminal and networks was put forward. In the slave terminal, a binocular vision camera was used for extracting the scope and contours of the objects on the platform. And a three-degree-of-freedom (DOF) force sensor was adopted to detect the force acting on the end effect of the robot or machine tool. The whole platform was controlled by a computer in which some Lab Viewalgorithms and Python algorithms were implemented. In the master terminal, a joystick was connected to a computer for obtaining the posture of the operator. The force and visual information generated from the force sensor and visual sensor in the remote platform was feedback to the master terminal through a local Ethernet. On the other side, the orders given by the operator to assist the remote platform was also transmitted through the same networks. An experiment testbed was established for verifying the proposed semi-autonomous teleoperation system. The experiment results show that the proposed scheme and measures are feasible in some certain.
Automatic Recognition of Learning Resource Category in a Digital Library

Digital libraries generally need to process a large volume of diverse document types. The collection and tagging of metadata is a long, error-prone, workforce-consuming task. We are attempting to build an automatic metadata extractor for digital libraries. In this work, we present the Heterogeneous Learning Resources (HLR) dataset for document image classification. The individual learning resource is first decomposed into its constituent document images (sheets) which are then passed through an OCR tool to obtain the textual representation. The document image and its textual content are classified with state-of-the-art classifiers. Finally, the labels of the constituent document images are used to predict the label of the overall document.
A Privacy-aware Computation Offloading Method for Virtual Reality Application

As a new technology, virtual reality (VR) is constantly enriching people’s experience. VR application has high requirements for the performance VR devices, but the computing resources of VR devices are limited. Mobile edge computing provides an effective solution to solve the above issue by offloading VR application to edge servers (ESs) for processing. Nevertheless, the resources of ESs are limited and heterogeneous. And thus, it is necessary to consider the load balancing of ESs. In addition, user privacy protection in the process of computation offloading is another issue that needs to take into consideration. In view of this, in this paper, we investigate the computation offloading for VR applications with privacy protection. Technically, we propose a privacy-aware computation offloading method based on multi-objective optimization genetic algorithm to obtain the optimal strategy for VR application. Finally, it is shown that the proposed method is effective through extensive experiments.
Bias Suppression Framework for Detrending Mean of Multi-Output Gaussian Process Regression in LED Remaining Storage Life Prognosis

Advancements in storage prognosis tend to be limited by the inherent challenge to collect sufficient significant degradation data over an extensive period. Using only sparse fleet data, multi-output Gaussian process regression (MOGPR) is one of the few techniques that offers a practical data driven approach to model non-monotonic degradation profiles with low mean absolute percentage error (MAPE). This accuracy in the storage prognosis context is, however, sensitive to the choice of the detrending mean. Working with light emitting diodes (LED) sparse lumen degradation data under storage conditions in this study, the MAPE is observed to be highly correlated to the detrending bias – the difference between the detrending mean and the test mean. We explore various approaches to suppress this bias and advocate a generic framework for fleet storage prognosis. The approaches include detrending using (A) static training data mean, (B) dynamic observed test data mean, (C) static bounded training data set pairs, (D) dynamic weighted mean of unbounded training data set pairs and (E) moving average of weighted mean of unbounded training data set pairs. Our analysis shows that the moving average approach (Method E) of computing weighted mean of unbounded training data set pairs results in the most stable detrending mean to suppress detrending bias and helps achieve an MAPE lower than 1%.
Machine Learning Methods for Detecting Fraud in Online Marketplaces

Connecting buyers and sellers in a safe and secure environment is one of the biggest challenges in online marketplaces. Probabilistic models built upon user-item databases address the challenge, but often encounter issues such as lack of stability and robustness. These issues are magniﬁed in fraud scenarios where datasets are highly imbalanced, noisy and malicious users deliberately adapt their behaviors to avoid detection. In this context, we leveraged the power of existing open sources machine learning libraries H2O and Catboost and designed a pipeline to collect, process and predict the likelihood of a private seller’s listing data to be fraudulent. We found that the stacked ensemble model provides the best performance (F1=0.73) when compared to other commonly used models in the ﬁeld. Further, our models are benchmarked on a public Kag-gle Dataset, TalkingData AdTracking Fraud Detection Challenge where we compared them to other studies and highlighted their generalizability and eﬀectiveness at handling online fraud.
Signal Processing Application for Musical Notes Recognition

Musical notes are the smallest elements used in composition of musical melody in the area of fine art. Technological advancement introduced digital application in musical composition by utilizing the area of signal processing. This paper presented musical notes recognition technique utilizing signal processing application. Samples of musical notes are composed in real time from chosen musical instruments. The collected musical notes are formed by two different types of guitars which are the acoustic and electric guitars. Signal processing algorithm is developed using MATLAB with the implementation of cepstral analysis method. The musical notes are analyzed by the algorithm to recognize the musical notes played by the instruments. The developed algorithm successfully exhibited the ability to recognize the presence of pitch related to musical notes from sound made by the chosen musical instruments. The outcomes also display that music notes matched perfectly in comparison from both chosen instruments.
Hopping on Spectrum: Measuring and Boosting a Large-scale Dual-band Wireless Network

In recent years, more and more wireless networks support both 2.4GHz and 5GHz bands. However, in large-scale dual-band wireless networks, lack of understanding on the behavior and performance makes the network diagnosis and optimization extremely challenging. In this paper, we conduct a comprehensive measurement to characterize the behavior and performance in a large-scale dual-band wireless network (TD WLAN). We make several meaningful observations. (1) Although the 5GHz band outperforms the 2.4GHz band, 60% of devices tend to be associated with the 2.4GHz band. The device association behavior has a large impact on the performance. (2) Rogue and non-WiFi devices are prevalent, wherein hidden terminal interference increases the average loss rate by 8%, carrier sense interference increases the average WiFi latency by 45%, and RF interference further aggravates both packet loss and channel contention. (3) The dynamic channel assignment strategy is not always effective. On this basis, we propose a novel and easy-to-implement strategy to improve the wireless performance by intelligent band navigation and heuristic channel optimization. The actual deployment in TD WLAN shows the packet loss reduces by 40% on average and the WiFi latency for more than 60% of devices is below 5ms.
Experimental evaluation of network packet latency within a distributed NFV Infrastructure

Network Functions Virtualization (NFV) architecture together with the Software-defined Networking (SDN) are the driver for the network services improvements. They complement the 5G expansion. Together they are the enablers of long awaited IoT penetration as well as the AI mass usage . With such a scenario, networks grow towards the edge with a need for the storage and compute resources to be brought in the proximity to the access networks, thus allowing network latency to be under 1ms and minimizing the backhaul traffic. It is important for reliable, cost effective, and low-latency services and improved end-user experience. We have made an experimental setup of a distributed NFV architecture to investigate the network packet delay in services that span across multiple geographically separated locations. The objective of the research is to come with a conclusion that will help network architects to assess the NFV architecture and to make a distribution of the infrastructure on which the services lay on.
ECO Timing Optimization with Data Paths and Clock Paths Considered

Metal-only ECO (engineering change order) is an important process for timing optimization after the layout has been completed. To fix timing violations, previous works use spare cells to revise data paths. Different from previous works, in this paper, we propose a new approach to fix timing violations by considering both data paths and clock paths. The proposed algorithm creates a candidate list (from both data paths and clock paths) based on current timing conditions and spare cell data, and then it calculates the costs to select the best revision method. The algorithm repeats until no further improvement can be made. Experiment results show that the proposed approach can achieve better timing results than previous works.
Audio-based Wearable Multi-Context Recognition System for Apnea Detection

Apnea or sleep apnea syndrome is a condition in which a person unconsciously stops breathing during a sleeping state for longer than a certain timeframe. Multiple long-term apnea events induce various impairments. However, apnea detection in hospitals is an intensive and complicated procedure, resulting in a high rate of non-diagnosis and a low awareness of the disease. Existing wearable devices for apnea detection mostly use heartbeat signal patterns and SpO2 levels to detect the disease; however, because apnea is a respiratory impairment, it is believed that using the breathing pattern is the most straightforward approach in apnea detection. Several recent studies investigated whether swallowing frequency during sleep can increase along with the apnea severity. However, the number of wearable devices using swallowing to detect apnea is extremely limited. Thus, this study proposes a wearable system to recognize human contexts such as breathing, heartbeat pattern, and swallowing using an audio sensor. Experiments were conducted to compare and obtain the most suitable parameters for the system such as the window sizes, types of audio feature values, and classification algorithms. A prototype of the device was built and is able to detect breathing, swallowing, heartbeat, oral sounds, and body movement. The results show the highest accuracy of 76.9% when using a 1-s window size and Mel's frequency cepstral coefficient features in contact microphone data.
A Global Orchestration Matching Framework for Energy-Efficient Multi-Access Edge Computing

Multi-access edge computing (MEC) enables mobile units (MUs) to offload computation tasks to edge servers nearby. This translates in energy savings for the MUs, but creates a joint problem of offloading decision making and allocation of the shared communication and computation resources. In a MEC scenario with multiple MUs, multiple access points and multiple cloudlets the complexity of this joint problem grows rapidly with the number of entities in the network. The complexity increases even further when some MUs have a higher incentive to offload tasks due to a low battery level and are willing to pay in exchange for more resources. Our proposed energy-minimization approach with a flexible maximum offloading time constraint is based on matching theory. A global orchestrator (GO) collects all the system state information and coordinates the offloading preferences of the MUs. A MU can lower the maximum time constraint by a payment. The GO allocates the shared communication and computation resources accordingly to satisfy the time constraint. The computation load of the algorithm at each MU is reduced to a minimum as each MU only has to take a simple offloading decision based on its task properties and payment willingness. In numerical simulations, the proposed matching approach and flexible resource allocation scheme is tested for fast and reliable convergence, even in large networks with hundreds of MUs. Furthermore, the matching algorithm, tested with different resource allocation strategies, shows a significant improvement in terms of energy-efficiency over the considered reference schemes.