Miracle-dz/UniFAR-FTUs huggingface dataset:
An ultra-sensitive optical angle sensor for pitch deviation measurement of diffraction gratings

A new method using an ultra-sensitive angle sensor to evaluate the grating pitch deviation is proposed in this paper. The principle and theoretical calculation are done to confirm the theoretical feasibility of the new proposed method. Theoretically, with a high resolution optical angle sensor, this method can reach an extremely high resolution of picometers for the evaluation of grating pitch deviation. Compensation methods of angle error during scanning process are also discussed.
Investigating Multiple Quality Objectives for Surface Roughness and Dimensional Accuracy of Carbon Steel Manufactured by Turn-Mill Multitasking Machining

This research in multiple quality objectives of the turning and milling multitasking machining is based on the Taguchi process and the fuzzy theory. These two methods can quickly find the best processing parameters to obtain the best surface roughness and dimensional accuracy and reduce the research cost and save research time. In this research, the Taguchi robust process $L9 (3^{4})$ orthogonal table was used to find the individual optimized process parameters. The control factors used were spindle speed (RPM), feed (mm/min), and C-axis brake pressure (kg/$cm^{2})$. ), the axial cutting depth, and then taking the surface roughness and dimensional accuracy as the characteristic indicators, and then the respective signal-to-noise ratio (S/N) is grayed out according to the experimental sequence to generate normalization, and the gray correlation coefficient is obtained. Further, a Multiple Performance Characteristic Index (MPCI) is obtained. The research results clearly demonstrated that the overall results of the process optimum parameters obtained by using the measurement indicators of multiple quality characteristics prevail the results of the individual optimized process parameters.
An Intelligent Traffic Load Prediction-Based Adaptive Channel Assignment Algorithm in SDN-IoT: A Deep Learning Approach

Due to the fast increase of sensing data and quick response requirement in the Internet of Things (IoT) delivery network, the high speed transmission has emerged as an important issue. Assigning suitable channels in the wireless IoT delivery network is a basic guarantee of high speed transmission. However, the high dynamics of traffic load (TL) make the conventional fixed channel assignment algorithm ineffective. Recently, the software defined networking-based IoT (SDN-IoT) is proposed to improve the transmission quality. Besides this, the intelligent technique of deep learning is widely researched in high computational SDN. Hence, we first propose a novel deep learning-based TL prediction algorithm to forecast future TL and congestion in network. Then, a deep learning-based partially channel assignment algorithm is proposed to intelligently allocate channels to each link in the SDN-IoT network. Finally, we consider a deep learning-based prediction and partially overlapping channel assignment to propose a novel intelligent channel assignment algorithm, which can intelligently avoid potential congestion and quickly assign suitable channels in SDN-IoT. The simulation result demonstrates that our proposal significantly outperforms conventional channel assignment algorithms.
Development of Rehabilitation System with Brain-Computer Interface for Subacute Stroke Patients

There have been recent advances in brain-computer interfaces for post-stroke rehabilitation. In particular, compact and embedded brain-computer interface systems with neuromuscular electrical stimulation have been developed by industry and academia, and some of them can potentially be used at the bedside. However, limited studies have demonstrated their safety and feasibility for treatment in subacute stroke patients. The aim of this pilot study was to first develop a brain-computer interface system for subacute stroke inpatients that is usable at the bedside and to show the safety and feasibility using a small cohort of inpatients. Four hemiplegic stroke inpatients in the very early phase (7-24 days from stroke onset) participated in this study. The portable brain-computer interface system shows the amplitude of sensorimotor rhythms extracted from scalp electroencephalograms in real time. Patients attempted to extend the wrist on their affected side, and neuromuscular electrical stimulation was applied only when the brain-computer interface system detected significant movement intention-related electroencephalogram changes. Each brain-computer interface training lasted 40 minutes. On average, 120-200 training trials of the wrist extension task were successfully and safely conducted over 3.3 days (range 2-4 days) with the bedside brain-computer interface system. Furthermore, electroencephalogram results showed a new significant event-related desynchronization in the damaged hemisphere after training. These results clearly show the proposed bedside brain-computer interface system's safety and feasibility and also demonstrated electrophysiological plasticity in the damaged hemisphere in subacute patients with post-stroke hemiplegia. Larger clinical studies are needed to identify the brain-computer interface system's clinical efficacy and its effect size in the subacute post-stroke patient population.
Deep Learning on Brain Cortical Thickness Data for Disease Classification

Deep learning has been applied to learn and classify brain disease using volumetric MRI scans with an accuracy approaching or even exceeding that of a human expert. This is typically done by applying convolutional neural networks to slices of a 3D brain image volume. Each slice of the brain volume, however, represents only a small cross-sectional area of the cortical layer. On the other hand, convolutional neural networks are less well developed for 3D volumes. Therefore we sought to apply deep networks to the 2D cortical surface, for the purpose of classifying Alzheimer's disease (AD). AD is known to affect the thickness and geometry of the cortical surface of the brain. Although the cortical surface has a complex geometry, here we present a novel data processing method to feed the information of an entire cortical surface into existing deep networks for more accurate early disease detection. A brain 3D MRI volume is registered and its cortical surface is flattened to a 2D plane. The flattened distributions of the thickness, curvature and surface area are combined into an RBG image which can be readily fed to existing deep networks. In this paper, the ADNI dataset of brain MRI scans are used and flattened cortical images are applied to different deep networks including ResNet and Inception. Two pre-clinical stages of AD are considered; stable mild cognitive impairment (MCIs) and converting mild cognitive impairment (MCIc). Experiments show that using flattened cortical images consistently leads to higher accuracy compared to using brain slices with the same network architecture. Specifically, the highest accuracy of 81% is achieved by Inception with flattened cortical images, as compared to 68% by the same network on brain slices and 75.9% accuracy by the best method in the literature which also used a deep network on brain slices. Our results indicate that flattened cortical images can be used to learn and classify AD with high accuracy.
Mobile Cloud-of-Clouds Storage Made Efficient: A Network Coding Based Approach

Cloud-of-clouds storage is a viable means to ensure security and reliability of distributed data storage, where data are encrypted, encoded, and stored in multiple clouds. However, it is a great challenge to adopt such a paradigm in mobile devices (e.g., smartphone). Mobile devices are generally incapable to perform the heavy-weight operations (i.e., data encryption, encoding, and transmission) required in such a paradigm, given the limited resources in such devices. This paper focuses on addressing this challenge, i.e., improving data storage performance in mobile cloud-of-clouds storage systems. The key of our proposal is to allow the low-capability mobile devices to offload the computational and transmission overhead to the clouds. In other words, we propose a Network Coding based Cloud-of-clouds Storage (NCCS) scheme, where the clouds can encode and exchange data collaboratively. We consider two state-of-the-art cloud-of-clouds storage approaches, i.e., AONT-RS and CAONT-RS, as example cases to deploy our scheme. Accordingly, we propose their network coding-based enhancements, namely NAONT-RS and NCAONT-RS. We implement a prototype cloud-of-clouds system to verify the efficiency of our proposal. We deploy the prototype on Microsoft Azure and conduct extensive experiments with real-world traces. The experimental results show that NAONT-RS and NCAONT-RS can reduce the time of data storage process by up to 50% and improve the throughput by up to 110% compared with their original versions, i.e., AONT-RS and CAONT-RS.
Convolutional Networks with Bracket-Style Decoder for Semantic Scene Segmentation

To build up a state-of-the-art semantic scene segmentation model, a balanced combination between coarsely and finely contextual details is required for eliminating class-wise ambiguities and reaching high accuracy of pixel-wise labeling, respectively. Accordingly, with deep learning integration, prior works have achieved impressive performance in general, but found difficulties in correctly labeling medium to small objects. For the purpose of overcoming such issue, this paper proposes a deep convolutional network with bracket-style decoder, namely B-Net, to leverage the utilization of features learned at middle layers in the backbone networks (encoder) for constructing a final prediction map of densely enhanced semantic information. In particular, every feature map of interest combines with its adjacent version of higher spatial resolution through lateral connection modules to produce finer outputs that repeat such routine round-by-round until retrieving the finest-resolution map for dense prediction. Consequently, benchmarking results on CamVid dataset showed the effectiveness of the proposed method with mean class-wise accuracy, pixel-wise accuracy, and mean union intersection of 76.2%, 87.1%, and 66.4%, respectively.
Training Neural Networks with Low Precision Dynamic Fixed-Point

Dynamic fixed-point (DFP) is one of the most successful attempts to reduce bit-widths in training neural networks. It has been reported that DFP can reduce the bit-widths of the training operations to 16 bits, except for parameter update operations; parameter updates for general networks need higher precision and are usually done with 32-bit floating-point operations. In this paper, we propose two methods of using 16-bit DFP for all the training operations including parameter updates; weight clipping and gradual batch size increase. Lastly, we combine the two methods to further explore their potentials. We successfully apply 16-bit DFP operations on the parameter updates of LeNet-5 and VGG-16 networks using CIFAR10 and CIFAR100 datasets.
An Image Sensing Method to Capture Soybean Growth State for Smart Agriculture Using Single Shot MultiBox Detector

Recently, smart agriculture, a new approach to farming using ICT, has been received great attention. To control cultivate condition precisely, it is important to capture the growth state of plants as well as environmental factors such as temperature, moisture, solar radiation, etc. In this paper, we propose an image sensing method to detect soy flowers and seedpods as growth factors using a state-of-the-art deep learning architecture called Single Shot MultiBox Detector (SSD). Images of soybeans were taken at Hokkaido Agricultural Research Center from Year 2015 to 2017 and we carry out the performance test for our system using a dataset of soybean images. The detection accuracy for seedpods and flowers are 0.586 and 0.646 in F-measure, respectively.
A Self-Adaptive Motion Scaling Framework for Surgical Robot Remote Control

Master–slave control is a common form of human–robot interaction for robotic surgery. To ensure seamless and intuitive control, a mechanism of self-adaptive motion scaling during teleoperaton is proposed in this letter. The operator can retain precise control when conducting delicate or complex manipulation, while the movement to a remote target is accelerated via adaptive motion scaling. The proposed framework consists of three components: 1) situation awareness, 2) skill level awareness, and 3) task awareness. The self-adaptive motion scaling ratio allows the operators to perform surgical tasks with high efficiency, forgoing the need of frequent clutching and instrument repositioning. The proposed framework has been verified on a da Vinci Research Kit to assess its usability and robustness. An in-house database is constructed for offline model training and parameter estimation, including both the kinematic data obtained from the robot and visual cues captured through the endoscope. Detailed user studies indicate that a suitable motion-scaling ratio can be obtained and adjusted online. The overall performance of the operators in terms of control efficiency and task completion is significantly improved with the proposed framework.
The Topic Extraction from the Discussion Data of Community Disaster Risk Management Workshops

In this study, the topic model was applied to specify the topics of the discussions in community disaster risk management workshops in Kunigami village, Okinawa, Japan. 20 topics were extracted from the text data of the workshop discussions held at 8 communities in the village. Not only the issues on natural hazards, but also the issues on local factors and mitigation activities were specified.
Improving Student's Engagement Through the Use of Learning Modules, Instantaneous Feedback and Automated Marking

Assessment is central in effective teaching. This research sets out to discover the impact and effectiveness of timely assessment and feedback on student performance and engagement. Qualitative and quantitative data is collected from two cohorts of students with different levels of engagement. We have shown that more regular feedback and engagement resulted in a significantly improved pass rate and average mark. In conclusion, enabling timely assessment and feedback can improve student performance and give educators tools that make this process more manageable.
An Improved Envelope Tracking SOA Amplifier for CO-OFDM Transmission by Using PSO

Envelope tracking (ET) is a popular technique for improving the power efficiency and linearity of radio-frequency power amplifiers. In this paper, we investigate its use in a Coherent-Optical OFDM transmitter using a Semiconductor Optical Amplifier (SOA) as a power booster. At a fixed bias current, such amplifier may lead to significant nonlinear effects, especially when the signal to be amplified has a non-constant envelope. Thanks to a bias current control scheme, an efficient linearization of the SOA can be achieved. It is shown that the bit error rate (BER) performance of a SOA-based CO-OFDM system can be largely improved by jointly tuning significant key parameters associated with the E T block. A particle swarm optimization (PSO) is applied for identifying the best operating conditions of the ET scheme, which is eventually combined with Peak to Average Power Ratio (P APR) reduction via nonlinear companding. Three distinct scenarios are proposed depending on the tuned parameters. An improvement of up to 8 dB is achieved at a BER of 10–3, for a 5 Gb/s QAM/CO-OFDM transmission.
Towards Efficient Microarchitecture Design of Simultaneous Localization and Mapping in Augmented Reality Era

Recently, augmented reality technologies are debuting to the mainstream markets. Simultaneous Localization and Mapping (SLAM), which serves as the core to drive augmented reality, enables mobile devices to understand "the reality" by recognizing and understanding the surrounding space. However, enabling SLAM on mobile devices still faces challenges due to computation and power limitations. Thus, this paper explores the microarchitecture design space of visual SLAM. First, we conduct a characterization to make the image signal processing stage in the front-end image acquisition stage configurable and explore SLAM's sensitivity to individual stages. Our characterization shows that only demosaicing, gamma compression, and denoising in the image signal processing process have significant influences on SLAM's accuracy. Thus, the image signal processor in SoC in traditional mobile devices can be replaced by an approximation logic to save hardware overhead and energy. Second, we propose a new CGRA architecture, called SL-CGRA, specially tailored to SLAM's workload characteristics. It features a two-level memory design, which includes data-redirection layer support for on-chip memory, and an efficient CGRA memory controller stacked through Through-Via Silicon (TSV) for highly efficient off-chip memory access. Besides, PEs in SL-CGRA support different execution modes to exploit the data-level parallelism and task-level parallelism of SLAM. Our evaluation results show that SL-CGRA achieves good performance and energy efficiency.
A Critical View of the NFV Dataplane Implementations for 5G Networks

In this paper we offer a critical view of the current NFV approach and the challenges that the system integrators will face when implementing 5th generation network platforms. The NFV concept is essentially applying the cloud ideas to the networking and telecommunication domains. Its promise is to provide a flexible and programmable infrastructure, which can host a variety of services, avoiding single vendor lock-in. This is achieved by moving the core functionality from the dedicated hardware (packet processors, DSPs etc.) to a virtualized environment running on top of generic purpose CPU cores.
Temporal Impact Analysis for Technological Innovation Based on Box-Cox Transformation

In addition to the preferential attachment mechanism, the evolving citation history of a technical paper is also influenced by time. However, the influence pattern varies a lot in real cases, and cannot be described by a certain distribution like normal distribution. In order to find a universal model to measure the influence of time, this paper proposed a model which combines the normal distribution with Box-Cox transformation. Compared with existing models such as Logistic model, Gompertz model and Wang's model (Science, 2013), our model explain the actual scientific research rules more widely and obtain higher accuracy by conducting experiments in four cases. It can not only describe emerging research topics but also explain the "Sleeping Beauties" phenomenon.
Performance Characterization of Underwater Visible Light Communication

In this paper, we investigate the performance limits of underwater visible light communication (UVLC) systems. We first develop a closed-form path loss expression as a function of transceiver parameters and water type. We then utilize this new expression to determine the maximum achievable link distance for UVLC systems in pure sea, clear ocean, coastal water, and harbor water. Our results demonstrate that the maximum achievable distance is limited to a few tens of meters. This necessitates the deployment of relay-assisted UVLC systems to extend the transmission range. We consider both detect-and-forward and amplify-and-forward relaying. For each relaying method, we first consider a dual-hop UVLC system and determine optimal relay placement to minimize the bit error rate (BER). Then, we consider a multi-hop system with equidistant relays and determine the maximum achievable distance for a given number of hops to satisfy a targeted end-to-end BER.
Decision-Forest Voting Scheme for Classification of Rare Classes in Network Intrusion Detection

In this paper, Bayesian based aggregation of decision trees in an ensemble (decision forest) is investigated. The focus is laid on multi-class classification with number of samples significantly skewed toward one of the classes. The algorithm leverages out-of-bag datasets to estimate prediction errors of individual trees, which are then used in accordance with the Bayes rule to refine the decision of the ensemble. The algorithm takes prevalence of individual classes into account and does not require setting of any additional parameters related to class weights or decision-score thresholds. Evaluation is based on publicly available datasets as well as on an proprietary dataset comprising network traffic telemetry from hundreds of enterprise networks with over a million of users overall. The aim is to increase the detection capabilities of an operating malware detection system. While we were able to keep precision of the system higher than 94%, that is only 6 out of 100 detections shown to the network administrator are false alarms, we were able to achieve increase of approximately 7% in the number of detections. The algorithm effectively handles large amounts of data, and can be used in conjunction with most of the state-of-the-art algorithms used to train decision forests.
A Wide-Angle Scanning Switched-Beam Antenna System in LTCC Technology With High Beam Crossing Levels for V-Band Communications

Wide-angle coverage, fine angular resolution, and low-power consumption are the key characteristics of millimeter-wave short-range wireless systems, such as access points in fifth-generation cellular networks and automotive radars. In this paper, we present a 60 GHz switched-beam antenna system addressing all these requirements. It consists of two identical parallel-fed arrays of eight slots, each fed by a passive pillbox beamformer. A switch network comprising four single-pole-multiple-throw switches excites one of the two arrays at a time and selects the radiated beam. The whole system is fully integrated into a multilayer low-temperature cofired ceramic module. The antenna covers a scan range of about ±39° in one plane using 11 beams between 57 and 66 GHz. The proposed architecture enables the simultaneous achievement of continuous coverage (beam crossover levels of about −3 dB) and of low sidelobe levels. Dedicated numerical tools are used for a preliminary design of the antenna. The design procedure, the technological development, and the experimental results are discussed in detail.
Configurable Multi-Port Dynamic Bitcell with Internal Refresh Mechanism

Embedded memories occupy an increasingly dominant part of the area and power budgets of modern SoCs. Multi-ported embedded memories, commonly used by media SoCs and graphical processing units, occupy even more area and consume higher power due to larger memory bitcells. Gain-cell eDRAM is a high-density alternative for two-ported operation with a small silicon footprint. However, conventional gain-cell memories have limited data availability, as they require periodic refresh operations to maintain their data. In this paper, we propose a novel 4-transistor gain-cell, which provides up-to two independent read and write ports (2R2W), with a configurable mode of operation, supporting a hidden refresh mechanism for improved memory availability. An 8 kbit memory macro was implemented in a 28 nm FD-SOI technology, offering up-to 3 × reduction in bitcell area compared to other dual-ported SRAM memory options, and 100% memory availability, as opposed to conventional dynamic memories.