Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Speed and Stepping MEMS Rotary Actuator for Multimodal, 360° Side-Viewing Endoscopic Probes

This paper reports, for the first time, a MEMS rotary actuator that enables side-viewing microendoscopic probes with different modalities for luminal tissue analysis. The developed tubular-form micro rotary actuator offers both high-speed and stepping motions that are compatible with the use for optical coherence tomography (OCT) and Raman spectroscopy, respectively. The actuator, improved from its preceding designs, shows up to 125× higher revolution speeds per power while enabling arbitrary angle-resolved stepping rotations in a ∼50% thinner body, with significantly reduced heating effect for biologically safe operation in vivo. Preliminary testing of the OCT endoscopic probe integrated with the developed rotary actuator/scanner successfully demonstrates real-time 360° imaging of live human skin tissue.
A Scientometric Analysis of Global Terahertz Research by Web of Science Data

Terahertz is a high-tech technology that has been developed rapidly in recent years. Relevant research has been extensively carried out in physics, chemistry, communication, biology, agricultural food safety testing, industrial non-destructive testing, biochemical, medical, and biological imaging. To provide a summary of the research trend in the field, there is a need to analyze the literature by scientometrics. In this work, we have an overview of the literature in the terahertz field, and then analyze the literature from the aspects of terahertz source, terahertz detector, terahertz spectroscopy and imaging, and terahertz applications respectively. The focus of this work is to automatically extract the trend by co-words and co-citation network analysis, and the comparative point of view is used to understand two closely related but different fields of terahertz basic research and terahertz application research. Through the scientific basis measurement, the research characteristics of these two fields, including annual outputs, active journals, key authors, key research directions and hotspots, important research projects and the countries behind them are immediately obtained.
A Novel Feature Selection Method Using Whale Optimization Algorithm and Genetic Operators for Intrusion Detection System in Wireless Mesh Network

Machine learning–based intrusion detection system (IDS) is an important requirement for securing data traffic in wireless mesh networks. The noisy and redundant features of network data tend to degrade the performance of the attack detection classifiers. Therefore, the selection of informative features plays a vital role in the enhancement to the IDS. In this paper, we propose a wrapper-based approach using the modified whale optimization algorithm (WOA). One drawback of WOA is that premature convergence results in a local optimal solution. To overcome this limitation, we proposed a method in which the genetic algorithm operators were combined with the WOA. The crossover operator was used to further improve the search space of whales, and the mutation operator helped to avoid being stuck in the local optimum. The proposed method selects the informative features in the network data, which helps to accurately detect intrusions. Using a support vector machine (SVM), we identified the types of intrusions based on the selected features. The performance of the improved method was analyzed by using the CICIDS2017 and ADFA-LD standard datasets. Our proposed method had better attack detection rate than the standard WOA and other evolutionary algorithms; it also had good accuracy and was suitable for IDS in the wireless mesh networks. The performance of the IDS was increased by selecting the informative features with the improved whale optimization algorithm. The attack detection ratio was higher than that of the standard WOA.
Natural Disaster Application on Big Data and Machine Learning: A Review

Natural disasters are events that are difficult to avoid. There are several ways of reducing the risks of natural disasters. One of them is implementing disaster reduction programs. There are already several developed countries that apply the concept of disaster reduction. In addition to disaster reduction programs, there are several ways to predict or reducing the risks using artificial intelligence technology. One of them is big data, machine learning, and deep learning. By utilizing this method at the moment, it facilitates tasks in visualizing, analyzing, and predicting natural disaster. This research will focus on conducting a review process and understanding the purpose of machine learning and big data in the area of disaster management and natural disaster. The result of this paper is providing insight and the use of big data, machine learning, and deep learning in 6 disaster management area. This 6-disaster management area includes early warning damage, damage assessment, monitoring and detection, forecasting and predicting, and post-disaster coordination, and response, and long-term risk assessment and reduction.
A Novel Controllable Image Segmentation Method Using the Inverse GAMMA Distribution Function Based on Histogram Specification

Image segmentation(IMSEG) is defined as a top of the basic and most significant process of digital image handling which refers to the techniques that used to partitioning and dividing an image into useful and meaningful parts, called segments. It's so important for many applications which difficult and inefficient to process the entire image, such as object recognition or image compression. So, IMSEG aims to segment the image into districts for further operations and processing. There are several techniques for image segmentation which divide the image into many parts regarding to some specific image properties such as the intensity value of pixels, relation between pixels, color, texture and so on. However, most image segmentation techniques have deficient ability to plainly tune the segment sensitivity of the segmented image. In our manuscript we will propose a new technique for image segmentations using the invers GAMMA distribution function based on histogram matching, the proposed technique provides a method to manage the segment sensitivity of segmented image via setting one parameter. Experimental outcomes pretend a superior execution of this created technique
Req2Lib: A Semantic Neural Model for Software Library Recommendation

Third-party libraries are crucial to the development of software projects. To get suitable libraries, developers need to search through millions of libraries by filtering, evaluating, and comparing. The vast number of libraries places a barrier for programmers to locate appropriate ones. To help developers, researchers have proposed automated approaches to recommend libraries based on library usage pattern. However, these prior studies can not sufficiently match user requirements and suffer from cold-start problem. In this work, we would like to make recommendations based on requirement descriptions to avoid these problems. To this end, we propose a novel neural approach called Req2Lib which recommends libraries given descriptions of the project requirement. We use a Sequence-to-Sequence model to learn the library linked-usage information and semantic information of requirement descriptions in natural language. Besides, we apply a domain-specific pre-trained word2vec model for word embedding, which is trained over textual corpus from Stack Overflow posts. In the experiment, we train and evaluate the model with data from 5,625 java projects. Our preliminary evaluation demonstrates that Req2Lib can recommend libraries accurately.
Memristor-based Reconfigurable Circuits: Challenges in Implementation

The emergence of memristor technologies has recently received much attention due to their promising features, expecting to be a key driver in the post-CMOS era. With its ultra-low power, higher density capability and non-volatile characteristics, memristor technology is considered as the best candidate to replace SRAM cells or be employed for routing in digital reconfigurable systems. Although memristor-based reconfigurable circuits can offer many advantages over the conventional CMOS designs, limitations in the utilization of memristor technologies such as electroforming or programming structures have not been thoroughly considered and discussed. This work looks into recent trends in exploiting memristor technologies in reconfigurable circuits and then discusses implementation challenges like memristor programming, reliability and operation of memristor-based memory cells for digitally reconfigurable circuits.
Secure Data Sharing and Customized Services for Intelligent Transportation Based on a Consortium Blockchain

In view of the security risks and centralized structure of traditional intelligent transportation system, we propose a novel scheme of secure data sharing and customized services based on the consortium blockchain (DSCSCB). The ciphertext-policy attribute-based proxy re-encryption algorithm has the function of keyword searching by dividing the key into an attribute key and a search key, which not only solves the problem that proxy re-encryption algorithm cannot retrieve data, but also realizes data sharing and data forwarding. Moreover, the algorithm effectively controls the access permission of data, and provides a secure communication environment for the vehicular ad-hoc network (VANET). Service sectors, such as insurance companies, the traffic police and maintenance suppliers, obtain the corresponding ciphertext and then apply the smart contract to provide customized services for the onboard unit after decryption. Security analysis and performance evaluation demonstrate that our scheme not only meets the requirements of data sharing in the security and confidentiality, but also has obvious advantages in the overhead of computing and communication.
Optical Dynamic Resource Allocation Based on Weighted Expected Spectral Efficiency

5G networks occupies a high interest recently because it enables internet of things applications. One of these networks is light fidelity (Li-Fi) technology that attracts high attention nowadays in wireless communication field. Because it has a large swath of spectrum hence the ability to provide high end user data rate up to multiple Gbps. However, Li-Fi suffers from big issues such as co-channel interference (CCI) between users. This problem highly degrades Li-Fi performance. To overcome on this challenge, an efficient resource allocation between users is needed to obtain optimum usage of available system spectrum. In this paper, the users' channel quality is taken into consideration to dynamically allocate resources to users in Li-Fi network. Based on this criterion, the network guarantees efficient spectrum distribution among users without wasting resources for users with bad channel conditions thus improving the system performance. Hence, the proposed scheme can achieve a high improvement in total system spectral efficiency and increase in average data rate per user comparable to other scheduling algorithms, i.e., round robin and proportional fairness scheduling, as it will be discussed in numerical simulation.
Joint Resources Allocation of Device-To-Device Communications Underlaying Cellular Networks

Device-to-Device (D2D) communications underlaying cellular networks have emerged as a necessity for a substantial increase in the system throughput and the number of active devices for the future cellular networks. In this paper, we investigate a joint power allocation and channel assignment problem for multi-pair D2D communications underlaying cellular networks over fading channels under different systems requirements. Specifically, we propose two primal-dual algorithms, where the objective of the first one is to maximize the total cell throughput under the different quality of service and power constraints, while the objective of the second one is to minimize the total power consumption. Simulations results show that the proposed algorithms achieve the targeted objectives in different signal-to-noise ratio regimes as well as enhance the D2D connectivity.
Intraclass Similarity Structure Representation-Based Hyperspectral Imagery Classification With Few Samples

Hyperspectral imagery (HSI) classification is one of the fundamental applications in remote sensing domain, which aims at predicting the labels of unlabeled pixels in an image with a classifier trained on a certain amount of labeled pixels. However, due to the expensive cost on manual labeling, only limited labeled pixels can be obtained in real applications, which is prone to result in the training of classifier to be overfitting. To address this problem, we present an intraclass similarity structure representation-based HSI classification method. First, according to the intraclass spectrum similarity of pixels, we establish a mixed labels-based annotation model. Given some randomly selected unlabeled pixels, we employ the proposed annotation model to assign each pixel a mixed label from the top-two possible classes, and then augment the original training set with those labeled pixels. On the augmented training set, we train a deep convolutional neural network-based classification model. With several individual rounds of the annotation and classifier training procedures, we obtain several independent classification models and predict the final labels as their fusion results with a voting strategy. Experimental results demonstrate the effectiveness of the proposed method in terms of HSI classification with few training samples.
Color image classification on neuromorphic system using reinforcement learning

Neuromorphic hardware system has been studied based on binary monochromatic numerical data. In this study, the input to neuromorphic hardware systems uses binary color images rather than monochrome. Color image classification is implemented using reinforcement learning. In addition, the smaller degree of freedom of synaptic weights is experimented to implement a binary neuromorphic hardware system. More complicated color image classification can be developed with this study.
PPG-based Personalized Verification System

Convergence between online and off-line systems gives us a great chance to enrich our societies but it also requires a high secure system to verify true user from fraud. In this paper, we propose a novel deep learning-based verification model using Photoplethysmography (PPG) signals. The goal of this paper is to build a personalized data-driven network by employing convolution neural network (CNN) with long-short term memory (LSTM), to model the time-series sequence inherent within the PPG signal. After building each personalized network, each network can be applied to distinguish a true user from others. The proposed network was evaluated on the BioSec. Lab PPG dataset at University of Toronto, which achieved an average of 10-fold cross-validation accuracy of 96% (in single-session) and 72.7% (in two-sessions).
Automated Deprecated-API Usage Update for Android Apps: How Far are We?

As the Android API evolves, some API methods may be deprecated, to be eventually removed. App developers face the challenge of keeping their apps up-to-date, to ensure that the apps work in both older and newer Android versions. Currently, AppEvolve is the state-of-the-art approach to automate such updates, and it has been shown to be quite effective. Still, the number of experiments reported is moderate, involving only API usage updates in 41 usage locations. In this work, we replicate the evaluation of AppEvolve and assess whether its effectiveness is generalizable. Given the set of APIs on which AppEvolve has been evaluated, we test AppEvolve on other mobile apps that use the same APIs. Our experiments show that AppEvolve fails to generate applicable updates for 81% of our dataset, even though the relevant knowledge for correct API updates is available in the examples. We first categorize the limitations of AppEvolve that lead to these failures. We then propose a mitigation strategy that solves 86% of these failures by a simple refactoring of the app code to better resemble the code in the examples. The refactoring usually involves assigning the target API method invocation and the arguments of the target API method into variables. Indeed, we have also seen such transformations in the dataset distributed with the AppEvolve replication package, as compared to the original source code from which this dataset is derived. Based on these findings, we propose some promising future directions.
Mitigation of Cryptojacking Attacks Using Taint Analysis

Cryptojacking (also called malicious cryptocurrency mining or cryptomining) is a new threat model using CPU resources covertly “mining” a cryptocurrency in the browser. The impact is a surge in CPU Usage and slows the system performance. In this research, in-browsercryptojacking mitigation has been built as an extension in Google Chrome using Taint analysis method. The method used in this research is attack modeling with abuse case using the Man-In-The-Middle (MITM) attack as a testing for mitigation. The proposed model is designed so that users will be notified if a cryptojacking attack occurs. Hence, the user is able to check the script characteristics that run on the website background. The results of this research show that the taint analysis is a promising method to mitigate cryptojacking attacks. From 100 random sample websites, the taint analysis method can detect 19 websites that are infcted by cryptojacking.
A 24 kb Single-Well Mixed 3T Gain-Cell eDRAM with Body-Bias in 28 nm FD-SOI for Refresh-Free DSP Applications

Logic-compatible gain-cell embedded DRAM (GC-eDRAM) is an emerging alternative to conventional SRAM for memory-dominated system-on-chip (SoC) designs due to its high-density, low-power, and two-ported operation. Although GCs have a limited data retention time (DRT) at deeply scaled technology nodes, there are many DSP applications which only require short-term data storage and can therefore avoid refresh. In this paper, we present a novel single-well mixed 3T GC implementation in 28 nm FD-SOI technology. The proposed GC is supplied with body-bias control to improve the DRT by suppressing the leakage through the write port, and extend the maximum operating frequency by forward body-biasing the read port. A 24 kbit GC-eDRAM macro implementing the proposed 3T GC was fabricated in 28 nm FD-SOI technology, resulting in the highest density logic-compatible embedded memory fabricated in any 28 nm process with over $2\times$ higher density compared to a 6T SRAM cell, over $4\times$ higher DRT compared to a conventional 3T GC, and $38\times-47\times$ lower static power compared to conventional single-ported and two-ported SRAMs.
An Octave Bandwidth RF Harvesting Tee-Shirt

This paper presents 16 and 81-element broadband rectenna arrays screen printed on a cotton tee-shirt for harvesting $4-130 \mu \mathrm{W}/\text{cm}^{2}$ power densities between 2–4 GHz. The antenna is a self-complementary tightly-coupled bow-tie array with a period of about $\lambda_{\mathrm{O}}/6$ at the highest frequency. Each array element feed is connected to a packaged Schottky diode using silver paint. Full-wave antenna simulations are performed with specific tissue electrical parameters for the torso, as well as for a body phantom. The diodes impedance as a function of dc load and input power density is analyzed using source-pull harmonic-balance simulations. Measurements using a water-filled phantom show up to $\mathrm{P}_{\text{DC}=}10 \mu\mathrm{W}$ for incident power densities of $4 \mu\mathrm{W}/\text{cm}^{2}$ at the center of the band, with a DC load of $\mathrm{R}_{\text{DC}}=2\mathrm{k}\Omega$.
Broadside/Endfire Switched Beam Double Ridge-Gap Waveguide H-plane Horn Antenna

In this paper, H-plane horn antenna based on double ridge-gap waveguide (RGW) technology is designed for satellite applications at 13.5 GHz. The double RGW surfaces are based on AMC-AMC parallel plate region around the metal horn-shaped ridge to enforce the propagating wave through the two parallel ridges. The AMC nail unit-cell has a stop-band of 10.5–22 GHz. The double-ridge horn antenna radiates end-fire beam along the x-axes with maximum gain of 9.45 dBi and high front-to-back (FBR) of 14.3 dB. A vertical beam switching from end-fire direction to broadside direction is achieved by adding rows of nail unit-cells in the front of the horn aperture. A metamaterial (MM) polarizer is added to the antenna structure to radiated circularly polarized waves.
Accurate Position Estimation of Mobile Robot Based on Cyber-Physical-Social Systems (CPSS)

When planning the trajectory of a mobile robot, it is usually necessary to use sensors to collect a large amount of position information. Because traditional computing methods cannot effectively use huge data sources, an edge computing that implements edge intelligent services on the side of network edge near the data source is proposed, which speeds up the process of data processing. However, the data collected through sensors may contain gross errors. In general, the influence of gross errors on state estimation are rarely considered when using particle filter algorithms for state estimation. In fact, the measurements of dynamic systems are often affected by different types of gross errors in the actual application process. Therefore, it is a problem worth studying that how to detect and compensate for different types of gross errors. In this paper, an improved particle filter algorithm is proposed for the position estimation of mobile robot dynamic system. Firstly, the gross error identification method is used to identify the types of gross errors, and then the various gross errors are compensated. Finally, the particle filter algorithm based on the measurements compensation is obtained. Simulation experiments on the position estimation of mobile robots are carried out to verify the effectiveness of the proposed method in solving the measurements with gross errors. The precise position estimation of the mobile robot is achieved. Through the simulation experiments on the position estimation problem of mobile robots, it is verified that the proposed method is effective in solving the measurements with gross errors. And the accurate position estimation of the mobile robot is realized.
A DC-43.5 GHz CMOS Switched-Type Attenuator with Capacitive Compensation Technique

This paper presents an ultra-broadband 5-bit switched-type attenuator (STA). The resistor values of the five attenuation cells are optimized to ensure accurate amplitude tuning. Capacitive compensation technique is adopted to reduce phase error and enhance the bandwidth. Fabricated in 65-nm CMOS technology, the 5-bit STA occupies a core chip area of 0.036 mm2. It exhibits ultra-broadband (i.e., DC to 43.5 GHz) operation with 15.5-dB amplitude tuning range and 0.5-dB tuning step. The insertion loss of the reference state is 1.5 - 5.3 dB from DC to 43.5 GHz. The RMS amplitude error and phase error are < 0.19 dB and < 3.1° over the whole measured band.