Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Review of Thermal Design and Analysis of Traction Motors

Traction motors used in electric vehicles have some definite operational requirements including high torque at low speed and constant high power at high speeds. These high torque and power requirements contribute directly to higher heat generation in the motors. Thus, an effective thermal design is needed during motor design for improved thermal performance. This review paper mainly focuses on different types of effective methods of thermal design analyzing the thermal performance of traction motors. In broad view, this review paper presents a brief discussion on comparison of different methods of thermal design and analysis for different types of traction motors. The final goal of the comparison is to show how improved thermal design can be achieved through optimum electromagnetic and structural design characteristics for different traction motor geometries.
Comparison of switching characteristics of HfOx RRAM device with different switching layer thicknesses

This paper presents switching characteristics of Ni/HfOx/p+-Si with different switching layer thicknesses (5/10 nm) in DC mode. Larger forming voltage and on/off ratio is obtained from the 10 nm HfOx RRAM while step-like reset process is seen from 5 nm HfOx RRAM. From the measurement results, fabricated RRAM device with thicker switching layer is more suitable for nonvolatile memory operation while thinner HfOx layer has potential for application in neuromorphic computing system.
The Attack Surface Shifting in the Mimic Defense System

The notion of attack surface shifting is introduced to describe the situation of shifting the attack surface in the moving target defense system. However, the situation of shifting the attack surface in the mimic defense system is different from the situation of shifting the attack surface in the moving target defense system. Hence, the existing notion of attack surface shifting can't be applied to describe the situation of shifting the attack surface in mimic defense system. In this paper, we redefine the notion of attack surface shifting for the mimic defense system, and provide a formal method for quantifying the shifting. Experimental results reveal that the new notion of attack surface shifting contributes to improve the strategy of scheduling the heterogeneous redundant executive entity so as to enhance the security of mimic defense system.
Statistical Max-SINR Beamforming for Wireless Powered Communications in Airborne Massive MIMO System

This paper proposes an airborne massive multiple-input multiple-output (MIMO) system for wireless energy transfer (WET) and information transmission. An air platform (AP) equipped with two-dimension rectangular antenna array is employed to broadcast energy and provide wireless access for ground sensors. By exploiting the statistical property of air-terrestrial MIMO channels, the energy and information beamformers are jointly designed to maximize the average received signal-to-interference-plus-noise ratio (SINR), which gives rise to a statistical max-SINR beamforming scheme. The scheme gets rid of the requirement on instantaneous channel state information (CSI), and thus results in significant saving in training resource and energy consumption. The simulation results show that the proposed scheme can achieve comparable performance with the schemes that requires instantaneous CSI.
An 8.5-11 GHz CMOS Transmitter with >19 dBm OP1dB and 24 % Efficiency

This paper presents the first reported highly linear, power efficient, and wideband $X$-band transmitter (TX) with a fully integrated power amplifier (PA) in 65 nm bulk CMOS. The TX features baseband current-bleeding, headroom-efficient and low-noise current mirror, harmonic trapping on the up-conversion mixer, wideband PA inter-stage matching (IM), bulk-floating connection on the PA output stage, as well as efficient output matching network. Fabricated in 65 nm bulk CMOS, the TX achieves 1dB bandwidth from 8.5 GHz to 11 GHz, >19 dBm $OP _{\mathbf {1dB}}$ with 24 % system efficiency, and - 134.9/-135 dBm/Hz out-of-band noise for the lower and upper sidebands. To the author’s best knowledge, this work is the highest efficiency $X$ -band, PA-included TX in CMOS at $OP _{\mathbf {1dB}}$.
Is there a risk for the astronauts' hearing in a microgravity environment? The Acoustic Diagnostics experiment on board the ISS

Acoustic Diagnostics is an experiment dedicated to monitoring the hearing function of the astronauts on board the International Space Station (ISS). The Italian Space Agency (ASI) provides access to the ISS as a laboratory in space to the Italian research community, through agreements between ASI and NASA for its use and between ASI and the European Space Agency (ESA) for the "BEYOND" mission, and via the Italian participation to the ISS as a member state of ESA. The main aim of the "Acoustic Diagnostics" experiment on board the space station ISS is investigating possible dysfunction of the human auditory system related to long-term missions on board the station. Some astronauts reported indeed mild hearing damage after long-term permanence in orbit. Probable hazard sources might be noise and micro-gravity, which affects intracranial fluid pressure. Before and after the mission, the astronauts will be subjected to full audiological examination, including pure tone audiometry, ABR, tuning and time-resolution measurements, impedance measurements, and otoacoustic emission (OAE) tests, which are a particularly sensitive tool for the early detection of mild hearing loss. The OAE test employed will be the measurement of distortion product otoacoustic emissions (DPOAEs) monitored during the flight, as a function of their permanence, in at least three in-flight sessions. OAE-based measurements are objective, non-invasive, and fast tests. The particular technology used for this type of tests on board will allow measurements relatively unaffected by the high noise levels of the ISS environment, and special precautions will be used to limit the EMI disturbances. High reproducibility of the measurements is guaranteed thanks to the forward-pressure calibration using the Thevenin methodology and a substantial (10dB) SNR increase will be provided by a customized time-frequency filtering procedure based on the wavelet transform.
Forecasting in Small Smart Grid

The wide proliferation of renewable energy and deregulation of power grid systems require small power utilization systems to deploy intelligent methods of adjustment to the user power demand. Small power utilization systems can benefit from the techniques developed for the smart grid in general. The present paper is devoted to the development of the forecasting model based on the Long Short-Term Memory (LSTM) method. The paper describes the small smart grid architecture and role of the LSTM in this architecture. The LSTM method is implemented with a number of interruptible appliances to predict power demand over 24 hour segments. The experiments demonstrate that the developed algorithm generates a stable pattern of daily power demand; therefore, it has a high predictive power.
Building 2D Maps with Integrated 3D and Visual Information using Kinect Sensor

RGB-D sensors can be used as a cost effective alternative to expensive laser scanners for small scale indoor mapping applications. Since RGB-D cameras provide both color and depth data, it is possible to fuse these two data frames in 2D mapping to exploit the advantages over laser scanners. An improved technique is to capture the navigation environment in an application for goal-oriented navigation and situation assessment using a mobile robot. The proposed technique is based on incorporating RGB images and 3D information to existing 2D Simultaneous Localization And Mapping (SLAM); which has not been clearly investigated before. By integrating visual and 3D information using an inexpensive RGB-D camera, the mobile robot is better equipped to perform tasks such as sign detection, object detection, and text understanding. The motivation for such an approach is to use 3D and visual information with reduced complexity. The performance of 2D mapping is exploited here by replacing the laser scanner with a RGB-D camera. In addition, memory usage is reduced by selecting key RGB-D frames from its sequence by comparing the number of overlapped RGB features. The proposed approach is evaluated for accuracy and consistency using experimental data gathered from a real environment.
Estimating Target Situation Based on 77GHz Radar Sensor for ACC System

Knowing the situation of the target in front is very important for safe driving when the vehicle is driving at a high speed. In this paper, a new method for estimating target situation based on 77GHz millimeter wave sensor for ACC system is proposed. First, Multiple sets of velocity and range values are obtained by taking multiple measurements of the target in short time periods. Then, using multipoint linear fitting to estimate the acceleration of the target. At last, judging the target situation by the magnitude of the acceleration. Simulation results prove that this method can efficient estimate the acceleration of the target and then achieve the purpose of judging its situation.
User and Product Attention Model Based on Recommendation Algorithm for Sentiment Classification

Document-level sentiment classification aims to predict user's overall sentiment in a document about a product. Existing sentiment classification methods just utilized the document content without consideration of user and product information, which have crucial effects on the sentiment polarities. To address the issue, this paper proposes a Hierarchical Bidirectional Long Short Term Memory (H-BiLSTM) model, where the user and product information captured by recommendation algorithm, and introduce IUF (Inverse User Frequence) as optimization objective, which proves effective. In addition, our model can adapt to the situation when the training data is sparse. Experiments on IMDB and Yelp datasets demonstrate that results are close to the state-of-the-art methods.
An aerial image segmentation approach based on enhanced multi-scale convolutional neural network

Aerial images are the images captured from high attitudes above the ground. Processing and analyzing aerial images play central roles in terrain modeling, agricultural monitoring, city planning, environmental surveillance, etc. Aerial images are developing towards high resolution and large size, which poses a major challenge in pixel-level image segmentation. With the rapid development of deep learning technology, the application of deep learning to image semantic segmentation has obtained satisfactory effect. In this paper, we propose a novel aerial image segmentation method based on convolutional neural network (CNN). The main structure of the proposed network adopts U-Net. In order to capture objects of different scales in the deep features, a group of cascaded dilated convolution is inserted at the bottom of U-Net which has different dilation rates. Furthermore, to better optimize the network at different scales, an auxiliary loss function is proposed to be integrated in the cascaded dilated convolution. The effectiveness of the proposed method is evaluated on the Inria Aerial Image Labeling Dataset. Experiment results show that the proposed method has better segmentation performance than existing approaches.
A Non-singular Terminal Recursive Surface Structure Design for Higher-order Systems

In this paper, the idea behind recursive surface structure design is further investigated for an arbitrary-order system. The sliding mode control is formulated for single-input single-output dynamical systems in the chain-of-integrator form. Terminal surfaces usually lead to singularity in control. Some of the methods are proposed in the past dealing with the aforementioned issue. This paper further investigates the elimination of singularity condition while preserving the terminal structure, leading to finite-time convergence during sliding. The conditions to ensure its applicability for singularity free control design are derived. It is interesting to find that the conditions obtained in the proposed case is similar to that of Fast Terminal Sliding-Mode (FTSM) control design. Therefore, it can be suggested that the FTSM surface structure can be modified to the proposed form in order to get the equivalent convergence speed while alleviating the singularity in control.
Role of Body Effect on Threshold Voltage of Strained $\mathrm{Si}-\mathrm{Si}_{\mathrm{X}}\mathrm{Ge}_{1-\mathrm{X}}$ MOSFET

Role of body effect coefficient on threshold voltage of strained Si/SiGe MOSFET is analytically investigated. Effect of dielectric thickness, doping concentration and dielectric material are computed on the threshold condition both in presence and absence of body effect. Simulation findings reveal that introduction of strained material in otherwise ideal structure enhances carrier mobility which, in turn, reduces threshold voltage. Sharp peak is observed when body effect is taken into account for a particular heterostructure composition due to enhancement of tunneling probability which decrease of barrier potential, and thus carrier flow is augmented. A few results for n-channel MOSFET are also represented to further justify the importance of novelty of the paper. Result also suggests that higher doping or thicker dielectric region leads to depletion mode of operation. An optimized design criterion is evaluated for the minimum threshold under inversion condition.
Achieving Efficient Similar Document Search over Encrypted Data on the Cloud

Cloud computing, which provides scalable computing resources at an economical rate, is more important than ever. More and more data owners are storing their data on the cloud. To protect private and sensitive data, user data should be encrypted by the data owner before it is outsourced to the cloud. However, this defeats the merits of cloud computing; the data needs to be decrypted and consumed on the client side. In this paper, we introduce a practical searchable encryption scheme which supports keyword search and similar document search, based on the Vector Space Model (VSM), by harnessing the power of homomorphic encryption (HE). HE is an encryption scheme where arithmetic calculations can be performed without decryption. We first build a term index tree to filter out irrelevant documents. Subsequently, we perform cosine similarity calculation upon search requests. Additionaly, we propose a parallel version of this scheme, which can effectively utilize the power of the cloud. Experiments on real-world datasets indicate that our scheme can effectively provide practical keyword search based on VSMs in a cloud environment.
I Can See Your Brain: Investigating Home-Use Electroencephalography System Security

Health-related Internet of Things (IoT) devices are becoming more popular in recent years. On the one hand, users can access information of their health conditions more conveniently; on the other hand, they are exposed to new security risks. In this paper, we presented, to the best of our knowledge, the first in-depth security analysis on home-use electroencephalography (EEG) IoT devices. Our key contributions are twofold. First, we reverse-engineered the home-use EEG system framework via which we identified the design and implementation flaws. By exploiting these flaws, we developed two sets of novel easy-to-exploit PoC attacks, which consist of four remote attacks and one proximate attack. In a remote attack, an attacker can steal a user’s brain wave data through a carefully crafted program while in the proximate attack, the attacker can steal a victim’s brain wave data over-the-air without accessing the victim’s device on any sense when he is close to the victim. As a result, all the 156 brain–computer interface (BCI) apps in the NeuroSky App store are vulnerable to the proximate attack. We also discovered that all the 31 free apps in the NeuroSky App store are vulnerable to at least one remote attack. Second, we proposed a novel deep learning model of a joint recurrent convolutional neural network (RCNN) to infer a user’s activities based on the reduced-featured EEG data stolen from the home-use EEG IoT devices, and our evaluation over the real-world EEG data indicates that the inference accuracy of the proposed RCNN is can reach 70.55%.
Application of Modified K-Means Clustering Algorithm in Battery Grouping

In the production process of lithium-ion battery, the inconsistency of single cell characteristics in battery pack will seriously affect the whole life and performance of battery pack. In this paper, a lithium-ion battery grouping model is proposed, which includes a data preprocessing model and a novel battery grouping method of K-means algorithm to meet the specific requirements. In the battery data preprocessing model, the faulty battery is removed by designing the process data preprocessing method and actual production standards. In the battery matching algorithm, an improved K-means algorithm is proposed. After the battery grouping is completed, the number of batteries in each cluster is equal. Additionally, experiments for battery grouping and method validation are designed. Compared with the actual method, the proposed method proves its effectiveness.
Comparative HRV Analysis of ECG Signal in the Context of Sportsperson under Post-Exercise and Relaxed Condition

Heart rate variability (HRV) analysis of electrocardiogram (ECG) signal is used to examine the health condition of the sportsperson. For the present work, a total of 22 numbers of data were taken from eleven participants in two different physical conditions who are under regular sporting activity. One set of data were taken just after playing badminton and 400–600 m run and another set of ECG data were taken in the normal i.e., relaxed condition. Acquired ECG signal is pre-processed for removal of powerline frequency, baseline wander (BW) removal, removal of low-frequency noise. The smoothening filter is also used for removal of high-frequency noise for smoothing of the wave. The filtered signals are used for HRV analysis. HRV is associated with average heart rate (HR) tachycardia and bradycardia. Main HRV parameters like mean heart rate, mean RR-interval, SDNN, RMSSD, NN50, pNN50 are analyzed in two different conditions namely post exercise and relaxed condition. In addition to HRV, the complexity analysis of the signal is also being done in the contest of sample entropy, approximate entropy etc.
Low-Jitter Phase-Locked Loop With Ring Voltage Controlled Oscillator Using a Prompt Phase-Error Compensation Technique

A ring-type voltage-controlled oscillator (VCO) based phase-locked loop (PLL) is presented, with low jitter. Type-I and type-II PLLs cannot provide a low-jitter performance. Injection-locked clock multipliers (ILCM) were a good solution, but they could not have a high multiplication factor because of reference spur problem, and also their jitter performance was still not preferable as they do not have integrator of a VCO. ILCMs cannot correct drifts of free running frequencies. So the prompt phase error compensation technique (PPEC) is introduced, which prototype is the phase-realignment mechanism of ILCM. Proposed design can achieve higher multiplication factor, lower jitter for frequencies different from free-running frequency, due to ring VCO. The main idea of the PPEC technique is to almost fully remove the jitter, that has been accumulated during the previous period of the clock signal, in a short period of time, thus achieving low-jitter performance. Suggested design includes switch-loop filter (SLF) and the PPEC technique realization circuit. The flaw of increase in area and power consumption will be considered.
The Information System for Critical Use Access Process Dependability Modeling

Objective reasons for the introduction of multi-server architecture for the creation of information systems for critical use (ISCU) have led to complications in ensuring their confidentiality, as a component of information security, and in assessment of their reliability. The paper proposes to comprehensively consider these characteristics of the ISCU within the framework of the integral qualitative characteristic of dependability. In order to assess the reliability of the procedure for access to ISCU, authors proposed a Markov model of the dependence of the functional capacity of ISCU on the influence of negative factors. This model describes a set of ISCU states and allows to get an analytical expressions to calculate the probabilities for the presence of ISCU in the corresponding states at selected moments of time depending on the probability of occurrence of negative factors and configurations of the access separation subsystem that controls the authorization process. For practical application of the model the appropriate methodology is developed. This methodology allowed, in particular, to confirm the adequacy of the model using standard methods of the planning experiment theory, including Fisher’s criterion.
A Sensor Glove for the Interaction with a Nursing-Care Assistive Robot

According to World Health Organization, the number of elderly people will rise rapidly between 2015 and 2050. Rates of physical disability or mobility problems are increasing due to population aging. With the continuous evolution of robotic technology, assistive robot shows great prospect to expand the existing healthcare services to address the above problems. As a result, nursing-care robotic systems are playing more and more important role to the elderly and disabled. In this paper, a sensor glove which is employed for human-robot interaction in an assistive robotic system is designed, produced and evaluated. The sensor glove is based on flex sensors which is utilized to detect user’s finger gestures and inertial measurement unit (IMU) which is used to detect arm movements of the user and other off-the-shelf electronic components. The main goal of this research is to make the interaction between human and assistive robots more intuitive and reliable. This paper mainly deals with three issues: 1) inventing a design pattern of this kind of sensor gloves, 2) performance evaluation of sensor gloves based on flex sensors, and 3) method of analyzing the human-in-the-loop system with the presence of the sensor glove. Our experimental results suggested that by using this design pattern, the sensor glove can be a reliable interface in controlling the assistive robotic system. Besides, our estimation model can be used in analyzing this human-in-the-loop assistive robotic system and able to find suitable parameters of this system theoretically.
Image Decoloring via Optimized Predominant Component Analysis for Segmentation of Microscopic Yeast Cell Images

Within the pipeline of microscopic yeast cell image operations, a problem of decoloring is addressed. The approach proposed by Grundland et al. is analyzed and improved. A special color phantom for yeast cell images is constructed. An optimum combination of two controlling parameters of this decoloring method is found for phantom segmentation, by precision and recall characteristics in comparison to Ground truth.
AM-PM: Efficient Network Telemetry using Alternate Marking

The fast evolution of high-speed networks has raised the need for accurate and scalable network measurement. Thus, in the last few years several new methods to collect network state information have been pursued by network operators and vendors across the board. This article focuses on the AM-PM approach, which allows accurate measurement of performance metrics, including packet loss and delay, using two bits or less in the header of each packet. This method was documented and recently published as an RFC by the IETF [RFC 8321]. This article provides an overview of AM-PM and how it can be applied in practice. The article also shares operational experience from a large-scale deployment and presents experimental results from an implementation of the AM-PM method.