Miracle-dz/UniFAR-FTUs huggingface dataset:
Proposal of Tear Meniscus Measurement for Minor Dry-eye Detection Using Smart-phone Camera and Ring-light

A meniscus height measurement method for dry-eye detection using a low-priced small portable LED ring-light and a smart phone was discussed. The ring-light was clearly reflected in the cornea and the lower part of the eye called tear meniscus, where tears were collected. The diameter of the circle reflected in the cornea and the length from the top to the bottom of the distorted circle reflected in the meniscus were measured through manual pointing operation. The length was normalized by the diameter to be the measured; the meniscus height was not affected by the distance from the camera to the eye. If the meniscus height is low, it indicates a dry -eye. Our experiments demonstrate that our method is as effective as a system using medical equipment.
Web User’s Access Pattern Identification Using Clustering Algorithms: A Survey

Users of web site usually perform their interest oriented actions by clicking or visiting web pages, which are traced in access log files. The clustering web client’s access pattern may catch common client interest in a website. The conventional web usage mining techniques for clustering web user sessions can discover usage patterns directly, but cannot identify the latent factors or hidden relationships among users’ navigational behaviour. One of the best tasks of internet usage mining, web client grouping, which sets up clusters of clients displaying comparative browsing designs gives beneficial information to customized web benefits. This paper presents a survey covering the strategies and techniques of clustering algorithms and demanding situations appear within the area.
Numerical modelling of broadband acoustic signatures for two Argentinian swimbladdered fish species

This work presents the first results concerning the analysis in the frequency domain of measured acoustic signatures for two swimbladdered fish species (Merluccius hubbsi and Engraulis anchoita) from the South West Atlantic Ocean. Experimental at-sea data collected off the Argentine coast, onboard the research vessel BIP V. Angelescu, is compared with numerically simulated responses by using a model based on the Boundary Element Method (BEM). The physical situation involved in the measurements (broadband signals for a frequency band spanning from 36 kHz to 85 kHz) is mathematically related to the Helmholtz differential equation which can be solved using a BEM approach considering the swimbladder as the dominant contribution to the scattering. Numerical evaluation of the acoustic response requires integration on the scattering surface (the swimbladder) which is carried out using a discretized version (mesh) of that surface obtained from X-ray computerized tomography scans. The backscattering Target Strength (TS) for each species was computed for two types of scatterers, a non-penetrable soft scatterer and a penetrable fluid scatterer. Reported comparisons between numerical and measured results in the frequency domain provide an encouraging first set of results and reveal a broad spectrum of further work in the future.
Measuring the Scintillation Decay Constant of PEN and PET with 120 GeV Proton Beam Excitation

We report the scintillation decay constants of polyethylene naphthalate (PEN) and polyethylene terephthalate (PET) determined by excitation of the plastic substrate with an accelerated beam of protons and resulting light yield measured as a function of time with a photomultiplier attached to an oscilloscope. The decay constant of PEN was found to be 35 ns and PET 7 ns.
Joint Angle Estimation with VIBE: an Evaluation Using Virtual Avatars

Work-related musculoskeletal disorders (WMSD) are health problems of the human locomotion system caused or intensified by inadequate work activities or environments. The usual alternative to prevent WMSDs is to implement an ergonomics program, which must include an evidence-based ergonomic risk assessment to recognize and rectify ergonomic deficiencies in a wide range of workplace situations. Ergonomic risk assessment is done traditionally through the completion of specific forms, through in-site observation by ergonomists. This is inaccurate and inefficient due to subjective biases. Also, it is not practical due to the cost, time, and technical knowledge required. For this reason, in this work we propose the standardization of data collection through computer vision, which offers the opportunity to obtain a considerable replication level, increasing the reliability of the results and the quality of the data available to ergonomists. As a methodology, we propose using the open-source neural network VIBE to detect skeletal joints in photos whose purpose is to calculate ergonomic risk. As an approach to rich and reliable data gathering, we carried out a virtual experiment, using controlled avatars in the Unity game engine. This allowed us to explore and quantify acquisition performances, in particular the occlusion effects generated by varying the camera angles concerning the worker’s bodies, and the errors induced by background clutter.
HGCROC-Si and HGCROC-SiPM: the front-end readout ASICs for the CMS HGCAL

1 Abstract—The two variants of HGCROC are the ASICs designed to readout the more than 6 million channels of the future HGCAL of CMS, which will consist of hexagonal silicon sensors for a large part but also SiPM-on-scintillators tiles. The SiPM version of the chip was made from the silicon version by adapting only the first amplifier stage. The first aspect is on the performance for both versions in terms of noise, charge and timing, the DAQ and Trigger paths, as well as results from irradiation qualification with total ionizing dose and heavy ions for single-event effects. The third version of HGCROC chip is a major digital release, with RadHard solutions and an additional buffer.
Stock Market Prediction using Bi-Directional LSTM

Stock market prediction is quite challenging as the market is volatile and its direction is stochastic. The stock market gets driven by several factors like investor sentiment, economic strength, market rumors, inflation. All these aspects together make the stock market quite turbulent and hence very difficult to predict with accuracy. In this paper, we analyzed traditional Machine Learning prediction models and figured out the drawbacks associated with them. Hence we scrutinized a range of stock prediction models and finally singled out the Bi-directional Long Short-Term Memory (Bi-LSTM) neural network. It intends to find out the title role of time series by analyzing historical data of different stocks and predict stock price trends. They form a unified framework for depth and time calculation learning faster than the one-directional approach. It can capture the temporal evolution of information which allows this model to attain the best performance.
A CNN-RNN based approach for Simultaneous Detection, Identification and Classification of Intracranial Hemorrhage

Intracranial Hemorrhage (ICH) refers to bleeding inside the brain or skull and is considered a life-threatening condition as the accumulation of blood can lead to increased intracranial pressure, which can crush delicate brain tissues or limit its blood supply. ICH accounts for 10 to 20% of all strokes and requires immediate diagnosis and treatment. In this study, our main aim was to precisely detect and classify Intracranial Hemorrhage simultaneously. The use of Recurrent Neural Network (RNN) in analysing the sequential information obtained from slice-level classifications has been proved to be beneficial [1]. Our proposed architectures combined a Convolution Neural Network (CNN) and a Recurrent Neural Network (RNN) i.e. CNN with Long Short Term Memory (LSTM) and CNN with Gated Recurrent Unit (GRU). Using RNN would enable the previously ignored small features to be easily detected while simultaneously improving the model’s capability to distinguish between the subtypes of Intracranial Hemorrhage. Also, by performing windowing in preprocessing certain features not previously visible would become clearer. This led to the framework of three architectures: Xception, Xception-LSTM, and Xception-GRU. The dataset collected by the Radiological Society of North America is used to train these architectures (RSNA) [2]. The dataset consisted of 752,803 DICOM files out of which 10% of randomly selected samples were used for testing. The performance of these architectures is evaluated and compared using different parameters like Loss, Accuracy, AUC-ROC score, Precision, Sensitivity, Specificity, and F1 Score. We found out Xception GRU outperformed the other two architectures in almost all the parameters having achieved the loss of 0.0659. Furthermore, we have also performed a detailed analysis of per-class results using the same parameters. We believe that the use of CNN-RNN architecture increases the accuracy of ICH detection, identification and classification which would help reduce diagnosis errors
Facial Video based Heart Rate Estimation for Physical Exercise

Heart rate estimation from facial videos is useful in applications such as telemedicine, public health monitoring, driver assessment, stress management and affective computing. Various studies have been done on evaluating remote photoplethysmography (rPPG) signals for subjects under different facial expressions to predict emotions. This paper proposed an analysis of heart rate measures from facial videos in the presence of heart rate variations for fitness applications. It is important to retrieve the health status of exercise and an optimized training program can be customized according to the preference physiological parameters. The state-of-the-art algorithm is applied to the raw RGB signals using Independent Component Analysis (ICA) method. The time-frequency domain of Fourier Transform is constructed to form PPG signals and estimate the heart rate. The analysis was carried out and validated using self-collected dataset using heart rate monitoring system prototype which developed using a pulse sensor as an input and Arduino microcontroller. The experimental results show that the state-of-the-art algorithm has an obvious low error index to proof efficiency and accuracy in various conditions of subjects with faster heartbeat after performing several physical exercises.
IoT based Novel Hydration System for Smart Agriculture Applications

Agriculture is a very important sector of the Indian economy and this sector has been highly dependent on the efficient usage of water resources. To measure and distribute the exact amount of required water in farm fields is a key problem. To solve this problem, smart sensors can be controlled by different Internet of Things (IoT). In this work, we propose a IoT based hydration system which consists of soil moisture sensor and humidity sensors to know the condition of the soil and aids them in watering the farms accurately. Our hydration system efficiently manages the water usage for farm fields which ensures the saving of water resources. NodeMCU microcontroller has been part of our system. It is programmed to detect moisture, temperature and humidity parameters. Moisture sensor for soil automatically detects the moisture levels in the soil and it alerts us for distributing the water timely. If the water levels are less than the intended levels, then we can remotely turn on the pumping motor and start watering until the moisture level reaches the expected range specified by using the Blynk application. Hence, it will reduce the need of human Labour as water usage.
Development of an Indoor Hybrid PLC/VLC/IR Communication System based on TCP/IP Model

In this paper, we report the results obtained from the experimental implementation of a hybrid communication system, composed by: power line communication (PLC), visible light communication (VLC) and infrared light communication (IR). This hybrid PLC/VLC/IR system consists of an uplink and a downlink, allowing full-duplex communication between two personal computers. An important contribution is that it has been possible to integrate the hybrid system to the TCP/IP model through the use of a virtual network interface that allows communication between the elements of the physical layer with the upper layers present in the TCP/IP model. Additionally, USRP software defined radio platforms and the GNU Radio program are used for implementing physical layer. Performed measurements show that the entire system has a latency of 2.85 ms and a bandwidth of 29 kbps. However, using only VLC and IR, latency is reduced to 2.42 ms and the network bandwidth is increased to 2.45 Mbps.
Mitigation of Kernel Memory Corruption Using Multiple Kernel Memory Mechanism

Operating systems adopt kernel protection methods (e.g., mandatory access control, kernel address space layout randomization, control flow integrity, and kernel page table isolation) as essential countermeasures to reduce the likelihood of kernel vulnerability attacks. However, kernel memory corruption can still occur via the execution of malicious kernel code at the kernel layer. This is because the vulnerable kernel code and the attack target kernel code or kernel data are located in the same kernel address space. To gain complete control of a host, adversaries focus on kernel code invocations, such as function pointers that rely on the starting points of the kernel protection methods. To mitigate such subversion attacks, this paper presents multiple kernel memory (MKM), which employs an alternative design for kernel address space separation. The MKM mechanism focuses on the isolation granularity of the kernel address space during each execution of the kernel code. MKM provides two kernel address spaces, namely, i) the trampoline kernel address space, which acts as the gateway feature between user and kernel modes and ii) the security kernel address space, which utilizes the localization of the kernel protection methods (i.e., kernel observation). Additionally, MKM achieves the encapsulation of the vulnerable kernel code to prevent access to the kernel code invocations of the separated kernel address space. The evaluation results demonstrated that MKM can protect the kernel code and kernel data from a proof-of-concept kernel vulnerability that could lead to kernel memory corruption. In addition, the performance results of MKM indicate that the system call overhead latency ranges from <inline-formula> <tex-math notation="LaTeX">$0.020~\mu \text{s}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$0.5445~\mu \text{s}$ </tex-math></inline-formula>, while the web application benchmark ranges from <inline-formula> <tex-math notation="LaTeX">$196.27~\mu \text{s}$ </tex-math></inline-formula> to 6,<inline-formula> <tex-math notation="LaTeX">$685.73~\mu \text{s}$ </tex-math></inline-formula> for each download access of 100,000 Hypertext Transfer Protocol sessions. MKM attained a 97.65% system benchmark score and a 99.76% kernel compilation time.
Musculoskeletal Model to Predict Muscle Activity During Upper Limb Movement

Assessing biomechanics of upper limb movement is essential for guiding targeted therapy to treat conditions such as spasticity and dystonia. Targeted therapy, including injections of medications into specific muscles (e.g., lidocaine, botulinum toxin type A), requires accurate identification (activity) and contribution of as many muscles as possible. Currently, this is achieved by visual clinical assessment or using surface electromyography (sEMG). Although sEMG could provide a reasonable estimate of muscle activity for certain superficial muscles after an intense filtering process, they are unable to provide separated activity and contribution for every superficial and deep muscle. Other proposed musculoskeletal and machine learning models similarly do not provide a detailed and accurate activity of every muscle. The objective of the study is to design a subject-specific musculoskeletal model to predict the activity and contribution of each muscle pertaining to any upper limb movement with improved detail and accuracy over existing methodologies. Performance metrics were calculated for validation by comparing the predicted muscle activity with the normalized sEMG data computed from 8 superficial muscles, while the deeper muscles were not included in the validation as the sEMG is unable to provide a separated activity for those muscles. The results show that the proposed model has a mean R2 value of 0.8190 and also indicated a statistically significant correlation ( $P < 0.0001$ ) between the calculated (normalized sEMG data) and predicted activity value. Additionally, and significantly, compared to earlier studies, the proposed model predicts the individual muscle activity and contribution of deeper muscles.
Design and Implementation of a Digital Front-End With Digital Compensation for Low-Complexity 4G Radio Transceivers

A digital front-end with digital compensation is designed and implemented for low-complexity 4G radio transceivers targeted for wearable devices such as smart watches. The proposed digital front-end in the radio receiver consists of an anti-drooping filter, a decimation chain, a DC offset cancellation circuit, and an in-phase and quadrature estimation and compensation circuit whereas the digital front-end in the radio transmitter includes an anti-drooping filter, a root raised cosine filter, and an interpolation chain. The proposed DC offset cancellation circuit is based on both infinite-duration impulse response filter and moving average. The proposed in-phase and quadrature estimation and compensation circuit attains lower complexity with negligible performance loss, compared with an existing circuit. A systematic top-down strategy is taken to design and implement the proposed digital front-end from the algorithm level to the application-specific integrated circuit or ASIC hardware level. The inter-symbol interference in the transmitter and the receiver is analyzed and the unwanted emission in the transmitter is simulated as well. For all the seven bandwidths or modes in 3G and 4G, the digital front end receiver ASIC satisfies all the interference requirements, namely, in-band blocker, narrowband blocker, and adjacent channel selectivity requirements whereas the digital front end transmitter ASIC meets all the unwanted emission requirements, namely, spectrum emission mask, spurious emission, and adjacent channel leakage ratio requirements. The proposed multimode 4G digital front end receiver and transmitter ASICs exhibit a >40dB mean signal-to-noise ratio for all the seven modes and are implemented in a 180nm CMOS process technology.
Enhanced Service Framework Based on Microservice Management and Client Support Provider for Efficient User Experiment in Edge Computing Environment

Leveraging the edge computing paradigm, computing resources are deployed in the network edge to provide heterogeneous services. Edge computing delivers sensing and actuating services to the Internet from the constrained Internet of Things (IoT) devices. Meanwhile, management of various elements is provided by offloading sufficient computing and storage to the edge of the networks for the IoT environments such as home, factory, and private spaces without cloud servers. In this paper, we propose an enhanced service framework based on microservice management and client support provider for efficient user experiments in the edge computing environment. For providing the edge computing service and management in the network edge, this paper presents an edge-computing architecture that provides various functions through microservice modules on the edge platform engine. Through the microservices, the interfaces are provided to the client to access the device, data, and additional services. Using Docker, the microservice modules are deployed in the edge platform to provide the services. However, the services and management functions need to be presented to the clients based on the friendly user interfaces. For providing the user interfaces of the services and Docker engine to the clients, the client support service provider is developed and deployed in the network edge based on the edge platform. Therefore, the proposed edge platform provides the services and management to the users for accessing the resources and functions through visualized interfaces in the IoT environment based on edge computing. The performance of our proposed system can be checked through the test result screen and delay time. Compared to controlling edge computing by using a command-line tool for users, we made it easy for general users who are not computer savvy to access edge services through a graphic user interface. And by measuring the delay time and comparing the execution time, it can be seen that the proposed system operates faster.
Online Speech Dereverberation Using Mixture of Multichannel Linear Prediction Models

We extend the state-of-the-art online dereverberation method, online weighted prediction error (WPE), which predicts late reverberation components using a multichannel linear prediction (MCLP) filter. The multi-input/output inverse theorem states that in general such an MCLP filter for WPE exists only if the number of sources is less than that of microphones <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> and there is no additive noise; otherwise, the WPE model has error, degrading its performance especially when <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> is small. To mitigate this WPE drawback, we recently developed an offline dereverberation method called switching WPE (SwWPE), which has multiple MCLP filters and switches them for each time-frequency bin. We here propose a recursive least squares (RLS) algorithm for the online optimization of SwWPE. Our proposed RLS has forgetting weights for each MCLP filter, and their decay rates are controlled based on a switching mechanism of SwWPE to stably improve the dereverberation performance of the online WPE. Experimental results show that our online SwWPE clearly outperforms online WPE in speech dereverberation tasks.
Predicting Power Density of Array Antenna in mmWave Applications With Deep Learning

In this paper, we present a method for obtaining the power density value, which is the standard for radio frequency (RF) electromagnetic field (EMF) human exposure from mmWave mobile devices, using a deep learning network. An mmWave mobile communication device that uses an array antenna requires a large number of phase conditions for covering a wide communication range. However, the power density values must be repeatedly obtained every time the phase conditions are changed, which incurs a lot of time and cost. For implementing the process seamlessly, we present a deep learning network that can input the phase conditions of the mmWave array antenna and simultaneously obtain the power density results for the phase conditions of the array antenna as an output. For a $4\times 1$ array patch antenna, which is commonly used in 5G mobile communication devices, the phases of the antenna were changed, and 5,832 electric and magnetic field data were acquired, which were then converted to power density values and learned thereafter. We examined whether appropriate power density values were output when inputting arbitrary phase sets of array antennas for the learned deep learning network. With the learned deep learning network, it was confirmed that when inputting unlearned phases for a $4\times 1$ array antenna, the power density values similar to the actual simulation were quickly obtained as output.