Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting faulty bottle caps using CNN model

One of the most important stages in the development of any product is quality control. It is the process of determining the precise investigation of each sub-item of the product prior to arriving at the best client. Each stage of quality control is critical, especially in the food and drug industries, where, in addition to visual issues, extra security guidelines are required. Numerous quality control measures are frequently controlled by cameras. These cameras can be used for computer vision inspections as their significant benefits are quick execution, less consumption of power. The use of AI technologies in production lines could move the production lines even faster. This paper presents an image preprocessing strategy for detecting caps on bottles, which utilizes an added skipped connection between the blocks. Global Average Pooling is used to reduce the size of the parameters on caps. The proposed model shows much more accurate results than well recognized existing models , without loss of precision. So, for checking of the flawed caps, we have predicted using CNN to comprehend an identification method for detection of bottle caps in a production line that move at high rpm. In spite of some issues that are caused by taking an image from side, a classification issue which was found in bottles, class profound learning methods is deployed to solve this problem. Our model out-performs the notable frameworks that are right now utilized and it is additionally more productive than the popular models like VGG, Inception, and Res Net
Real-Time Ultrasound Open Platform with an Extendable Number of Channels

Ultrasound open platforms are ideal instruments to experiment innovative modalities in transmission and reception. So far, the high number of channels, involved in applications based on 2-D arrays, has been managed through complex combinations of scanners and personal computers, at the expense of ease of use, portability, and real-time performance. This work presents a new flexible architecture, based on the full (hardware and software) synchronization of multiple ULA-OP 256 scanners, in a Master-Slave architecture, and controlled by a single computer. For the hardware synchronization, a Master scanner provides two digital signals (clock and transmission start) to the Slave scanners, while the software synchronization is managed by two purposely developed MATLAB classes that allow the user to interface the scanners as a single system. To demonstrate the effectiveness of the architecture, a 512-channel prototype, composed of two ULA-OP 256 connected to a host PC and to a sparse array, was implemented and tested through applications that demonstrate the high real-time processing power.
A Comprehensive Survey on Identification of Malware Types and Malware Classification Using Machine Learning Techniques

Malware is malicious code that has an effect on the user or device and allows an attacker to do significant harm to the machine. Malware is a kind of computer virus that increases in number and severity with each passing day, posing a major danger to the security of the Internet. This is a never-ending fight between security experts and malware producers, with the sophistication of malware increasing at the same rate as technological advancement. Current state-of-the-art research focuses on the development and use of machine learning methods for malware detection owing to the capacity of these techniques to stay up with malware evolution and keep up with the speed of technological advancement. The purpose of this study is to provide a systematic and comprehensive review of machine learning methods for malware detection, with a special emphasis on deep learning techniques, in order to aid in the identification of malware. The paper's primary contributions are (i) it provides a comprehensive description of the methods and features used in a traditional machine learning workflow for malware detection and classification; (ii) it examines the challenges and limitations of traditional machine learning; and (iii) it examines recent trends and progress in the field, with a particular emphasis on deep learning approaches. Furthermore, (iv) it addresses the research problems and unresolved obstacles associated with state-of-the-art methods, and (v) it discusses the future directions of study in the field. A better knowledge of malware detection and the new advances and research paths being explored by the scientific community to combat the issue is provided by the survey results, which aid researchers in their research efforts.
Development of Frequency Tunable Fluid Loaded PMUTs

We report on the development of a novel technique for creating real-time frequency tuning in Piezoelectric Micromachined Ultrasound Transducers (PMUTs). This technique employs controlled virtual added mass in a confined microfluidic environment as a switch to tune the resonant frequency of a PMUT. Using this technique, we could vary the frequency of a $500\ \mu\mathrm{m}$ circular PMUT from 489 kHz to 210 kHz, corresponding to a tuning range of 57%. This shows the potential of using such a technique in creating PMUTs with a wide range of frequency tuning.
Effects of SiO2 layer on the performance of a LNOI Acoustic Wave Resonator

Acoustic Wave resonators based on thin-film LiNbO3 piezoelectric layer have gained a lot of interest due the high intrinsic electromechanical coupling constant for a SH0 in a 30° YX cut. LiNbO3-on-insulator (LNOI) structures allow to manufacture AW resonators with good performance using simple fabrication process. In this work, the 3D simulation of LNOI structures is presented with the aim to analyze the effect of the SiO2 layer on the performance of the resonator. Several LNOI resonators have been fabricated which validates previous analysis.
High-Precision and Customized Ring-Type Virtual Keyboard Based on Layout Redesign

Virtual keyboards could potentially replace traditional physical keyboards for text input and human-computer-interface due to their portability and comfortable usage characteristics. However, typical virtual keyboards such as wearable keyboards and camera-based keyboards have unsatisfactory input recognition accuracy, and hence, preventing them from gaining pervasive acceptance by users. In this study, we demonstrated that the layout optimization of keyboards can potentially improve recognition accuracy for ring-type motion sensing virtual keyboards, and analyzed several layout designs for the configuration of typing keys. Moreover, we proved that the new layout designs have significant advantages in reducing muscle oppression for the users. The layout designs were mainly improved through three aspects: arc and split design, key position adjustment, and letter arrangement. Three new layouts were tested, which show significant improvements ranging from 5.74% to 7.51% in recognition accuracy, i.e., achieving up to 97.57% accuracy for ring-type virtual keyboards. Additionally, muscle oppression test results (using surface myoelectricity data) indicate that our new keyboard layouts could provide a significant difference ( $\text{p}< 0.05$ ) in reducing muscle oppression from traditional QWERTY keyboards. As for the moving distance of finger tips, our customized layouts could even reduce half of the movement distance compared with traditional keyboards. The experimental results demonstrate that our motion sensing rings combined with customized keyboard layouts could greatly overcome several disadvantages of traditional QWERTY keyboards.
Transcranial Ultrasound Imaging with Estimating the Geometry, Position and Wave-Speed of Temporal Bone

Transcranial ultrasound imaging is a suitable technology for diagnosis of strokes as it is safe, portable, relatively inexpensive and available in emergency medicine services, however it currently offers poor image quality due to the phase aberration caused by the human skull. In this work, we evaluate an approach for two-dimensional transcranial ultrasound imaging through the temporal window of a sagittally-cut human skull using the commercial P4-1 phased-array probe, where the position and true geometry of the bone layer is estimated for accurate phase aberration correction. The medium is described with four layers (probe lens, soft tissue, skull, soft tissue). A synthetic aperture imaging scheme is used as the transmission of spherical wave-fronts facilitates the modeling of refraction. First, the bidirectional headwave method estimates the compressional wave-speed in the temporal bone. Next, a fast marching method calculates the travel times between individual array elements and image pixels to be used with delay-and-sum reconstruction algorithm. Sound speed maps are generated with adaptive beamforming including the successive segmentation of the near and far surfaces of the cortical bone layer. The proposed method reconstructs the scatterers with an average lateral and axial localization error of about 1.25 mm and 0.37 mm, compared to the ground truth, respectively. In average, it improves the contrast ratio and lateral resolution by 7 dB and 36%, compared to the conventional method, respectively.
Lowering the Computational Load in High Frame Rate 2-D Vector Flow Imaging

The market of portable, mid-range ultrasound imaging systems for ambulatory care has steadily grown. Hence, high-resource-demanding modalities must be adapted to be compliant with these systems. A high frame rate (HFR) 2-D vector flow imaging (VFI) algorithm was recently shown suitable for real-time implementation in a high-end system over a limited-size region of interest (ROI). In this work, we propose a solution to lower the computational load of HFR VFI without reducing the size of the ROI. The VFI algorithm estimates the displacement between kernels in consecutive frames. The pulse repetition interval (PRI) impacts the aliasing limit, but the needed estimation rate depends on the flow temporal gradients and may correspond to several PRIs. The VFI algorithm can thus be limited to pairs of consecutive frames, having a time-lag longer than the PRI. The higher the time-lag, the fewer the required estimates per second. The novel algorithm was tested in simulation on steady parabolic flow conditions. For time lags between 0.4 and 4 ms, the accuracy was in the range [2.4%, 3.1%] while the standard deviation slightly increased from 7.4% to 10.9%. On the other hand, the computational load decreases up to 85% for a time-lag equal to 10 times the PRI. Preliminary tests on pulsatile flow confirm the efficacy of such an approach.
Deep Learning Based Real-time Segmentation in Ultrasonic Imaging Following the Doctor's Voice Guide

Deep learning models such as convolutional neural network have been widely used in clinical diagnosis biomedical segmentation and achieve state-of-the-art performance. However, most of them often adapt a single modality or stack multiple modalities as different input channels. They all analyze medical images that have already been obtained. To better make use of the real-time and portable advantage in ultrasonic instrument, we propose a model called Voice U-Net was proposed for real-time segmentation guided by the doctor's voice instruction during ultrasonic scanning. Through the instructions, the network can identify and segment organs in different directions. Experimental results on phantom and public dataset show that our method outperforms state-of-the-art biomedical segmentation approaches.
XBAR physics and next generation filter design

XBARs are laterally-excited bulk acoustic resonators consisting of interdigital transducers (IDT) on thin piezoelectric plates. Their low loss and excellent piezo-coupling are ideal for wideband 5G cellular and next-generation Wi-Fi filters. We present measurements of 5 GHz and 6 GHz Wi-Fi filters fabricated by Resonant with respective bandwidths exceeding 600 MHz or 1200 MHz respectively. Avoiding spurs over such large bandwidths while maintaining high power performance requires careful choice of the electrode thickness. FEM simulations demonstrate a minimum in spurious content at particular values of the metal thickness. This minimum can be understood by calculating propagating mode dispersion curves in two 2D structures: 1) an infinite lithium niobate (LN) plate which matches the XBAR structure between electrodes, and 2) an infinite composite plate with LN and thick metal which matches the XBAR structure in the electrode region. We show that the resonant XBAR mode for electrode thickness near the spur minimum can be approximately described as Al between electrodes, and A3 in and beneath the electrodes. The minimum in spur content occurs close to the metal thickness where the A1 mode of the open plate between electrodes is best matched to the A3 mode of the composite LN and thick metal plate at the electrodes.
Pipeline Inspection Gauge Positioning System Based on Optical Fiber Distributed Acoustic Sensing

To maintain pipeline transportation’s optimal efficiency and safety, pipelines must be regularly inspected and cleaned by the pipeline inspection gauge (PIG). Since the PIG passes through the whole pipeline, which is generally tens of kilometers, observing where the PIG is working in real-time is necessary. When the PIG passes through the pipe joint, the vibration signal will generate by the collision between the PIG and the girth weld, which can transmit along the fiber cable besides the pipeline. In this paper, we demonstrate that the optical fiber distributed acoustic sensing (DAS) can acquire the collision vibration in terms of the V pattern in the waterfall diagram, whose vertex is the collision position of PIG. The vertex will not exist when the cable positions are far away from the pipeline, which is relatively common in practice. We proposed an algorithm based on the clustering algorithm and the Hough transform to detect the position of the PIG even when the vertex of the V pattern does not exist. A field experiment shows that the proposed positioning algorithms applied to the DAS can effectively track the PIG online.
Automatic optical failure detection on large ultrasonic arrays

The search for improved resolution in super-resolution ultrasound (SRUS) imaging leads to larger ultrasound arrays and smaller elements. A current large row-column addressed (RCA) array spans $\approx 21\times 21\ \text{mm}^{2}$ with $190 +190$ elements separated by a kerf of $3\ \mu\mathrm{m}$. Increasing array dimensions and decreasing feature sizes not only makes fabrication more complex, but makes quality control more challenging. In this work, we present a method for acquiring a series of high resolution microscope images of an array, combining them to a large gigapixel mosaic, making dynamic and robust optical artefact correction, before performing automatic detection of optically visible defects on such large arrays. These in combination are meant as a tool for a first line of elimination of non-ideal arrays.
Viscoelastic properties of a corneal stromal model measured by surface acoustic wave optical coherence elastography (SAW-OCE)

viscoelastic properties of the cornea are mainly provided by the stromal layer, acting as the important parameters to diagnose corneal diseases and access the effectiveness of surgery and treatment. In this study, collagen-based hydrogels incorporated with keratocytes were reconstructed to mimic the human cornea stroma. An air-pulse-based surface acoustic wave optical coherence elastography (SAW-OCE) was used to examine the changes in the viscoelastic properties of the hydrogels with different initial collagen concentrations of 3, 5, 7 mg/ml over 7 days. The results showed that the hydrogels with a lower initial collagen concentration had a greater increase in the elastic modulus and the viscosity with the culturing time. Moreover, the SAW-OCE estimated viscoelasticity of the no cell hydrogels on day 1 was validated with the outcomes from plate compression, ramp-hold relaxation test.
A Real-time Dual-mode Ultrafast High-frequency Beamformer

This work presents a custom-developed dual-mode ultrafast high-frequency imaging system with real-time hardware-based processing and display. The work builds on a previously designed ultrafast high-frequency hardware-based beamformer capable of performing diverging wave imaging with up to 0.72 kHz frame rates. In this study, the system performs focused and ultrafast imaging. Focused imaging provides high spatial resolution and contrast, while ultrafast imaging provides high temporal resolution. Using a 30 MHz 64 element phased array endoscope, the system transmitted 16 diverging waves/frame at a frame rate of 0.9kHz. This high acquisition rate enabled capturing and processing a single focused B-mode frame and 28 ultrafast frames at a 10 Hz display rate.
Demystifying assessment with 'bells and whistles' in a first year communication course

This paper reports on the use of multimedia technology to help students (particularly external students) understand the requirements and processes involved in two linked pieces of assessment in a first year undergraduate communication core course. The use of a CD for course materials linked to a wider Transmodal environment at the University of Southern Queensland attempts to give the students greater access to resources via a variety of modal channels beyond a static written presentation. Research to date suggests that many students are finding the interactive elements of the Transmodal environment helpful in breaking down the requirements of assessment items. This case study investigates two linked pieces of assessment (preliminary plan Assignment 1 and final essay Assignment 3). Some of the multimedia elements used to support these assignments items are described in the paper to reveal that 'bells and whistles' can be more than novelty attention-grabbers but can give practical and clear guidance to students as they approach their assessment.
Cloud Based Optimized Technique for Classification of Covid -19 patients with Convolution Neural Network

In the year 2020, from the month of January to the present date, the novel corona virus has affected to the life of many. In India especially the second wave of covid-19 has taken the life of many individuals. Not the old people, but also many youngsters also lost their life due to covid infection. Nature of covid-19 varies from person to person and even the doctors not able to decide the kind of medication suggested to the patients. Here Convolution neural network based classification of covid-19 patients with support of cloud platform is proposed which not only classifies the covid-19 patients but also the details of the nature of infection is also stored in cloud and the kind of medication used for a particular kind of nature of covid-19 so that the same kind of medication can be applied on future patients with same nature of affect of covid-19.The proposed model successfully achieved the accuracy of 0.93.
Non-Binary Polar Codes for Spread-Spectrum Modulations

This paper proposes a new coded modulation scheme for reliable transmission of short data packets at very low signal-to-noise ratio, combining cyclic code shift keying modulation and non-binary polar coding. We consider non-binary polar codes defined over Galois fields, and propose a new design methodology, aimed at optimizing the choice of the kernel coefficients. Numerical results show that the system performance is close to the achievable limits in the finite blocklength regime.
IoT Based Smart Automated Agriculture and Real Time Monitoring System

In Bangladesh, agriculture is the primary source of income. It has a significant impact on the country's economy. However, agriculture is being hampered these days due to citizens shifting from rural to urban areas. Monitoring environmental factors is not a natural remedy for increasing crop production. Several causes have a significant impact on efficiency. Consequently, to address these issues, agriculture must incorporate automation. A farmer can save time, cost, resources, and energy by using an automated irrigation device. Traditional agricultural irrigation methods necessitate human interference. Human interference can be reduced with automated irrigation technology. This research work has designed and developed a reliable smart farming system (IoT) to reduce farmers' time costs and resources. Our proposed system can detect temperature, detect the moisture level and water level of the agricultural land, and remotely monitor the land crops. The proposed model sort out into four modules: Water Le vel Detection Module (WLDM) always detect the water level to avoid drops destruction; Soil Moisture Detection Module (SMDM) calculate the soil moisture level from the land, if the level goes down its start the water pump; Temperature Detection Module (TDM) is always counting the temperature and humidity of the air if its high then it will start the fan; Cloud and Notification Module (CNM) is handled the user notification through message and remotely monitoring the data of first three modules to take the necessary steps. The result shows the system successfully performed and it can be noted that our proposed can be implemented with any type of environment and agricultural land.
A Benchmark of Preprocessing Strategies for Autism Classification from Resting-State Functional Magnetic Resonance Imaging

Currently, the diagnosis of autism spectrum disorder (ASD) is a subjective behaviorally-based process, which requires one or more professionals. In recent years, strategies based on the automated analysis of resting-state functional magnetic resonance imaging (rs-fMRI) have become a promising candidate that could reduce subjectivity in ASD diagnosis. Unfortunately, the outcome of computerized rs-fMRI analysis is highly dependent on preprocessing stages. To date, there is no consensus regarding which preprocessing methods are more suitable according to the techniques used in the analysis of the images. For this reason, in this work we analyze the performance of different machine learning algorithms when classifying ASD patients vs typically developing patients using the four preprocessing strategies provided by Configurable Pipeline for the Analysis of Connectomes. Specifically, this study is based on rs-fMRI data from the Autism Brain Imaging Data Exchange I dataset. The objective is to evaluate the impact in the performance on a given machine learning model when using different preprocessing strategies on rs-fMRI sequences. Experiments with a dataset of 175 rs-fMRI sequences show that the best results are obtained using the strategy of global signal regression with support vector machines, achieving an accuracy and a $F_{1}$-score of 69.7% and 60.5%, respectively.
Learning Enhanced Beamforming Vector From CQIs in 5G NR FDD Massive MIMO Systems: A Tuning-free Approach

In the fifth generation new radio (5G NR) frequency division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems, downlink beamforming relies on the codebook design and limited feedback schemes. Particularly, based on the Type I codebook introduced by 3GPP and the two feedback values (i.e., precoder matrix indicator (PMI) and channel quality indicator (CQI)), base station (BS) aims at acquiring the optimal beamforming vector. Different from previous schemes that focus on the exploitation of PMI, this paper firstly reveals the link between the optimal beamforming vector and CQI, and then devises an intelligent tuning-free algorithm that can learn both the beamforming vector and the associated regularization parameter from the collected CQIs. Numerical results based on channel samples from QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) have shown the excellent performance of the proposed algorithm in terms of both beamforming vector acquisition and regularization parameter learning.