Miracle-dz/UniFAR-FTUs huggingface dataset:
H-scan ultrasound imaging for the preclinical assessment of liver cancer treatment with transarterial chemoembolization

H-scan ultrasound (US) is a new imaging technique based on a simplified framework for characterizing the relative size of acoustic scatterers and visualizing the results as a color overlay onto the registered B-scan US image. The purpose of this research was to explore H-scan US imaging of liver cancer-bearing animals after receiving transarterial chemoembolization (TACE) treatment. Radiofrequency (RF) data was collected using a preclinical US system (Vevo 3100, FUJIFILM VisualSonics Inc) integrated with a MX201 linear array transducer. US imaging was performed at a center frequency of 15 MHz with a single focal position. The H-scan US analysis involved use of two convolutional filters composed of 2nd and 8th-order Gaussian-weighted Hermite polynomial functions that were applied in parallel to the RF data sequence to assess the relative strength of the backscattered US signals. Filter outputs were combined and color coded to form the final H-scan US image. A total of nine Sprague-Dawley rats (Charles River Laboratories) were used in this study. All animals were imaged at baseline and one week after receiving TACE treatment. Texture analysis was also performed on the H-scan US images, and features such as contrast (CON), homogeneity (HOM), sum average (SA), and sum variance (SV) were projected onto spatial maps. Mean changes in H-scan US image intensity and textural parameters were quantified and compared with pathologically determined tumor response. Experimental results indicated that the SA feature significantly distinguished between the responder and non-responder TACE group animals. Overall, H-scan US imaging with texture analysis has the potential to improve detection of early liver cancer response to TACE treatment.
Displacement and Cavitation Monitoring During Focused Ultrasound Neuromodulation of the Sciatic Nerve In Vivo

Focused ultrasound (FUS) can be used to excite peripheral neurons, inducing local motor activity. Sciatic nerve displacement, the amount by which FUS physically moves the nerve, has strongly correlated to the degree of motor activation; however, the potential relationship between in vivo cavitation and nerve activation remains unclear. In this study, sciatic nerve displacement and cavitation were imaged during FUS neuromodulation. Nerve activation was recorded using electromyography (EMG) electrodes and the amplitude of compound muscle action potentials (CMAPs) was correlated to both displacement and cavitation. FUS pulses that induced CMAPs were associated on average with a peak interframe nerve displacement of 46.1 ± 24.1 μm compared to only 20.6 ± 16.5 μm for pulses with no activation. In contrast, no significant differences were found between passive cavitation images, stable cavitation doses (SCD) or inertial cavitation doses (ICD) in activation and no activation conditions. Peak interframe displacement also demonstrated a strong correlation (r = 0.53) with CMAP amplitude, in contrast to both ICD and SCD. Thus, nerve displacement appears to be the driving mechanism behind FUS peripheral nerve excitation.
FindMySpace: A Cost Effective Carbon Reduction Solution for Parking

Parking is a major worldwide problem. Emissions generated by drivers searching for parking contribute to the global climate crisis. The goal of my work is to solve the carbon emissions problem created by drivers' search for parking. I have achieved this by building a hybrid deep learning neural network and edge device solution that gives consumers real-time availability of parking and predicts future availability while also being extremely cheap and easy to implement in societies worldwide. I have created a convolutional deep-learning neural network that uses classification to determine whether a single parking space is free or busy. With this tool, I can take a picture of a parking lot and send each space to my model to know how many spaces are free or busy. I also built a smart camera using a raspberry pi and built a prototype app to showcase how the full solution can be used. I have vigorously tested my model with data sets containing individually cropped spaces, videos of parking lots online, and a real parking lot. My solution demonstrated accuracies from 90-97% for all of these, proving that my model can be instrumental in solving the parking crisis worldwide with easy accessibility and low costs.
Quantitative ultrasound assessment of healthy and degenerated cartilage

Ultrasound (US) imaging represents a safe option for cartilage monitoring. However, at present, it is not established as an objective and quantitative evaluation method. In this work, we introduce a novel quantitative US assessment based on six radiofrequency (RF) metrics intended to discriminate between healthy and chemically degraded cartilage tissues. Bovine cartilage samples were degraded with collagenase (responsible of collagen network deterioration) and scanned with a 15 MHz US imaging probe, which allowed RF data access. Results revealed a significant reduction in the values of approximate entropy, root mean square, and sample entropy after collagenase treatment, paving the way for the use of these techniques as a future diagnostic tool for cartilage monitoring.
Quantization of Raw Channel Data for Plane-Wave Fourier-Domain Beamforming

Plane-wave ultrasound image reconstruction based on Fourier-domain beamforming is known to be highly efficient, but it needs to access individual frames of raw channel data all at once. To reduce data storage and transfer costs, we propose to quantize channel data samples using a small codebook, whose dynamically scaled entries are then used during reconstruction. Based on publicly available experimental data, we show that it is possible to obtain good-quality images with a 16-entry codebook, i.e., only 4 bits per sample are needed.
Concurrent Validity of the Human Pose Estimation Model “MediaPipe Pose” and the XSENS Inertial Measuring System for Knee Flexion and Extension Analysis During Hurling Sport Motion

Hurling is a stick-and-ball field game, predominantly played in Ireland. Knee injuries are frequent in Hurling and are associated with ongoing issues such as reinjury and decreased performance. Analysis of sport motion is crucial for understanding the quality of the technical aspects of sport skill, and to identify risk factors of injury. The aim of this study was to compare the use of a human pose estimation (HPE) model “Mediapipe Pose” to the XSENS MVN Link inertial measuring unit (IMU) system for calculation of knee flexion and extension during Hurling sport motion. To assess concurrent validity, 10 trials of an overhead catch were performed by an experienced Hurling player where knee flexion and extension angles of the right knee were captured simultaneously by an inertial measuring unit (IMU) system and a video camera. A HPE model “MediaPipe Pose” was subsequently applied to the video footage. The correlation between the knee joint angles measured by the two systems was assessed using Spearman's rho correlation coefficient. Results suggest a significant positive correlation between the IMU system and the HPE measurements for the right knee $(\rho=\ 0.838^{**},\ \mathrm{p} < 0.01)$. This indicates good concurrent validity of the HPE model for calculating and tracking knee flexion and extension angles. However, the mean absolute error in knee joint angles between the IMU system and HPE was between 4.91 and 12.2°. These results provide valuable insights into the use of HPE and computer vision techniques as a valid and non-obtrusive method of calculating joint angles in Hurling sport motion. The authors aim to conduct an additional validation test to verify these results. Future research shall test a greater sample of subjects and enhance the analysis methods by incorporating high quality depth cameras. This information may be used by biomechanists and sport scientists to explore the potential for applying artificial intelligence and biomechanical analysis to improve performance in other sports or applications.
A High-Quality-Factor Aluminum Nitride Resonator with Silicon Dioxide Composite Structure

A high-quality-factor composite structure resonator is presented, which consists of a silicon dioxide (SiO2) composite structure driven by an aluminum nitride (AlN) transducer positioned underneath. With the kinetic energy primarily concentrated in the upper SiO2 composite layer, the series quality factor (Qs) of this resonator is significantly enhanced. The spurious modes are effectively eliminated by modifying the composite layer into split rods-shaped structure. A simulated series quality factor of 3110 is achieved at 1.45 GHz, with a spurious-free frequency spectrum spanning over a range of more than 1.4 GHz. This resonator holds great potential for high-precision oscillator applications.
Supporting End-to-End Coding and Use of Arduinos in a Formal Classroom Environment

This paper presents the design of a browser-based Arduino programming tool and learning management system (LMS), CASMM, that offers end-to-end support for learners utilizing Chromebooks in a classroom environment. This tool aims to support learners through the entire process of coding and using Arduinos in group projects at scale in formal classrooms. The novelty of this tool and its discussion for the VL/HCC community lies in the design and customization of this tool to meet real world constraints of formal classrooms. In addition, it encourages expansion of who we consider users and requires inclusion of where and how learning takes place to truly support human-centered development of programming tools. In this paper, we shift the focus from individual users to multiple groups of student users and 1–3 teachers/mentors in a classroom environment. In particular, this paper aims to make explicit the unique needs of teachers and students who may have limited technology expertise both in coding and using Arduinos in formal classroom environments, the human and technological constraints of a formal classroom and features we've designed into CASMM to address these needs. Through this paper, we aim to spark discussion about the human-centered requirements of these users and how tools that support learners end-to-end in the development process may be necessary to truly provide accessible programming languages and environments for a wide range of novices (i.e., students, classroom teachers, and college mentors/volunteers).
Manipulators with Machine Learning-based Control with Reinforcement

This industry is created for a number of tasks in which there is no unambiguous solution algorithm. Accordingly, these tasks cannot be solved in a fully automated way. Such tasks include welding of steel with a fuzzy weld, cutting of the forest, sorting, packaging, conveyor tasks with variable environment, all these tasks are currently solved by a person. As a result of using algorithms, it is possible to achieve results up to 70% savings on debugging smart manipulators and on automation tasks for the entire industry 4.0. The size of this market, i.e. the market of robot manipulators, is approximately 35 billion dollars in global terms, and in particular in Russia, with a low level of automation, 15 billion rubles. The target audience is a part of the production facilities that implement automation, robotics and, accordingly, they are engaged in the processing of parts. The business model by which we are developing is the creation of a hardware complex to upgrade customers’ equipment. The DDPG algorithm is used for training. The training itself is carried out in two versions -using HER (Hindsight Experience Replay) and without. The results are compared and the best model is taken for work.
Sensorless End-to-End Freehand Ultrasound with Physics Inspired Network

Three-dimensional ultrasound (3D US) imaging finds broad applications in clinical practice. Compared to using 'wobbler' (motor embedded) or 'matrix array' transducers which suffers from a restricted field of view, freehand US offers more flexibility in mapping the 3D volume along the scanning path. Nevertheless, current methods for reconstructing 3D US volumes using freehand scanning are challenged by significant elevational shifts along the scanning trajectory. Previous research explored the integration of motion sensors to resolve transducer drift, yet frequent motion artifacts often compromise the benefits brought by these sensors. Recent work turned to deep neural networks (DNNs) for estimating the relative pose of imaging planes between frames in the scanning trajectory, allowing for sensorless freehand US. However, these data-driven models fall short in accuracy as they lack physical constraints, hence limiting their use in clinical practice. Inspired by the physical properties of US, we design a physics inspired DNN architecture that can better leverage the contextual cues between elevational frames for sensorless freehand 3D US reconstruction, and demonstrate substantially improving reconstruction accuracy.
Flood Detection Using Variational Autoencoder in SAR Images

Monitoring large areas to detect unexpected changes is beneficial in disaster response preparations, particularly when using a persistent information source such as Synthetic Aperture Radar (SAR) images. However, the complexity of radar images makes it difficult to select features that differentiate relevant and irrelevant changes. In this research, a Variational Autoencoder (VAE) was trained in a self-supervised way to learn meaningful representations of the multitemporal SAR dataset. The embeddings from adjacent timeframes were then compared using a distance function. Direct pixel comparisons using the Cosine Similarity and embedding comparisons with the Wasserstein divergence were both able to highlight the flood event in the final SAR image pair; however, the latter managed to ignore changes in the previous pairs better than the former.
Network Traffic Anomaly Detection Using Quantile Regression with Tolerance

Network traffic anomaly detection describes a time series anomaly detection problem where a sudden increase or decrease (called spikes) in network traffic is predicted. Data is modeled with the trend and heteroscedastic noise component. Traditional autoregressive models struggle to capture data changes effectively, making anomaly detection difficult. Our approach is to generate upper and lower limits by using quantile regression. We use a deep learning based multilayer perceptron model to predict five data quantiles 1, 25, 50, 75, and 99. The upper and lower limits are calculated as differences between the quantile-1 and quantile-99. Any data that is outside these limits are considered as an anomaly. We also add tolerance to these limits to add flexibility to anomaly detection. Anomalies and non-anomalies are labeled to get a binary classification task. Anomaly detection is class imbalanced by nature; therefore, precision, recall, and F-1 score are computed to evaluate the proposed anomaly detection method. We conclude that choosing tolerance is a tradeoff between false alarms and missing anomaly detections.
E-Serve for Environment Monitoring Using LoRaWAN

The number of forest fires have been increasing rapidly in different parts of the world and has caused a major public concern which can jeopardize our ecosystem. By 2100, it is estimated that over 49% of our forests will be exposed to pollution and this can cause the world climate to worsen. This has led to an increasing demand in environment monitoring applications. The use of remote sensing and development of weather stations have several challenges in terms of coverage and cost. However, wireless sensor networks technologies and the existence of Internet of Things (IoT) has made better communications in monitoring environment in a large area. In this paper, a prototype, called e-Serve, implements the Long Range (LoRa) technology (a branch of IoT), to collect and monitor the environment. The prototype will be equipped with a LoRa node consisting of sensors to measure the temperature, humidity, and air quality. Gathered data will be sent to the server and alert the administrator for any abnormalities in order to deter the environmental risk such as forest fires and pollution.
Development of the Biomech-Wrist: A 3-DOF Exoskeleton for Rehabilitation and Training of Human Wrist

This work describes a three-degrees-of-freedom rehabilitation exoskeleton robot for wrist articulation movement: the Biomech-Wrist. The proposed development includes the design requirements based on the biomechanics and anthropometric features of the upper limb, the mechanical design, electronic instrumentation, software design, manufacturing, control algorithm implementation, and the experimental setup to validate the functionality of the system. The design requirements were set to achieve human wrist-like movements: ulnar-radial deviation, flexion-extension, and pronation-supination. Then, the mechanical design considers the human range of motion with proper torques, velocities, and geometry. The manufacturing consists of 3D-printed elements and tubular aluminum sections resulting in lightweight components with modifiable distances. The central aspect of the instrumentation is the actuation system consisting of three brushless motors and a microcontroller for the control implementation. The proposed device was evaluated by considering two control schemes to regulate the trajectory tracking on each joint. The first scheme was the conventional proportional-derivative controller, whereas the second was proposed as a first-order sliding mode. The results show that the Biomech-Wrist exoskeleton can perform trajectory tracking with high precision ($RMSE_{max}= 0.0556$ rad) when implementing the sliding mode controller.
Underwater Localization based on Robust Privacy-Preserving and Intelligent Correction of Sound Velocity

The privacy-preserving localization of hydroacoustic sensor networks plays a critical role in the communication and control of marine environments. The performance of underwater location varies with constrained the complex underwater environment, such as openness, inhomogeneity, temperature, press, and so on, which make it much more challenging to ensure privacy preserving methods and obtain accurate acoustic speed used for localization computation. To address the above issues, this paper innovatively constructs Privacy-preservation Three-dimensional Underwater Location (PTUL). Firstly, the maximum distance separable coding algorithm which is designed one-time aggregated mask reconstruction by mask coding of online beacon node signals to ensure privacy-preserving and robustness is introduced into this localization model. Secondly, it relies on the sound speed modified model to compensate for the error of acoustic speed, through which an iterative regression strategy is used to deal with the change of acoustic speed. Finally, the experiments are provided to illustrate the feasibility of the proposed model. The proposed localization algorithm can efficiently improve the localization accuracy and ensure the privacy of the localization data compared with the other localization algorithms.
Current Control of Grid-Connected Simplified Split-Source Converters (S3I) for Single Phase Photovoltaic Microinverter Application

This paper presents design of grid-connected single phase simplified split source inverter and effective grid current control strategy for photovoltaic (PV) microinverter application. The operation of principle of the split-source inverter is discussed and how the proposed current control method is applied to the related system is explained in detail. The simulation is carried out by using Powersim (PSIM) software environment to demonstrate the operating of the proposed system. The proposed microinverter system includes the PV module and simplified split source inverter. The grid current is adjusted to a value that corresponds to maximum power operating point of the PV module and this power is transferred to grid in a microinverter system. The maximum power of PV module is obtained using perturb and observe (P&O) method and unipolar sinusoidal pulse width modulation (SPWM) is used for simplified split source power converter. Various simulation tests have been performed on the proposed micro inverter system at various and constant irradiation values. The simulation results show that grid current control strategy is implemented on simplified split source inverter for the single-stage PV -grid-connected power generation competently, producing constant DC-link voltage. Reduction of number of switches, high voltage boosting gain and no need for additional DC-DC converters are features of high efficiency simplified split source inverter.
Human-Centered Functional Task Design for Robotic Upper-Limb Rehabilitation

Robotic rehabilitation has demonstrated slight positive effects compared to traditional care, but there is still a lack of targeted high-level control strategies in the current state-of-the-art for minimizing pathological motor behaviors. In this study, we analyzed upper-limb motion capture data from healthy subjects performing a pick-and-place task to identify task-specific variability in postural patterns. The results revealed consistent behaviors among subjects, presenting an opportunity to develop a novel extraction method for variable volume references based solely on observations from healthy individuals. These human-centered references were tested on a simulated 4 degrees-of-freedom upper-limb exoskeleton, showing its compliant adaptation to the path considering the variance in healthy subjects' motor behavior.
Compensating Quadrature Hybrid Mismatch Effects in Integrated Balanced Power Amplifier

This paper presents techniques to mitigate frequency dependent amplitude response of quadrature hybrid in an integrated quadrature balanced power amplifier (PA). The studied structure consists of stacked pseudo-differential power amplifiers and transformer-based quadrature hybrids designed with 22 nm CMOS FDSOI operating at 26 GHz. We propose two techniques to compensate frequency-dependent amplitude response of the quadrature hybrid when operating away from the center frequency. First one involves dual input drive and second asymmetric biasing. Measurements with continuous wave (CW) and high dynamic range fifth generation (5G) modulated signals demonstrate that the described techniques improve output power that can be reached within the linearity specifications when operating away from the center frequency.
Relationship between feldspar alteration and the precise post-crystallization movement of rubidium and strontium isotopes in a granite

Preparation of high-prurity plagioclase separates from a coarse-grained granite (Heemskirk granite, western Tasmania) allows, for the first time, the exact delineation of the amounts of radiogenic Sr, Rb, and common Sr migrating between cogenetic K-feldspar and plagioclase in a typical post-crystallation granite environment. Further, the isotope relocation is demonstrated to occur in the alteration products of one of these feldspars (plagioclase) rather than in the crystal structure. Relative to their calculated, unaltered present-day compositions the most altered plagioclase has gained 15.8 times the radiogenic Sr, 8.8 times the Rb87, and 1.7 times the Sr86, whereas the K-feldspar has lost 19, 14, and 34% of these isotopes, respectively. Whereas the alteration isochron for the plagioclase is governed by the radiogenic Sr and Rb87 gain, the K-feldspar alteration isochron reflects the relatively high loss of common Sr. As a result, the calculated present-day composition of the unaltered K-feldspar in this granite occupies a surprising position on the reference isochron. The normal interpretation of discordant K-feldspars in terms of radiogenic Sr loss only must now be viewed as a gross simplification of the isotopic migration pattern. The larger amounts of isotopes recognized to be involved in this migration must also be taken as firm indication of the possibility of open system total-rock behavior in granites displaying feldspar alteration.