Miracle-dz/UniFAR-FTUs huggingface dataset:
OneAI - Novel Multipurpose Deep Learning Algorithms for UWB Wireless Networks

In this paper, novel multipurpose deep learning algorithms are proposed for ultra-wideband (UWB) wireless networks that are capable of identifying the channel environment, estimating the SNR level, and performing ToA estimation, simultaneously. UWB technology is among the rapid-growing solutions for the next generation of deep learning-based wireless communication and localization systems. Existing deep learning algorithms for UWB wireless networks have addressed the various signal processing tasks individually in separate deep learning modules. This, however, increases the computational complexity, power consumption, and overall latency of the models. In this paper, unlike the existing methods, the desired signal processing tasks are performed in one single deep learning module. The proposed model consists of a main deep learning module as the core of the model that extracts low-level information from the signal and several shallow learning networks to extract high-level information. We demonstrate that the low-level information that is extracted in the core deep learning module can be reused in all separate tasks. The performance of the proposed models is investigated against the standard IEEE 802.15. 4a channel model by evaluating various metrics such as accuracy, area under the curve (AUC), precision, mean absolute error (MAE), and mean square error (MSE).
Tumor photoacoustic image reconstruction method based on deep learning

Photoacoustic imaging combines the advantages of ultrasound imaging and optical imaging, has the characteristics of cross-scale, deep penetration, and non-radiation, providing a new plan for cancer prevention and treatment. The combination of deep learning and tumor photoacoustic image reconstruction has important technological innovation and clinical application value for tumor prevention and treatment. In this paper, a photoacoustic image reconstruction network SEU-Net is designed. Through the algorithm evaluation system and network training strategy, the network can achieve fast and accurate image reconstruction on the basis of sparse sampling, effectively reducing equipment costs. Based on the analysis of tumor tissue structure and physiological characteristics, two models of geometrical figure and blood vessel grayscale image are designed to simulate tumor tissue, and a simulation data set is established through k-Wave. The model is trained to remove artifacts and the initial sound pressure signal map reconstruction, and compared with other classic reconstruction networks, the algorithm proposed in this paper has a good reconstruction effect.
Towards ultrathin fiber-optic probe for simultaneous photoacoustic and fluorescence endoscopy

A dual-modal photoacoustic and fluorescence endoscopy probe based on a single multimode fiber with a diameter of 140 µm was developed. By using a digital micromirror device with a high-speed algorithm for modulating the incident optical wavefront, the light transmission distortion through a multimode fiber was characterized and thus a focused laser beam was scanned at the distal fiber tip for microscopy imaging. Evaluation of the imaging performance was conducted with a dual-modal phantom comprising carbon fibers and fluorescence beads, and mouse red blood cells. The imaging probe has the potential to be further miniaturized using a fiber-optic ultrasound sensor so that it could be integrated into surgical tools such as medical needles for the guidance of minimally invasive procedures.
Purposeful Proposal for CP-afflicted Upper Limbs Exoskeletons

Cerebral Palsy (CP) is a debilitating neurological disorder that reduces motor function for children with CP. This paper presents the latest trends in the development of the arm exoskeleton for children afflicted by CP. Furthermore, it discusses the prospects for achieving an optimal outcome in rehabilitation and assistance. Nine upper limb exoskeletons, which targeted CP-afflicted children and were developed in recent years, are presented. Three of these exoskeletons are most commonly used in rehabilitation, and the other six are used for assistive purposes. Henceforth, it discusses when CP-afflicted children can make good use of this rehabilitation or assistance. In conclusion, this research should focus on a scalable upper limb exoskeleton that would benefit the majority of children afflicted by CP.
Worker Exposure to Nickel-Containing Aerosol in Two Electroplating Shops: Comparison Between Inhalable and Total Aerosol

Abstract This article describes a study that was carried out at two electroplating shops to investigate the levels of personal exposure to inhalable nickel-containing aerosols and the impact of introducing new personal sampling instrumentation with performance consistent with the latest criteria proposed by the International Standards Organisation, the Comite Europeen Normalisation, and the American Conference of Governmental Industrial Hygienists (ACGIH). Experiments were carried out to investigate the effect of changing from the current widely used method for total aerosol (using the closed-face, 37-mm filter holder) to a new method for inhalable aerosol (using the IOM inhalable aerosol sampler). The results showed that measured inhalable aerosol exposures—for both overall aerosol and total nickel—were consistently and significantly higher than the corresponding measured total aerosol exposures. Weighted least-squares linear regression yielded biases for one company ranging from about 1.3 to 2.5 and for.
Multi Coding Rates Nested Recursive Convolutional Doubly-Orthogonal Codes

This article presents a class of multi-coding rates time-invariant Recursive Convolutional Doubly-Orthogonal codes (RCDO). The Nested-RCDO (N-RCDO) codes are generated from their RCDO mother code which has a Tanner graph with a girth equal to 10 due to the doubly-orthogonal conditions imposed onto the connections positions of the mother recursive convolutional encoder. This implies that the girths of the N-RCDO codes are also equals to 10. Moreover, results are showing that regular (3, dρ) N-RCDO codes nearly achieve their asymptotic decoding limits for all the desired coding rates obtained from the regular mother RCDO code. These codes also offer a substantial additional coding gain as compared to the non-recursive multi coding rates CDO codes.
Dynamics and control of a single-line maneuverable kite

Dynamics and control of a single-line maneuverable kite Christopher Joseph Donnelly Supervising Professor: Dr. Mario W. Gomes Through simulation, an automated control system for a single-line maneuverable kite is developed for application in kite wind energy production. The kite used in this study is a small, tension-controlled, single-line kite, commonly known as a fighter kite. These kites have a simple design, but flying them requires complex control of line tension and visual input. At low tether tension, the kite is unstable; spinning about the tether. Increasing tension in the tether causes the kite to deform and fly in the direction it was facing. Experienced fliers can produce intricate maneuvers and often participate in competitions with other fliers. A simplified physical and behavioral numeric simulation of the kite’s dynamics was created and shown to closely approximate the actual kite’s flight characteristics. This model was used to develop successful control algorithms for autonomous flight. Information of the kite’s state and orientation used by the controller was gradually reduced to that which is physically measurable from the ground. An experimental test rig was designed and constructed for future testing in real wind conditions.
Temperature stable 65 nm UWB CMOS LNA with 1.5 dB NF for K-Band Applications

K band has received a lot of attention in recent years due to applications like automotive radar systems and satellite communication. This paper demonstrates a 65 nm temperature stable UWB CMOS LNA with 1.5 dB NF for K-Band applications The proposed LNA consists of three-stage topology using 65 nm CMOS technology which operates at an input voltage of 0.7 V. This LNA has a maximum gain of 25 dB achieved at 26 GHz, while the power consumption is obtained to be 6.3 mW. The 3-dB bandwidth (BW) exists in the operating range of 23 GHz-29 GHz. NF is 1.5 dB at 26 GHz, while it is found to be below 1.7 dB from 23 GHz to 29 GHz. S11 and S22 are -20 dB and -21.7 dB respectively at 26 GHz. With the help of appropriate biasing, the proposed LNA is designed by keeping a constant transconductance at 0.7 bias voltage. Due to this, a little variation in NF and gain is observed to be 0.12 dB and 5.1 dB respectively, when the temperature varies between -40 °C to 100 °C. These results demonstrate that proposed LNA is temperature stable.
Observation of the Blood-Brain Barrier Opening by Ultrasound with Microbubbles on Mice Using Intravital Imaging with Two-photon Microscopy

Low-intensity focused ultrasound (FUS) has been shown to temporarily increase the permeability of the blood-brain barrier (BBB) in the presence of circulating microbubbles (MB), facilitating the delivery of therapeutic agents to the brain. It is important to understand the short-term or long-term effect of FUS/MB on cerebrovascular health and the dynamic diffusion process of therapeutic agents in the brain interstitium in vivo. In this study, an ultrasound treatment platform was established to observe the FUS/MB-mediated BBB opening utilizing two-photon fluorescence imaging with both temporal and spatial resolution in real time. First, cranial window chamber models were established. The BBB disruption was visualized by the application of Evans blue (EB) dye and two types of leakage pattern were observed. One was fast leakage that the EB extravasation occurred drastically and quickly in less than one minute during the sonication. The other was slow leakage that the extravasation of EB generally happened several minutes after sonication and lasted a relative long time slowly and continuously.
A Novel Blind Detection Method and FPGA Implementation for Energy-Efficient Sidelink Communications

A novel physical sidelink control channel (PSCCH) blind detection method based on demodulation reference signal (DMRS) detection is proposed for sidelink communications in cellular vehicular-to-everything (C-V2X). In the proposed method, the user equipment (UE) first performs coherent energy detection on the DMRS positions. According to the information of the time/frequency location where the DMRS is detected, the UE can adjust the decoding area to minimize unnecessary blind decoding attempts. Based on the proposed algorithm and the channel estimation method, a VLSI architecture of joint energy detection and channel estimation (JEC) is proposed. Reference implementation results for a Xilinx Virtex-7 FPGA show that our design can reduce hardware complexity and energy consumption.
Automated Particle and Cell Phenotyping Using Object Recognition and Tracking Based on Machine Learning Algorithms

Knowledge of the acoustic contrast factor of biological entities can provide useful information regarding defects or cell stages in biological applications of microfluidics. It is also a valuable input in the design of acoustic particle manipulators or sorters. To calculate the contrast factor, the required physical properties can be obtained using contact measurements, but these are not desirable as they can damage particles or cells. In indirect approaches, reference particles are employed and the behavior of the unknown particles or cells is compared with that of the reference particles. Here we propose an image recognition-based framework to automate the entire characterization workflow and obtain acoustic contrast factor without intervention. We use 10 micron diameter polystyrene particles as reference and obtain contrast of 6 and 15 micron particle as a proof of concept. Excellent agreement with expected value within 5% is seen for the 15 micron diameter particles.
Make the most of MUST, an open-source Matlab UltraSound Toolbox

Medical ultrasound imaging requires recurring use of elementary principles, namely, the choice of the transducer, the design of the emitted waves and their wavefronts (e.g. focused or wide), the demodulation of the received signals, their beamforming, and their post-processing to generate B-mode or flow images. The ultrasound signals used for research purposes can be synthetic or acquired. To make the whole pipeline easily accessible to many researchers and students, the objective was to provide an open-access toolbox, widely documented, and adapted to ultrasound imaging research involving experimental or simulation methods. The MUST Matlab UltraSound Toolbox contains algorithms that focus on the development, simulation, and analysis of ultrasound signals for medical imaging. The user can design various ultrasound-imaging scenarios and analyze their performance using simulated or acquired data. The MUST functions allow studying the characteristics of transducers and waveforms, analyze signals, and construct ultrasound images. The many examples provide a starting point for students and researchers to quickly gain an understanding of the essentials of ultrasound imaging. The simulators integrated into MUST provide very realistic acoustic pressure fields and ultrasound images. The Matlab MUST toolbox is freely available at https://www.biomecardio. com/MUST. Before engaging in advanced ultrasound techniques and comparing them with so-called standard methods, it is necessary to have a good understanding of the latter and their advantages and limitations. With this in mind, the MUST toolbox includes everything needed for comprehensive ultrasound imaging: simulators of acoustic pressure fields and backscattered RF signals, delay-and-sum beamforming, B-mode imaging, wall filtering, color or vector Doppler, speckle tracking … The documentation and open access facilitate easy and intuitive use. If the MUST toolbox proves to be interesting, the author plans to integrate advanced features depending on the demands of the ultrasound community.
Interactive and Ubiquitous Audiovisual Media in Solving Verbal Arithmetic Problems

Currently online teaching has become the new educational scenario, which favors instruction and interactive processes between teacher and student through digital tools. In this sense, it is intended to evaluate the level of learning and its effectiveness in the management of audiovisual media in the resolution of verbal arithmetic problems. As well as the impact of feedback mediated by technology and the teacher. 13 learning sessions were designed in Powtoon hosted by EDpuzzle offering immediate feedback and then, through personalized video calls, reflective feedback, using Polya’s heuristic method. This learning strategy was applied to third-grade students. A study was designed with a quantitative approach and a quasi-experimental design. The results show significant differences in obtaining the learning achievement in favor of the experimental group. Therefore, it is concluded that audiovisual media promote greater motivation, interaction, opportunity, and error management, in a ubiquitous work. The findings showed that reflective feedback proved to be more effective compared to immediate feedback.
Closed-loop photothermal therapy system based on photoacoustic and ultrasonic dual-mode temperature feedback

Photothermal therapy (PTT) is a new type of noninvasive tumor treatment method that has appeared in recent years, and the key to PTT is to kill cancer cells with high temperature through the difference in temperature tolerance between healthy tissue and cancer tissue. However, due to the lack of noninvasive temperature measurement and precise imaging method to clearly define the edge of the tumor, there is a high possibility of under-treatment and over-treatment in the current PTT operation. In this study, a closed loop PTT system based on photoacoustic (PA) and ultrasonic (US) dual-mode temperature feedback is proposed, and an integrated dual-mode PTT treatment probe is designed. A closed-loop control algorithm based on PA and US dual-mode is proposed to achieve precise temperature regulation. The phantom experimental results show that the temperature control accuracy can reach 0.72 °C. In conclusion, the proposed system has a good application prospect in the field of PTT.
Recovery of Full Synthetic Transmit Aperture Dataset with Well-preserved Phase Information by Self-supervised Deep Learning

Thanks to two-way dynamic focusing, synthetic transmit aperture (STA) imaging can obtain images with high spatial resolution. However, STA imaging suffers from low signal-to-noise ratio (SNR) and low frame rate. In our previous study, the array elements were spatially encoded with partial Hadamard matrix to transmit plane wave for higher energy and to reduce the number of transmissions for higher frame rate. Thereafter, a compressed-sensing algorithm (CS-STA) was used for the recovery of full STA dataset. However, CS-STA is time-consuming and may destroy the phase information of radio-frequency (RF) signals. In this study, a self-supervised learning (SSL) method was proposed to recover the STA dataset from the encoded PW transmissions, to both accelerate the reconstruction and better preserve the RF phase information for motion estimation. Phantom experiments show that the proposed method achieves similar resolution and improved contrast, compared with STA. Simulation results demonstrate that the proposed method obtains higher performance in lateral strain estimation than STA and CS-STA, in noisy situation. Moreover, the proposed method significantly reduces the reconstruction time from ~1 hour (CS-STA) to ~10 seconds.
Design of Codebooks Optimized for Noncoherent Transmission over Fast Fading Channel

A method of codebook optimization for noncoherent transmission over fast fading channel is presented. Noncoherent reception is considered in a wide sense when the instantaneous channel state information (CSI) is not available, but statistical properties of the CSI are known. Proposed approach is based on an autoencoder network that simulates end-to-end operation of communication system. Signal reception algorithm implemented in the network is based on generalized likelihood ratio test (GLRT). Obtained codebooks show good performance in fast fading channel and provide diversity effect if the signal is sufficiently long. Power loss at slow fading does not exceed 0.7 dB. When GLRT receiver is not matched with actual Doppler spread of the channel, obtained codebooks still demonstrate good results with power loss not exceeding 2 dB.
Computational Assessment of the Anderson and Nesterov acceleration methods for large scale proximal gradient problems

Proximal gradient (PG) algorithms target optimization problems composed by the sum of two convex functions, i.e. $F=f+g$, such that $\nabla f$ is $L$-Lipschitz continuous and $g$ is possibly nonsmooth. Accelerated PG, which uses past information in order to speed-up PG's original rate of convergence (RoC), are of particular practical interest since they are guaranteed to, at least, achieve $\mathcal{O}(k^{-2})$. While there exist several alternatives, arguably, Nesterov's acceleration is the de-facto method. However in the recent years, the Anderson acceleration, a well-established technique, which has also been recently adapted for PG, has gained a lot of attention due to its simplicity and practical speed-up w.r. t. Nesterov's method for small to medium scale (number of variables) problems. In this paper we mainly focus on carrying out a computational (Python based) assessment between the Anderson and Nesterov acceleration methods for large scale optimization problems. The computational evidence from our practical experiments, which particularly target Convolutional Sparse Representations, agrees with our theoretical analysis: the extra burden (both in memory and computations) associated with the Anderson acceleration imposes a practical limit, thus giving the Nesterov's method a clear edge for large scale problems.
Version Attack Detection using Claim Algorithm in RPL based IoT Networks: Effects and Performance Parameters Evaluation

Internet of Things (IoT) is a collection of things or objects that are uniquely identified in the network. All the devices communicate through the Internet. The devices are also resource constrained. Since the devices are resources constrained, Routing protocol for Low Power Lossy Network (RPL) is used as a network layer protocol. It is suitable for network that is susceptible to high packet loss and has low power devices. The version number is the active type of attack possible in RPL protocol of IoT. RPL constructs Destination Oriented Directed Acyclic graph (DODAG) topology using control packets. Each DODAG is assigned a version number. If there is any change in the topology, then the version number is incremented. If any version attacker is introduced in the network, it falsly advertise the control packet and leads to frequent DODAG formation. So, that the control packets are generated in large volume. This leads to performance degradation in the network. Version attack also leads to various other attacks. So, it is important to address the version attack. In this paper, working of RPL, during formation and maintenance of topology, a version number attack detection Claim algorithm for securing RPL is proposed along with existing countermeasures and the effects of version attack are discussed.
Comparing 3D Rendering Engines in Blender

Using most 3D modeling and/or animation software, there are multiple choices for visualization. As Computer Graphics have evolved over the years, there has been an increased demand for computer generated visuals in movies, advertisements, product designs and so much more. Render engines are an essential part of turning project ideas into real products and different engines can produce different results. This raises the question: " What render engine is right for me? ". This paper has compared the three easiest and most accessible engines within Blender: Cycles, LuxCore and EEVEE, showing the differences between the subjective and objective differences of the end products, while monitoring hardware usage and measure theing render times to conclude what render engine could be the most optimal choice for a given scene.
Restricted Natural Language and Model-based Adaptive Test Generation for Autonomous Driving

With the aim to reduce car accidents, autonomous driving attracted a lot of attentions these years. However, recently reported crashes indicate that this goal is far from being achieved. Hence, cost-effective testing of autonomous driving systems (ADSs) has become a prominent research topic. The classical model-based testing (MBT), i.e., generating test cases from test models followed by executing the test cases, is ineffective for testing ADSs, mainly because of the constant exposure to ever-changing operating environments, and uncertain internal behaviors due to employed AI techniques. Thus, MBT must be adaptive to guide test case generation based on test execution results in a step-wise manner. To this end, we propose a natural language and model-based approach, named LiveTCM, to automatically execute and generate test case specifications (TCSs) by interacting with an ADS under test and its environment. LiveTCM is evaluated with an open-source ADS and two test generation strategies: Deep Q-Network (DQN)-based and Random. Results show that LiveTCM with DQN can generate TCSs with 56 steps on average in 60 seconds, leading to 6.4 test oracle violations and covering 14 APIs per TCS on average.