Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Satellite-Terrestrial Relay Network Over Generalized-K Fading Channel

Current satellite communication solutions propose a network solution that is compatible with the existing network and 6G architecture in order to provide global coverage for rural areas and emergency applications worldwide. Therefore, integrated satellite-terrestrial technologies will enable ubiquitous connectivity for any type of end user. In this paper, we propose cooperative communication in which the satellite station transmits its information to the ground base station. The signal is then transmitted to the receiver using a decode-and-forward relaying protocol. The satellite link is presumed to be shadowed Rician, whereas the terrestrial link is referred to as Generalized-K fading since it is the generalized representation of conventional small-scale channel models in cellular communications. Alternatively, assuming a special case in which the end user is a vehicular node, the appropriate channel is selected as double Rayleigh fading on the terrestrial link. Due to the mobility of the ground user, it is assumed that the distance over the terrestrial link is uniformly distributed. In addition, several system parameters are considered when evaluating system performance, including outage probability and bit-error probability. In order to confirm the derived mathematical formulas, computer-based Monte Carlo simulations are conducted. The results indicate that the shadowing parameter of the satellite link and the distance of the terrestrial link has a significant impact on the proposed system performance.
Enhanced Decision Mechanism for RAN Subslicing in Management Closed Control Loop

5G radio access network (RAN) slicing enables better satisfaction of different quality of service (QoS) requirements than without network slicing. A handful of slice types are specified for various service verticals; however, the number and size of those slices is unspecified. Subslicing refers to grouping slice users into smaller groups, subpartitioning slice bandwidth, and allocating smaller bandwidth parts to smaller user groups. State of the art subslicing has been done to better satisfy the QoS requirements inside the service vertical. Slice performance improvement was not the purpose of subslicing, but the positive effect was noticeable. In this paper, the subslicing decision is done with the aim of improving slice performance. The decision mechanism for management closed control loop is proposed. The input dataset consists of 6 key performance indicators, namely slice bandwidth utilization, slice goodput (application level throughput) per one allocated resource block (RB), and slice block error ratio (BLER), both in uplink and downlink. This dataset is clustered, and the result is learned by a classifier to decide whether the slice is too large and should be split or too small and should be merged with another too small slice or subslice. The results show that by knowing the slice utilization and goodput per one allocated RB, the slice reconfiguration action regarding subslicing can be determined using machine learning tools.
A Review of Optimization Algorithms for Training Neural Networks

The selection of the optimization algorithm (optimizer) is one of the most essential endeavors in Deep Learning and across all categories of Neural Networks. It's a matter of experimenting by making mistakes and learning from them. In this research, we will conduct experiments using 7 of the most common optimization algorithms, namely: Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient Algorithm, Adadelta, Adaptive Moment Estimation, Adamax, and Nadam on MNIST datasets, to determine which one provides the deep neural network with the highest accuracy and performance. A data scientist will benefit from the insightful analysis provided by this study in selecting the most appropriate optimizer to use while modeling their deep neural network.
A Sparse Bayesian Learning Method of Joint Activity Detection and Channel Estimation for LEO Grant-Free Random Access

The low earth orbit (LEO) satellite communication network has attracted extensive attentions owing to its advantages of seamless coverage and low propagation delay, which provides a promising solution to realize massive access for Internet-of-Things (IoT) devices. In this paper, we investigate the problem of joint activity detection and channel estimation (JADCE) in the uplink multi-input multi-output (MIMO) LEO satellite communication system based on the grant free random access (GF-RA) scheme, owing to the effectiveness of the GF-RA scheme for massive machine type communications. Considering the sporadic traffic of IoT devices, the problem can be solved via compressive sensing (CS) technology. To facilitate cost-effective hardware implementation, we utilize the Toeplitz matrix to generate the preamble. A multiple measurement vector algorithm based on sparse bayesian learning is developed for performing active device detection and channel estimation by fully exploiting the sparsity of the device state matrix. Simulation results indicate that the proposed method achieves lower device activity detection error probability and better channel estimation performance than the baseline method in the literature.
Problems of Building Machine Learning Models in Teaching Students

The article deals with the problems that arise in the process of teaching students in courses involving the development of machine learning models. The subject area is analyzed. The main problems are the low speed of building models and their verification. A solution method based on the use of fog and cloud computing environments is proposed. The conducted experimental studies showed that the time for building models decreased by 22.17 times, the speed of the teacher’s work increased by 47 %. The presented method of increasing the speed of building models can be applied in laboratory work, in the management of final qualifying works. Also, the method is applicable for building machine learning models in solving practical problems in the field of production, urban economy, in the provision of transport services, etc.
Emerging trends in OBIC characterization technique for Wide Bandgap semiconductor devices

Wide-bandgap semiconductors, like silicon carbide (SiC), gallium nitride (GaN), and diamond (C), outperform silicon (Si) based power electronic devices. However, the peripheral protection of these wide-bandgap devices needs to be carefully designed to handle high voltage. This article demonstrates the potential of using the OBIC (Optical Beam Induced Current) technique to analyze different protection methods’ effectiveness and provide feedback to device designers regarding peripheral termination efficiency.At the beginning, the article presents a theoretical approach to introduce the OBIC method. Subsequently, the electro-optical characterization technique is applied to high-voltage power devices within a vacuum chamber, enabling the examination of the electric field’s spatial distribution in the semiconductor. The focus is on SiC devices due to their availability. Throughout the years, this technique has evolved, with advancements made in reducing spot size and enhancing sample placement precision. The article will also showcase new results obtained from components with new-generation peripheral protection.
AI-assisted Material Property Extraction for Piezoelectric Resonators

New piezoelectric materials such as ScAlN, sputtered PZT and NaNbO3 with much higher piezoelectric coefficients than AlN, have been reported in the literature. However, their exact material properties are not reliably known. We demonstrate, for the first time, an AI-assisted method which combines a fast forward solver neural network with a minimizing optimizer. We extracted the material properties which give the best match of the impedance spectra of a coupled bulk acoustic resonator (CBAR) between simulation and experiment.
Blind MIMO Detection for Doubly-Selective Fading Channels

Blind MIMO detection is an interesting and challenging problem. Existing works mainly focus on block fading channels. In practice, wireless channels generally present doubly-selective fading. In this paper, we address blind MIMO detection in doubly-selective fading channels. Leveraging the Bayesian framework, a BBD-IBR receiver, consisting of two modules named block blind detection (BBD) and inter-block refinement (IBD), is proposed by dividing the whole frame into blocks. A linear interpolation (LI) based technique is further proposed to improve the accuracy of channel modeling in each block and thereby the performance of the whole algorithm, resulting in a BBD-IBR-LI receiver design. Simulation results demonstrate that the proposed designs obtain noticeable performance improvements compared to existing alternatives, and have a comparable or even better performance than the pilot based linear minimum-mean-squared-error (LMMSE) receiver.
Quantifying 3D Tumor Microvascular Heterogeneity via Super-resolution Ultrasound Imaging to Categorize and Characterize Microvascular Subgroups in U87 Glioblastoma

Cancer treatment response has traditionally been monitored by measuring tumor volume without considering tumor microvascular heterogeneity (TMH), which is known to profoundly influence treatment outcomes. As TMH presents as distinct vascular subtypes with different functions and structures, we employed super-resolution ultrasound imaging (SRUS) to resolve various tumor vascular subgroups. The feasibility of characterizing tumor vascular subtypes in evaluating anti-angiogenic treatment was also validated. Compared with traditional vascular parameters, the evaluation of multiple vascular subtypes provides a new approach to investigating the complex vascular dynamics induced by antiangiogenic drugs. It is reasonable to believe that tumor microvascular categorization allows the identification of at-risk vascular subgroups with unfavorable functional properties. In summary, this preliminary study of SRUS in the non-invasive characterization of microvascular subtypes sheds light on personalized cancer management.
Self-Adaptive Genetic Algorithm For Permutation Flow Shop Scheduling Problems

The permutation flow shop scheduling problem (PFSSP) is a well-known, extensively researched, and heavily applied non-polynomial (NP)-Hard combinatorial optimization problem. It is encountered in various real-life manufacturing problems such as automotive manufacturing, integrated circuit fabrication, and agricultural food industries. It continues to gain popularity in operational research areas as new manufacturing areas are developed. Therefore, finding a solution to these NP-Hard problems attract the attention of scientists. In this paper, we studied a PFSSP and proposed a new heuristic for its solution: The Self-Adaptive Genetic Algorithm (GA). This proposed algorithm uses a conventional GA with cycle crossover and random swap mutation. Its novelty, on the other hand, lies in incorporating an adaptive mechanism in the GA. The proposed algorithm uses three different local searches (i.e. 2-Opt, Greedy Insert, and Greedy Swap local searches) based on their successes. In other words, the proposed algorithm evaluates the performance of each local search at each generational iteration and makes a decision on which one to use based on their previous performances. The more successful local searches increase their probability of selection and vice versa. This way, Self-Adaptive GA, hence the name, adapts and directs its exploitation by the information it obtains in its previous generations. The proposed algorithm was applied to a subset of well-known Taillard problem instances. The experimental studies show its successful performance. Self-Adaptive GA obtained the optimum results for 7 out of 18 benchmark instances. In the rest of the 11 instances, the differences between the results of the proposed method and the optimum values are less than 2%.
Compound Angles Phased Array Imaging Method Based on Intracardiac Echocardiography Probe

Intracardiac echocardiography (ICE) is one of the most widely used echocardiography techniques in clinic. At present, most of the imaging systems of intracardiac echocardiography adopt the traditional beamforming method of phased array, that is, the lateral resolution near the focal point is higher. For phased array ICE, there is room for improvement in imaging quality at the far field of traditional phased array imaging method, in order to obtain deeper intracardiac structure. In this paper, we have proposed a new imaging method, i.e., compound angles phased array imaging method. This work is aimed at improving the imaging quality of ICE probe in the far field. The results of field II simulation show that the lateral resolution at the depth of 10 mm to 100 mm improved by 41% in average in comparison to traditional beamforming method of phased array.
Development and Testing of an Angled, High Frequency Ultrasound Probe for Minimally Invasive Spine Surgeries

As spine surgeries for decompression of the spinal cord and nerve roots trend towards minimally invasive approaches, conventional visualization methods for surgical guidance, such as ultrasound, are often ineffective. This is because conventional imaging probes are too large to fit down the limited access routes used in these approaches. We have developed a miniature high-resolution ultrasound phased array specifically for this application. The probe has a cross section of 4 mm by 4 mm, length of 16 cm from the handle, and an angled tip that enables imaging at a 40-degree angle from forward looking. The image window was steered between +/-32 degrees with an imaging depth of 15 mm. The array was micromachined using a picosecond laser, had 64 sub-diced elements, and an operating frequency of 30 MHz. The axial and lateral resolution were measured to be 33 μm and 116 μm, respectively, and the secondary lobes were suppressed to -60 dB. During preliminary clinical studies, patients were imaged during minimally invasive spine surgery, where the spinal cord anatomy and compression points could clearly be visualized.
AoI Minimization for Sensor Networks With Adaptive Packet and Energy Arrival

This paper explores the design of packet scheduling policy in wireless sensor networks that feature adaptive packet and energy arrival. Using the metric Age of Information (AoI), we model the problem of minimizing the long-term average AoI as a Markov decision process (MDP) and propose a simple single-threshold policy in which the transmission is triggered when the gap between the receiver AoI and the data generation time exceeds certain threshold. We also derive the expected AoI performance and the optimal threshold in closed-form expressions.
Monitoring microvascular tortuosity and velocity distribution response of 4T1 tumor to the DOX-NDs treatment using super-resolution ultrasound imaging

Super-resolution ultrasound (SRUS) imaging has been gradually employed for the non-invasive evaluation of tumor intervention, while the comprehensive indicators such as vessel density (VD), tortuosity (VT) and velocity (VV) could easily conceal the local changes of the heterogeneous tumor microvasculature. Herein, our study attempts to construct the novel monitor manner for the local microvascular changes and verify its non-invasion quantitative evaluation of tumor response during intervention. Tumors were delineated into central and peripheral regions based on B-mode images. VD, regional mean VT, and VV indices were quantified for subsequent comparisons of tumor angiogenesis and vessel perfusion over time. SRUS images depicted a gradual reduction in tumor vascularity during treatment. Traditional quantitative data indicated solely a decline in central VD, with marginal VD and VT exhibiting negligible variations. Conversely, a distinct leftward shift in the VT distribution center was evident for peripheral vasculature. Moreover, the VV distribution illustrated a progressive rise in the count of rapidly flowing vessels in the periphery, implying a gradual shift of the tumor's blood supply center from the core to the outskirts. These findings offer a fresh perspective for utilizing SRUS imaging in evaluating intervention responses of highly heterogeneous tumors and guiding more precise therapeutic strategies.
Development of an AI Enabled Yoga Posture (Aasans) Prediction System Using Deep Neural Network Model

“Yoga does not just change the way we see things; it transforms the person who sees”. This is a statement that highlights not just suggests the greatness we have in the practice which was once seen as just a hobby - YOGA In this paper, we propose a method to detect correct yoga postures using deep learning techniques. Our approach involves using a dataset of various yoga asanas to train our model to detect any abnormalities between the desired pose and the user's real time pose. By using this approach, we hope to provide a useful tool for individuals who want to learn and practice yoga at home, without the need for expensive trainers or yoga centers. AI Trainer will provide an interactive platform for beginners to learn about the fundamentals of yoga and asanas, as well as provide a recommendation system for users based on their postures. With the help of MoveNet, the human pose estimation can be done fast and can be easily deployed considering and keeping the inference times as low as possible during that. The outcome of this is a model which can implement and can achieve accurate key points for estimating a wide variety of gestures, movements, environments, and setup of hardware. The proposed model is giving an accuracy of 0.9929% and we have prepared an application is built using react javascript in which the pose estimation model is integrated. This application will help the user to easily maneuver and interact with the pose estimation model.
Forests and Global Change: Forests and global change: an overview

Introduction Forests provide a range of goods and services upon which humanity depends, from local (e.g. flood prevention) to global (e.g. carbon sequestration). Yet some 13 Mha of forest is lost each year, mostly in the tropics (Canadell & Raupach 2008). Considerable political, media and scientific attention has focused on this forest destruction and fragmentation and its implications for livelihoods, biodiversity and ecosystem services. However, human influences on forests now go beyond deforestation and degradation, to changing the states of atmosphere and climates and altering biogeochemical cycles, which in turn bear heavily on the functioning and composition of forest ecosystems. A range of disciplines are required to chart such change, understand how it works and predict where it is going. They span a broad range of scales from photosynthetic machinery in leaves, to the dynamics of forests across wide regions, to global atmospheric circulation. Integration of these multiple strands and scales of investigation has only recently begun, and this volume makes an important contribution to weaving them together into a more cohesive, albeit still incomplete, picture. The story is told in three parts, beginning with a collection of perspectives on the global environmental drivers of forest change, the complexity of their interaction and effects, and important feedbacks. The second section concentrates on species-level traits and trade-offs, and how these explain the composition and dynamics of forests in a changing world. Finally, a number of approaches and tools are presented for measuring forest change and forecasting its future direction.
Facial Landmark Detection Approaches for Remote Medical Applications

Nowadays, telemedicine systems capable for monitoring human physiological parameters are a promising area of research. These systems are usually used for non-invasive express diagnostics of pathological conditions. Computer vision and machine learning algorithms are applied to obtain and process the necessary information. This paper considers the most promising methods for solving the problem of facial region of interest detection. The methods are based on the use of convolutional neural networks. The MobileNet v3 architecture is used as a machine learning model. The training results of two configurations of the selected neural network are presented. A comparison of training parameters that provide the best accuracy results is provided. The analysis of the obtained results is carried out.
Tribological Behavior of Medium Molecular Weight Chitosan in Acid Aqueous Solution

Natural biomacromolecules are widely used in biomimetic medicine engineering. Especially, polysaccharides have been extensively exploited in biomedical applications due to featuring desirable lower toxicity, biocompatibility, and degradability. Chitosan, as a typical natural polysaccharide, was widely utilized as a potential vector for gene/drug delivery according to its adhesion. However, it usually coexists with tribological behaviors in these application scenarios. Many studies have explored its adhesion properties, but the tribological behavior is not clear between the two chitosan-coated surfaces. Here, we investigated the tribological behavior of chitosan films by virtue of the surface forces apparatus (SFA) under shearing in 150 mM acetic acid. The thickness of chitosan films was observed, which would gradually increase and become stable once the two chitosan films under sliding, and the variation of the chitosan thickness is reversible. It could be attributed to the generation of a hemifusion layer at the bearingload area under the high load, and the new contact interfaces were generated when relative sliding occurred. Meanwhile, the configuration of chitosan films would be rearranged with elastic swelling deformation during a running-in stage. Interestingly, the kinetic friction exhibited a negative correlation with load, in which increasing the load would result in a decrease of friction force. Such phenomenon is primarily attributed to form a more compact configuration of the new contact interfaces with a higher charge density, which reduced the opportunities for the polymer interpenetration of entanglement of the chitosan chains and enhanced the electrostatic repulsion between the new contact interfaces, leading to smaller friction. Our findings provide useful information in understanding the tribology mechanism of soft materials, with implications onto designing chitosan-based lubricating materials for biomedical and bioengineering.
MLIRSmith: Random Program Generation for Fuzzing MLIR Compiler Infrastructure

MLIR (Multi-Level Intermediate Representation) compiler infrastructure has gained popularity in recent years to support the construction of many compilers. Instead of designing a new IR with a single abstraction for each domain, MLIR compiler infrastructure provides systematic passes to support a wide range of functionalities for benefiting multiple domains together and introduces dialects to support different levels of abstraction in MLIR. Due to its fundamental role in compiler community, ensuring its quality is very critical. In this work, we propose MLIRSmith, the first fuzzing technique for MLIR compiler infrastructure. MLIRSmith employs a two-phase strategy to generate valid and diverse MLIR programs, which first constructs diverse program templates guided by extended MLIR syntax rules and then generates valid MLIR programs through template instantiation guided by our designed context-sensitive grammar. After applying MLIRSmith to the latest revision of MLIR compiler infrastructure, we detected 53 previously unknown bugs, among which 49/38 have been confirmed/fixed by developers. We also transform the high-level programs generated by NNSmith (a high-level program generator for deep learning compilers) to MLIR programs for indirectly fuzzing MLIR compiler infrastructure. During the same testing time, MLIRSmith largely outperforms such an indirect technique by detecting 328.57% more bugs and covering 194.67%/225.87% more lines/branches in MLIR compiler infrastructure.