Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards an Interpretable AI Framework for Advanced Classification of Unmanned Aerial Vehicles (UAVs)

With UAVs on the rise, accurate detection and identification are crucial. Traditional unmanned aerial vehicle (UAV) identification systems involve opaque decision-making, restricting their usability. This research introduces an RF-based Deep Learning (DL) framework for drone recognition and identification. We use cutting-edge eXplainable Artificial Intelligence (XAI) tools, SHapley Additive Explanations (SHAP), and Local Interpretable Model-agnostic Explanations(LIME). Our deep learning model uses these methods for accurate, transparent, and interpretable airspace security. With 84.59% accuracy, our deep-learning algorithms detect drone signals from RF noise. Most crucially, SHAP and LIME improve UAV detection. Detailed explanations show the model's identification decision-making process. This transparency and interpretability set our system apart. The accurate, transparent, and user-trustworthy model improves airspace security.
Lane-Changing Decisions Making for Autonomous Vehicles via Behavior Cloning and Decision Tree

The use of behavior cloning technology for autonomous driving decision-making has become a research hot spot at home and abroad. Existing research methods are limited to existing data sets. The data is single and scarce, and the robustness of the algorithm cannot be verified interactively. The accuracy and convergence speed of the algorithm are limited. In this paper, we divide the lane-changing decision-making process of autonomous driving vehicles into two layers. The upper layer uses the decision tree algorithm to select tasks, selects lane-changing task or lane keeping task according to road conditions, subdivides traffic scenes, and simplifies the data space for behavior cloning. The lower layer uses end-to-end behavior cloning and outputs the steering wheel angle and the throttle angle control amount. Firstly, build the lane changing scene on the CARLA simulator, collect and preprocess the driving data, then use attribute reduction to filter appropriate attributes, and use decision tree algorithm to extract driving rules, finally the lane-changing task and the lane-keeping task are learned through the behavior cloning algorithm respectively. The training results are analyzed and simulated in CARLA. The results show that the accuracy and convergence speed of the behavior cloning algorithm are improved after the division of decision tree algorithm, the lane changing task loss is reduced by 6.25%, and the lane keeping task loss is reduced by 9.38%.
A Circularly Polarized Four Port MIMO Antenna with Split Ring Metamaterial for dual band IoT Applications

MIMO (multiple-input multiple-output) antennas are widely used in numerous applications to enhance the speed of communication through high frequency signals, which are provided by MIMO antenna. Several MIMO antenna systems in recent research are focusing on ways to attain high isolation among antenna units and to reduce mutual coupling because lesser dimensions and areas are limited when organizing antennas. In this research work, a dual band circularly polarized split ring Meta MIMO antenna is designed to obtain less loss of polarization and better reliability. The presented four port MIMO antenna is applied over a dual band IoT application that resonates over dual frequencies between 2.95 GHz to 8.2 GHz. The MIMO antenna is designed on the Rogers RT DUROID 5880 substrate and uses a Microstrip feed line. Rectangle shaped defected ground structure (RDGS) is used the work to minimize antenna’s mutual coupling and cross polarization issues. The proposed model can analyze various performance metrics such as bandwidth, gain, return loss, radiation pattern, isolation, DG (Diversity Gain), TARC (Total Active Reflection Coefficient), and so on.
H_infty Performance Analysis and Controller Design for Data-Driven Dynamic Event-Triggered Systems

This article is concerned with $\mathbf{H}_{\infty}$ performance analysis and controller design for the dynamic event-triggered systems through noisy input-state data. A novel dynamic event-triggered scheme is proposed. Based on a data-driven representation, a new data-based analysis method for $\mathbf{H}_{\infty}$ performance of the dynamic event-triggered system is introduced by using Lyapunov stability theorem, which relies on the noisy data rather than the model parameters. Beyond that, a data-based method for co-designing the $\mathbf{H}_{\infty}$ controller and the event-triggering matrix is provided, through which the controller gain matrix can be optimized. Finally, a numerical example is given to illustrate the effectiveness of the proposed methods.
Propagation through Electrically Coupled Cells: Effects of Regional Changes in Membrane Properties

The normal process of excitation of the heart involves propagation of action potentials through cardiac regions of different anatomy and different intrinsic membrane properties. Although our understanding of these properties is still incomplete, it is well accepted that the parameters measured from a single cell penetration in an electrical syncytium (e.g., action potential duration, rate of rise, and velocity) reflect not only the properties of that cell but also the electrotonic interactions with other cells to which the recorded cell is electrically coupled. We have used simulation techniques to predict the spatial distribution of action potential parameters resulting from discretely localized alterations in the intrinsic membrane properties of some of the cells of an electrical syncytium. We have shown that the resulting spatial distribution is markedly different for alterations in plateau and pacemaker currents vs. rising phase currents, and that other factors, such as the site of stimulation and the underlying spatial pattern of cell-cell coupling resistance, also modify the spatial distribution of action potential properties resulting from a discrete regional change in intrinsic membrane properties.
Context-aware Conversational Management System for Human-robot Interaction by Incorporating User Query-based Context Classification

Context awareness in conversational human robot interaction is an interesting but still challenging topic in the research arena. Context awareness in conversational human robot interaction improves the human robot interaction by providing exact information to the user's queries as well as maintaining a human friendly interaction. We propose a conversational management system that is aware of the context of the user. Context awareness is identified from the verbal interactions with the user. In addition, a response cloud structure that is embedded in the conversational management system is presented to choose the appropriate responses for user queries, and at the same time, it can also be updated according to the wishes of the user. A user study was done to evaluate the performance of the system. The results of the user study show that the proposed method is suitable for domestic service robot-based applications.
Privacy Preservation on the Cloud Data Multikeyword Search Using Deep Learning

The technology was vulnerable to online hackers and assaults on the open and dispersed nature of the cloud. To recognize and stop both internal and external attacks in a cloud ecosystem with high identification accuracy and minimal false alerts, it should be essential to create an anomalous network platform. In this study, researchers proposed an intelligent method of instantaneously creating an effective and hybrid classification method called Network Privacy Preservation based K-Means Recurrent Neural Network (NPK-RNN) predicted using both of normalized K-means segmentation method to RNN & Privacy Preservation that results would be known as NPK. The researchers use three freely accessible data sets to detect negative/offensive comments on social media in multilingual languages to respond to such an in-depth study. The proposed approach is to improve the privacy preservation on searching the data on cloud using multikeyword. As demonstrated empirically by comparison with other classification techniques developed successful invasions.
Models of Regional Habitat Quality and Connectivity for Pumas (Puma concolor) in the Southwestern United States

The impact of landscape changes on the quality and connectivity of habitats for multiple wildlife species is of global conservation concern. In the southwestern United States, pumas (Puma concolor) are a well distributed and wide-ranging large carnivore that are sensitive to loss of habitat and to the disruption of pathways that connect their populations. We used an expert-based approach to define and derive variables hypothesized to influence the quality, location, and permeability of habitat for pumas within an area encompassing the entire states of Arizona and New Mexico. Survey results indicated that the presence of woodland and forest cover types, rugged terrain, and canyon bottom and ridgeline topography were expected to be important predictors of both high quality habitat and heightened permeability. As road density, distance to water, or human population density increased, the quality and permeability of habitats were predicted to decline. Using these results, we identified 67 high quality patches across the study area, and applied concepts from electronic circuit theory to estimate regional patterns of connectivity among these patches. Maps of current flow among individual pairs of patches highlighted possible pinch points along two major interstate highways. Current flow summed across all pairs of patches highlighted areas important for keeping the entire network connected, regardless of patch size. Cumulative current flow was highest in Arizona north of the Colorado River and around Grand Canyon National Park, and in the Sky Islands region owing to the many small habitat patches present. Our outputs present a first approximation of habitat quality and connectivity for dispersing pumas in the southwestern United States. Map results can be used to help target finer-scaled analyses in support of planning efforts concerned with the maintenance of puma metapopulation structure, as well as the protection of landscape features that facilitate the dispersal process.
Genotyping of Chlamydophila psittaci by Real-Time PCR and High-Resolution Melt Analysis

ABSTRACT Human infection with Chlamydophila (Chlamydia) psittaci can lead to psittacosis, a disease that occasionally results in severe pneumonia and other medical complications. C. psittaci is currently grouped into seven avian genotypes: A through F and E/B. Serological testing, outer membrane protein A (ompA) gene sequencing, and restriction fragment length polymorphism analysis are currently used for distinguishing these genotypes. Although accurate, these methods are time-consuming and require multiple confirmatory tests. By targeting the ompA gene, a real-time PCR assay has been developed to rapidly detect and genotype C. psittaci by light-upon-extension chemistry and high-resolution melt analysis. Using this assay, we screened 169 animal specimens; 98 were positive for C. psittaci (71.4% genotype A, 3.1% genotype B, 4.1% genotype E, and 21.4% unable to be typed). This test may provide insight into the distribution of each genotype among specific hosts and provide epidemiological and epizootiological data in human and mammalian/avian cases. This diagnostic assay may also have veterinary applications during chlamydial outbreaks, particularly with respect to identifying the sources and tracking the movements of a particular genotype when multiple animal facilities are affected.
High-resolution pore-water sampling with a gel sampler

Sediment pore-water profiles were sampled at high resolution (millimeter scale) with a polyacrylamide gel probe. This simple procedure involves inserting a 1-mm-thick gel held in a plastic probe into sediment. The gel reaches diffusive equilibrium in <1-2 h. For anions, the gel was sectioned, back-equilibrated into distilled-deionized water, and anions determined by high-performance liquid chromatography. Laboratory trials showed recovery of 104+/-4% Cl, 102+/-2% NO3, 101+/-1% SO4, and 102+/-2% NH4. For Fe and Mn, the gel was fixed in 0.01 M NaOH for similar to 3 h, subsectioned, extracted with 1 M HNO3, and analyzed by atomic absorption spectrometry. Field trials were undertaken in Esthwaite Water, a seasonally anoxic lake in the English Lake District. Gel probe data compared well with conventional pore-water extractions.
A Non-Work Conserving Algorithm for Dynamic Scheduling of Moldable Gang Tasks on Multicore Systems

In this paper, we consider a dynamic scheduling algorithm for moldable gang tasks on multicore systems. While previous algorithms have been proposed, they predominantly adhere to work-conserving scheduling algorithms. In this paper, we introduce a non-work conserving dynamic scheduling algorithm aiming to reduce the makespan. Our proposed algorithm employs beam-search to perform non-work conserving scheduling. Through experiments, we demonstrate that under certain conditions, our method improves the makespan by 10% compared to existing methods. Furthermore, we discuss the variance in results based on the characteristics of the input tasks.
The Stable Homotopy Category Has a Unique Model at the Prime 2

The stable homotopy category has been extensively studied by algebraic topologists for a long time. For many applications it is convenient or even necessary to work with point set level models of spectra as opposed to working up-to-homotopy, and the outcome of a calculation can depend on the choice of model. In recent years many new models for the stable homotopy category have been constructed. It is especially useful to have the structure of a closed model category in the sense of Quillen [Qui] and many examples of spectra categories t into this context [BF, Rob87, EKMM, HSS, Lyd, MMSS]. Moreover all known examples capture the ‘same homotopy theory’ { in technical terms one speaks of Quillen equivalent model categories [Hov, Def. 1.3. 12]. Hence not only the homotopy categories, but also higher order information such as Toda brackets, homotopy colimits and homotopy types of function spaces coincide. In two Quillen equivalent model categories the answer to every homotopy theoretic question comes out the same. In a model category one can pass to the associated homotopy category by formally inverting the class of weak equivalences. However, passage to the homotopy category loses information and in general the ‘homotopy theory’ can not be recovered from the homotopy category, see 2.1 and 2.2 for two examples. In this paper we show that in contrast to the general case, the stable homotopy category completely determines the stable homotopy theory 2-locally. We prove a uniqueness theorem which says that there is essentially only one model category structure underlying the stable homotopy category of 2-local spectra | the stable homotopy category has no ‘exotic’ models at the prime 2.
Characterizing CNN-Based Vessel Detection Algorithm Sensitivity to Optical Sensor Artifacts

The number of space-based optical imaging systems have substantially grown in recent years. Many of these commercial based vendors are exploring concepts to move processing applications directly to the space segment or to incorporate advanced algorithms in their ground segment. Typically, Convolutional Neural Network (CNN)-based detection algorithms are considered due to their high reliability in laboratory settings. However, this class of algorithms routinely demonstrates degraded performance when unexpected phenomena are introduced to the data. As more near-real-time applications are considered, CNN-based algorithms will be subjected to sensor calibration artifacts. This paper seeks to characterize the response of CNN-based vessel detection algorithms to common electro-optical sensor calibration artifacts. Several artifacts are explored, including added sensor noise and failed detector artifacts. Each of the explored artifacts uniquely impact the algorithm detection performance. It is found that applying Poisson distributed random noise to the imagery substantially degrades model performance by increasing false alarm rate. Likewise, the application of a uniformly distributed random scale factor to the imagery degrades model performance by lowering positive detection rate. These results may inform plans to implement CNN-based algorithms directly in the space-segment by characterizing typical performance variations due to underlying sensor calibration issues.
Utilizing the molecular gateway: the path to personalized cancer management.

BACKGROUND Personalized medicine is the provision of focused prevention, detection, prognostic, and therapeutic efforts according to an individual's genetic composition. The actualization of personalized medicine will require combining a patient's conventional clinical data with bioinformatics-based molecular-assessment profiles. This synergistic approach offers tangible benefits, such as heightened specificity in the molecular classification of cancer subtypes, improved prognostic accuracy, targeted development of new therapies, novel applications for old therapies, and tailored selection and delivery of chemotherapeutics. CONTENT Our ability to personalize cancer management is rapidly expanding through biotechnological advances in the postgenomic era. The platforms of genomics, proteomics, single-nucleotide polymorphism profiling and haplotype mapping, high-throughput genomic sequencing, and pharmacogenomics constitute the mechanisms for the molecular assessment of a patient's tumor. The complementary data derived during these assessments is processed through bioinformatics analysis to offer unique insights for linking expression profiles to disease detection, tumor response to chemotherapy, and patient survival. Together, these approaches permit improved physician capacity to assess risk, target therapies, and tailor a chemotherapeutic treatment course. SUMMARY Personalized medicine is poised for rapid growth as the insights provided by new bioinformatics models are integrated with current procedures for assessing and treating cancer patients. Integration of these biological platforms will require refinement of tissue-processing and analysis techniques, particularly in clinical pathology, to overcome obstacles in customizing our ability to treat cancer.
A Novel Framework for Classification of MRI Images to Diagnose Brain Tumors using DenseNet 201

Brain cancer tumors are often undetected by radiologists due to the lack of clarity in some MRI images, making early detection of a potentially malignant cancer very difficult. One way to address this issue is with the use of convolutional neural networks (CNNs) with segmentation and classification steps to detect the unseen tumors. These networks can be utilized by radiologists as a computer aided tool to improve proficiency and develop pattern recognition of biomarkers. CNNs take in MRI data inputs and separate each scan into edges, colors, and sizes to apply an algorithm for classification, minimizing the error value with each iteration. The computer vision methods of image preprocessing, image segmentation, classification and pattern detection allow the model to be a viable supplement to clinician based diagnosis. Currently, studies have used various algorithms to improve cancer detection, but the accuracy rates have not been high enough for proper detection. This research aimed to produce a network with a higher validation set accuracy rate, training on 25 epochs. The model was trained on 70% of the images in the selected dataset, 20% for validation, and 10% of the data for testing. This study utilizes DenseNet architecture and is able to reach an accuracy rate for the brain tumor images of about 97%, higher than many other models and fit for classification usage. A limitation of this study is that the selected data set is confined to glioma and meningioma cancer types, leaving out other potential cancer a patient may have. However, as models continue to develop, using CNN based detection will ultimately benefit patients with various types of cancer in finding their tumors earlier for better prognosis.
Bleeding risk, physical functioning and non-use of anticoagulation among patients with stroke and atrial fibrillation.

BACKGROUND Atrial fibrillation (AF) is common among people with stroke. Anticoagulation medications can be used to manage the deleterious impact of AF after stroke, however, may not be prescribed due to concerns about post-stroke falls and decreased functioning. Thus, the purpose of this study was to identify, among people with stroke and AF, predictors of anticoagulation prescription at hospital discharge. METHODS This is a secondary analysis of a retrospective cohort study of data retrieved via medical records, including National Institutes of Health Stroke Scale score, Functional Independence Measure (FIM) motor score (motor or physical function), ambulation on second day of hospitalization, Morse Falls Scale (fall risk) and HAS-BLED score (Hypertension; Abnormal renal and liver function; Stroke; Bleeding; Labile INRs; Elderly >65; and Drugs or alcohol). Data analyses included bivariate comparisons between people with and without anticoagulation at discharge. Logistic-regression modeling was used to assess predictors of discharge anticoagulation. RESULTS There were 334 subjects included in the analyses, whose average age was 75 years old. Anticoagulation was prescribed at discharge for 235 (70%) of patients. In the adjusted regression analyses, only the FIM motor score (adjusted OR = 1.015, 95% CI 1.001-1.028) and the HAS-BLED score (adjusted OR = 0.36, 95% CI 0.22-0.58) were significantly associated with anticoagulation prescription at discharge. CONCLUSION It appears that in this sample, post-stroke anticoagulation decisions appear to be made based on clinical factors associated with bleed risk and motor deficits or physical functioning. However, opportunities may exist for improving clinician documentation of specific reasoning for non-anticoagulation prescription.
Deep Census Weight Correlation Stereo Network

Stereo depth estimation is of great importance in autonomous driving, unmanned system operation and other intelligent visual tasks. At present, large textureless and uneven illumination regions are still the difficult problems for stereo matching tasks. To promote the stereo matching network to effectively utilize the feature information of the uneven illumination regions and the large textureless regions, we proposed the census weight correlation cost volume construction method which can selectively construct the illumination feature informaiton into the cost volume. The census weight correlation volume is a differentiable transformation of the traditional census-based matching cost computation. Extensive experiments show that the proposed census weight correlation volume can effectively improved the performance in the large textureless and uneven illumination regions compared with the original correlation volume and our network has achieved excellent performance on Scene Flow datasets, KITTI 2012 datasets and KITTI 2015 datasets.
Dual-mode Resonator Filter Using Directed-feed Line

This paper introduces a dual-mode stepped-impedance resonator filter with a directed-feed line for 5G communication systems. Implementing directed input and output feeds reduces insertion loss within the passband channel. The perturbation stub lengths are designed for adjusting two degenerate modes and tuning the second mode, which moved to the first passband. The dual-mode configuration is verified and analyzed by the IE3D simulator to choose a proper impedance ratio and tune stubs. The center frequency selected for the design of the proposed filter is 3.5 GHz. The simulated insertion loss (IL) in the passband channel is below 0.12 dB, and its related return loss (RL) stands higher than −25 dB.