High dimensional classification problems have gained increasing attention in machine learning, and feature selection has become essential in executing machine learning algorithms. In general, most feature selection methods compare the scores of several feature subsets and select the one that gives the maximum score. There may be other selections of a lower number of features with a lower score, yet the difference is negligible. This article proposes and applies an extended version of such feature selection methods, which selects a smaller feature subset with similar performance to the original subset under a pre-defined threshold. It further validates the suggested extended version of the Principal Component Loading Feature Selection (PCLFS-ext) results by simulating data for several practical scenarios with different numbers of features and different imbalance rates on several classification methods. Our simulated results show that the proposed method outperforms the original PCLFS and existing Recursive Feature Elimination (RFE) by giving reasonable feature reduction on various data sets, which is important in some applications. With the increased development of machine learning concepts and related topics, feature selection has become crucial as most real-world data sets suffer from many features. This problem is known as the curse of dimensionality , and manSelecting fewer features, known as feature selection, provides several significant advantages. With feature selection, dimensionality reduction can decrease the size of the data without harming the overall performance of the analytical algorithm . The decThe main three categories of feature selection techniques are filter, wrapper, and embedded methods. Filter methods measure the feature relevance to the dependent variable; hence, only features with meaningful relationships would be included in a classification model. They use statistical methods such as Pearson’s Correlation, Analysis of Variance (ANOVA), Linear discriminant analysis (LDA), and Chi-Squared statistics to select a subset of features. By training a model, wrapper methods measure the usefulness of a subset of features . ForwardVarious approaches and extensions in the literature have been suggested to the existing feature selection mechanisms such as RFE. Usually, the two main objectives for feature selection are to select the smallest possible subset with a given discrimination capability and to find the subset of features with the minimum possible generalization error . This arWe propose a method as an extended version of the suggested PCLFS  method  explainePrior research also compares the impact of class re-balancing techniques on the performance of binary prediction models for a different choice of data sets, classification techniques, and performance measures. Hence, in this article, we focus on binary classification problems with only two possible outcomes. Class imbalance occurs when the number of instances in the small (minority) class is significantly smaller than that in the large (majority) class. It produces a significant negative influence on standard classification learning algorithms. The minority class is important in many practical situations; therefore, it requires an intense urgency to be identified . HoweverAlthough we can already reduce the number of features using PCLFS according to a given selection scoring criteria, there is room to improve it further. We observed that the number of features of the chosen subset by PCLFS might not be the expected quantity if the desire is to have a smaller number of features. In particular, there are other selections of a lower number of features with negligibly lower model accuracy. Therefore, we consider the challenge of finding an optimal threshold to identify this minuscule difference. We also compare simulation and application results with existing PCLFS and RFE results.To illustrate this procedure with a contrived example we will consider a simulated data set with ten informative features out of 30. But, example 1 in Our primary focus in this article is analyzing the behavior of the PCLFS method towards classification accuracy and suggesting an improved extension for selecting a smaller number of features with similar performance with the previous method. Hence, we introduce an algorithm with a threshold to achieve this objective. Besides choosing the minimal number of features, we suggest the appropriate feature subset by considering the informativeness of features. To cover most practical scenarios, we synthetically simulated data using the scikit-learn python library  and compThe remainder of this article is structured as follows. Section 2 describes the data preparation introduces the methods used in the study with the experimental design. Section 3 presents the results of the simulation studies, and the results in a real-world application are illustrated and interpreted in Section 4. Finally, Section 5 of this article is included with a discussion of its contributions and limitations.RFE can be fitted on any classification model with an inherent quantification of the importance of a feature. It removes the weakest features by a step count, where the step is the number of features removed at each iteration. This process repeats until the stipulated number of features is reached. Features are ranked according to the importance identified by the model. Then, to find the optimal number of features, cross-validation is used in each iteration and selects the subset giving the best scoring value as the desired feature subset.i.e., F1-score) and, in the end, obtains the feature subset which gives the maximum F1-score.Principal Component Loading Feature Selection uses the sum of absolute values of principal component loadings to order features and capture the most informative feature subset, which is the subset that obtains the maximum F1-score using a sequential feature selection technique . This meinputs:Training samples: Class labels: outputs:Feature ranked list: Grid scores: Number of selected features by PCLFS: n is the number of features in the data set, and ℓ is the number of samples in the training set. Grid scores (g) are the F1-scores such that i features of the PCLFS ordered feature list.Here, PCLFS is a newly introduced feature selection method . TherefoIn this article, we propose a new algorithm based on PCLFS. The suggested method is an extension of the PCLFS method, and the results that come out of the PCLFS algorithm are fed into the new algorithm to get the desired output. The main difference between the new method and the original PCLFS is that the original PCLFS chooses the feature subset giving the maximum score. In contrast, the suggested method identifies a feature subset under an applicable threshold to obtain a smaller feature subset with similar performance and minimal loss. We compare PCLFS and the extended method on various synthetic data sets and show that the suggested method reduces the number of features with a bearable score reduction. The algorithm for the new method is described below.inputs:Grid scores: Number of selected features by PCLFS: nTotal number of features: Feature importance scores (obtained from the classifier): T (User-defined).Maximum tolerable F1-score reduction: procedure:Step 1: Consider all the local maximum grid scores .Step 2: Connect each point with the maximum point  can be interpreted as the tolerable reduction of the F1-score to reduce one feature, where,The threshold . Then obtain the best s).Step 5: To get the relevant feature subset, use feature importance scores , and there is only one cluster per class. Several numbers of features were considered to compare different situations. Since different classification models perform uniquely in different data sets, we aim to introduce a general tool that works with multiple models. We train different binary classification models in data sets with different numbers of features and imbalance rates to ensure this. Initially, five different binary classification models were trained with PCLFS. They are Logistic Regression (LOGIT) , Linear This section illustrates the results obtained through synthetic samples and the simulation study results on all three methods, existing RFE, PCLFS, and proposed PCLFS-ext.To capture the variability of the final F1-scores of each method, we conducted a simulation study to determine the validity of the suggested combined approach. One hundred samples are simulated from each scenario to reduce the variability in experimental results, while the number of informative features is increased from one to the total number of features. All features are classified as informative or non-informative. No redundant features or repeated features are included in simulated data sets. We generated data for 50%:50% balanced data and two other imbalance rates, 70%:30% and 90%:10%. Two sample sizes with 200 and 1,000 samples were also examined, and results were discussed only for sample size 200 unless there is a notable discrepancy to emphasize. Most importantly, in this analysis, the models were fitted on original data and re-sampled data with SMOTE. Here, the results are only illustrated for the logistic regression model. Results obtained for the comparison of model F1-scores and feature selection correct percentages of RFE and PCLFS are shown in To further understand the selection of features, we plotted the number of selected features and feature selection true positive rate , PCLFS and PCLFS-ext methods even pick a lower number of features than RFE when there are few informative features in the data set  heart data set , which dThe sample consists of data from 267 patients with 44 continuous features that have been created for each patient. Hence, it has 267 instances that are described by 45 attributes (44 continuous and one binary class). We also divided the data set into two groups, 75% training samples and 25% test samples. The class-imbalanced rate for the data set is 79.4%:20.6%, where the minority class represents the abnormal patients. The imbalance is the same in the training and test set.Then we applied Synthetic Minority Oversampling Technique (SMOTE) to handle imbalanced data to achieve higher accuracy in classification models. The SMOTE aims to balance class distribution by randomly increasing minority class examples by creating similar instances.We compare the proposed PCLFS and PCLFS-ext model results with the final F1-scores of the existing RFE method. The results are shown in To further evaluate the performance of the existing and proposed approaches, we used five different continuous data sets which downloaded from UCI machine learning repository . They aletc. In such situations, we have to limit the number of features to a reasonable value; identifying the most important feature subset is crucial. Not only that but having fewer features also increases the computational efficiency and the prediction performances of the model. As a solution, we have proposed a new approach for the existing wrapper methods to select a minimal number of important features with similar performance. Hence, this is an important contribution as it reduces costs, especially in data collection.Feature selection has become an essential aspect of matured machine learning methods. Feature selection is also known as variable selection, feature reduction, attribute selection, or variable subset selection . This prMost of the wrapper feature selection methods compare scores of several feature subsets and select the one that gives the maximum score. There are other selections of fewer features with lower-score, yet with little difference in score. This article proposes and applies an extended version of selecting a minimal number of features subset instead of having the subset with the maximum score. PCLFS uses the sum of absolute values of principal component loadings to rank features and capture the most informative feature subset. It obtains the best feature subset by comparing the scores, where the feature subset that gives the best score is identified as the optimal feature subset. Still, some other feature subsets practically reduce the number of features with minimal score loss.Our proposed method assesses the number of features below the maximum and receives the most beneficial smallest number of features and the feature subset with a tolerable score deduction. For the extended version, we only consider the feature subsets smaller than the previous subset selection; therefore, having a minimal feature set is guaranteed by the proposed approach under the threshold. The threshold plays a vital role in the introduced algorithm as the numerator, the maximum tolerable F1-score, is decided by the user using their domain knowledge and desire. The selection of the threshold is sensitive to the imbalance rate of the data. We can use a relatively larger threshold for highly imbalanced data to achieve a similar result. Although we have considered only five classification models in examples, like in PCLFS, the proposed method can also be fitted on any classification model as feature ordering is entirely independent of the classification method .Although the underlying truth of the real-world data is hidden, we compare the result of the proposed method with existing PCLFS, RFE, and some other PSO-based feature selection methods on the same real data sets to compare the accuracy of each method.This study introduces a novel gradient-based algorithm to further reduce the number of features with a similar performance to existing greedy feature selection approaches. The extended version of the existing PCLFS method was implemented to identify the most informative features first. First, we compare the proposed approach to PCLFS and RFE results on simulated data sets. and real-world data sets. Simulation results clearly shown that the proposed method makes a reasonable improvement over existing results, especially when we have a balanced data set and large sample size. For this purpose, we can re-balance the data set using existing methods such as SMOTE . Then thReferring to the Simulation Results Section, 10.7717/peerj-cs.1081/supp-1Supplemental Information 1main.py runs the simulation study and application data where Grid_Score_Calc.py contains the introduced algorithm for the extended version of the PCLFS method. The SPECT.csv file is a publicly available Single-photon emission computed tomography (SPECT) heart data set, which describes diagnosing cardiac abnormalities using SPECT. Response of the data set consists of two categories: normal (0) and abnormal (1), by considering the diagnosis of images. This data consists of binary class imbalanced data with a higher number of numerical features and a lower number of instances.Click here for additional data file.10.7717/peerj-cs.1081/supp-2Supplemental Information 2Click here for additional data file.


The standard detection method is based on the measurement of the shift of some optical quantity induced by the immobilization of target molecules at the surface of an integrated optical element upon biomolecular recognition. However, this requires the acquisition of said quantity over the whole hybridization process, which can take hours, during which any external perturbation  can seriously affect the measurement and contribute to a sizeable percentage of invalid tests. Here, we present a different assay concept, named Opto-Magnetic biosensing, allowing us to optically measure off-line  tiny variations of the effective refractive index seen by microring resonators upon immobilization of magnetic nanoparticles labelling target molecules. Bound magnetic nanoparticles are driven in oscillation by an external AC magnetic field and the corresponding modulation of the microring transfer function, due to the effective refractive index dependence on the position of the particles above the ring, is recorded using a lock-in technique. For a model system of DNA biomolecular recognition we reached a lowest detected concentration on the order of 10  In diagnostics and biosensing, the demand for new tools is increasing year by year. Selectivity, sensitivity, response time, stability, reliability and reproducibility are just some of the parameters the researchers are looking to improve when developing a new technology .Over the past decades many different biosensing platforms based on light have been proposed ,3, aiminm of cerebrospinal diseases’ biomarkers, and [SPR and Localized Surface Plasmon (LSP) are among the most sensitive platforms under development, with detection limits down to  sensing  in a comers, and  detectedm or 1 PC are gaining attention, with notable results introducing new detection techniques. Researchers detected avidin protein down to 15 o 100 ag  and a usExploiting the knowledge coming from telecommunication field and the improving quality in SiPh foundries , researc−7 RIU detection limit [2 for IgG antibodies with protein G [MZI in particular are very promising solutions, with proven bulk sensitivities up to 20 on limit . Researcrotein G .−6 RIU and sensitivity of 850 Resonating cavities biosensors exploit the high Q-factor achievable to detect very low concentrations of analyte, down to single molecules. Spherical and thoroidal reached detection limits in the order of 100 nm/RIU , while [0 nm/RIU  reportedr scheme  reached r scheme  obtainedIn parallel to optical solutions, we assisted a rapid growth of magnetic biosensors ,25, explThe sensing principle is based on the detection of a phase shift or absorption induced by the biological bindings of the analytes on the sensor surface. Both label-free and labelled approaches can be used with PICs. While un-labelled methods, in which just the analytes are the source of the effect on the sensor, usually allow a simpler sample preparation, the use of labelling techniques can improve performances in terms of sensitivity and speed. Among the various particles that can be used as a label, Magnetic NanoParticles (MNP) ,31 can bIn this work we present a novel biosensing technique, named the Opto-Magnetic technique, which combines SiPh microring resonators and magnetic nanoparticles on a PIC. With the use of oscillating magnetic field and lock-in detection of the phase variation induced by the motion of immobilized MNP we show that quantitative analyte measurements on biomolecules such as DNA can be performed in a few minutes after biomolecular recognition, thus avoiding long measurements usually carried out in label-free optical platforms where the signal proportional to the analyte concentration is the difference of the sensor response after and before hybridization. In our approach, the presence of MNP labelling the target molecules on top of a surface specifically functionalized is detected after hybridization, thus solving typical issues of signal stability and parasitism affecting conventional measurements in which the signal must be continuously recorded over the hybridization time, on the order of hours.In Integrated optical biosensors are based on the perturbation that the analytes induce on the light propagating in the waveguides. The surface of the waveguide core is in direct contact with the fluid containing the analyte that perturbs the exponential evanescent electromagnetic field extending outside the core. The target molecules immobilized on the waveguide surface therefore induce a variation of the phase of the optical mode proportional to the difference of the refractive index of the bound molecules with respect to the refractive index of the fluid surrounding the waveguide. In general the induced attenuation variation is negligible. The phase variation has to be converted in intensity variation in order to be easily detected with photodiodes and hence the sensible waveguide must be inserted in an interferometer. The literature is rich with theoretical studies and experimental results on different kinds of interferometric structures such as Mach–Zehnder, ring resonators and Fabry–Perot cavities, Bragg gratings and more, realized on a plethora of technological platforms ,14,15,40However, the binding process between the molecules and the probes on the waveguide surface occurs on a time scale of minutes or hours; meanwhile the output power can also change due to other effects such as temperature variation of the overall device, density and refractive index change of the fluid, mechanical stress, misalignment of the input–output coupling, acoustic and environmental noise and so on. If, on one side, the basic principle of a PIC biosensor is pretty simple, on the other side a reliable and quantitative data detection requires accurate overall control and stabilization that often impair the exploitation in real applications.In this work we propose a label approach for PIC biosensing to circumvent most of the environmental sources of noise, drifts and the relative risk of false detection. The proposed technique consists of labeling the analyte with MNP, for which the diameter is of the order of 100 terature , also beHere, instead, a periodic time varying magnetic field is used to induce a coherent and synchronous oscillation of all the bound MNPs and, as explained in the next section, of the output power signal that can be advantageously detected with a locking technique. The magnetic field is produced by an electromagnet placed under the SiPh chip. The electromagnet is designed to produce a large magnetic field gradient in the proximity of the sensing elements and to provide an attractive force to any susceptible material nearby.The key advantage of this interrogation method is twofold: first, it is not necessary to monitor the entire long biological binding process while trying to extract the effect of the analyte from all the other parasitic effects by comparing with control sensors, and, second, the sensor can be interrogated after the binding occurs in an extremely rapid way. In The variation itten as:ΔPoPi=SsΔterature ,15,41 anThe effective index change tructure ,43 that From Equation  it is evThe choice of the waveguide technology and shape would permit us to optimize two sensitivity factors. ctronics  and so octronics ,45 and h−9 RIU/A comprehensive analysis and optimal design of MRR and Mach–Zehnder interferometers for biosensing can be found in . The chol issues .In this work, a ring resonator is used mainly to reduce the footprint, design a compact multipoint biosensor and ease the microfluidic circuit. The platform used for the measurements is a tion see a, each oFor a radius Equation , a sensiFinally, to evaluate The sensitivity of an optical biosensor sometimes indicates the so called bulk sensitivity olutions ,47, our The magnetic nanoparticles used in this work are ensembles of superparamagnetic particles grouped together by a polymer matrix and functionalized on the surface. Magnetic objects are defined as superparamagnetic when they are smaller than a critical dimension, typically in the order of a few  microns ,49.As schematically shown in The force is directed towards the electromagnet placed under the chip for both positive and negative values of the magnetic field, attracting the MNP towards the chip surface. For a sinusoidally varying magnetic field at frequency rce Fmag  oscillatThe wavelength shift ntensity , these tThe Silicon Photonic chip has been realized with an MPW run in STm. Waveguides are buried in a multilayer structure of multiple alternated silicon dioxide  probes, the chips were functionalized at the SCITEC-CNR laboratories, with the polymer MCP-4, copoly(DMA-NAS-MAPS) ,53,54, ow/v in water solution of ammonium sulfate at 20% saturation, for 30 m sodium phosphate pH 8.5, 0.01% Sucrose monolaurate) to a concentration of 10 m and printed by a piezoelectric spotter, SciFLEX ARRAYER S12 , onto the coated chip. After the spotting step, the chip was incubated overnight, and all residual reactive groups of the coating polymer were blocked by dipping the chip in a blocking solution  for 60 The surface was first cleaned with  0.34 nm , 60 baseThe hybridization process is implemented in the microfluidic cell in which the chip is embedded. First, the ssDNA probe immobilized on the microchip is hybridized with the complementary biotinylated ssDNA target diluted at different concentrations in a saline sodium citrate solution (SSC).The second step binds the magnetic nanoparticles. The used beads have a diameter of 130 The needed solutions are listed in The dsDNA forms fast: in 10 All the solutions, after passing through the fluidic cell, enter the syringe where they are mixed together and so considered waste. However, a different type of fluidic system can keep the fluids separated to be used again, maybe by putting in a series of other cells, yet a small loss in efficiency shall be expected in the steps following the first hybridization due to analyte depletion and partial cross-contamination.The experimental setup is illustrated in The laser temperature driver is a Temperature Controller TED200, while the current driver is a Laser Diode Controller LDC210. The laser source is a commercial DFB diode laser from JDS Uniphase for telecom applications with 700 The laser output is coupled to a Single Mode Fiber (SMF) and then to an SM 1 × 4 PLC Splitter. The four output SMFs are connected to a custom made 8-channels Fiber Array interposer from W2 Optronics to inject light into the photonic chip through grating couplers. The precise alignment is obtained with a 3-axis XYZ micropositioner and an ad-hoc 3D-printed holder for the fiber array.The photonic chip is mounted on a custom made Printed Circuit Board (PCB) specifically designed to hold the chip and the fluidic cell and the photodiodes are wire-bonded to the PCB pads. A flat cable with an edge connector brings the signal to the amplification stage, designed and fabricated by Elite srl, Italy. The photodiodes are reverse biased at The internal signal generator of the LIA generates the sine wave signal with frequency A custom LabView software controls the temperature laser driver to obtain a temperature ramp to scan the wavelength across an FSR of the MRR while acquiring both The fluidic system uses the syringe pump AL-1000 from WPI Ltd, manually operated in aspiration mode, with a 15 MMA cell d, designm concentration. Besides shifting the resonance, the presence of MNPs increases the roughness of the waveguide surface and hence the attenuation and scattering of the light field. The main affected parameters of the ring spectral response are the quality factor Q of the resonance and the Extinction Ratio, both decreasing as more beads attach to the surface, as shown in The hybridization processes, dsDNA formation and MNP binding, produce a resonance shift of the functionalized ring with respect to the control ring that undergoes only common fluctuations. The difference between the two acquired shifts represents the molecular recognition signal, while thermal fluctuations and non-specific binding are cancelled by the subtraction. An example of differential acquisition is shown in m concentration, sensing and control MRR show a different response, while at 1 m they are no more distinguishable, meaning that the Lowest Detectable Concentration (LDC) has been reached and it lays in the range 1–100 m. The same effect is visible for labelled measurements, but in this case sensing and control MRR show a comparable shift at lower concentrations. The shift enhancement produced by the MNP is large enough to push the LDC down by at least one order of magnitude of target concentration, in the range 0.1–1 m.The resonance detection algorithm performs a Lorentzian curve fit to the data to find the resonating wavelength of each MRR. The difference between sensing and control ring responses is evaluated at the end of both processes, considering 5 min of plateau data before and after the process. Average shifts are plotted in The common definition of Limit of Detection ex σneff , such thThis analysis clearly shows the weak points of the conventional technique for optical biosensing. First, high stability over hours is needed to measure small variations of resonance wavelength disentangling them from spurious effects due to thermal, optical or mechanical drifts. Second, the optimized biological process of molecular recognition seldom requires conditions that are not easily compatible with the optical tracking method such as sample shaking or stirring and temperature and humidity values inside specific ranges. The Opto-Magnetic technique, instead, interrogates the system after the biomolecular recombination, allowing the binding to be performed in the required conditions to optimize the recognition efficiency, a dominant factor when dealing with low analyte concentrations.The Opto-Magnetic technique is now presented and discussed. As soon as the hybridization process is over, the chip is kept in a PBS bath and the chip can be interrogated even after several days.An oscillating magnetic field at frequency The DAQ acquires the second harmonic signal Equation :(8)Δλ2ω0Scan and calculation are repeated for both functionalized and reference MRR, and are performed for different concentrations of the target solutions.The Opto-Magnetic measurements are now presented, with the collection of acquisition showed in From the spectrum intensity, Equation  and FiguEquation b, producm, or by using Equation  is propT the temperature, To properly put in motion the MNP and maximize the oscillation swing, the magnetic field has to oscillate at the right frequency. The correct range should be high enough to avoid ≃ 1 mPas  and a hyH on the chip) for the functionalized sensor is visible, while the reference shows a steady increase mostly due to electromagnetic induction in the nearby readout electronics. Measurements were conducted at With a similar process, the correct intensity of the magnetic field has been found sweeping the voltage m, the sensing and reference signals in At the lowest tested concentration of 10 m tested so far and considering a To overcome this difficulty we developed a second detection method at a fixed wavelength. First, a scan is used to evaluate the transfer function slope, then the laser wavelength is tuned on the position of maximum derivative, the LIA filter is set to a narrower bandwidth of Equation  we obtaiThis is a remarkable result: obtained with a measurement time in the order of just few seconds, better than any other rib silicon waveguide-based MRR platforms and comparable with the state of the art in optical biosensing ,58. So, m concentration, extracting a theoretical limit of detection To conclude, in this work we presented a novel technique, called Opto-Magnetic, to perform actively-labelled biosensing with a silicon photonics ring-resonators’ based platform. The labels are magnetic nanoparticles, which can be actuated with the use of an external magnetic field. An oscillating field produces a shift of the resonance condition of the cavity that causes a variable optical output intensity. The frequency component associated with the magnetic actuation can be extracted with a locking demodulator. For a model system of DNA recognition, we tested successfully down to 10 It is worth noting that the time needed to measure the target concentration goes from a few minutes for the whole FSR scan, to just a few seconds for the On-Off mode, as the readout can be performed after the hybridization protocol. This approach can be used for each kind of target molecule  provided that a suitable step for magnetic labelling is added to the bioassay. Elisa-like protocols being widely used in biological applications, several steps in molecular binding are easily accessible for a large variety of biomolecules. The interrogation being performed after the molecular binding, the biorecognition step that usually lasts up to several hours, with our technique can be performed without worrying about measuring the resonance condition, thus it can be accomplished with temperature, humidity and mechanical agitation parameters optimized for the specific analyte. The Opto-Magnetic technique improves the performance, robustness, speed and reliability of existing refractive index-based integrated photonics biosensing platforms.http://hdl.handle.net/11311/1126863 accessed on 29 March 2022).“Opto-Magnetic sensor device and molecular recognition system” (


This paper discusses the perception and tracking of individual as well as group targets as applied to multi-lane public traffic. Target tracking problem is formulated as a two hierarchical layer problem—on the first layer, a multi-target tracking problem based on multiple detections is distinguished in the measurement space, and on the second (top) layer, group target tracking with birth and death as well as merging and splitting of group target tracks as they evolve in a dynamic scene is represented. This configuration enhances the multi-target tracking performance in situations including but not limited to target initialization(birth), target occlusion, missed detections, unresolved measurement, target maneuver, etc. In addition, group tracking exposes complex individual target interactions to help in situation assessment which is challenging to capture otherwise. Advances in automotive sensing technology and the ever-increasing demand for environmental perception have stimulated researchers and practitioners alike in the development of tracking algorithms that address the accuracy and computational requirements of autonomous vehicles, while sophisticated tracking algorithms as demonstrated in airborne/ground target tracking applications underwent profound treatment following world war II, the presence of clutter, false alarms, missed detections as well as maneuvering targets pose challenges in tracking. Perception and tracking in the automotive industry are no different. In fact, owing to a significant leap in the development of automotive sensors, such sensors as RADAR and LIDAR can generate multiple detections/returns making measurement-to-track data associations even more complicated. The improved sensor resolution has revitalized research in the tracking of extended and group targets. In this paper, we focus on the perception and tracking of individual as well as group targets as applied to multi-lane public traffic. We formulate the tracking problem as a two hierarchical layer: at level 1, we distinguish multi-target tracking based on multiple detections represented in the measurement space. A situation assessment layer at level 2 tracks group targets with birth and death as well as merging and splitting functionalities as they evolve. This arrangement enhances the multi-target tracking performance in situations including but not limited to target initialization (birth), target occlusion, missed detections, unresolved measurement, target maneuver, etc. In addition, group targets expose complex individual target interactions to help in a situation assessment study which are otherwise challenging to capture.In short, we monitor both the group and individual extended object (EO) tracks from sensors’ detections mounted on the moving ego vehicle. In addition to group behavior, which is directly observable from sensor detections, the behavior of individual EO is also equally important both in the short and long horizon control decisions. For instance, due to transient occlusions, detections from an EO might be missing over consecutive scan times. The group state that the target was known to be a part of can be used to complement missing detections, i.e., the group state essentially plays a role of a virtual measurement to update target state predictions of the EO. Furthermore, before an EO (or groups of EO) splits away from a group that is being tracked, the tendency for such an event  together with the time history of the trajectory before and following the event, can be used to make informed decisions about the evolving traffic. Some of these decisions could be critical such as when the splitting EO (a group of EO) ultimately joins the current or planned lane of the ego vehicle. In general, group tracking can improve individual EO tracking performance under missing detections, merged detections, and data association uncertainties.Data Association: The advent of high-resolution automotive sensors resulted in multiple detections which are distributed over the extent of a target vehicle. This in turn necessitated the use of algorithms capable of extended object tracking, which is a departure from conventional approaches to point target tracking that assumes at most one detection per object vehicle. The data association was simpler: the detection could be from a target or a nearby non-target or clutter. Under multiple detections, the sheer amount of data association that needs to be resolved especially under groups of extended objects poses a computational challenge. With group tracking, we can simplify the data association problem to mutually exclusive regions of clusters of detections which are treated as independent group detections. By way of contrast, the complexity of data association over a cluster of detections is less demanding than solving the same over all the measurement space.Merged Measurement: Even under EO considerations, cases of unresolved measurement  can be a challenging problem in multi-target tracking problems. A Merged measurement occurs when the sensor returns a single detection for multiple objects in the scene. This is mainly due to sensor imperfections and poor resolution for sufficiently distant detections. Generally, modeling and integrating merged measurements into the tracking scheme, improve tracking by preventing premature track terminations. However, this comes with an added computational complexity. The literature under merged measurement tracking cases is very rare, often ignored under the independent measurement assumption. The earliest work that models merged measurements and integrates it into the Joint Probabilistic Data Association (JPDA) algorithm is presented in . In 2],,2], a reMissing Detections: The rationale for using the group state as a virtual measurement to update the target track (with missing detections) which is known to be a member of a group is discussed above.Track Initialization: To start a new target track, we employ a one-point track initialization method. Furthermore, any detections which are not associated with a confirmed track are used to initiate tentative tracks. The target velocities and accelerations are assumed to be ioned in .Next, we shall highlight the objective of group tracking and comment on how it fits into the overall scheme of multi-target tracking. In short, the relevance of group tracking, especially under EO (multiple detections) scenarios is evident in four main areas:Before closing this section, we shall clarify the use of the terms “extended” and “groups” targets which are often interchangeably used in the literature without distinctions. From the viewpoint of sensor detections, an extended object tracking problem is closely related to group tracking, but there are multiple important differences. Groups have an internal degree of freedom that affects the shape of the group whereas the extent of EO is relatively fixed in most circumstances and for the most part of its motion. Moreover, groups possess unique behaviors of splitting and merging which, generally, are not characteristics of an EO. It also needs to be clarified that the objective here is not to model inter-group interactions, but rather to focus on the use of the group behavior in improving individual EO tracking performance. In that sense, we propose a hierarchical structure with feedback from the group to individual track layers arranged as shown in The self-organizing ability of vehicular traffic is the result of intelligent decisions by autonomous agents and/or human-driven vehicles. These decisions are influenced by the desire to avoid collision while navigating a structured multi-lane road network (repulsion) and by the intention to reach a common goal such as driving through an exit (cohesion) , while iBy far, a theoretically unified and a rigorous framework for group detection, tracking, and identification is presented by Mahler . Using “N) increases could be a challenge in real-time applications. Here, s is the dimension of the state variables. A group tracking scheme, similar in spirit to [In , a dynampirit to , that jopirit to . The nodpirit to ,11. Bothpirit to ,11 buildThe work in  presentsThe measurement model that extends the point-source model to multiple detections of EO based on Poisson target-measurement model was discussed in ,15. The Our objective is to improve the tracking performance of individual extended objects during common public traffic events involving occlusions, track initiation, and merged measurements. To that end, we propose a hierarchical tracking structure where the lower layer deals with the multi-detection multi-target track estimation of individual extended objects while the upper layer executes group tracking to facilitate a feedback mechanism that provides group state to the lower tracking layer.The specific objective at the upper layer is not to model inter-group interactions, but rather to emphasize the use of group behavior as a mechanism to enhance the performance of individual target tracks via feedback to the lower layer. This approach was first suggested in  and was In order to address the large data association uncertainty in the presence of high-resolution detections, we use the linear multi-target (LM) Integrated Probabilistic Data Association (IPDA) approach for handling the data association problem. The joint kinematic and extent estimation is facilitated through the random matrix approach outlined in . The worFor extended target tracking, the joint density of the kinematic state rated as :(2)pp(xkThe two densities can then be independently integrated out from Equations  and 9)..9).(8)pThe update step incorporates new data pykiLet Equation .(13)p(YFor the set of measurements quations  and 15)Yk, definX with an expected SPD matrix Equation , can be Applying the Chapman-Kolmogorov theorem and the concept of conjugate priors, a recursive joint state estimation scheme is derived in . The joiUnder the assumed probability density functions, this further results inR and an additional term that includes the spread contribution of the object extension scaled by a factor of z. Further, from Equation . The approximate trajectory that both vehicles traverse is shown in First, we fix Poisson distribution with parameter Next, we fix the Poisson rate at For the same scenario given in As shown in The mmwave radar sensor used in the both single EO and group EO target experiments has the specifications as shown in The positive directions of the x-y coordinate axes of the radar sensor are established as shown in the experimental set up of The rover unit of the RTK GPS is mounted on the target vehicle; logging positional data at a rate of 10 Hz, will serves as the ground truth. Radar point cloud detections are sampled and recorded on a tracking computer at a rate of 20 Hz. Ultimately, the GPS data is transformed to the local sensor frame coordinates and the GPS and radar timestamps are synchronized to use the RTK GPS location data to validate the performance of the tracking algorithms.As shown from the pose of the target at In In Next, group target tracking is demonstrated in a multi-target scenario that involves two target vehicles. The availability of resources limit the number of targets to two, we expect the result to extend easily to scenarios involving more than two target vehicles. The reason being, the complex data association step that is computationally prohibitive as the number of target vehicles increases, is simplified in LMIPDA algorithm by its very design.In The MD-LMIPDA tracker is able to resolve measurement-to-target data association without the need to compute the data association jointly for all the measurements and targets simultaneously. In addition, it is shown in The applicability of the tracking algorithm under real-time requirement is further justified in the results plotted under The notion of group objects is interpreted under stricter constraints of inter-target distances, relatives speeds and orientations. Targets in formation are required to have “similar” velocities and tight inter-object distances  to maximThe application of high-resolution automotive radar to public traffic presents a multi-detection tracking problem. In earlier work , we propThe evaluation of the tracking algorithm for real radar detections is conducted for a single ET and promising results are obtained for real-time application. In particular, we noted the mean and maximum execution time per iteration in the order of 2.5 and 10.4 ms, respectively. A further experiment exploiting two extended targets was setup to emulate a dynamic group target. Radar detections from both target vehicles as well as RTK GPS data on one of the target vehicles is collected from two vehicles driving closely with each other and infront of the ego vehicle. The tracking problem is formulated as a two hierarchical layer: at the bottom layer, extended multi-target tracking algorithm is presented; at the top layer a group target tracking algorithm captures group evolution including merging and splitting of groups targets. The observed mean and maximum execution time per iteration are, respectively, 8.5 and 19.8 ms, respectively, justifying the use of the proposed tracking scheme for real-time applications.More reliable extent estimation for both individual ET and group targets could be obtained under more dense sensor detections that also preferably take a uniform distribution across the extent of the ET and/or the group target. In this study, we employed an assumption on the extent evolution that tends to constrain the extent to vary only gradually. Practical traffic scenarios support this assumption: owing to the presence of structured lanes, tight traffic regulations and the inherent desire to avoid collisions, the extent varies rather slowly. This assumption also favors radar detections which tend to resemble a line segment or an “L-shape” even for high resolution options. Adding miss-detections and the possibility of relatively larger ego-to-target distances that reduce target detections, the extent estimation under the above assumption is clearly justified. However, for highly maneuvering targets and if the sole objective is to get reliable extent estimation, other sensors such as LiDAR and camera could be explored.


Active learning improves knowledge acquisition and provides medical students with learning habits that become an integral part of their behavior. As an integral element of our institution’s transition from a lecture hall teaching culture to active learning, the current project, conducted with fourth year students, aimed to examine the effects of the two envelopes method of teaching on students’ knowledge. The class of 120 students was divided into 12 groups of 10 students each. Six experienced senior cardiologists were assigned to teach the 12 groups. When the students arrived at the classroom, they received two envelopes. Students were instructed to open the first envelope and answer a 10-question test in 15 minutes. After completing the test, they returned the tests to the envelope, sealed it, and then opened the second envelope which included the same test and relevant patient information. They then spent the next 30 minutes discussing the test as a group and familiarizing themselves with the patients’ case histories and clinical data. After completion of the group discussion, the tutor entered the room for a two-hour discussion of the patients’ disease entities including the anatomy, physiology, pathology, clinical presentation, diagnostic measures, and potential therapies. We compared grades and standard deviations of grades between two classes: one learned in the lecture hall format (2018) and the other learned employing the two-envelopes method (2019). There was a non-statistically significant trend toward better grades with reduced dispersion of grades in the class that learned with the two-envelope method. We describe a novel method for active learning that enhances self-learning and peer learning, and we observed better knowledge acquisition and reduced knowledge dispersion that were not statistically significant. Teaching medicine has been an ever-changing challenge since the sentinel publication of Flexner , 2], , , 5], , , 8], , , 11], , . th grade) by Chi et al was based on the so called ICAP model . T. T14]. TAP model  which diAP model , 15]. . th gradWe initiated a faculty-wide effort to introduce active learning to our curriculum which is currently based on lecture-embedded knowledge transfer with a minor element of active learning, mostly in laboratories. In this manuscript we describe a novel method that melds active learning with peer learning of heart valve-related diseases as part of the cardiology course, and we tested its effect on student knowledge acquisition in the final examination. This novel method, namely the two envelopes method, transfers responsibility for learning to the students by assigning compulsory reading and autonomous peer discussion to small groups. In the present study, we hypothesized that students’ knowledge would improve with the two envelopes method as compared with traditional lecture-based pedagogy. Fourth year students in our 6-year medical school program study the main subjects of internal medicine integrated with system pathology and clinical pharmacology. As part of learning cardiology, the topics of valvular disease and rheumatic heart disease were chosen for the introduction of active learning. The other topics of cardiology are taught in the classroom, and coronary artery disease is taught in the hospital using a problem-based learning methodology. As part of the active learning protocol the students were asked to read about five disease entities in Harrison’s Principles of Internal Medicine including mitral stenosis, mitral regurgitation, aortic stenosis, aortic regurgitation, and rheumatic heart disease. The class of 120 students was divided into 12 groups of 10 students each. Six experienced senior cardiologists were assigned to teach the groups. The teachers met and reached a consensus regarding the central learning points for each disease entity prior to meeting the groups. Five detailed patient histories relating to each of the valvular pathologies and rheumatic heart disease were provided to the teachers as materials for the group learning sessions. The introduction of the novel learning method was discussed with the student union prior to its introduction, and the students received a letter describing their learning assignment and the way the group meeting would be conducted, including the two envelopes method. Three subjects were discussed in the first session and the remaining two disease entities in the second session. When the students arrived at the faculty, each group was reintroduced to the two envelopes method. They then received two envelopes which included a short test on the subject syndromes. The students were allowed 15 minutes to complete the test in the first envelope. The tests were then returned to the first envelope which was sealed. The students subsequently opened the second envelope which included the same test as well as patients case studies including history, physical examination, laboratory and imaging evaluation, treatment and outcome of the syndromes that were the subject of the meeting. The students then spent the next 30 minutes discussing the test as a group and familiarizing themselves with the patient details. After completion of this group conference, the tutor-instructor entered the room for a two-hour discussion of the disease entities including anatomy, physiology, pathology, clinical presentation, diagnostic measures, and therapies. The same methodology was used for the two other disease entities. After completion of the 3 week cardiology course, the students completed a test which included multiple choice questions (MCQ) and a comprehensive integrative puzzle (CIP) on all cardiology topics including the five entities studied using the two-envelope methodology. The CIP included five different clinical diagnoses, such as acute myocardial infarction and mitral stenosis. The students were requested to complete the following sections regarding each clinical diagnosis: patient’s complaints, physical findings, laboratory findings, pathological findings, and course and treatment. The students were asked to choose the best of five possible answers for each section. Once the CIP was completed, a comprehensive description of the diagnosis should become evident. We identified the questions in the test related to the five disease entities taught using the two envelopes method, studied the number of correct answers and compared these results to previous years’ tests in which the five disease entities were taught using a standard frontal lecture format. We analyzed the average grades of the students in 2018 when the teaching of valvular disease was presented in lecture halls. We documented the average grades on questions related to valve disease and the standard deviations both in the multiple-choice part and in the comprehensive integrative puzzle. We then documented the average student grades from 2019 when valvular disease was taught using the 2 envelopes methods. We compared the grades for questions related to valvular disease and questions related to non-valvular disease both in the MCQ and CIP portions of the examinations for the two years. Grades data were tested for normality using the Shapiro-Wilk test and equality of variance using the Levene’s test. Because the Shapiro-Wilk test indicated that grades were not normally distributed, grades are presented as medians (interquartile range [IQR]) and compared with the Mann-Whitney U test. Differences were considered statistically significant at the 2-sided P<0.05 level. Statistical analyses were performed using the Stata Version 16.1 . In 2018 120 students were tested and in 2019 122 students were tested. A summary of the number of questions in valve-related diseases and the average grades in 2018 and 2019 are presented in table 1 When we compared the grades of the valve related and non-valve related questions in 2018 and 2109, we could not find significant statistical differences. There was a tendency for higher grades in valve-related MCQ questions and narrower variances in 2019 (see table 2 th year medical students. This method promoted individual active learning and peer learning of 4 heart valve disease entities and rheumatic heart disease. Based on the final examinations, we observed a trend, which was not statistically significant, for better knowledge acquisition based on the two-envelope methodology, both in MCQ and CIP questions. We also observed lower dispersion of grades as an indicator of better knowledge dispersion using the two envelopes method. In the current manuscript we describe a novel method to introduce active learning to 4The changing methodology and culture of learning over the last several decades reflect the following paradigms: In light of the current explosion of knowledge, students must be given the intellectual tools to seek and identify valuable information rather than merely acquire it in classes. Technological changes have changed the interface between individuals and the traditional teaching hierarchy. Social changes in society have created different expectations from individuals whether they are students, teachers or professionals and have promoted nonconventional behaviors, and better understanding of learning mechanisms have given rise to novel teaching methodologies , 5], , , 8], , , 11], , . Currently, many topics are taught in lecture halls in our faculty and the students are tested on the topic at the end of the semester. As part of the effort to change teaching methodology from frontal lectures to active learning we developed the two-envelopes method. The two-envelope method that we describe encompasses several aspects of modern educational theory, and updates methodologies with transfer of learning responsibility to the student. Providing a specific compulsory reading task and a short test makes all students study the relevant material on their own before class. It was recently shown that tests improve learning beyond simply testing the information taught . After cAfter completion of the test, the students are asked to discuss the cases provided in the second envelope. In this part of the process, they are required to integrate learned data with patient histories. Again, group dynamics improves motivation and self confidence in the students . After cLearning in a small group and using a patient case study as the basis for discussion provides an invaluable platform to develop knowledge, as well as an opportunity to develop clinical reasoning and pattern recognition , 20]. B. B20]. BAn indirect educational benefit of the program is that the students conducted the first part of the encounter, including the test, without faculty supervision. This provides a strong message that we trust our students and expect ethical behavior from them. A successful program such as the one we describe requires a dedicated and capable group of tutors. We selected experienced cardiologists and gathered all tutors for a detailed project description and discussion. We highlighted the importance of engaging all the students in the discussion and conducting the encounter as a creative conversation and not a lecture. We asked the tutors to elucidate the important points of each syndrome, and then asked all of them to teach and emphasis these points. Reaching consensus on what is important secures standardized teaching in all the groups. While Chi et al.  focused Another important aspect of the two envelopes method is that students realize they can learn complex subjects on their own, and that the knowledge they gain is a direct result of their effort and interaction with their peers. During student reviews at the end of the course, many students shared with us their sense of accomplishment, but this aspect was not studied systematically. Such success, as evidenced in the present study by a trend toward better final test grades, promotes self-efficacy and provides reassurance that our students are indeed capable of profiting from active learning . Active learning theory which is based on better understanding of how we learn and how we develop cognitive skills, encompasses many methods. The two envelopes method engages the students actively in learning and teaches them to reflect on what they have learned by allowing them to discuss the knowledge gained with peers and with the tutor. It also engages problem-solving and critical thinking skills while learning the patient cases provided in the second envelope, initially on their own, then with their peers and finally with the tutor. As previously reported in other manuscripts describing transitions to active learning, the students expressed a high level of satisfaction, as did the tutors, but this was not tested systematically. Study limitations include comparison of test results of different classes that learned the five disease entities in the classroom using conventional lectures and the relatively small number of questions focused on the 5 disease entities. Additional studies testing the two envelopes method should be conducted. Students’ responses to learning using this method should be included , 25] in in25] inWe introduce a novel method of active teaching that encompasses multiple innovative pedagogical methods. The two envelopes method as employed in the present study resulted in a nonsignificant trend toward enhanced student knowledge acquisition. The authors declare that they have no competing interests.


Bronze cultural heritage in urban areas is susceptible to decay due to the significant amount of pollutants present in the air. This causes the dissolution of bronze and the patina on its surface. The efficient protection of outdoor bronze cultural heritage is still an unresolved problem. The aim of this work is to investigate 16-phosphonohexadecanoic acid as an environmentally friendly and non-toxic corrosion inhibitor for patinated bronze. The corrosion protection of sulphide-patinated bronze by phosphonic acid alone and in combination with acrylic coating Paraloid B-72 is examined. In order to achieve efficient corrosion protection, various parameters of the phosphonic acid application were studied. The efficiency of protection is examined by electrochemical impedance spectroscopy (EIS) during the immersion in simulated acid rain solution and after exposure to a corrosion chamber. It was found that the studied phosphonic acid provides corrosion protection to patinated bronze and significantly improves the protective properties of Paraloid B72. This was also confirmed by scanning electron microscopy (SEM) examination of the coating surface after exposure to a corrosive environment. The patina layer covering bronze cultural heritage has an important historical and aesthetic value but also provides corrosion protection to the underlying bronze substrate. In urban atmospheres, patinas directly exposed to rain are subjected to significant dissolution, which leads to the corrosion of underlying bronze ,5,6,7,8.The metallic surfaces of cultural heritages differ significantly one from another in terms of the degree of corrosion, different types of corrosion products, and the presence of microorganisms. It can be challenging to achieve good coating adhesion on such surfaces, as not every protection is equally effective on every type of patina ,28. NumeDespite the extensive scientific research on new types of coatings, those from the Paraloid family  are still the most popular for archaeological or historical copper alloy objects , but the2 on brass to obtain superhydrophobic coating, but it had strong effects on the color of treated brass, which is not in accordance with the requirements of the restoration and conservation profession. Cano et al. , where Q is the CPE’s frequency-independent parameter, which represents pure capacitance when n = 1. The proposed three-time constants EEC consist of Rf − Qf, Rct − Qdl, and RF − QF couple. The high-frequency Rf − Qf couple represents the capacitance and resistance of the non-reactive oxide film within the patina layer. Rct − Qdl couple represents the corrosion reaction at the metal substrate/solution interface, where Rct is charge transfer resistance and Qdl is the constant phase element representing the double layer capacitance. The low-frequency RF − QF couple is allocated to the Faradaic resistance and Faradaic capacitance, implying the oxidation-reduction processes of the reactive patina layer [Rel.In order to fit EIS spectra, it was necessary to use the model with three-time constants . Due to na layer . The eleRf − Qf couple cannot be reliably determined and are thus not shown in Rct values are quite low at the beginning of immersion in the acid rain solution, which could be due to the presence of corrosive S2− ions inside the patina layer [Rct values and lower Qdl values compared to the unprotected sample. This confirms that phosphonic acid film slows down the corrosion process of bronze. The highest Rct value is observed for the film obtained by dip coating. The probable explanation is that the longer contact of the sample with the PA solution, compared to the brushing and spraying method, resulted in the diffusion of a higher amount of PA molecules into the pores of the patina layer, reaching the bronze surface and protecting it more efficiently.na layer . HoweverRF values of all samples also increased in time, while the QF values decreased, which can be attributed to the stabilization of the patina. Reactive species in the patina are transformed into less reactive compounds, which can now be observed as the Rf − Qf couple in the high-frequency region. The resistances of the reactive (RF) and non-reactive patina layer (Rf) are always higher in the presence of PA film. This confirms that PA effectively protects the bronze substrate, as well as the patina.The Rp and EIS measurements confirmed that brushed samples exhibited less significant deterioration of protective properties during the aging in artificial acid rain compared to dip-coated samples.With the dip coating and spraying method, the darkening of the patina was observed by the naked eye after the application of the films. Given that in the case of the dip coating method, the patina is in longer contact with ethanol, in which PA is dissolved, it is possible that longer exposure to ethanol caused its darkening. On the other hand, it cannot be excluded that thicker PA surface films formed by dip coating and spraying resulted in color alteration. Thus, although the dip coating method initially resulted in the most protective PA film, from the practical point of view, it would be recommended to use the brush application method in order not to alter the visual appearance of the surface. In addition, Rp is obtained when the PA solution is applied only once, and the films are dried at room temperature. Given that the patina is porous, a single application of the acid solution is probably not sufficient to cover the entire surface. The increased number of film applications increased the amount of PA molecules on the outer surface and inside the patina layer, which resulted in higher corrosion protection. After three weeks of immersion in a solution of simulated acid rain, the samples, on which the PA solution was applied five times and the obtained films were dried at a temperature of 80 °C, exhibited the highest corrosion resistance, even higher than for the samples on which PA film was formed by the dip coating method. It is interesting to notice that for the dip-coated samples, longer exposure to a corrosive medium resulted in a decrease in polarization resistance, both in the case of the PA film alone or in combination with the Paraloid coating.The use of phosphonic acid as the only mean of corrosion protection of bronze does not provide long-term protection in an aggressive environment. Therefore, the combined protection system PA/Paraloid B-72 was examined. For this purpose, only brushing and dip coating were studied as methods for PA application, as described in Rpo represents the resistance of the ionically conducting paths across the coating (“pores”) and Qcoat represents the capacitance of the coating [Rct − Qdl couple represents the corrosion reaction at the metal substrate/solution interface, as described previously.In addition to polarization measurements, samples coated with Paraloid B–72 alone or combined with PA 5×, D 80 °C brushing pretreatment  pretreatment, were also exposed to the humidity corrosion test chamber with NO of 740× . The samIn this work, a novel method of bronze cultural heritage protection, by surface treatment with selected long-chain phosphonic acid, was examined. The corrosion protection of sulphide patinated bronze with phosphonic acid films prepared by dip coating, spraying, and brushing was studied. The polarization resistance measurements in time showed that the elevated drying temperature of the films improved their protective properties and stability. PA films prepared by all three methods improved the corrosion resistance of patinated bronze if the drying step was conducted at 80 °C, while the PA films dried at room temperature exhibited protective properties only for the brushing method. The EIS results confirmed that such applied 16-phosphonohexadecanoic acid efficiently inhibited patina dissolution as well as the dissolution of the bronze substrate.The corrosion protection of studied phosphonic acid was also tested in combination with acrylic coating Paraloid B-72. Electrochemical measurements showed a significant improvement in the protective properties of the coating when the patina was pretreated with phosphonic acid film dried at elevated temperatures. It was also found that a five-time brush application of PA resulted in significantly higher corrosion protection compared to a single application. In addition, the protective coating with PA applied by brush  exhibited a more stable protection level compared to the coating with PA applied by dip coating.SEM images of the samples after three weeks of exposure to a simulated acid rain solution exhibited significantly fewer defects in the coating when the patina was pretreated with phosphonic acid film. Optical microscopy images of the samples after two weeks in the corrosion chamber also showed that the coating had significantly less deteriorated when the patina was pretreated with studied phosphonic acid.The results of the studies conducted in this work clearly show that five-time brushing of PA solution accompanied by drying at 80 °C is an effective and simple pretreatment that significantly improves the efficiency and the durability of corrosion protection of sulphide patinated bronze by Paraloid B-72 coating.These findings open the possibility of PA application in the field of bronze cultural heritage protection. However, additional issues, such as the dependence of protection level on the bronze composition and surface roughness, should be examined, which will be the focus of our further studies.


One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real medical challenges such as the building of computer-aided diagnosis (CAD) systems for breast cancer. Transfer learning is one of the recent emerging AI-based techniques that allow rapid learning progress and improve medical imaging diagnosis performance. Although deep learning classification for breast cancer has been widely covered, certain obstacles still remain to investigate the independency among the extracted high-level deep features. This work tackles two challenges that still exist when designing effective CAD systems for breast lesion classification from mammograms. The first challenge is to enrich the input information of the deep learning models by generating pseudo-colored images instead of only using the input original grayscale images. To achieve this goal two different image preprocessing techniques are parallel used: contrast-limited adaptive histogram equalization (CLAHE) and Pixel-wise intensity adjustment. The original image is preserved in the first channel, while the other two channels receive the processed images, respectively. The generated three-channel pseudo-colored images are fed directly into the input layer of the backbone CNNs to generate more powerful high-level deep features. The second challenge is to overcome the multicollinearity problem that occurs among the high correlated deep features generated from deep learning models. A new hybrid processing technique based on Logistic Regression (LR) as well as Principal Components Analysis (PCA) is presented and called LR-PCA. Such a process helps to select the significant principal components (PCs) to further use them for the classification purpose. The proposed CAD system has been examined using two different public benchmark datasets which are INbreast and mini-MAIS. The proposed CAD system could achieve the highest performance accuracies of 98.60% and 98.80% using INbreast and mini-MAIS datasets, respectively. Such a CAD system seems to be useful and reliable for breast cancer diagnosis. According to the global cancer statistics in 2020, GLOBOCAN 2020 , an estiArtificial intelligence, or AI, is a well-known technology that has recently been used in the development of effective CAD systems for the classification of lesions in mammograms. Numerous researchers have lately developed AI-based solutions for the automated detection and diagnosis of breast anomalies in mammography pictures. These solutions can be divided into two types: classical and deep learning models. Although the deep learning approach has achieved exceptional performance in the context of medical image analysis, several problems remain, as discussed in . Over-fiA hybrid deep learning model of CNN-based LR-PCA has been designed for breast lesion classification purpose using the X-ray breast cancer images .Generating more useful and powerful input information based on pseudo-colored images to enrich the input knowledge of deep learning models.A comprehensive study of transfer learning based on multiple convolutional networks has been conducted in order to extract the high-level deep features directly from the pre-trained backbone models.Resolving the multicollinearity problem that occurs among the derived high-level deep features from pre-trained by introducing a new method called LR-PCA. The optimized PCs are selected to perform the final classification purpose.A comprehensive evaluation process of the proposed CAD system is performed using two different public benchmark datasets: INbreast and mini-MAIS.To highlight our contribution, this study extends our prior work  in constArtificial intelligence, AI, is a well-known technology that has recently been employed in developing innovative solutions in the healthcare sector. These solutions can help patients in receiving an earlier diagnosis and precise treatments while also increasing the efficiency and effectiveness of healthcare services. The governance and regulation of AI services in healthcare is a major concern. The BSI (body substance isolation) and AAMI  are non-profit standardization organizations that are investigating the role for standards in the deployment of innovative AI solutions within the healthcare sector . RegulatThe AI-based solutions for the automated detection and diagnosis of breast abnormalities in mammography images have recently been proposed by numerous researchers. These solutions can be classified into classical and deep learning models. Early CAD systems have been developed using the classical machine learning models such as the research conducted in ,28,29,30With the current artificial NN and optimization skills, large-scale deep NN have been successfully developed, with a greater performance with deeper depth . HoweverIn this study, a hybrid deep learning CNN-based LR-PCA for breast lesion classification is proposed. Such a CAD system involves three main stages. First, three-channel pseudo-colored images are prepared based on the input grayscale images allowing deep learning models to extract more powerful deep features for better diagnosis performance. Second, backbone deep convolutional networks are used to extract the high-level deep features based on the transfer learning strategy avoiding the overfitting problem . For deeThe proposed CAD system comprises five modules including the preprocessing of images, features extraction using the pretrained CNN, dimensionality reduction using PCA, feature classification using the traditional machine learning techniques, and classifier evaluation using the performance metrics of CAD systems. The proposed framework is depicted in The data used in this work were obtained from different public datasets including the mini-MIAS for film-based, reduced resolution data, and the INbreast for full-field high-resolution digital mammography. The mini-MIAS is a popular Mammography Image Analysis Society (MIAS) database . The minThe data preparation stage includes the following steps: Region of Interest, ROI, extraction, data augmentation, and generating pseudo-color images. ROIs fed to the CNN are derived from the suspicious areas of mammograms. Data augmentation is used to enlarge the number of samples in the training dataset. A 32 × 32 square region of interest (ROI) was extracted from the lesion. Such an ROI size was selected to directly compare our proposed framework against earlier work that addressed the same classification problem using various techniques . HoweverThe created datasets contain 144 ROIs with equal number, 72 abnormal and normal regions for the mini-MIAS collection, and 34 and 73 benign/malignant ROIs for the INbreast dataset. Data augmentation, which was primarily based on the flipping (up/down and left/right) and rotation technique, was utilized in order to generate greater size of the dataset that were utilized. The ROIs have been rotated by multiple different angles: 0 degrees, 90 degrees, 180 degrees, and 270 degrees. The number of samples included in each category of the labeled ROI dataset that was generated is 576 and 1095 for the mini-MIAS and the INbreast, respectively.Channel 1 (Red) includes the original grayscale image.Channel 2 (Green) includes the enhanced image contrast of the grayscale image by transforming the values using contrast-limited adaptive histogram equalization (CLAHE) .Channel 3 (Blue) includes the processed image that maps the intensity values in the grayscale image to new values by saturating the bottom 1% and the top 1% of all pixel values. This operation increases the contrast of the output image.The pseudo-colored images were generated and used to obtain rich information allowing deep learning models to derive more accurate deep features. Instead of repeating the same image in all three-color channels, only one channel gets the original image while the other two channels receive different processed images that allow some additional global information to be incorporated within each pixel. The pseudo-color image display has been widely used in medical imaging since the digital imaging revolution and gives doctors new ways of detecting subtle variations because of the way colors are perceived by the human eye. An example of an input image that was used during the data preparation phase can be seen in The conventional computer-aided diagnosing system in medical field relies heavily on the human designer’s efforts to extract handcrafted features, such as the density and shape of the cancer region . This isAlexNet  was intrVGG  has beenGoogleNet  has alsoThe number of extracted features from transfer learning when applied in the classification of medical images is huge. Although this creates opportunities to improve model performance, it also creates serious problems during the data analysis process. One of these problems is known as the multicollinearity problem . MulticoIt is well known that principal component analysis is the original and preeminent dimension reduction technique, and that it has been utilized in numerous studies for the classification of mammograms utilizing conventional machine learning techniques. The process of PCA consists primarily of transforming the feature space through the relationship between attributes, mapping the initial feature space to the low-dimensional one to accomplish the goal of dimension reduction, and then analyzing the transformed feature space . PCA is p-value less than 0.05. As a result, the LR-PCA algorithm is as follows:Create a covariance matrix for your dataset.Determine the eigenvectors and eigenvalues of that matrix.Choose the desired number of dimensions and filter the eigenvectors to match, sorting them by their associated eigenvalue.Multiply the original space by the feature vector produced in the preceding step.Build a Multinomial Logistic Regression model for the retrieved features.Pick the principal components of the newly created LR model that have significant coefficients.The main principal in selecting the Principal Components, PCs, retrieved from the PCA is that the directions with the greatest variations contain the most information about classes. PCA is most commonly derived from a standard linear projection that maximizes the variance in the projected space. The most common method for selecting the PCs to be used is to first establish a threshold for the percentage of variance that can be explained, such as 80 percent, and then choose the number of components that generate a cumulative sum of variance that is as close as possible to that threshold. This is the standard approach to selecting the Principal Components to be used. This method suffers from the two primary weaknesses. It is necessary to make a personal decision regarding the threshold. The 80 percent or 90 percent thresholds are arbitrary and do not have a fair motive for being chosen in the majority of cases; instead, they are chosen at random. There is a chance that some portion of that variation is just noise and not an actual signal. It is not possible to know in advance whether or not the selected threshold eliminates only noise or it may remove important information from the signal itself. To overcome that problem, we are proposing a hybrid usage of Logistic Regression, LR, and PCA to select the significant components as a further reduction of features yielded from the transfer learning. LR is a common ML approach that can be utilized to explore the relationship between the input feature and outcomes. Logistic regression is a simple and effective binary classification model. It is very easy to implement LR and it works very well with classes that can be separated by a straight line. In this work, a Binomial Logistic Regression model has been constructed using a subset of the retrieved PCs from the PCA analysis. The selected PCs from the LR are the one that have a significant coefficient in the constructed LR model, In this work, the statistical machine learning system comprised the implementation of six distinct families of traditional classifiers. These classifiers included both parametric and nonparametric classification strategies. Methods such as decision trees, discriminant analysis, ensemble, KNN, naive Bayes, and SVM are examples of these types of techniques. By using 5-fold cross-validation to obtain a reliable estimation of the performance, the various parameters and variants that make up each classifier were optimized in order to achieve the best possible performance and reduce the issue of overfitting as much as possible. In each fold, different portions of images are assigned for each set. This process is carried out five times, which is the same as the number of folds, and the results obtained from each fold are averaged together to determine the overall performance of the system. In particular, the data for benign and malignant cases are randomly split into three sets including the training, validation, and testing sets. The percentage of division between the different splits are 70%, 15%, and 15% for training, validation, and testing sets, respectively.To evaluate the proposed CAD framework, Accuracy (Acc.), sensitivity (SE), specificity (SP), False Negative Rate (FNR), False Positive Rate (FPR), Area Under the Curve (AUC), Matthews Correlation Coefficient (MCC), and F1-score were used ,71. All In this study, we have proposed an innovative framework of a CAD system for the classification of breast cancer lesions by the use of a hybrid pretrained CNN and LR-PCA learning model. The framework comprises four stages including data preparation, feature extraction, feature reduction, and classification. In order to highlight the effect that the newly developed method in the data preparation stage has on the overall performance of the CAD system, we have generated two distinct sets of images for each database that was used in this investigation. These sets include grayscale and pseudo-colored images, respectively. In total, we have four different datasets being utilized in the testing of the adequacy of the introduced CAD system.® CoreTM i7-6700HQ that operates at 2.60 GHz. It also has 16 gigabytes of random access memory (RAM), and a CUDA-supported graphics card (NVIDIA GeForce GTX 950M with 4 gigabytes of memory). Although the retrieved processing time of the conducted experiments depends on the execution environment specifications, the insights gained could be reflected by comparing the derived overall performances of the alternative methodologies. As depicted in The pre-trained CNN has been employed as a feature extraction stage. The ROIs in each dataset have been resized to the corresponding size of each network  using bilinear interpolation with an anti-aliasing filter in trying to attain network requirements and preserve the image quality and maintain them free of aliasing artifacts, as the input layer must not be altered as a part of the transfer learning strategy. During the training of CNNs, the learning parameters should be estimated. In this work, a stochastic gradient descent with momentum, SGDM, optimizer was utilized. The learning rate for this optimizer was set to 0.0001, the momentum term factor was set to 0.9, the L2-Regularization setting was set to 0.0005, and the gradient threshold method used the L2-norm. The number of training epochs that could be performed was set at 100, and the mini-batch size that could be used was 16. These training options were chosen by examining the validation results obtained through the running experiments. They were applied across all networks to enable a direct comparison of the networks’ respective results as well as the training time incurred by each of them. p-values for each feature pair in order to validate the necessity of applying principal component analysis and demonstrate that the extracted features are affected by the multicollinearity problem. The range of possible p-values is from 0 to 1, with values close to 0 indicating a significant correlation between the extracted features. For illustration, p-value. p-values for the correlation coefficient lie in the bin containing the numbers 0 to 0.1, which indicates that the coefficients are significant.We have computed the correlation coefficients between the extracted features as well as the corresponding PCA is implemented by first computing the covariance matrix of the extracted features, and then calculating the PCs by employing eigenvalue decomposition. This is done so that PCA can more accurately analyze the data. After sorting the eigenvalues, the 50 values with the highest absolute value are chosen. The projection is carried out with the help of the corresponding eigenvectors. The 50 selected PCs has retained a high amount of energy within the components as illustrated in t), and the p-value of the coefficient estimates. The retrieved results of the test for the coefficient estimates of the fifty extracted principal components are presented in p-values that were retrieved show that the implanted LR model is effective in selecting the significant PCs that should be utilized in the subsequent classical classification model. The PCs that were chosen from the LR model were the ones that had a significant coefficient in the constructed LR model with a p-value that was lower than 0.05 as highlighted in gray color in Utilizing the Logistic Regression approach allowed for an additional level of filtering of the PCs to be carried out in an effective manner. Following the completion of the PCA analysis, a Binomial LR model was developed using the retrieved set of principal components (50 PCs). We have used the ANOVA F-statistics test as a method of hypothesis testing in order to evaluate the significance of the LR model in terms of choosing the significant PCs. This test was carried out so that we could assess the significance of the LR model. The significance level of the estimated coefficients that are yielded from the binomial logistic regression model has been measured. The significance has been calculated using the following metrics including the standard errors (SE), the t statistics . In particular, the numbers of parameters for these networks are approximately 61 million for AlexNet, 7 million for GoogLeNet, and 138 million for VGG-16. Even with the least complex of them, the huge number of parameters suggests that it is not possible to properly train such networks with the limited data set available in this study and outline the value of using them as pretrained feature extractors rather than attempting to retrain them. Furthermore, these networks were used to classify the same data sets of this study through transfer learning. This allows direct comparison of the results. Naturally, there are additional options for pretrained CNNs that appear to be significant; nevertheless, it is challenging to address more models in a brief presentation.A correlation analysis has been accomplished on the extracted features to examine the necessity for applying PCA on the feature matrix and demonstrating that the extracted features are affected by the multicollinearity problem. The Binomial Logistic Regression model has been developed using the retrieved set of principal components (50 PCs). The selected subset of these PCs is 23 according to their significance in the yielded results from the ANOVA F-statistics test on the developed LR model. The twenty-three PCs have been presented to six distinct families of traditional classifiers including the decision trees, discriminant analysis, SVM, KNN, naive Bayes, and Ensemble. Twelve classification experiments have been performed based on all features with or without the utilization of LR-PCA. The PCA has been examined with all classes as opposed to PCA with combined PCs that are class specific. The application of LR-PCA on the extracted features utilizing AlexNet for each class of the INbreast/mini-MAIS (pseudo-colored pictures) individually has provided the best performance. The performance of the proposed framework of our CAD system was compared to that of similar systems found in the literature, and the results of the comparison showed that the proposed one records the highest performance compared to all other systems.


This study presents a vision-based water color identification system designed for monitoring aquaculture ponds. The algorithm proposed in this system can identify water color, which is an important factor in aquaculture farming management. To address the effect of outdoor lighting conditions on the proposed system, a color correction method using a color checkerboard was introduced. Several candidates for water-only image patches were extracted by performing image segmentation and fuzzy inferencing. Finally, a deep learning-based model was employed to identify the color of these patches and then find the representative color of the water. Experiments at different aquaculture sites verified the effectiveness of the proposed system and its algorithm. The color identification accuracy exceeded 96% for the test data. Aquaculture plays an important role in fisheries and feeds large populations of people worldwide. According to a long-term report by the Food and Agriculture Organization, marine fish resources are depleting . TherefoVision-based monitoring techniques have become widely used in many fields, including aquaculture, as computer vision technology has matured . CurrentThe water identification of such a vision system is generally divided into several stages, including the steps of (1) restoring the color of the captured image as close to the human eye sees as possible; (2) extracting the water area within the image; and (3) recognizing the color of the water area and obtaining the representative water color of the aquaculture farm. For the color issue, the majority of methods considered color correction problems as finding the transformation between captured and ideal colors. In , color hWe designed a color checkerboard based on 24 colors commonly used for color correction. We then adopted this checkerboard to correct the colors of images captured under various lighting conditions in the outside environment.We proposed a scheme for extracting candidate patches from the water regions in an image. These candidate patches are further used to identify the representative color of a pond. The proposed scheme consists of two main steps: semantic segmentation and fuzzy inferencing to determine the degree to which a specified image patch is considered to be the candidate patch.A simple color identification model with a deep CNN was implemented. The model’s output is the probability of belonging to one of the predefined color categories.Based on the above investigations, we designed an algorithm suitable for our proposed system that can identify water color in an aquaculture pond. The main contributions of this study are summarized as follows:The remainder of this paper is organized as follows. The proposed water color monitoring system consists of three main components: (1) a color camera fixed by the pond; (2) a color checkerboard placed on the water; and (3) a cloud-based computing platform where the proposed algorithm is deployed. The aforementioned color checkerboard with 24 color blocks, as illustrated in In this subsection, we describe the main procedure of our proposed water color identification algorithm, which includes four stages: (1) image color correction, (2) image segmentation, (3) candidate patch extraction, and (4) color identification of candidate patches.Owing to the diverse changes in outdoor lighting conditions, errors may occur while identifying the water color unless the image color can be corrected. To make the correction easy and stable, we used a color checkerboard to correct the color information of the captured image. Because the information of 24 color blocks on this checkerboard is known, a transformation for restoring the colors to their ideal values for capture can be found. Thus, the color of each image shot was corrected using this transformation. In our method, the checkerboard is first localized using the four ArUco markers. Given an image containing ArUco markers, the position and ID number of each marker can be easily obtained using the algorithm in . FurtherLet For an image captured at timestamp Equation (5) can be abbreviated as Algorithm 1: Proposed color correction algorithm (Pseudocode)Input: an image capture  is proposed to determine the above probability. Let According to the aforementioned selection criterion for a water-only image patch, we used the variables The consequent variable Algorithm 2: Proposed water-only candidate extraction algorithm (pseudocode)Input: a color-corrected image  color correction, (2) image segmentation, and (3) water color identification. GPU-accelerated techniques were used to implement the proposed method to satisfy the computational requirements of running a deep learning-based model. The algorithm was programmed using Python language.The proposed system was first set to capture an image once every 30 min; thus, we could observe the color correction results at different times under various lighting conditions. From our experiences in farming fields, the correction results were almost the same as what we saw. In the present study, a fully convolutional model, YOLACT++ , was impThe final result of the proposed system is the representative color of the monitored pond. In this subsection, the detailed implementation of training a color identification model is introduced; thus, the results of model inference are provided later. We also collaborated with experts in aquaculture fields to provide us with correct ground truths for classification labeling. For any captured image, the candidate patches were extracted and identified. In the detailed implementation, we first cropped During this experiment, we collected 9500 samples to form our training dataset, and 1900 samples for testing. Because of the difficulty in gathering sufficient numbers of real samples of various colors, a part of the dataset was generated by data augmentation and synthesis techniques. The samples were manually classified into 19 classes of water color, as listed in After the water color identification stage, the proposed system provided The key methodologies of the proposed system have been described in the previous sections. Here, we discuss the deployment of the proposed system at an experimental site.As shown in In In this work, we have presented an identification system and an algorithm for monitoring the color of water in aquaculture ponds. The proposed system primarily comprises a camera, a color checkerboard, and a cloud computing platform, whereas the algorithm comprises a sequence of stages in which color correction, image segmentation, water-only patch extraction, and color identification of patches are successively performed. The effectiveness of color correction using our own checkerboard was verified under different lighting conditions. We then applied instance segmentation followed by a fuzzy inference system to extract candidate water-only image patches. Finally, a color identification model based on deep learning was used to determine the representative color of the monitored aquaculture pond. The output of the proposed system can be used directly as a periodic log report to the FA-COA, Taiwan.In this study, we have mainly considered a vision-based system designed to monitor the color of water in an aquaculture pond. Therefore, the results of color identification are consistent and do not differ individually. Moreover, the proposed system provides the functionalities of uploading log reports and recording all historical data. Based on these recorded data, farmers or managers of aquaculture fields can make farming decisions accurately. Some important issues remain to be investigated, such as replacing traditional water quality sensors with remote sensors. We are also evaluating the feasibility of using a camera to measure water quality. The changing trend of the color of the water could be used to estimate whether water quality is deteriorating if an indicator could be defined in terms of vision-based recognition. We intend to continue pursuing this research at more experimental sites.