Ectopic crossed testis is a rare condition in which both testes descend through the same inguinal canal. The most common presentation is an ipsilateral inguinal hernia with contralateral cryptorchidism. This is a case report of a six-year-old male child who had an empty right scrotal sac. Diagnostic laparoscopy is useful for both diagnosis and management. Management is determined by the anatomy of the vas, vessels, and testis discovered during surgical exploration. Transseptal contralateral orchidopexy results in good tension-free testicular fixation in the scrotum. Testes may be ectopic in the abdomen and inguinal area and can also descend into the contralateral scrotal sac . CrossedA six-year-old male child presented to the hospital complaining of an empty right scrotum since birth and swelling on the left side of the scrotum. According to the mother, she noticed the empty right scrotum shortly after the child's birth and consulted a private doctor, who advised observation only then. When the child reached the age of six years, he directly presented to the doctor with a complaint of an empty right scrotum and swelling on the left side of the scrotum.Upon examination, the doctor found that the right scrotum was empty, and the left testis was palpable. There was also swelling on the left side of the scrotum. To confirm the diagnosis, an ultrasound (USG) was performed in the inguinoscrotal region, which revealed two testes on the left side, an empty right scrotum suggestive of an undescended right testis, and a left-sided crossed ectopic testis, as shown in Figure A rare condition known as transverse testicular ectopia (TTE) is marked by the migration of both testes to the same hemi-scrotum. Less than 65% of instances of TTE are detected prior to surgery, which is a low incidence. The majority of instances (65%) are often found when an inguinal hernia is repaired . Based oA recommended surgical treatment option is orchiopexy to preserve fertility following the identification of TTE. TTE and related abnormalities can be diagnosed and treated well using laparoscopy . TransseIn cases of undescended testes, the condition of the crossed ectopic testis is a rare phenomenon that requires diagnosis prior to surgery. It is recommended to undertake magnetic resonance imaging (MRI) and/or ultrasonography (USG) in cases when there is a suspicion of such a problem before performing a diagnostic laparoscopy. Both the diagnosis and treatment of the illness are aided by this strategy.


Inertial kinetics and kinematics have substantial influences on human biomechanical function. A new algorithm for Inertial Measurement Unit (IMU)-based motion tracking is presented in this work. The primary aims of this paper are to combine recent developments in improved biosensor technology with mainstream motion-tracking hardware to measure the overall performance of human movement based on joint axis-angle representations of limb rotation. This work describes an alternative approach to representing three-dimensional rotations using a normalized vector around which an identified joint angle defines the overall rotation, rather than a traditional Euler angle approach. Furthermore, IMUs allow for the direct measurement of joint angular velocities, offering the opportunity to increase the accuracy of instantaneous axis of rotation estimations. Although the axis-angle representation requires vector quotient algebra (quaternions) to define rotation, this approach may be preferred for many graphics, vision, and virtual reality software applications. The analytical method was validated with laboratory data gathered from an infant dummy leg’s flexion and extension knee movements and applied to a living subject’s upper limb movement. The results showed that the novel approach could reasonably handle a simple case and provide a detailed analysis of axis-angle migration. The described algorithm could play a notable role in the biomechanical analysis of human joints and offers a harbinger of IMU-based biosensors that may detect pathological patterns of joint disease and injury. Lapresa et al. 2022) presented the validation of inertial systems using an anthropomorphic robot . Recent 022 preseThese approaches rely on measuring the three-dimensional linear and angular positions and accelerations of subject joints and limbs generated by micro-electromechanical systems (MEMS) such as an IMU. Effectively, an IMU is a localized biosensor accelerometer and gyroscope that estimates an object’s biomechanical position and orientation. IMUs can be single-point sensors or more complex single-pack arrays when including an additional magnetometer and sensor fusion algorithm, providing more accurate movement data and reduced sensor drift. A common artifact of accelerometer measurements is manifested in velocity and displacement trajectory drift obtained when integrating the raw acceleration record.MEMS-based IMU sensors can be used in computer vision techniques that track the location of a person through a combination of their pose and orientation with applications in robotics, personal navigation, and virtual reality. Furthermore, recent studies confirm IMU sensor applications for human motion analysis, enhancing biomechanics, rehabilitation, ergonomics, and sports assessments . This rePrevious work addressing joint biomechanics identified the geometric change to the knee joint’s dynamic alignment as an influence on the stance phase of foot loading . The resThe present study describes the characteristics of an IMU wearable sensor platform that provides a critical biomechanical parameter during the assessment of joint disease and injury. Here, the instantaneous axis-angle representation (IAA) of limb function is a vector identified as a metric to assist human movement analysis for rehabilitation and sports. The estimation of the IAA and its variant motion is strongly related to the joint’s functionality and ligament health  as well The specific objective of this study is to present a new algorithm for Inertial measurements unit (IMU)-based motion tracking with quaternions. Axis-angle representation for rotation, instead of representing a 3D rotation using a sequence of rotations around the sensor coordinates system, as Euler angles do, the axis-angle representation uses a normalized vector There are two main advantages to using the axis angle representation for describing limb kinematics. The first is that they allow a global description of rigid body motion that does not suffer from singularities due to local coordinates. Such singularities are inevitable when one represents rotation via Euler angles. The second advantage is that the axis-angle provides a very geometric description of rigid motion, which significantly simplifies biomechanism analysis and is handy for describing the kinesthesis, “feeling of movement,” in all skeletal and muscle structures. The axis vector is not moving instantaneously, occupying a stationary axis in the global frames.2.2.1.The newest generation of cost-efficient inertial motion trackers features a lightweight design, wireless connectivity ), and robust sensor fusion algorithms to provide accurate data for human movement applications . Software manipulation tools are provided  to facilitate the customization of mobile applications based on the available output data, thereby allowing developers to integrate the sensor into a wide range of solutions. Robust algorithms  and a sensor fusion framework  run onboard the sensor to provide accurate physical orientation estimates and minimize the effects of magnetic distortion [The IMUs applied in this work contain MEMS-type gyroscopes, accelerometers, and magnetometers. These individual sensor signals are fused through a statistical estimation framework to obtain three-dimensional (3D) limb and joint orientation. The output provided by the three main device components is then fed into the signal processing pipeline. The two main algorithms noted above are run onboard the motion tracking sensor . The senBefore describing the output data, the different types of reference systems used in the study are presented. Data shall be expressed in terms of local  and global, earth-fixed  coordinate systems. The SCS is a right-handed, cartesian coordinate system that is body-fixed within each sensor identified with lowercase d z axes . The locThis coordinate system is known as East-North-Up (ENU) and is the standard framework in inertial navigation for aviation and geodetic applications. Note that positive global orientations can be established for any application while maintaining the right-hand configuration, i.e., 2) and angular velocity . The TRF algorithm was previously applied in the optimization of smoothing parameters  during mAs a result of the TRF, thirteen numerically smoothed state variables are then present in the filtered vector The data in this application were then recorded through local resources . One healthy male, well-trained subject provided his written informed consent to participate in this study.It has frequently been assumed in previous methods that the point of observation for motion is unoccupied because it is measured in a SCS, while the point of observation in this work is occupied in GRCS. When a point of observation is occupied, there is also information to specify the motion of the subject, and the limb of the person in action instantaneously occupies some portion of the space in a way that is unique to the person as presented as the instantaneous axis-angle representation (IAA.) This information is unique to that person. The IAA is not moving but stationary in the GRCS, occupying the specific axis in the freedom space. Therefore, the innovation brought by this research is to propose the measure of the feeling of the self-movement, i.e., proprioception, in terms of the IAA meaning that it specifies the self-movement as distinguished from an object moving in the environment.2.2.Our solution method is based on an axis-angle representation by applying vector algebra quaternions as a motion operator. All rotating screw motions are represented as a rotation about an axis with respect to the global GRCS. Two quaternions describe general movement positioning: one for orientation and the second for translation.All the data processing was implemented in a commercial programming and computing platform . Here, the module “Quaternion.m” was applied . QuaternQuaternions represent complex numbers within a four-dimensional vector space (rank 4) over a real number field . A quateThe conjugate of the quaternion can be expressed as:A unit quaternion can be further defined as a vector rotation operator. Rotation about a unit axis entation (10)q=2.3.Earlier work has demonstrated how human perception and motor control interact continuously with external physical systems . The axiA general rigid-body transformation has 6 degrees of freedom (DOF) accounting for linear and angular translations or as defined here: 3 DOF for orientation and 3 DOF for translation. A unit-quaternion can be used as a rotation operator as shown in A vector In this application, we use the axis-angle representation to obtain the inverse kinematics solution of the kinematics of the dummy and then apply it to the elbow during simple flexion and extension within the healthy range of motion of upper limb movement. The immediate objective is to identify the forward kinematics of the hand. For this purpose, it suffices to identify the values of the axis-angle of the elbow joint and its location with respect to the GRCS system. The estimation of the IAA representation of limb movement, also known as a biomechanical screw axis , can pla2.4.Poor accuracy and precision when determining the IAA origin obtained from the IMU data are typically due to the lack of a consistent ground reference system within the sensor . TherefoIn the proposed algorithm, when estimating the IAA origin, the IAA direction Identifying the screw axis location vector ovariant . AlternaMore systematically, an IMU-based kinematic manipulator identification algorithm can be used , where aration α . IdentifHere, linear acceleration The mathematical arrangement is a skew-symmetric matrix representation of the vector cross product where ef subscript) can be given by:Using As described above, the IMU consists of three linear acceleration sensors and three rotational rate gyroscopes with the transformation from the SCS frame to the GRCS frames.A mathematical-specific aim satisfied in this line of research is to geometrically view the quaternion operator’s vector-frame action characterized by point rotation.The first perspective is through observations fixed with respect to the coordinate frame The second perspective is that observations are made with respect to the fixed vector 3.Experimental validation of the proposed method is carried out through soft exoskeletons on a dummy model. To validate our approach, the authors utilized lower limbs while modeling was conducted using upper limbs due to data availability. It is assumed that the motion tracking methodology can be applied to any joint ,27. ThesNext, we demonstrate the described IMU-based approach by applying the axis-angle representation of healthy upper limb movements. The authors used lower limbs to validate our approach, while modeling was conducted using upper limbs due to the availability of data. It is assumed that the motion tracking methodology can be applied to any joint based on previous studies ,27. ThesThe forward kinematics was performed in that sensor trajectories were reconstructed as  humerus . The X-aData were generated by the IMU accelerometer and gyroscope, as provided in the SCS, and combined through a sensor fusion algorithm measuring the orientation with respect to the GRCS. Therefore, it was necessary to align the SCS frame in which three linear accelerations and three rotational rate gyroscopes were measured to the global coordinates as described above, allowing the IAA to be computed by the global system.To visualize the inverse kinematics as a line representation of the IAA in space, we identified the line geometry as determined by its direction and a point on the line itself. We can write the vector equation of the line as:The orthogonality condition can then be written as:ustrated .The Further, a given line We used a commercial programming platform as previously noted (MATLAB NULL Operation) to solve for the coordinates al plane . This apal plane  includinal plane .The virtual sagittal plane was defined relative to the geometric representation of the IAA from the geometric center . This alWhen comparing the Euler angle procedure with the Axis angle procedure, Euler angles tried to force the body to move along the certain route that it had arbitrarily chosen but which the body had not chosen. In fact, the body would not take any one of its routes separately, though it would take all of them together in the most embarrassing manner-goal-directed behavior. The axis angle procedure had no preconceived scheme as to the nature of the movements to be expressed. A subject simply found the body in a certain position, A, and then he coaxed the body to move, not in this way or in that particular way, but any way the body liked to any new position B.Once the IAA was defined, forward kinematics obtained the vector trajectories . FinallyIn the present study, we computed 2D angular measurements obtained from IMUs relative to those obtained from compass systems; compared to IMU angular measurements , the rea4.Theoretically, for our study, the inverse kinematics, which is ruled by an IAA, is used to visualize the migration of biomechanical action Another piece of information provided by forward kinematics as ruled by the end effector is formed by visualizing the migration of the motion at the distal or proximal ends of the limb itself. In this way, the characteristics of the elbow motion can be estimated intuitively based on the shape and alignment relative to each of the limb segments. From a mechatronic perspective, we use position and orientation data to control the end effector of a robotic arm. From that application, identifying the joint variables that generate that desired position and orientation will ultimately control the end effector. However, human movement control is continuous and processed concurrently with afferent and efferent inherent modulation.Limitations of this study are that one IMU sensor was used during the activity of a single subject. The optimal system for joint biomechanics should be characterized using two IMUs where each sensor is worn at the proximal and distal segments containing the target joint. However, previous studies have validated the use of a single IMU to measure joint angles in children with cerebral palsy  and to aBy using the traditional optical-based motion tracking system, we have characterized the concept of a “knee axis” and further the concept of “invariant ,32”. We Global Description: Axis-angle representations allow for a global description of rigid body motion without suffering from singularities due to local coordinates. Unlike traditional Euler angles, which can result in singularities and ambiguities, axis-angle representations provide a more robust and accurate representation of limb kinematics.Geometric Description: Axis-angle representations provide a geometric description of rigid motion, simplifying biomechanical analysis and facilitating the understanding of kinesthesis (the feeling of movement) in skeletal and muscle structures. This geometric description is useful for applications such as computer-aided graphics, vision, and virtual reality.Quaternion Operations: Axis-angle representations can be easily converted to quaternion representations, which have well-defined operations for vector addition, multiplication, and interpolation. Quaternions offer a more efficient and accurate way to represent rotations compared to other methods.Simplified Biomechanics Analysis: Axis-angle representations simplify the analysis of joint biomechanics by providing a clear and intuitive representation of joint function and ligament health. They can be used to study the instantaneous axis of rotation, which plays a crucial role in joint functionality and overall locomotion perception and motor control.There are several advantages of using axis-angle representations for describing limb kinematics:Overall, using axis-angle representations for describing limb kinematics offers advantages in terms of accuracy, robustness, and simplicity of analysis.The invariant combination of the axis-angle representation could open a new era of quantifying biomechanical perception-action systems as interactions with the natural or built environment. The overall performance metrics of many motor activities could be extended to real-world and clinical settings within multiple spatial and temporal frameworks . FurtherThe kinesthesis, the awareness of one’s own motion, cannot be measured in a SCS. However, they have unity relative to the posture and behavior of the subject being considered. The results exert goal-directed feedback control by using the IAA to guide our motion continuously. Our assumption is that goal-directed feedback could be applied to many more rehabilitation application routines. Real-time posture correction and motion change instruction could ultimately optimize motor learning, reducing injuries caused by excessive motion and bad postures.


Doppler ultrasound (DU) is used in decompression research to detect venous gas emboli in the precordium or subclavian vein, as a marker of decompression stress. This is of relevance to scuba divers, compressed air workers and astronauts to prevent decompression sickness (DCS) that can be caused by these bubbles upon or after a sudden reduction in ambient pressure. Doppler ultrasound data is graded by expert raters on the Kisman-Masurel or Spencer scales that are associated to DCS risk. Meta-analyses, as well as efforts to computer-automate DU grading, both necessitate access to large databases of well-curated and graded data. Leveraging previously collected data is especially important due to the difficulty of repeating large-scale extreme military pressure exposures that were conducted in the 70-90s in austere environments. Historically, DU data (Non-speech) were often captured on cassettes in one-channel audio with superimposed human speech describing the experiment (Speech). Digitizing and separating these audio files is currently a lengthy, manual task. In this paper, we develop a graphical user interface (GUI) to perform automatic speech recognition and aid in Non-speech and Speech separation. This constitutes the first study incorporating speech processing technology in the field of diving research. If successful, it has the potential to significantly accelerate the reuse of previously-acquired datasets. The recognition task incorporates the Google speech recognizer to detect the presence of human voice activity together with corresponding timestamps. The detected human speech is then separated from the audio Doppler ultrasound within the developed GUI. Several experiments were conducted on recently digitized audio Doppler recordings to corroborate the effectiveness of the developed GUI in recognition and separations tasks, and these are compared to manual labels for Speech timestamps. The following metrics are used to evaluate performance: the average absolute differences between the reference and detected Speech starting points, as well as the percentage of detected Speech over the total duration of the reference Speech. Results have shown the efficacy of the developed GUI in Speech/Non-speech component separation. Automatic speech recognition –3 refersAutomated telephone systems and medical dictation software were among the first implementations for speech recognition –20. ThesTo ensure that the dialog system provides relevant replies at the proper moment, a highly accurate and rapid speech recognition system must be built. Many cloud-based speech recognition services are available, including the Google Cloud Speech application programming interface (API) , IBM WatThe Google speech recognizer uses a neural network to model speech recognition and allows developers to transform audio files into text together with corresponding timestamps in the form of an API that supports 120 different languages around the world. For the best performance of the Google speech recognizer engine, audio files with a 16 kHz sample rate should be used . We haveDoppler ultrasound (DU) is used in decompression research to detect venous gas emboli in the precordium or subclavian vein, as a marker of decompression stress , 29. A 1Meta-analyses, as well as efforts to computer-automate DU grading, both necessitate access to large databases of well-curated and graded data. A large amount of post-dive audio DU data was recorded in the 1970s and 1980s and recently digitized using audio software tools. In these experiments, DU was recorded on physical tapes, an example of which is shown in An example of audio activity timeline for these data is shown in In this work, we proposed to employ the Google speech recognizer to separate Speech/ Non-Speech components in DU audio files. To conduct a large-scale experiment on recognition and separation tasks, a graphical user interface (GUI) is developed using the license-free Tkinter package of Python. First, the end-user must select the desired audio file to be separated. Then, after recognition of the long human voice component , the corresponding timestamps of the Speech part were used to apply the separation process. During the recognition process, the end-user was notified by the progress of the program. Once the recognition is finished, the end-user must corroborate the effectiveness of the separation process before saving the output of the GUI. The separated Speech component together with its spectrogram  is displayed for the end-user information.In this section, the methodology of the speech recognition and separation is provided together with the GUI structure and functionality of the buttons. Moreover, the dataset used and performance metrics are provided at the end of the section.The block diagram of the developed method is depicted in The noisy recorded post-dive DU data were recently digitized. The presence of noise in the background of the speech signal makes detection and recognition difficult. Power spectral density of non-stationary noise was used and implemented in MATLAB to enhance the noisy speech .The noise estimation is updated by averaging the noisy speech power spectrum using time and frequency dependent smoothing factors. Signal presence is controlled by computing the ratio of the noisy speech power spectrum to its local minimum, which is updated continuously by averaging past values of the noisy speech power spectrum. The enhanced audio is then split into one-minute audio chunks so that it can be fed through the free version of Google speech recognizer for offline processing without being uploaded to the cloud , 27.Traditional speech recognition systems have three major components : the acoOnce the audio recognition is completed, the algorithm asks the end-user for final verification of separated audio files before saving into the memory. This is a critical step, since the algorithm may not be reliable in all the detections especially when dealing with old data, and in this way the end-user must corroborate the recognition and separation tasks. To help the end-user decide and save the correct files, the spectrograms of separated audio files (Speech and Non-speech components) is shown on the GUI platform as a piece of complementary information. A sample result of the separation step is provided in the next section.A GUI was established and designed using the license-free Tkinter package of Python . TkinterThe first step in the pipeline (shown as part (a) in This button (shown as part (b) in By clicking on the Audio chunking and selection button, these files get pruned from all detected human voice activity for final verification in the next step.In this step, the separated Speech and Non-speech components are displayed to the end-user. Spectrograms are shown, an example of which can be seen in The dataset comprised analogue recordings of human precordial and subclavian audio Doppler signals that were made as part of a study conducted by the United States Navy Experimental Diving Unit in the 1980s. The analogue tapes were then converted to a digital format using an analogue to digital converter  connected to a PC, where they were saved as digital files in the .flac format. The duration of each audio file is summarized in r and Dd are the reference duration of Speech component and detected duration Speech component, respectively. Furthermore, the error in start time detection of each voice segment, M2, in seconds, is calculated as:r and Td represent the reference start of Speech component and detected start of Speech component, respectively.The start and end points of the Speech component were annotated manually as the reference for the entire dataset to test the effectiveness of the developed algorithm. Two complementary metrics were used for evaluation, as defined in The estimation of the power spectral density of the speech and non-speech components in each recording were analyzed, which converts the signal from time domain into the frequency domain. This method was used for the estimation of the power signal at different frequencies in this study. One representative example of the results is shown in The performance results for different audio files are reported in To show the effectiveness of the developed algorithm visually and make the interpretation of the results easier, four sample audio files were randomly selected, and the bar chart of system performance is plotted in While not a perfect surrogate marker for DCS , VGE remLeveraging previously collected DU data is especially important in diving research due to the difficulty of repeating large-scale military hyperbaric exposures that were conducted in the 70–90s in austere environments. Historically, these were often collected on cassettes as one-channel audio with superimposed human speech describing the experiment, making digitization and separation of these audio files a lengthy, manual task. Since the processing of this data relies heavily on the effective separation of the human voice from the ultrasound audio, we have developed a novel graphical user interface (GUI) to aid in these recognition and separation tasks. We used the Google speech recognizer within our developed GUI to extract the timestamps of Speech component and perform separation. Speech separation technology has not previously been used in post-dive Doppler ultrasound recordings. Here we show promising preliminary performance for its capacity to help separate long back to back recordings that could help accelerate the reuse of large amounts of unique previously-collected data. The developed algorithm tested on our private domain dataset shows that the recognition and separations tasks are performed with good accuracy. This may allow a human operator to save time in reviewing historic cassettes, where the approximate times of Speech/Non-speech transition are presented, and they can selectively listen to those to expedite manual separation of the segments of interest.S1 File(PDF)Click here for additional data file.


Biomagnetism is the measurement of the weak magnetic fields produced by nerves and muscle. The magnetic field of the heart—the magnetocardiogram (MCG)—is the largest biomagnetic signal generated by the body and was the first measured. Magnetic fields have been detected from isolated tissue, such as a peripheral nerve or cardiac muscle, and these studies have provided insights into the fundamental properties of biomagnetism. The magnetic field of the brain—the magnetoencephalogram (MEG)—has generated much interest and has potential clinical applications to epilepsy, migraine, and psychiatric disorders. The biomagnetic inverse problem, calculating the electrical sources inside the brain from magnetic field recordings made outside the head, is difficult, but several techniques have been introduced to solve it. Traditionally, biomagnetic fields are recorded using superconducting quantum interference device (SQUID) magnetometers, but recently, new sensors have been developed that allow magnetic measurements without the cryogenic technology required for SQUIDs. This year marks the 60th anniversary of the birth of biomagnetism: the study of magnetic fields produced by the body. Ultrasensitive sensors let researchers record the magnetic field of the heart and brain. Studies of isolated nerves and muscle have answered fundamental questions about how biomagnetic fields relate to their current sources. Innovative technologies may encourage new medical applications.In 1963, Gerhard Baule and Richard McFee first measured the magnetic field generated by the human body [The ferrite core was about one-third of a meter long, so the magnetic field was not measured at a single point above the chest, but instead was averaged over the entire coil. One question repeatedly examined in this review is spatial resolution. Small detectors are often noisy and large detectors integrate over the area, creating a trade-off between spatial resolution and the signal-to-noise ratio.The heart’s magnetic field is tiny, on the order of 50–100 pT . A picotBaule and McFee suppressed background noise by subtracting the output of two pickup coils. A distant source of noise gave the same signal in both coils and did not contribute to their difference. One coil was placed over the heart, and the magnetocardiogram was larger there and did not cancel out. The two coils formed a rudimentary type of gradiometer .Most laboratories contain stray magnetic fields from sources such as electronic equipment, elevators, or passing cars . Baule aOne limitation of a metal pickup coil is the thermal currents in the winding due to the random motion of electrons, creating extraneous magnetic fields caused by the measuring device itself. The ultimate source of noise is thermal currents in the body, but fortunately, their magnetic field is miniscule .The magnetocardiogram resembled the electrocardiogram (ECG) sensed by electrodes attached to the skin. Baule and McFee speculated that the MCG might contain different information than the ECG, another idea that reappears throughout this review. In a follow-up article, they theoretically calculated the magnetic field produced by the heart . The intAfter Baule and McFee’s groundbreaking study, research into magnetocardiology was taken up by David Cohen, one of the founders of biomagnetism. Cohen performed his experiments inside a magnetically shielded room . The walThe voltage induced in Cohen’s pickup coil had a time course that was the derivative of the magnetic field, but he electronically integrated his signal before comparing the MCG to the ECG. In some cases, he averaged over 150 heartbeats to further lessen the noise.In the 1960s, an instrument was invented to detect extraordinarily weak magnetic fields: the superconducting quantum interference device (SQUID) magnetometer . In 1970After their initial study, Cohen and Zimmerman continued to investigate the magnetocardiogram. Zimmerman and Nolan Frederick showed that by utilizing a first-order gradiometer design for the superconducting pickup coil , SQUID mwww.biomag2024.org).At the same time, other research teams became interested in the growing field of biomagnetism. One of the earliest and most successful groups was led by Toivo Katila at the Helsinki University of Technology  in Finland. In 1974, they reported the first observation of a fetal magnetocardiogram, which allows a doctor to monitor the heart rate of a fetus . They alSimilar to the electrocardiogram, the magnetocardiogram consists of three deflections: a P wave caused by activation of the atria, a larger QRS complex from activation of the ventricles, and a T wave corresponding to recovery of the ventricles . In 1975David Geselowitz and, later, Robert Plonsey analyzed the relationship between the biomagnetic field and the current sources that gave rise to it ,25. TheyMilan Horáček, of Dalhousie University, implemented a computational method to solve these integral equations . He reprWhile the forward problem of determining the magnetic field from the current is important, of even greater interest is the inverse problem of determining the current from the measured magnetic field. Indeed, how to solve the inverse problem is the paramount question in biomagnetism. The simplest way to approach it is to assume the primary source consists of a single dipole. The forward problem can be solved to calculate the magnetic field it produces . This caA vital question is: does the magnetocardiogram contain information not present in the electrocardiogram? Plonsey pointed out that the ECG is produced by the divergence of the impressed current density, while the MCG is produced by its curl . Since tMany of the early recordings of the MCG were performed with a single SQUID sensor. To determine the spatial distribution of the magnetic field, an experiment had to be repeated many times, each time with the sensor in a new position. Beginning around 1990, multichannel systems became available that allowed scanning at numerous locations simultaneously. One of the first such devices had seven channels , but sooRiccardo Fenici and his colleagues have reviewed the medical applications of magnetocardiography , includiWhile much of the early biomagnetic research centered on measuring the magnetocardiogram, Wikswo took a different track—he sought to measure the magnetic field produced by isolated tissue. In 1980, Wikswo, Barach, and John Freeman dissected a sciatic nerve out of a frog, threaded it through a wire-wound ferrite-core toroid, immersed the toroid in a saline bath, and then electrically stimulated the nerve . An actiThe magnetic field sensed by the toroid  was 70 pT, which is similar to the magnetic field of the magnetocardiogram. When the saline bath was removed, so merely a thin layer of fluid clung to the nerve’s outer surface, the magnetic field disappeared. If the action potential propagated in the opposite direction, the magnetic field changed polarity. In their original study, Wikswo et al. detected the current in the toroid coil with a SQUID magnetometer. Later, they found that the extreme sensitivity of a SQUID was not needed, and they developed a room-temperature, low-noise amplifier for subsequent experiments . Toroids have been used in a variety of experiments to measure the magnetic field from isolated tissue. One of these experiments was to measure the field of a single nerve axon. Wikswo and his graduate student Brad Roth simultaneously recorded the magnetic field (using a toroid) and transmembrane potential (using a glass microelectrode) of a crayfish giant axon having a radius of 0.1 mm . The ratThe magnetic field of an axon can be calculated using either Ampere’s law or the law of Biot and Savart ,55,56. AThe law of Biot and Savart, on the other hand, integrates the intracellular current along the length of the axon. The extracellular current contributes little or nothing because it is the sum of the current coming out of the axon at different points, and the magnetic field from the current radiating outward from a point is zero. The magnetic field of an axon is not the same as for a long, straight wire because the action potential has a finite length. The depolarization current points opposite to the repolarization current, so they partially cancel each other in the integral.Wikswo and Roth, working with Jim Woosley, performed these integrals using Fourier transforms and modified Bessel functions . The matFor measurements of the nerve magnetic field with a toroid, the interpretation based on Ampere’s law is particularly useful. For instance, the reason the magnetic field was zero when Wikswo removed the saline bath was that the net current threading the toroid vanished: the intracellular current was canceled by an equal but opposite extracellular current passing through the thin layer of saline on the nerve surface . NeverthWikswo’s laboratory studied other tissue besides nerves. Roth and Marcella Woods examined an action potential propagating through a two-dimensional sheet of cardiac muscle . In FiguAn illuminating case study is propagation originating at the apex of the heart . SupposeThe experiments confirming the predictions in In order to interpret microSQUID data, Roth and Wikswo, working with Nestor Sepulveda, derived a general mathematical technique to determine the two-dimensional current distribution from the measurement of its magnetic field . The proInvestigations of isolated nerves and muscle provide insight into many basic concepts underlying biomagnetism. However, nowadays, the field is driven by clinical applications, especially for the study of the brain.In 1968, biomagnetism pioneer David Cohen performed the first measurement of the magnetic field of the brain: the magnetoencephalogram (MEG). He detected the brain’s largest signal: the alpha rhythm . This neThe MEG is primarily produced by currents in the dendrites of neurons in the brain ,74. MagnA radial dipole produces no magnetic field . This reThe radial component of the magnetic field is the same as for a dipole in a homogenous, unbounded conductor. In other words, the conductor–insulator interface at the surface of the sphere does not affect the radial component of the magnetic field. Furthermore, this result holds for a heterogeneous sphere, as long as the conductivity varies only with depth. For these reasons, investigators prefer measuring the radial component of the magnetic field when studying the MEG.The tangential component of the magnetic field is affected by the conductor–insulator boundary, but it does not depend on a conductivity that radially varies.The initial calculations of the magnetic field underlying the magnetoencephalogram were performed assuming that the head is a sphere ,89,90. AThese results have a significance for the magnetoencephalogram. You cannot record a magnetic field from radial dipoles, such as those that may exist in cortical folds or gyri —they areThe last section analyzed some of the relative merits of the magnetoencephalogram and the electroencephalogram. Are there enough advantages of the MEG over the EEG to justify the cost and complexity of cryogenics? The magnetic field pattern is rotated by 90° relative to the electrical voltage distribution, so the MEG should provide better localization perpendicular to a dipole, while the EEG should better localize parallel to it. In addition, the MEG might reveal tangential dipoles that are obscured in the EEG by radial ones . In 1990, Cohen created a stir by claiming that the MEG did not localize a source better than EEG did . He and Reviews of Modern Physics [By the mid-1990s, the MEG was becoming an established technique for investigating brain activity. In 1993, Matti Hämäläinen and his collaborators in Finland surveyed magnetoencephalography in the  Physics . It is tHow to solve the inverse problem—determining the sources of brain activity from magnetic measurements—is the central question in magnetoencephalography. In general, the solution is not unique. For example, if you assume a spherical head, then you may always add a radial dipole to any solution . In addiThe inverse problem was initially solved for a single dipole. Its location, orientation, and strength were determined by a least-squares fit to the experimental data. The technique can be generalized to multiple dipoles ,108, butOften, least-squares minimum-norm methods are applied at one instant. If solved at different times, the resulting dipoles could move or rotate. An alternative approach is to assume a few fixed dipoles that each produce their own individual (asynchronous) time series. The fit is performed over their postulated locations and time . John Mosher and Richard Leahy have applied this multiple-signal classification (MUSIC) algorithm, and a recursively applied and projected MUSIC algorithm (RAP-MUSIC), to MEG data ,112,113.The MUSIC algorithm has been generalized by employing spatial filtering (beamformers), where the filter emphasizes regions of interest within the brain while attenuating other locations ,115. BeaStill another approach to solving the MEG inverse problem is to use known anatomical and physiological constraints. The least-squares minimum-norm fit is performed subject to these constraints by adopting a Bayesian formulation to produce a “maximum a posteriori” estimate of brain activity . One impIf you have no anatomical information, or you do not wish to form any hypotheses about the source, you can go to the other extreme and construct a solution that directly relates the magnetic field to the current, bypassing any restrictive assumptions or least-squares fit. An example was described in A variation of linear estimation for the inverse problem is the focal underdetermined system solution (FOCUSS) . The algThis review cannot examine all the various algorithms for solving the inverse problem, there are just too many. For example, whole families of methods are based on coherence  and connMagnetoencephalography is able to diagnose and help treat many illnesses. Daniel Barth and his coworkers at UCLA used MEG to localize interictal spikes: transient, brief discharges, often observed in patients with epilepsy . Such spIn addition to epilepsy, magnetoencephalography has other medical applications . It has One way to encourage medical doctors to adopt MEG techniques is to make them easier and cheaper. SQUIDs constructed using high-temperature superconductors may allow the coolant to be liquid nitrogen instead of the more expensive and difficult to handle liquid helium ,138. EnoBoth the MEG and EEG must compete with other methods for imaging the brain, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). MEG and EEG provide better temporal resolution than fMRI and PET, but fMRI and PET provide better spatial resolution and do not require solving an ill-posed inverse problem. Although MEG is more expensive than EEG, it is not as expensive as fMRI or PET. Traditional MEG, similar to fMRI and PET, is completely non-contact . MEG and EEG do not require injecting any radioactive isotopes into the patient, as PET does. fMRI measures blood flow in the brain and PET measures metabolic activity, whereas MEG and EEG directly record brain electrical activity. Ultimately, combinations of these techniques may be used together to learn more about how the brain works.One of the most remarkable developments in biomagnetism over the last couple of decades is the introduction of new ways to detect the magnetic field, such as optically pumped magnetometers ,144,145.Optically pumped magnetometers have advantages and disadvantages compared to traditional devices for measuring biomagnetic fields . The magAnother type of magnetic field detector contains nitrogen-vacancy defects in a diamond, which creates a spin system sensitive to a magnetic field that can be optically detected with a photodiode ,153. TheAnother type of magnetic field probe is based on giant magnetoresistance ,155. TheRecently, magnetic sensors constructed from layered ferrite/piezoelectric composites have been proposed for measuring biomagnetic fields . High-teBiomagnetism remains a growing and developing field of study. New instruments may mean that biomagnetic measurements no longer require the complicated cryogenic technology associated with SQUID magnetometers. If these sensors can be arranged as gradiometers, or if active shielding can cancel out magnetic noise, then the need for expensive shielded rooms might be eliminated. In that case, the cost and complexity of devices to measure the MEG should become competitive with EEG machines, encouraging further medical applications. The next sixty years of biomagnetism might well be more momentous than the first sixty.


Therefore, this paper presents an automated machine learning (AutoML) to simplify and accelerate the modeling tasks. Focused on Python and RapidMiner rapid modeling tools, Tree-based Pipeline Optimization Tool (TPOT) and AutoModel were used. This paper presents a comprehensive comparison between these tools with regard to the prediction accuracy and Area Under Curve (AUC) in classifying real cases of whistleblowing academic dishonesty among undergraduate students of two universities in Indonesia. Additionally, the correlations weight from demographic and Theory of Planned Behavior (TOB) attributes in the different machine learning models are also discussed. All the machine learning algorithms from TPOT and AutoModel are considerable powerful to generate good accuracy level (between 70–93% of AUC) in classifying both cases of whistleblowing and non-whistleblowing on the hold-out samples from the testing process. Generally, based on the validation results of the prediction models, demographic attributes presented more importance than the TBP attributes. The findings of this study will be a great interest of many research scholars to conduct a more in-depth analysis on AutoML for many domains mainly in education and academic misconduct fields.  Specifications tableInexpert data scientists often deal with challenges to implement machine learning such as identifying the best features selection, hyper-parameter tuning, and handling imbalance dataset. Additionally, insufficient skills for computer programming and the complexity of extended codes scripts for machine learning implementation have given a significant hurdle for those data scientists. Decreasing the difficulties of the design and development for machine learning has become a major concern by many researchers recently due to the massive emergence of complex problems that need a more efficient and intelligent solutions. Machine learning is a kind of complex computing technique with artificial intelligence mechanisms that has the ability to infer new knowledge and redevelop learning like the way a human brain works One of the crucial problems in education that has received major concern is education dishonesty. Academic dishonesty can be defined as an intentional act of fraud Despite the importance of whistleblowing in containing academic dishonesty, a review of literature on whistleblowing shows that most of prior studies more emphasize on whistleblowing practice in corporate setting This paper has two main contributions. First, it demonstrates the implementation of whistleblowing academic dishonesty prediction model using AutoML method to enable simple and efficient prediction analysis and to encourage the acceptance of whistleblowing as one of the universities mechanisms in mitigating academic misconducts. Second, it provides another design aspects of the machine learning whistleblowing prediction model by using a unified construct from the attributes of Theory of Planned Behaviour (TPB) as additional attributes of demographic. The influences of the attributes with regards to the performances of the machine learning models are also discussed in this paper. Therefore, this paper provides an important empirical contribution that fill the gap of research on whistleblowing academic dishonesty with the intelligent prediction approaches.Cumulative Grade Point Average or CGPA), level of religiosity, integrity culture, course and their fear retaliation (the act or intention of hurting or doing something harmful to someone). Additionally, the data composed of factors that affecting such whistleblowing intention, which were constructed based on Theory of Planned Behavior (TPB). As listed in affective attitude and instrumental attitude), subjective norms toward the behavior (injunctive norm and descriptive norm), and perceptions of behaviour control (self-efficacy and perceived controllability).The dataset for this research was collected through survey questionnaires that personally disributed to undergraduate students of two universities in Indonesia. A total of 166 valid replies were used for the study out of the 292 questionnaires that were distributed, giving a response rate of 57 percent. The whistleblowing dataset from the questionnaire consists of students’ demographic information, such as academic grade  and (2) respectively. Affective attitude stresses more the emotional behavior aspects while instrumental factor in attitude emphasizes more the cognitive aspects of behavior.Attitudes refers to the degree to which a person has a favorable or unfavorable measure of the whistleblowing interest either through subjective norms is descriptive norms measured with Eq. (3), which is the perception of other peoples’ attitude towards whistleblowing. Another type is injunctive norms or social norms, calculated with Eq. (4) refers to the social support to perform the behavior.Subjective norms towards the behavior refers to the belief on approving or disapproving. It relates to a person's principles about their peers or peoples surround either should engage in the behavior or not. A type of Self-efficacy and perceived controllability. Self-efficacy as measured in Eq. (5) is defined as the student's confident to carry out the whistleblowing behaviour. The ability to control the whistleblowing behavior is defined as perceived controllability (Eq. (6)).Behavioral control or intention reflects to the motivational factors that control the behavior including The classification of whistleblowing is a type of binary classification in such that if the prediction probability value is 0.5 and above, the case is classify as 1 (whistleblowing). Otherwise, prediction value with less than 0.5 is resulted the model to classify the case as 0 (non-whistleblowing). On measuring the students’ whistleblowing intention to be used as the dependent variable (DV) of the prediction model, nine indicators have been used, including a general intention to whistleblowing and another whistleblowing measures on eight indicators of academic misconducts. Eq. (7) measure the value of whistleblowing intention.RQ1: Does each attribute is being utilized in all the machine learning algorithms suggested by AutoML?RQ2: How variances of importance can be presented by each of the attribute in the different machine learning algorithms?Demographic attributes are CGPA, Gender, Religiosity, Fear Retaliation and Integrity culture while Controllability, Self-efficacy, Injunctive norm, Descriptive norm, Instrumental attitude and Affective attitude are the TPB attributes. The main problem appeared in the whistleblowing dataset is each of the attribute/feature has weak associations to the DV (below 0.3 correlation coefficients). With AutoML that able to optimize the best combination of features, the following research questions have been constructed to be justified from the results generated in each of the machine learning algorithm.CGPA) has the strongest correlation to the whistleblowing intention in To elaborate the answer for RQ1, it is useful to get insight on the implication of weight of correlations to the algorithms’ performances. Moreover, by observing the variances of important to answer the RQ2, different effect between Demographic and TPB can be generally described. Academic performance , it's important to note the presence of imbalanced features within the dataset. Both TPOT and AutoModel incorporate algorithmic and ensemble approaches that take into account not only the distribution of the target label but also the potential consequences of skewed feature representations.TPOT in PythonTPOT library can be imported in any programming files for developing the machine learning that can be written in any Python development editor. The basic codes for TPOT library in Python codes written with Google Colab, a web browser for Python development editor. The main part to install TPOT library and to read the dataset is given in TPOT uses Cross-Validation (CV) training approach that has been set with 4 K-Folds and the test size is 0.25 representing 42 numbers out of 166 data were used for testing and the rest 124 for training and validation Refer . From thGeneration is the number of iterations for the optimization search while population is the number of maximum individual (pipelines) to be randomly selected in the optimization search. The findings in AutoModel in RapidMiner with Graphical User Interface (GUI) is easier than TPOT that used Python programming. Based on the whistleblowing dataset, AutoModel suggested a series of machine learning to be observed. As seen in The evaluation of prediction accuracy in this study is emphasized by the most common metrics of accuracy and classification error. However, these metrics have a limitation as they assess the prediction model's performance without distinguishing its performance with respect to specific classes. Since the machine learning models in this research address a binary classification problem involving whistleblowing and non-whistleblowing students, it becomes essential to gauge the machine learning ability to accurately predict each class. Moreover, this becomes particularly crucial with the inherent imbalance in the target label distribution of the dataset. To address this, sensitivity (True Positive Rate) and specificity  were employed as supplementary metrics. Sensitivity denotes the total number of correct predictions for the whistleblowing cases, while specificity signifies the accurate prediction of non-whistleblowing cases. To provide a more comprehensive visualization of the trade-off between sensitivity and specificity, this study employed a Receiver Operating Characteristics (ROC) Curve graph. The Area Under Curve (AUC) of the ROC curve was then utilized to quantify the model's overall classification ability. A higher AUC indicates a more potent model in effectively classifying both cases of whistleblowing and non-whistleblowing students. By utilizing these metrics and techniques, the research design gains robustness through a clearer description of the model's performance with respect to the specific challenges posed by the imbalanced distribution of the target label.Furthermore, The following findings are described based on the ROC and AUC of the AutoML. Furthermore, it is important in this research to understand how each of whistleblowing attributes from the demographic and TPB effect the results of the different machine learning models. These can be interpreted from the weight of correlations of each attribute in all the machine learning models. Furthermore, in order to provide a rationale for addressing Research Question 2, the information presented in These findings emphasize the significance of CGPA not only within various machine learning algorithms but also outside the model contexts Refer to . The posCompared to demographic attributes, every TPB attribute demonstrates a diminished impact on all machine learning algorithms, with the exception of the Instrumental attribute that emerges as the primary influencer in the Naive Bayes algorithm. The results of this reduced influence are in accordance with the observations presented in The performance of different machine learning algorithms can be explained by the findings of this weight correlation analysis. Within the Generalized Linear Model and Fast Large Margin algorithms, the attribute with the highest influence is CGPA, leading to AUC scores of 0.78 for both algorithms. This underlines that CGPA's significant predictive power in discerning the behavior under consideration within these models.Notably, the attribute of religiosity emerges as the most crucial factor within the algorithm of Gradient Boosting Classifier, resulting in an impressive AUC score of 0.9. This highlights the essential role of religious inclination in predicting and explaining certain behaviors within the context of this algorithm.In the Naive Bayes algorithm, the most influential attribute stems from the TPB framework, which is the instrumental attitude. This attribute is closely followed by CGPA, which collectively contributes to an AUC score of 0.84. This signifies that while religiosity and academic achievement play pivotal roles in other algorithms, the Naive Bayes algorithm places emphasis on the role of instrumental attitude from TPB in making accurate predictions.This paper presents a comprehensive comparison between software tools that support AutoML. With the utilization of real data in this study, a critical challenge to implement machine learning is the data had very weak connections between its variables. This problem made it hard for the conventional prediction model to give really accurate results and lengthy time is involved for the inexpert data scientists to conduct hyper-parameters tuning. Thus, the introduction of AutoML has become advantageous as it automates the identification of the optimal variable combinations for enhancing model performance. Within the tested dataset, TPOT is proven to have better performances than AutoModel in term of accuracy and AUC but the implementation of TPOT is more difficult than AutoModel. Nevertheless, the Python codes for TPOT are very easy to be replicated by researchers. Additionally, this paper presents comparison for variance of contributions between the attributes that ware constructed for the whistleblowing academic dishonesty classification model based on demographic and TPB theory. The findings reveal the intricate interplay between attributes and algorithms in machine learning. The algorithm-specific importance of certain attributes and emphasizing the need to consider different attributes depending on the chosen algorithm is demonstrated from the findings. The strong association between religiosity, CGPA, and instrumental attitude in their respective algorithms showcases the nuanced nature of predictive factors and their impact on model performance. Further research could investigate deeper into the underlying reasons for these attribute-algorithm associations and potentially yield insights into the psychological and behavioral dynamics they represent.Participants are aware of the specific privacy statements and confidentiality protections in online surveys. Participants' consent was collected through a Google form, and the data has been completely anonymised.Rahayu Abdul Rahman: Conceptualization, Methodology, Formal analysis, Data curation. Suraya Masrom: Writing – original draft, Formal analysis, Data curation. Masurah Mohamad: Conceptualization, Methodology, Writing – review & editing. Eka Nurmala Sari: Data curation. Fitriani Saragih: Data curation. Abdullah Sani Abd Rahman: Conceptualization, Methodology.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Drying is one of the common procedures in the food processing steps. The moisture content (MC) is also of crucial significance in the evaluation of the drying technique and quality of the final product. However, conventional MC evaluation methods suffer from several drawbacks, such as long processing time, destruction of the sample and the inability to determine the moisture of single grain samples. In this regard, the technology and knowledge of hyperspectral imaging (HSI) were addressed first. Then, the reports on the use of this technology as a rapid, non-destructive, and precise method were explored for the prediction and detection of the MC of crops during their drying process. After spectrometry, researchers have employed various pre-processing and merging data techniques to decrease and eliminate spectral noise. Then, diverse methods such as linear and multiple regressions and machine learning were used to model and predict the MC. Finally, the best wavelength capable of precise estimation of the MC was reported. Investigation of the previous studies revealed that HSI technology could be employed as a valuable technique to precisely control the drying process. Smart dryers are expected to be commercialised and industrialised soon by the development of portable systems capable of an online MC measurement. The high moisture level of agricultural products will lead to microbial spoilage. Therefore, the chemical reactions and the activity of the microorganisms could be minimised by decreasing the moisture content (MC) of the food products to a specific level, which can prolong the durability of the final product . Dryers In this process, the drying condition and quality of the product are continuously and non-destructively controlled . If the A proper understanding of the HSI technology is essential for some analyses. In this regard, the principles of the HSI technology will be addressed in this section.The hyperspectral imaging technique is a newly emerging and non-destructive approach that relies on a combination of spectrometry and imaging technologies. According to Sun et al. , the mecAt the macroscopic level, electromagnetic waves can be observed as lights that might be reflected, transmitted, or scattered . The choAs the absorbed part of the light penetrates the sample, the emission and absorption intensity and wavelength depend on the physical and chemical modes of the sample. Using the technology of HSI, this transmission can be converted into a spectrum and an image . The imaAn HSI system includes two main parts: software and hardware. The hardware components of the HSI systems are almost the same. According to According to Principal component analysis (PCA) is one of the multivariable statistical methods with extensive application in the chemical analysis ,32. HoweThe HSI data are subjected to factors such as light scattering by altering the detector, changes in the sample size, surface roughness and temperature-induced noises, which can affect the calibration accuracy. The data should be thus pre-processed to achieve sustainable, precise and reliable calibration models ,33. In oSNV is one of the pre-processing methods capable of normalising spectral data. This method can correct the effect of variations in the detector-sample distance and the sample size. In other words, SNV is focused on eliminating the scattered light interference and changes in the path length . In the Multiplicative scatter correction (MSC) is one of the well-known normalisations methods capable of correcting the additive (baseline shift) and multiplicative (tilt) effects due to the physical influences on the spectral data. These effects can now be due to the nonuniform distribution throughout the spectrum (the degree of diffusion depends on the wavelength), sample size, and light refractive index . Therefo (3)–(5) :(3)X¯=∑iIn which:i and bi = simple regression constants of each spectrum against the mean spectrum of the samples, and XiMSC = the corrected spectrum of each sampleThis method was utilised in the present research to normalise the spectra.i,yj+ii,) I and j = 1, …, n) in which Cj and yj+ii are independent and control variables. According to Equation (6):Smoothing methods  can decrease the noise in the spectral data. Although these filters can effectively reduce noise, they should be employed with extra care to avoid variations in the important information. Moreover, edge detection or enhancement should not be applied before smoothing. In the Savitzky-Golay algorithm, a polynomial should be matched around several points chosen for smoothing, which can be achieved by a relative minimum square regression method. Smoothing can improve the state of the Vis/NIR spectrum, but it can also result in the loss of important information for the modelling. Despite the extensive investigations to select the optimal point, this selection is made experimentally. An empirical law states that the width of the optimal number of points should not exceed the width required to cover half of the smallest peak of the spectrum . The datNormalisation results in similar data. It also modifies for the change in the conditions.2). Generally, a good model should offer the highest R2 and lowest RMSE [Modelling is a monitored classification method used based on the differences between several classes using a specific calibration model to differentiate unknown samples. For instance, Lin et al. (2022) utilised various regression models such as least square support vector machines, nonlinear regression model, partial least squares regression (PLSR) and multiple linear regression between the MC and spectral data of four vegetable samples in various drying intervals . These cest RMSE . Furtherest RMSE .(7)R2=1HSI encompasses several wavelengths, most of which are not useful to increase the model’s performance as they contain irrelevant information. Some wavelengths may contain similar useful information. Therefore, the modelling process can be simplified by selecting the optimal wavelength and eliminating irrelevant wavelengths to improve the model’s accuracy ,29. TherRegarding the complexity of the HSI data, several different algorithms should be compared to select the optimal wavelength to determine the best algorithm. Chen et al. (2022) proposed a successive projection algorithm to select the optimal wavelength based on the complete spectral range to predict the chemical composition of the product . Lin et The development of models using the selected wavelengths was performed to simplify the prediction process and improve the calculation speed. One of them was the MLR models .Different techniques are available to determine moisture content during the drying of food, such as computer vision technology , Near IMoreover, this technique is insensitive to impurities, and only the surface moisture of the material can be determined due to the short wavelength. The use of MRI-enabled visualisation of spatial changes during drying also proves that the MRI method offers valuable information for process optimisation in the commercial processing of different food materials. Still, its application for the online system has a high cost. Microwave dielectric spectroscopy needs more expensive electronic components due to the requirement of applying high frequency, and it must be calibrated separately for different materials. Moreover, it is difficult or even infeasible to attain an appropriate spatial resolution because of the relatively long wavelengths.In conclusion, the commercial application of microwave dielectric spectroscopy still needs further research . On the No requirement for sample preparation; environmentally friendly, non-invasive, and non-destructive method, economical as compared with traditional techniques, recording a spectral volume as compared to spectroscopy (collecting a single spectrum at one spot on a sample), obtaining both qualitative and quantitative properties, reporting the spatial distribution and concentration of the chemical composition of samples, distinguishing samples with similar colours, incomparable for process monitoring and real-time inspection.As shown in Lin et al. (2022) employed the HSI technology to predict the MC of four vegetables  during their drying by a vacuum-microwave dryer. They employed a spectrograph made in Finland  with a spectral range of 950–1655 nm . They emIn another study, the application of the HSI system and the partial least squares regression model was examined for the measurement and detection of MC and colour of the apple slices during the drying process. The results indicated that the wavelengths of 580, 750 and 970 nm can predict MC with high accuracy .Crighton et al. (2018) used an HSI system manufactured by Specim  in the spectral range of 400–1000 nm with the partial least square regression (PLSR) model to predict the moisture content and colour of raw and pre-treated apple slices (Golden Delicious variety). They indicated the capability of the HSI technology of the real-time detection of moisture content and colour of samples during the drying process [Arefi et al. (2021) employed an HSI system, which was a combination of the VisNIR system with a spectrum range of 1010–396 nm and a NIR system with a spectrum range of 1718–937 nm, to predict MC and some qualitative properties of apple slices . RegardiChen et al. (2022) studied the HSI method for online and non-destructive measurement of moisture content and soluble solid content (SSC) of persimmon characteristics during drying . They apA Finnish HSI technology  was employed to evaluate the quality of dried wolfberry fruit under different conditions. SVM, artificial bee colony (ABC), and grey wolf optimiser (GWO) algorithms were utilised to model and optimise. In contrast, Savitsky-Golay (SG) and standard normal variate (SNV) algorithms were used for spectral data pre-processing. The researchers showed that using the HSI technology and model integration (LS-SVM) with SG-SVN pre-processing methods can accurately predict the quality of dried fruit. All the analyses were performed by Unscambler X software and MATLAB R2015 [Seventeen pre-processing methods were adopted for noise elimination of the spectral data to predict the moisture content of potatoes using a Chinese his technology . Four machine learning algorithms, including Extreme Gradient Boosting (XGBoost), Gradient Boosting Categorical Features (CatBoost), Light Gradient Boosting Machine (LightGBM) and Integrated Stacking (Stacking), were compared to predict the moisture content in potatoes. ENVI 4.8  software was used for all the analyses. Based on their results, the XGBoost model at the wavelength of 400 nm offered the best prediction of the potato moisture content during the drying process .MC of a carrot was measured during hot air drying using a Finnish HSI technology  in the spectral range of 400 to 1010 nm. The analyses were performed with MATLAB software , and the results showed the ability of the PLSR model to precisely determine the MC of carrots .Zhang et al. (2022) investigated the prediction of MC of single-grain corn using technology manufactured by Specim Company of Finland in combination with deep learning . All theAn HSI technology  in the spectral range of 900–1700 nm was adopted for rapid and non-destructive determination of MC, free fatty acids (FFA) and peroxide value (PV) of the shelled almonds . All anaThe moisture content of peanuts was assessed using a Finnish HSI technology manufactured by Oulu company  accordinThe HSI technology of the IMEC model  was adopted using three pre-processing methods as implemented in Unscrambler software  to predict and determine the moisture content of blueberry during drying. The regression coefficients were high at 706, 790, 827, 868 and 894 nm, and these wavelengths were introduced as the best wavelength for the prediction of MC. The two pre-processing methods of SVN and MSC, along with the PLS model, were the best models for MC prediction . The res® 2010a software. The results showed promising hyperspectral images in evaluating the drying process of the melon.In another study, researchers determined MC of the melon samples during drying by the hot air method (temperature 60 °C and air speed of 2.5 m/s) with various pre-treatments, including ultrasonic (US), vacuum (VC) and US+VC pre-treatments, in different time intervals  by the HSI technology [In recent years, the development of suitable methods and technologies for continuous observation of the drying process and, thus, process control has received much attention. Smart drying is one of the emerging drying technologies within a multidisciplinary and interdisciplinary field. Its recent developments include the following research and development areas: artificial intelligence, biomimetics, computer vision and microwave/dielectric spectroscopy, visible and infrared spectroscopy, near-infrared (NIR), cloud/multispectral imaging and magnetic resonance imaging, ultrasound imaging, electrostatic sensing and control system for the drying environment .As previously mentioned, hyperspectral imaging (HSI) includes the simultaneous spatial and spectral detection of the product non-invasively in a wide range of wavelengths. It is referred to as a versatile technology. Therefore, by modelling the data obtained through the hyperspectral image during drying, useful information about the chemical substances and physicochemical changes of the product can be obtained. With all this information, a smart dryer can be designed. The researcher’s results showed that despite the necessity to overcome the significant computational effort required for data processing, a significant step could be taken towards the HSI development on a large scale as a rapid and non-destructive detection method based on an intelligent dryer system. However, there are many limitations to the transfer of HSI technology from the laboratory to the industrial  besides on many advantages of HSI. These restrictions include the high cost of HSI instruments and a lot of redundant data, which restrict the online processing of hyperspectral data. However, there are some research trends for reducing these limitations in the future. Firstly, cost reduction of HSI instruments must be attempted to develop and improve the hardware and software of the HSI system. Second, the existence of a lot of redundant data will be resolved with further advances in computational technology. So, despite the necessity to overcome these limitations, a significant step can be taken towards developing large-scale HSI-based intelligent dryer systems.In this review, the feasibility of the HSI technology in predicting and measuring MC of different agricultural products during drying was explored by reviewing previous works. The results showed the applicability of non-invasive methods such as HSI to measure the MC of different agricultural products during drying. Some researchers have recently proposed machine learning to analyse spectral data and introduced the HSI technology combined with machine learning as a promising tool for the non-destructive and accurate detection of MC during drying. Future research can focus on the application of the HSI technology during the drying process. By expanding the studies on the integration of the HSI technology with machine learning, the prediction accuracy of MC can be increased and the chemical composition can be controlled during the drying process to take major steps towards developing a smart dryer as quickly as possible. In other words, this review suggests developing a machine learning system to create a smart dryer using the HSI analysis for future research. Therefore, by having smart dryers, high-quality and low-price products can be offered to consumers all over the world.


General target detection with deep learning has made tremendous strides in the past few years. However, small target detection sometimes is associated with insufficient sample size and difficulty in extracting complete feature information. For safety during autonomous driving, remote signs and pedestrians need to be detected from driving scenes photographed by car cameras. In the early period of a medical lesion, because of the small area of the lesion, target detection is of great significance to detect masses and tumors for accurate diagnosis and treatment. To deal with these problems, we propose a novel deep learning model, named CenterNet for small targets (ST-CenterNet). First of all, due to the lack of visual information on small targets in the dataset, we extracted less discriminative features. To overcome this shortcoming, the proposed selective small target replication algorithm (SSTRA) was used to realize increasing numbers of small targets by selectively oversampling them. In addition, the difficulty of extracting shallow semantic information for small targets results in incomplete target feature information. Consequently, we developed a target adaptation feature extraction module (TAFEM), which was used to conduct bottom-up and top-down bidirectional feature extraction by combining ResNet with the adaptive feature pyramid network (AFPN). The improved new network model, AFPN, was added to solve the problem of the original feature extraction module, which can only extract the last layer of the feature information. The experimental results demonstrate that the proposed method can accurately detect the small-scale image of distributed targets and simultaneously, at the pixel level, classify whether a subject is wearing a safety helmet. Compared with the detection effect of the original algorithm on the safety helmet wearing dataset (SHWD), we achieved mean average precision (mAP) of 89.06% and frames per second (FPS) of 28.96, an improvement of 18.08% mAP over the previous method. The current progression in the fields of deep learning (DL), image processing, and computer vision (CV) technologies has changed the thinking about different features of day-to-day living . The DL However, detecting small targets is still a challenging problem. There is still a large performance gap between small and normal scale targets. Taking RetinaNet , one of The definition of “small target” varies in different scenarios and there is not yet a unified standard. The existing small target definition methods are mainly divided into the two categories of relative scale and absolute scale. Relative scale refers to the definition of small targets from the perspective of the relative proportion of the targets and images . HoweverCenterNet  recentlyWe propose a SSTRA algorithm to select all small targets by pixel filtering. By copying the identified small targets, it can effectively increase the number of small target samples.We propose TAFEM to obtain feature maps of multiple receptive fields , by combA novel structure, AFPN, is proposed to enhance feature extraction and fusion, allowing TAFEM to detect small target regions more accurately.To address the above problems, we present a novel small target detection network with adaptive data enhancement (ST-CenterNet) for accurately increasing target samples directly and enhancing shallow semantic information. First, we design a novel selective small target replication algorithm (SSTRA) method to scale limited small targets. Then, shallow semantic information enhancement is performed by our proposed target adaptation feature extraction module (TAFEM), and this TAFEM uses the residual network ResNet and AFPN successively connect to retain more shallow features. Our main contributions of this paper include:The rest of this paper is arranged as follows: With the massive progress of convolutional neural networks (CNN), it has become mainstream in target detectors to adopt the modernized state-of-the-art CNN models as feature extractors . The tarLarge numbers of detectors in deep learning have been designed for general targets. However, the insufficiency of small target detection and the extraction of feature information based on deep learning lead to unsatisfactory effectiveness. Small target detectability remains to be improved.Data enhancement methods enhance the value of limited data. Mishra et al.  used theThere have been some methods that have achieved some effects through feature enhancement. SSD detects targets with different scales and aspect ratios utilizing a multi-scale feature pyramid. The method uses convolution layers to predict the category fraction and the position offset of bounding boxes. Then, it obtains positioning bounding boxes by a non-maximum suppression post-processing strategy . The modNonetheless, the deep convolutional networks are prone to lose the key position information of small targets when extracting feature information. Some algorithms determine the location region by the contextual information  of targeAiming at these problems, some algorithms have proposed an attention mechanism  to improSome algorithms replace the sparse coding labels with dense coding labels based on angle classification ,58. The CenterNet is a one-stage target detection model with high accuracy. Target features are extracted from the inputting images by the baseline network and then introduced into the fully convolutional networks (FCN) to obtain a heatmap. Peak points of heatmaps are the centroids of targets. The position and category attributes of targets are obtained by using centroid location regression, which transforms the target detection problem into a key point prediction problem.Motivated by these studies, we propose the ST-CenterNet model to expand small target samples based on the original dataset and to strengthen the extraction of small target feature information, improving the capability of small target detection.Experiments are carried out on the open-source safety helmet wearing dataset (SHWD), which is used to provide head detection and safety helmet wearing. Referring to the standards of the PASCAL VOC dataset, an image dataset containing multiple scenes and targets was established, so that the target detection model has detection ability in different scenes, so as to facilitate the model for training, testing and analysis. The training and testing dataset contains 7581 images in which the targets are divided into two categories: hat and person. Among them, including 9044 people wearing helmets correctly  and 111,514 people without helmets .The images were acquired under complex lighting conditions, and there are complex backgrounds and small targets. Unlike the targets in natural scenes which are often taken from horizontal perspectives, images of the building site in SHWD are typically taken from several positions in diverse directions. The adopted dataset is inundated with low-resolution images with significant amounts of distractors and confusing target orientation. Moreover, our labeled frame only shows the head part of the workers, which further makes it difficult to detect whether a helmet is worn or not. These difficulties suggest that targets in SHWD usually are of diverse sizes and orientations with complex backgrounds, making them an excellent dataset for detection in complicated scenes.The images in the dataset contain various angles, multiple and single targets, distances, occlusions, etc. The used public dataset is an xml file in PASCAL VOC format and was converted into a txt tag file in the CenterNet format. The dataset was labeled with labelImg for image targets, and the entire dataset was randomly divided into a training set and a test set, with the ratio of 8:2. The number of training set images in the 7581 images dataset is 6064, and the number of test set images is 1517.The performance of detection results for small targets is weak due to the insufficient number of small targets in SHWD. Therefore, the network model will pay more attention to the training of medium and large targets during training, and ignore small targets, resulting in unsatisfactory detection effect of small targets.In order to enrich the detection background and increase the number of small targets in SHWD, the proposed SSTRA is used to replicate and flip the small targets in the dataset preprocessing stage. Targets with image resolution less than or equal to 32 × 32 are considered small targets. To further verify the effects of SSTRA, we represent the visual results of images processed by SSTRA in In the experiment, our SSTRA detects and replicates only the header information of objects, because the specific category can be judged only through the detection of header information in the experimental detection stage. Our proposed SSTRA is implemented through the real box annotated by the dataset, then by finding the small target according to the size of the targets, and finally by copying the bounding box of the small target. Since the dataset is labeled with the header, subsequent operations are processed against the target header.Our proposed ST-CenterNet algorithm samples an inputting image of uniform size by the feature extraction network. The feature information extraction network subsequently extracts the target feature information. After that, the extracted information is passed into FCN. The target centroid, the width and height of targets, and the offset value of the centroid for heatmap prediction are obtained. The position, size and category of targets are obtained by centroid regression.The SSTRA is proposed to address the shortage of the number of small target samples in the dataset. Targets with pixel values less than or equal to the number in the images are selected and considered as small targets. Then, all small targets are replicated and flipped to achieve the oversampling of small targets, thus increasing the sample size for them.Utilization of low-level features is one way to pick up information about small targets. The backbone network uses ResNet-50 to down-sample the image and combines it with AFPN to obtain feature maps of different sizes. This allows more complete extraction of feature maps with strong deep semantic information and shallow position information during inference processing.Compared with the conventional size targets, small targets have fewer available pixels, which makes it difficult to extract complete feature information. With the increase of network layers, their semantic information and position information are gradually lost and become difficult for the network to detect. The features cannot provide effective expressions of semantic information. To deal with that, multi-scale learning is used, as a strategy to effectively integrate two types of feature information and perform better semantic representation. According to ,47, it iIn the proposed algorithm, the loss function is divided into the following three parts: the loss of the heatmap, the loss of the width and height of the bounding box, and the offset loss of the central key point. The loss function is formulated as follows:When calculating the loss value of the heatmap, the idea of focal loss  is used The number of small target samples in SHWD is less than that of general targets. This leads us to pay more attention to the training of large and medium-sized targets, neglecting the training of small targets when using the network model for training. Because of the small size of small targets or the small proportion in the image, the position diversity of small targets in the image is insufficient.Considering the above two problems, we utilize SSTRA to selectively oversample all targets in the image and to achieve separate replication of small targets to enrich their sample size. Extracted targets are used to obtain targets with pixel values less than or equal to 32 × 32 against the background of a construction site by screening. These are considered small targets. All of the original images that contain small targets are determined and then individually extracted. The extracted small targets are replicated twice and horizontally flipped at the second copy, and then are put back into the corresponding original images according to initial sizes of extraction. Related information about these small targets is added to the extensible markup language file corresponding to the image. The architecture of SSTRA is demonstrated in Our SSTRA focuses on two replications of small targets, which individually match pixel value screening. Then, there is repeated sampling processing of small targets to obtain a larger number of samples and to further increase the diversity of positions to improve the detection performance of small targets.The detection task requires models to identify targets on more scales than the classification task requires, in order to preserve information from various layers. This is especially important for detection because each layer has different receptive fields. CenterNet  makes prThe AFPN constructs a four-layer feature pyramid by up-sampling the deep convolution feature map and fusing this with the shallow features through horizontal connection. The details of TAFEM are shown below.For the network structure of the deeper layers, the parameters are generally set close to zero during initialization. This leads to problems such as gradient explosion as the network layer deepens during training to update the parameters of the shallow-layer network. The result may make it impossible to update the parameters of the shallow-level network. We set up a linear combination of the network layers based on the original network structure. With an increased number of network layers, the linear combination for the mapping is as follows:The feature information of each predicted feature map is enhanced by boosting each feature map to the same number of channels by convolution with a convolution kernel size of 1. It is then fused backward and forward with the shallow features by lateral connections, which are similar to the jump connections in the residual structure. The features of each layer obtained after independent up and down-sampling are passed to an activation gate consisting of a combination of multiple convolutions and sigmoid activation functions. Thus, generated lateral features are used to fuse location features with semantic feature information:Deep and adjacent feature layers are added and element-wise multiplication fusion processing is conducted. Convolution is used to eliminate the aliasing effect of the up-sampling and to generate a new feature map according to the sizes of the targets:After the convolutional layers, the three feature maps in  are flatIn order to conduct model training and performance testing, we set up the experimental environment configuration. Detailed experimental settings are presented in −4, momentum factor of 0.9, and a step learning rate decay [−4. The updated formula of the weighting is as follows:We adopt a stochastic gradient descent (SGD) algorithm for model training to update and optimize the weight of the network model. The model training uses synchronous batch normalization . Accordite decay . In the To speed up the training, the backbone of the proposed model is initialized from a checkpoint pre-trained by ResNet-50 with self-training . All oth−2), to evaluate the performance of the proposed detector.According to the standard protocol defined by the public dataset, we adopt the mean average precision (mAP), precision, recall, and f1-score to evaluate the detection accuracy of the algorithm in SHWD. We use frames per second (FPS) as the evaluation index of detection over speed. Because we expect to detect whether the remotely operating workers are wearing their helmets correctly, we also use the evaluation metric in pedestrian detection, log-average miss rate . The CenterNet model detected a total of four targets, two missed detections, and one false detection. The ST-CenterNet model correctly detected a total of six targets, zero missed detections, and zero false detection. It can be seen from the above that, in the CenterNet model, there were serious false detection and missed detections for small targets and occluded targets. Compared with the original model, the qualitative detection results in  illustraAccording to the different improvement schemes, we conducted ablation experiments to explore the effects of the SSTRA module and TAFEM module on the model performance. The results of the ablation experiments are shown in In As can be seen from Four groups of models were trained, respectively, and each module was successively added on the basis of the CenterNet model. Four groups of models were tested in turn on the same test set, and the AP curves of hat and person detection are shown in Compared with the results of “CenterNet”, the AP values of the “CenterNet+SSTRA” and “CenterNet+TAFEM” model were slightly improved. It was found that adding the SSTRA selected processing function effectively optimized the model performance and improved the detection accuracy of the model by increasing the number of small targets. The extraction of effective feature information and multi-scale feature fusion enabled the model to better detect targets. It can be found that adding the TAFEM feature enhancement function optimized the feature extraction efficiency and improved the detection performance. Compared with the “CenterNet+SSTRA” model, the AP of the “ST-CenterNet” was increased by 3.12% and 5.08%, respectively. Compared with the “CenterNet+TAFEM” model, the AP of the latter was increased by 3.22% and 6.36%, respectively. On the whole, each module in the proposed ST-CenterNet model effectively improved the detection accuracy, met the actual detection requirements, and verified the feasibility of the model.The confusion matrix is a situation analysis table that summarizes the prediction results of the classification model in deep learning. It summarizes the records in the data set in the form of a matrix according to the true category and the category judgment standard predicted by the classification model. The confusion matrix generated by the CenterNet and ST-CenterNet technique on the classification of hat and person under 200 epochs is in −2) to evaluate the performance of the proposed detector. The purpose is mainly to verify the effectiveness of the proposed model in reducing the proportion of missed detection of the detection results. MR−2 represents the miss rate of the model, and the pedestrian detector performance is measured by the curve of the average miss rate versus False Positives Per Image (FPPI) as the abscissa, and Log (MR) as the ordinate. The expected result is a reduction of MR−2. As can be seen in −2 for both types of targets detected (hat and person), when compared to the original model.In addition, we adopt the log-average miss rate (MRThe experimental results on SHWD show that the mAP index of the model is improved by 13.9% after replicating and flipping small targets through the SSTRA. Besides, the TAFEM considerably improves the ability of feature extraction, and the mAP of the model is increased by 13.22% compared with the original model. Compared with the CenterNet, the mAP index of the proposed algorithm is increased by 18.08%, which proves the superiority of ST-CenterNet in small target detection. SOTA performance on various methods demonstrates the superiority of ST-CenterNet in small target detection. SSTRA and AFPN can be combined with diverse detectors and various backbones to strengthen small target detection. This ability is transferrable to more specific situations. During the experiment, we found that there are still some missing targets that have not been detected, so the future improvement needs to further strengthen the capture of shallow feature information. We may consider trying to add learning to strengthen the correlation between parts using a self-attention mechanism.Our proposed algorithm for small target detection with adaptive data enhancement aims to solve the lack of shallow feature information extraction and insufficient numbers of small targets. SSTRA utilizes a screening and oversampling method, which brings great benefits to increasing the number of samples and to detection effectiveness, while maintaining the total quantity of images in the dataset. TAFEM adopts the combination of ResNet-50 and AFPN to enhance the performance of extracting shallow semantic information, so as to obtain more complete semantic feature information. For future work, we are committed to using a more lightweight network to reduce the computational complexity of the model parameters. We intend to study the combination with insights from the recently proposed transformer model to accurately deepen the self-attention mechanism and more perfectly explore the relationship between different levels of feature maps.