The main limitation of high-temperature piezoresistive pressure sensors is the variation of output voltage with operating temperature, which seriously reduces their measurement accuracy. This paper presents a passive resistor temperature compensation technique whose parameters are calculated using differential equations. Unlike traditional experiential arithmetic, the differential equations are independent of the parameter deviation among the piezoresistors of the microelectromechanical pressure sensor and the residual stress caused by the fabrication process or a mismatch in the thermal expansion coefficients. The differential equations are solved using calibration data from uncompensated high-temperature piezoresistive pressure sensors. Tests conducted on the calibrated equipment at various temperatures and pressures show that the passive resistor temperature compensation produces a remarkable effect. Additionally, a high-temperature signal-conditioning circuit is used to improve the output sensitivity of the sensor, which can be reduced by the temperature compensation. Compared to traditional experiential arithmetic, the proposed passive resistor temperature compensation technique exhibits less temperature drift and is expected to be highly applicable for pressure measurements in harsh environments with large temperature variations. In recent years, universal high-temperature piezoresistive pressure sensors have been extensively applied in the fields of petrochemicals, energy and electric power, and aerospace. Thus, improving the measurement accuracy is likely to play an increasingly important role in various industries, especially those that encounter high-temperature environments . Among tThe main measuring principle used by high-temperature piezoresistive pressure sensors is the Wheatstone bridge. The output voltage of a Wheatstone bridge must be compensated in terms of the offset voltage, the temperature coefficient of the offset, and the temperature coefficient of the sensitivity. Current bridge temperature compensation methods can be divided into hardware, software, or hybrid techniques. Hardware methods typically use an extra thermistor, a low-temperature coefficient resistor network, a diode, a triode, an adjustable gain operational amplifier, and so on . The sofIn this study, we describe a passive resistor temperature compensation technique that uses a low-temperature coefficient resistance network. Unlike other hardware methods, which require the compensation circuit to be in the same temperature field as the bridge arm piezoresistor, our passive resistor temperature compensation circuit does not need to be at the working temperature of the bridge arm piezoresistor, allowing more flexibility and convenience in packing. Moreover, the compensation accuracy of the proposed method is independent of the temperature difference between the compensation circuit and the bridge arm piezoresistor. A typical circuit diagram of a low-temperature coefficient resistor network model is shown in B is the bridge arm resistance, TCRB is the temperature coefficient of RB, VOS is the bridge zero output voltage, and α is the temperature coefficient of the output voltage sensitivity [The traditional theoretical compensation formulas for the offset voltage and temperature coefficient of the offset are given in Equations (1) and (2), respectively; the compensation formula for the temperature coefficient of the sensitivity is given in Equation (3).sitivity .These equations assume that the four bridge arm resistances and their temperature coefficients have almost the same initial values, and neglect the influence of residual stress caused by the fabrication process. Otherwise, the compensation applied to the temperature coefficient of the offset and the sensitivity would be invalid ,8. In coPassive resistor temperature compensation is based on the assumption that passive resistance is temperature-independent. This means that the temperature coefficient of the passive resistor should be less than 10% of that of the bridge arm resistor. In this case, the temperature coefficient of the passive resistor can be ignored. As the temperature coefficient of a typical silicon piezoresistor is greater than 2000 ppm , the temt can be expressed as:The temperature of the bridge arm resistor RR is given by:The temperature coefficient expression αCompensation can be accomplished by correcting the temperature coefficient of the bridge arm resistor. This can be done using simple circuit techniques with passive shunt or series resistor elements with temperature-independent resistance.t is αR+G, as shown in Equation (6). A comparison of Equations (5) and (6) shows that αR+G is less than αR. This implies that the temperature coefficient of the bridge arm resistor with a series compensation resistor is less than that of the bridge arm resistor itself.t is αR‖G, as shown in Equation (7). A comparison with Equation (5) shows that αR‖G is less than αR, which means that the temperature coefficient of the bridge arm resistor with a shunt compensation resistor is also less than that of the bridge arm resistor itself.From the above analysis, we can infer that increasing the serial compensation resistance or decreasing the shunt compensation resistance can reduce the temperature coefficient of the bridge arm resistor with a compensation resistor and adjust the offset. This is the principle of compensation for the offset voltage and the temperature coefficient of offset.s under a constant voltage supply, as shown in P under a constant current supply, as shown in The temperature coefficient of sensitivity can be compensated using a serial temperature-independent resistor RIN is the supply voltage, RB(T) is the equivalent bridge arm resistor, S(T) is the sensitivity, and P is the load pressure. As the temperature rises, RB(T) increases and S(T) decreases. Obviously, the temperature drift of the sensitivity can be compensated by selecting a suitable value of RS according to Equation (8).For the example illustrated in S works is that, as the temperature rises, the resistance of the bridge RB(T) increases, whereas the resistance of RS is almost unchanged with a near-zero temperature coefficient. This leads to an increase in the proportion of the supply voltage in the bridge and an increase in the bridge output voltage. The resistance of RS is chosen such that the decrease in the sensitivity of the bridge as the temperature increases can be compensated by the increase in the bridge output voltage with temperature. A similar interpretation can be applied to the case of a constant current supply, as in A simple qualitative explanation of how the compensation resistor ROur model for passive resistor temperature compensation is based on the principles described above. As a simple supply circuit is best suited to high-temperature applications, a constant voltage supply is usually adopted by high-temperature pressure sensors. The following describes the model and algorithm for passive resistor temperature compensation with a constant voltage supply. +  and K-  are the positive and negative voltage division factors, respectively, and VB is the supply voltage of the bridge, which can be written as:The sensor output voltage is:According to the initial offset voltage and sensor test data, we select the compensation model in The compensation model in Bridge resistance:Voltage division factors:Z, RP, RS are the model parameters to be determined, and Ri  are the known measurement parameter values in The output voltage expression of the model in According to the demands of the bridge temperature compensation, the algorithm for passive resistor temperature compensation can be written as:0 (P1) to be independent of the temperature. This means that the partial derivatives of VOUT  and VOUT  with respect to temperature T must remain equal to zero.It can be seen from these equations that compensating for the temperature coefficient of offset (sensitivity) requires the sensor output voltage under the initial (higher) load pressure PZ, RP, RS in the passive resistor temperature compensation model can be determined from Equation (15) using computer software such as MATLAB [The values of Rs MATLAB .The uncompensated high-temperature pressure sensor can be tested using the high-temperature and pressure calibration device shown in The test results are shown in The traditional temperature compensation model and experiential arithmetic shown in The passive resistor temperature compensation model and differential equations derived in this paper were applied to the bridge arm resistors at temperature thresholds of 20 °C and 220 °C and load pressures of 200 kPa and 600 kPa. The results are listed in 0 = 4 mV).Because of the negative initial offset output voltage, the compensation model circuit shown in 0; the green surface is drawn according to the second equation in Equation (15), representing the compensation of the temperature coefficient of offset; the blue surface is drawn according to the third equation in Equation (15), representing the compensation of the temperature coefficient of sensitivity.In the The parameter values were varied within the following compensation resistance ranges:From this, we obtained the minimal compensation resistance parameters:The passive resistor temperature compensation circuit was then established using the data in The test results for the sensor under passive resistor temperature compensation are shown in From the above results, it is clear that the uncompensated sensor calibration curve  shows siOne limitation of this compensation approach is its effect on the output sensitivity. As can be seen by comparing in+ and Vin- are the output voltages of the bridge, Vref is the output zero reference voltage of the circuit, and R5 (the gain adjustment resistor) determines the output voltage of the high-temperature signal-conditioning circuit, which can be expressed as:In The test results for the high-temperature piezoresistive pressure sensor with passive resistor temperature compensation and the high-temperature signal-conditioning circuit are shown in As can be seen from these results, the uncompensated high-temperature pressure sensor suffers from a significant degree of parameter drift across the whole working temperature range, but the sensor with passive resistor temperature compensation and the high-temperature signal-conditioning circuit can achieve similar levels of accuracy and temperature drift as the XTE-190. Additionally, in order to fully verify the effect of passive resistor temperature compensation, the same batch of six sensors has completed the temperature compensation calibration test by the same method, and the compensation effect reaches the same level. The sensor device pictures are shown in In this paper, we have presented a broadly applicable method for passive resistor temperature compensation for high-temperature piezoresistive pressure sensors. The proposed method uses an algorithm based on the solution of differential equations. Our passive resistor temperature compensation technique is not affected by the characteristic deviation between bridge arm resistors or residual stress. Using only four bridge arm resistance measurements at different temperature thresholds and different load pressures, the temperature compensation circuit and compensation parameters can be determined computationally. Additionally, a high-temperature signal-conditioning circuit can be used to improve the output sensitivity of the compensated sensor. The compensation effect of the passive resistor temperature compensation on a high-temperature pressure sensor was demonstrated to be significantly better than that of the conventional compensation technique across a wide range of temperatures and pressures, suggesting that our method is worthy of popularization and application in sensor fabrication.


We aim to build the simplest possible model capable of detecting long, noisy contours in a cluttered visual scene. For this, we model the neural dynamics in the primate primary visual cortex in terms of a continuous director field that describes the average rate and the average orientational preference of active neurons at a particular point in the cortex. We then use a linear-nonlinear dynamical model with long range connectivity patterns to enforce long-range statistical context present in the analyzed images. The resulting model has substantially fewer degrees of freedom than traditional models, and yet it can distinguish large contiguous objects from the background clutter by suppressing the clutter and by filling-in occluded elements of object contours. This results in high-precision, high-recall detection of large objects in cluttered scenes. Parenthetically, our model has a direct correspondence with the Landau - de Gennes theory of nematic liquid crystal in two dimensions. To recognize an object in a visual scene, humans and other primates process visual signals relayed through the retina in vivo, the error rate is reduced by orders of magnitude by contextual information that influences local processing, which may not be captured fully in such models It is believed that, the simplest model of the primary visual cortex that simultaneously achieves two contradictory tasks: clutter removal and occlusion filling. We do not aim at the state of the art performance on complex natural images, but rather ask what is the smallest set of computational primitives that must be implemented in a model to achieve such detection and integration of long contours in a nontrivial setting. For this, we focus on a proposal of a specific lateral connectivity among V1 neurons coarse-grained, continuous neural field, which we model as a complex-valued field on the complex plane, The goal of this paper is to build Importantly, our complex field approach is significantly simpler than most other coarse-grained models, thus pushing the limits in identification of the minimal set of the required computational primitives. Indeed, typically the neural firing rate is represented as a real function of three variables  directorWe define the dynamical variables in our coarse-grained model as the neural firing rate A. At the same time, neurons responding to high spatial frequency clutter elements do not get sufficient lateral excitation, and their activity decays. These collective dynamics integrate information over large spatial scales.Neurophysiological and psychophysical experiments We represent these phenomena in a traditional linear-nonlinear model, where the neural field at a point Here The excitatory input, Co-circular excitation may be represented asThe first term, derived in We define the input nonlinearity using a complex step function:The inhibition term Here Since our focus is not on practical image processing algorithms, we focus on synthetic images in this work, as in The “amoeba” objects (long closed contours with gaps) are generated by choosing a center at a random point in the image, and then drawing the amoeba around this point in polar coordinates, with the radius as a superposition of periodic functions with different radial frequencies, We simulate occlusions and noise in real-world images by removing parts of amoebas. A random number of 2–4 segments with random angular length combining to the total of We need the clutter to be indistinguishable from the targets by curvature, brightness, and other local statistics, so that object detection is impossible without long-range contextual contour integration afforded by co-circular connectivity. Thus clutter is generated by first generating an amoeba as described above, partitioning it into segments, and then randomly shuffling and rotating the segments to break long-range contour continuity. Specifically, the model cortex is divided into One or two targets and clutter resulting from breakup of one or two additional targets were then superimposed together to form test images, see Images used previously in psychophysics experiment  were impThe time evolution of the model is studied on a square lattice of a linear size kernel lattice. Square discretization destroys the angular symmetry of the kernel evaluated at an arbitrary In each iteration cycle we first calculate the total input In our simulations, the excitation range ‘Ex’ is We first chose the parameter The code was implemented in C, compiled with the gcc v. 4.7, and optimized with OpenMP libraries. Simulations were performed on a computer with Intel i7 2600k (clock speed 3.4 GHz). The simulation time for 250 iteration cycles for one image took about 10 s. All model dynamics times were measured in units of We quantified the performance in terms of precision, mulation . Hence dA gives the variation of precision and recall at various cutoffs at particular times during the simulation. At ad hoc details of the simulations and the data. For example, defining the threshold parameter not as an absolute value, but as a fraction of the maximum activity of the field at a given time point did not change the precision-recall curves much (B). This is because the time scale of the clutter decay depends on the size of the segments, and not on their number. For longer segments, the decay takes longer, and hence the optimal processing time increases. The optimal processing time also increases with the linear dimension of the occlusions present in the target amoebas and with the number of occlusions (B). However, for all of these cases, the maximum precision and recall remain simultaneously high.Performance depends only weakly on the ves much . Similarves much B. This clusions B. Howevcontinuum, coarse-grained model of V1 to study contour detection in complex images, which is substantially simpler than other models in the literature, and yet still performs nontrivial visual computation. While borrowing heavily from previous research, our model differs from most previous approaches by forgoing individual neurons and describing the neural activity as a parity-symmetric continuous director field, which makes expressions for Hebbian connectivity and solutions of the model dynamics expressible in the closed form. We incorporate some experimentally observed properties of the visual neural dynamics, namely non-linear excitation, thresholding, cross orientation suppression, local relaxation, global suppression, and, crucially, co-circular excitatory connectivity We developed a fill in the occlusions particularly distinguishes our approach from the previous work on co-circular excitatory feedback The model identifies long object contours in computer-generated images with simultaneous recall and precision of over 90% for many conditions. It happens even though initially large parts of objects are invisible , and clutter is present (decreasing precision). The model fills in the occlusions and filters out the clutter based on the presence or absence of co-circular contextual edge support. In addition to the substantial simplification, this ability to not crucial for the studied visual processing function. The reduced complexity is not only conceptually appealing, but also can result in more efficient computational implementations. For example, it should be possible to augment practical feedforward models of object detection, such as The model performs on par or better than agent-based three-dimensional models , with complex, empirically specified co-circular interaction kernel The model makes predictions that can be tested experimentally, such as regarding the amount of neural excitation in V1 as a function of the computation time and the duration of exposure to an image. Additionally, it predicts that the neural activity localizes to long contours with time, which can be tested with various imaging technologies. Finally, it can be used to predict the dependence of the contour detection performance on the statistical structure of images and on the exposure time. Testing such predictions in psychophysics experiments Finally, we notice that the neural field This may help solve a crucial difficulty in implementing an artificial laterally-interacting neural model: the computational cost of long-range communication. Indeed, one can think of materials with symmetry and dynamical properties such that the neural computation and the communication are performed by the intrinsic dynamics of the material itself. Potential implementations can include polarizable liquid crystals with long-range magnetic interactions, polar colloidal materials, or heterogenous solid state materials with long-range connectivity. The liquid crystal analogy suggests the use of the well-developed repertoire of theoretical physics to understand the impact of different terms in the model neural dynamics, Eq. (1). In particular, one can hope that the future renormalization group treatment of this dynamics will reveal the terms in the interaction kernel


Control and reversibility of biomolecular interactions at engineered interfaces presents opportunities to develop highly efficient substrates and devices for a wide range of biomedical applications.FigureDynamic surfaces are particularly attractive for biomedical applications and are playing an increasingly important part in the development of highly sensitive biosensors,While the charged molecular backbone or the end group on the structure of the reported electrically switchable SAMs are the hypothesized basis for controlling such biomolecular interactions,Herein, we address this challenge by studying the biotin-KKKKC:TEGT mixed SAM as a model system and report the first observation of biotin orientations in the charged mixed SAM in response to an applied potential, using in situ SFG spectroscopy. This technique takes advantage of even-order non-linear optical selection rules that precludes signals from isotropic environments or molecular arrangements that possess inversion symmetry.The SFG characterization was performed with an easy to assemble and purpose built electrochemical cell that allowed recording of SFG spectra while applying an electrical potential. Furthermore, the conformational changes of the biotin-KKKKC:TEGT SAMs observed by SFG at negative and positive potential are compared with densely packed biotin-KKKKC SAMs to understand the importance of the presence of TEGT as a spacer group. SPR data are also discussed to further strengthen our findings. Finally, the reversibility of the conformational changes is verified.2 prism (size 25 mm). The prism was then placed onto a purpose built Teflon electrochemical cell , the peak around 3245 cm−1 can be attributed to molecular vibrations within the heterocyclic imidazole moiety of the biotin group.37SFG measurements were performed at static potentials. First, a potential of +0.3 V was applied to the biotin-KKKKC:TEGT SAM and the corresponding SFG spectra was recorded. Subsequently, a potential of –0.4 V was applied to the same substrate while the SFG setup was left unchanged. The corresponding spectra  are shown in Besides the narrow band visible in An analysis of the relative phase in between the resonant and non-resonant signals can retain information on the orientation of this particular group. A dip in the spectrum is related to a mean orientation of the corresponding transition dipole moment (TDM) away from the substrate (destructive interference), while a peak is indicating an orientation towards the substrate (constructive interference). Applied to the spectra in this present research, the biotin moiety contributing in this spectral region has its mean TDM orientation pointing away from the substrate at positive potential resulting in a dip in the spectrum. In this scenario, the positively charged peptide chain is prone to adopt a conformation that will extend itself away from the substrate due to electrostatic repulsion resulting in an anisotropic upright orientation of the biotin group.At negative potential, the peptide chains are likely to adopt a collapsed folded conformation due to electrostatic attraction between the negative potential of the surface and the positive charges on the peptide backbone, which appears to have resulted in a disordered biotin group since the SFG signal is no longer visible , in situ SFG spectra of biomolecules at surfaces are rather complex and relatively weak due to the isotropic nature coming with a less tightly packed arrangement of molecules. The mixed biotin-KKKKC:TEGT SAM used in our study has been previouslyOftentimes FigureThe conformational change of the biotin-KKKKC peptide is further illustrated by SPR measurements of the binding events between the biotin end-group of the biotin-KKKKC:TEGT SAM and the neutrally charged protein Neutravidin at different applied potential c. For biAlthough it is important to study the switching behaviour of SAMs with different experimental detection techniques, no information at the molecular level can be gained by SPR. Furthermore, the reversibility of the switching monitored by SPR implies the use of specific analytes which could lead to several issues such as the occurrence of non-specific binding and the irreversible chemical bonds between the analyte and the ligand. Both these circumstances have an impact on the reversibility performance and therefore on its analysis. However, these difficulties are overcome by using SFG spectroscopy where no extra binding processes are needed a and b.−1 at –0.4 V suggest that the biotin end-group is facing in the opposite direction compared to the initial measurement (+0.3 V). Such information has the potential to positively impact the field of biosensors as well as surface engineering where the direct knowledge of the structure and the geometry of the molecules at interfaces are vitally important.We have demonstrated that SFG spectroscopy is a highly sensitive tool able to provide an in depth characterisation of the reversibility of electrically switchable biotin-KKKKC:TEGT SAMs. By studying the orientation of the SFG peak's characteristics of the biotin end-group, the determination of the structural orientation under electro-induced switching was ascertained. The switchable process and its reversibility were assessed by repeatedly switching between positive and negative surface potentials. Monolayers of single component biotin-KKKKC SAMs exhibited no conformational change upon the application of an electrical potential, indicating that due to the high level of packing the oligolysine chains are constrained to one extended conformation (anisotropic molecular ordering). On the contrary, when a negative potential of –0.4 V is applied to the mixed biotin-KKKKC:TEGT SAM, a change in conformation occurs owing to the presence of the spacer, TEGT, presumably allowing the folding of the oligolysine backbones (isotropic molecular ordering). Furthermore, the reverse phase of the SFG signal in the region between 3200–3300 cm


Daily Increased rate of anterior cruciate ligament (ACL) injuries in athletes calls for more investigation in these patients to differentiate copers from noncoper ACL-deficient (ACLD) knees as soon as possible.The current study aims to introduce a new electromyographic protocol, named voluntary response index (VRI), that might help to categorize and differentiate patients with ACLD knee from others in the early stage.Thirty-four patients with ACLD knee were allocated into two equal groups, namely, coper and noncoper groups, based upon their ability to return to sport during the preceding six months. The patients with ACLD knee were asked to perform a jump on a force platform from a 75-cm distance.The results were compared with 17 matched healthy participants. The electromyographic disposable electrodes were attached to the seven muscles of the lower extremity of the participants before performing the test. The outcome measures were the magnitude and similarity index of the VRI, time to stop, vertical ground reaction force, the displacement of the center of pressure’s (COP) path line length, and the participants’ Tegner, IKDC  as well as KOOS (Knee injury and Osteoarthritis Outcome Score) questionnaires scores. Using the appropriate statistical analysis, the electromyographic and force plate data were compared among the three groups. All efforts went into determining whether an association exists between the findings of each group and the participants’ functional questionnaires scores.The results of this study would be helpful in objectively differentiating the patients with ACLD knee into coper and noncoper groups to receive appropriate treatments before their return to the competitions. Tearing of the anterior cruciate ligament (ACL) is one of the most common musculoskeletal injuries affecting athletes with the frequency rate of 250 000 injuries per year or 30 people in 100 000 population in the United States . SeventyIn brief, due to the high rate of ACL-deficiency in athletes and shortage of useful information provided by the study of the activities of individual muscles, a comprehensive EMG protocol to study the prototype of all muscles involved in a hard task such as landing, as the commonest cause of ACL-rupture, is necessary.The current research aimed to introduce the VRI technique to study the magnitude and the similarity index of seven muscles of the lower extremity in both coper and noncoper ACLD subjects and to compare them with the matched healthy participants.This case-control study assessed the coper and noncoper individuals with ACLD knees and compared their results with that of matched healthy controls. We used a convenience sampling, which is a nonrandom sampling method. The ethical approval code was achieved before conducting the study.Thirty-four recreational athletes with age between 20-29 years old who had ACL-deficiency at least six months ago or longer will be recruited in this study. The subjects were allocated into coper and noncoper groups (17 patients in each group) based upon the idea of Fitzgerald et al. (2000) . All subThe subjects were recruited based on the convenience sampling via the following Using a pilot study, estimation of variance was 0.0015 and means were 0.85 and 0.89, respectively.All subjects were asked to sign an inform consent regarding their voluntarily participation in the study and to complete the published Farsi version of the KOOS, IKDC, and Tegner activity scale questionnaires -19 as stThe EMG activities of the seven muscles of the lower extremity including the vastus medialis, vastus lateralis, biceps femoris, semitendinosus, gastrocnemius , and the gluteus medius were recorded during a landing test. The electrodes were placed based on the SENIAM’s (Surface Electromyography for the Non-Invasive Assessment of Muscles) recommendations . The sitn muscles in a task and (1)Ri = Response vector (RMS) of each muscle(2)At the end, all efforts were made to find out any association between the VRI and the questionnaires’ scores of each group. In addition, the sensitivity and specificity of the VRI method in differentiating individuals with ACLD knee as copers or noncopers was studied.Before starting the test, all subjects were asked to stand on the ground and jump vertically for three times to record their maximum height jumping. Then, they were asked to stand 75 cm far from the force platform and jump on it on one leg, i, the injured leg in patients with ACLD and the matched leg in healthy controls. The important point on landing was that the trials was accepted only if the subjects jumped so high that their head was located between the two lights placed in 50% to 60% of their maximum jump height. In other words, any jumps lower or higher than these two lines was discarded as it might ruin the ground reaction force data. The subjects had to jump while standing on both legs, but they had to land only on one leg. The outcome measures of this study were the magnitude and similarity indices of the tested muscles (VRI), the time to stop landing on the force platform, the maximum vertical ground reaction force, and the path line length of the center of pressure (COP) of the participants.A Kolmogorov-Smirnov test was used to assess whether the recorded data were normally distributed. A multiple regression analysis was used to determine any correlation between the independent and dependent measurements. Finally, a one-way ANOVA analysis was used to study the magnitude, similarity index, and the force platform data between study groups.The annual reports of the World Health Organization (WHO) routinely express a rapid increase in musculoskeletal injuries, particularly, ACL-deficiency in both recreational and professional athletes. Finding the coper patients with ACLD knee has great importance as they are able to continue exercising without reconstructive surgeries. Currently, the electromyographic tools are essential diagnostic apparatus in kinesiological research by use of the parameters such as magnitude, duration, onset activation time (latency) and recently the voluntary response index (VRI). The latter parameter may help opening new insights in diagnosis and finding a more objective and scientific way to divide the ACLD knee subjects into coper and noncopers. This also might help to be able to change the potentially noncoper ACLD knee subjects into coper ones in near future.During landing, the knee joint, as a joint between the ankle and hip joints, encounters the most impact forces to enable the subjects to obtain their balance as soon as possible. During landing, a smooth force transmission from the ground to the trunk requires an intact ACL. Therefore, studying the methods for better controlling the knee joint during landing may help the researchers to understand how some patients with ACLD knee are able to overcome this ligament deficiency (copers). Due to the all aforementioned reasons, it might be helpful if the muscles responsible for landing are studied as a whole so that it might help clinicians for a better evaluation, diagnosis, and treatment plans.


This paper introduces a method to remove the unwanted interdependency between vertical and horizontal eye-movement components in electrooculograms (EOGs). EOGs have been widely used to estimate eye movements without a camera in a variety of human-computer interaction (HCI) applications using pairs of electrodes generally attached either above and below the eye  or to the left and right of the eyes . It has been well documented that the vertical EOG component has less stability than the horizontal EOG one, making accurate estimation of the vertical location of the eyes difficult. To address this issue, an experiment was designed in which ten subjects participated. Visual inspection of the recorded EOG signals showed that the vertical EOG component is highly influenced by horizontal eye movements, whereas the horizontal EOG is rarely affected by vertical eye movements. Moreover, the results showed that this interdependency could be effectively removed by introducing an individual constant value. It is therefore expected that the proposed method can enhance the overall performance of practical EOG-based eye-tracking systems. An electrooculogram (EOG) is the electric potential measured around the eyes, which is generated by the corneo-retinal standing potential between the front and back of the eye . In receet al. [et al. [et al. attempted to recognize ten eye-written digits and four extra symbols from 11 subjects. The overall accuracy was reported to be 72.1% [To date, many studies have strived to recognize user intention using both the horizontal and vertical EOG components; however, most of these studies have been limited to only the estimation of momentary eye movements. The most successful application of EOG was as a substitute for the standard four directional (arrow) keys. For example, EOG-based gaze-direction estimation has been applied to simple eyeball input devices , a wheelet al.  sought t [et al. . It clasbe 72.1% .et al. [Many possible reasons exist for the limited performance of EOG-based eye tracking. Muscular artifacts, eye blink artifacts, and brain signals ) are the major artifacts contaminating EOG . Moreoveet al.  estimateet al. .The main objective of the present work is to introduce a method to eliminate the undesired interdependency between horizontal and vertical EOG components. To the best of our knowledge, this objective has not been considered in previous studies to date. Through experimental studies, we investigate whether the proposed method can enhance the performance of EOG-based eye tracking.EOG signals were recorded at a sampling rate of 2048 Hz by using an ActiveTwo biosignal recording system  with six flat-type active electrodes. Two pairs of electrodes were placed at the most widely used locations to record EOG: to the left and right of the eyes, and above and below the right eye. Reference and ground electrodes were attached at left and right mastoids. Details of the electrode configurations are illustrated in When placing the electrodes on the skin, a conductive gel was applied on the electrodes together with double-sided adhesive disks. This gel reduces variations of the skin-electrode capacitance due to the irregular surface of the skin and thereby enables more reliable bioelectric signal measurements. We performed no skin preparation processes  before attaching the EOG electrodes.EOG signals were acquired from ten participants (between 18 and 25 years of age). Prior to the data acquisition, a comprehensive summary of experimental procedures and protocols were explained to each participant. All participants signed a consent form and received monetary reimbursement for their participation. The participants were situated a distance away from a monitor in a quiet room. The distance from the eyes to the monitor was set to 62.5 cm in our experiments. To exclude the potential influence of head movement, the head of each participant was fixed using a type of chin rest generally used in ophthalmic applications . The heiThe dots were displayed via E-Prime , which is a software package for synchronizing various events with biosignal recording systems via a parallel port. E-Prime was used to visualize graphical instructions and target dots on the monitor, as well as to synchronize the experimental paradigm with the recording device by transmitting trigger signals to the system (receiver). A snapshot of the overall experimental environment is shown in For better estimation of eye movements from EOG, noises and artifacts were removed according to the general steps used for previous EOG-based eye movement estimation studies, which were high-frequency noise removal, baseline removal, eye blink removal, and saccadic detection ,9,10. Tht, ith local maximum and minimum in the filtered signal, respectively, ith local maximum and minimum, respectively. Any Eye blink artifacts are often included in EOG and must be detected in the preprocessing phase because they are easily misrecognized as vertical eye movements. An eye blink detection method  utilizinet al. [s at scale a and position b is defined as:et al., and the threshold was empirically set to 0.08. The signals were normalized to have a width of one before the saccade detection in order to apply the same threshold.To remove the unknown sources of artifacts and extract signals only related to saccadic eye movements, we adopted the continuous wavelet transform-saccade detection (CWT-SD) algorithm of Bulling et al. . This alIn this section, we visually investigate the changes in the EOG signals recorded while the subjects moved their eyes to draw a specific pattern. EOG signals were recorded from ten participants, as described in As clearly seen from the example of the EOG components in For further analysis of this interdependency, we visualized EOG components of all participants, as shown in h = R − L and U, D, R, and L denote the signals recorded from the corresponding electrodes shown in In the previous section, we confirmed that the vertical EOG components are highly influenced by horizontal eye movements, and that the degrees of the interdependency differ among individuals. This interdependency can be readily removed by estimating the individual degree of interdependency if it is assumed to be stable over time. Under this assumption, the vertical component is compensated by:An experiment was designed to investigate the enhancement of EOG signals by the introduction of an individual constant given in Equation (7). For the validation of the proposed compensation method, we compared the original (uncompensated) EOG components with those obtained after the proposed compensation process, as well as the ideal (desired) EOG components. EOG signals were recorded from the ten participants introduced in In this experiment, the participants were provided with a short practice session to foster familiarity with the experiment before the data acquisition . The participants were asked to gaze at a fixation cross at the center of the monitor for 3 s. Then, a target pattern was displayed together with a red dot marking its starting point see a. When tThe procedure of the data acquisition was similar to the practice session; however, the guide lines were not provided during the pattern drawing. Instead, the target pattern and starting point were displayed on the monitor for 3 s before the presentation of the fixation cross. After gazing at the fixation cross for 3 s, the participants started to draw the target pattern without guide lines, and they pressed a key to finish the eye drawing. The four target patterns were drawn three times for the nine subjects and twice for one subject. All events, such as pressing a key or changing pages, were also recorded and synchronized with EOG signals using E-Prime.n times that of the criterion points. We set After the data acquisition and preprocessing procedures, the signals were resampled to obtain a similar Euclidean distance between adjacent data points. The resampling process is a recommended procedure  for charNext, the accuracy of the compensation was quantitatively evaluated by comparing the compensated signals with the ideal signals. For the quantitative comparison, the similarity between the two vertical components was evaluated using Pearson’s correlation coefficient together with dynamic time warping (DTW) . DTW is t-test, α does not need to be updated for a given subject unless the electrode locations change. It would be noteworthy to examine in future studies the cause of these large individual variations in the compensation constant. Furthermore, this individual difference in the interdependency between two EOG components may be potentially used as a new feature for biometric verification [fication , althougIn this study, we investigated the changes of vertical and horizontal EOG components during eye tracing of specific patterns. Experiments conducted with ten participants showed that the horizontal eye movement can influence the vertical EOG component, although the degrees of this interdependency showed large inter-individual variability. Therefore, we proposed a method to eliminate this unwanted interdependency between horizontal and vertical EOG components by introducing an individual constant, which can be readily obtained from a short period of EOG signals recorded during a single “left-to-right” movement. The experimental results showed increases in the correlation coefficient with ideal EOG waveforms (


A protein's function is determined by the wide range of motions exhibited by its 3D structure. However, current experimental techniques are not able to reliably provide the level of detail required for elucidating the exact mechanisms of protein motion essential for effective drug screening and design. Computational tools are instrumental in the study of the underlying structure-function relationship. We focus on a special type of proteins called "hinge proteins" which exhibit a motion that can be interpreted as a rotation of one domain relative to another.rigidity theory, an area of mathematics that studies flexibility properties of general structures. Given a single conformational state, our analysis predicts a relative axis of motion between two specified domains. We analyze a dataset of 19 structures known to exhibit this hinge-like behavior. For 15, the predicted axis is consistent with a motion to a second, known conformation. We present a detailed case study for three proteins whose dynamics have been well-studied in the literature: calmodulin, the LAO binding protein and the Bence-Jones protein.This work proposes a computational approach that uses the geometric structure of a single conformation to predict the feasible motions of the protein and is founded in recent work from Our results show that incorporating rigidity-theoretic analyses can lead to effective computational methods for understanding hinge motions in macromolecules. This initial investigation is the first step towards a new tool for probing the structure-dynamics relationship in proteins. Proteins play a significant role in virtually all biological processes. These macromolecules are composed of sequences of amino acids folded into 3D shapes of varying size and complexity. The structures of many proteins have been determined experimentally and are easily accessible . The keysingle conformational state.In this work, we demonstrate the applicability of rigidity theory, an area of mathematics that studies the flexibility properties of general structures, to the analysis of protein dynamics. In particular, we focus on a set of proteins that exhibit "hinge" behavior, a rotational movement of one domain of the protein relative to another , which assigns a velocity vector to each joint such that the bars maintain their lengths infinitesimally; refer to Figure x- and y-directions and rotation about the origin) are always contained in the motion space, additional "pinning" rows are often added to the rigidity matrix so that the null space contains only internal motions.degrees of freedom available to the framework; this is equivalent to the minimum number of bars whose addition would stabilize the framework. For example, the 4-bar mechanism in Figure generic rigidity of a bar-and-joint structure is characterized by a graph-theoretic property proven by Laman , depicted in Figure open Ca2+-bound calmodulin binds a peptide; the two globular domains "wrap" around the peptide, leading to the closed state [PDB:2BBM] shown in Figure open conformation [PDB:1CLL] is shown faded, aligned along residues 64-75.The first conformational change is triggered when calcium binds to calcium-free (apo) calmodulin and causes the central helix to straighten, correlated with a relative rotation of the two globular domains. The resulting conformation is Caopen [PDB:2LAO] and closed [PDB:1LST] structures, determined by x-ray crystallography, indicate a rotation of two "lobes" relative to each other  seemed to exhibit the same behavior as the sample set of 1000. Since the current approach required close interaction with KINARI to produce an appropriate cluster decomposition, we plan to automate this part of the process in the future, enabling an evaluation of the method on a larger dataset. We ultimately expect to develop a web tool that will allow users to analyze a single structure by uploading or choosing a PDB file. Finally, we seek to develop a computational measure for evaluating the validity of our results instead of visually comparing two conformations using Jmol.All protein structures were obtained from the RCSB Protein Data Bank (using the indicated PDB IDs) . FiguresThe authors declare that they have no competing interests.RS and MD developed the codebase and performed analysis on an initial set of proteins under the guidance of AL while undergraduates at Mount Holyoke College. SJ and KM provided domain expertise for the hinge protein dataset and subsequent analysis of conformational changes. AL wrote automation scripts and performed experiments on the entire dataset. All authors read and approved the final manuscript.


The examination of joint range of motion (RoM) is part of musculo-skeletal functional diagnostics, used, for example, in occupational examinations. Various examination methodologies exist that have been optimized for occupational medical practice, which means they were reduced to the most necessary and feasible measures and examinations for efficiency and usability reasons. Because of time constraints in medical examinations in occupational settings, visual inspection is commonly used to quantify joint RoM. To support medical examiners, an inertial sensor-based measurement system (CUELA) was adapted for joint RoM examination in these settings. The objective of the present study was to evaluate the measurement tool in functional diagnostics under conditions close to clinical practice.ICCk3,), and Bland-Altman-Plots were calculated using MatLab for statistical analysis.The joint RoM of twenty healthy subjects were examined by three physicians, who were simultaneously using the measurement tool. Physicians were blinded to the measurement results and the other physicians. Active RoM was examined on the cervical, thoracic and lumbar spine while passive RoM was examined on the shoulder, elbow, wrist, hip, and knee, resulting in a total of 40 joint examination angles. The means, standard deviations, intraclass correlation coefficients .The reliability and objectivity of active RoM examinations were improved by the measurement tool compared with examiners. In passive RoM examinations of upper and lower extremities, the increase of objectivity by the measurements was limited for some examination angles by external factors such as the individual examiner impact on motion execution or the given joint examination conditions. Especially the elbow joint examination requires further development to achieve acceptable reliability. A modification in the examination method to reduce the examiner impact on measurement and the implementation of a more complex calibration procedure could improve the objectivity and reliability of the measurement tool in passive joint RoM examination to be applicable on nearly the whole body. Musculo-skeletal disorders (MSD) have been the main reason for illness induced work disability in Germany in recent years . To asseEven though measurement accuracy is not a major concern of the examination methodologies, objectivity of the assessment could be supported and improved by technical tools. To meet the requirements of efficiency and usability, such a tool needs to be simple to handle and fast in application.Ⓒ physical examination methodology [Ⓒ method is part of the G46 examination - an occupational medical check-up of the musculo-skeletal system of employees in Germany [Inertial measurement units (IMU) provide an adequate technology for ambulatory acquisition of human movement, especially joint angles ,9. An IMhodology . This me Germany . This meThe objective of the present study was to evaluate the validity, reliability and objectivity of the measurement tool in functional diagnostics under conditions close to clinical practice. The validity is evaluated by comparing the measurement results to expected RoM results from literature. The reliability is evaluated by analyzing repeatability under constant conditions and reproducibility under varying conditions. Finally the objectivity is evaluated by analyzing the rater agreements of measurements and examiner ratings. To the authors’ knowledge, this is the first evaluation study to apply IMUs for joint RoM measurement on nearly the whole body under these conditions.Twenty subjects  volunteered to participate in the study after giving their informed written consent. The local ethical committee of the RWTH Aachen University approved the study. Only healthy subjects without or with minor known functional deficits were recruited. Functional deficits could appear for example in combination with joint diseases, after a bone fracture or after developmental disorders. Subjects with minor functional deficits were recruited depending on a sufficient state of healing and relevance to the examined movements. In the medical history taking, which took place before the examination, the participants declared themselves to be free of musculo-skeletal complaints for at least one week before the examination. Participants unfulfilling these requirements were excluded. Three physicians (with 2-4 years of experience in joint RoM examination) conducted the physical examinations of the subjects. Each physician was supported by one assistant. Examination rooms, equipped with a couch, an IMU-examination-set, and a camera for a video recording of the examination, were assigned to eachexaminer.To measure the joint RoM, IMUs were placed at the body segments of interest. The IMUs used Figure  are partBased on the attached sensors and identified sensor positions, the examination software provided a list of applicable RoM examinations. When the assistant chose one joint RoM examination area, the software selected the IMUs corresponding to the joint, the relevant axis of rotation and the expected initial segment posture. Equally to the conventional examination, the subject had to start in a joint specific neutral starting-position and to move the adjacent, distal segment to the end of range of motion , while tA simplified IMU to segment calibration was used, based on the defined neutral start posture, to initialize the IMU orientation at the beginning of each examination. The procedure is comparable to standard anatomical positions , but useThe IMUs were placed on the forehead, on the back at the level of L5/S1 and Th4 , lateralAll participants were examined by the three physicians and their assigned IMU-examination-set. To keep the physical state of the participants as constant as possible, all three examinations of each participant took place within one day, one directly after the other in random order. The examiners were blinded to the measurement results of the IMUs and to the otherexaminers.All measurements and examination data were collected in an MS-Access database and prepared for statistical analysis with Matlab . Comparator basis was the mean of the five examination repetitions by the physicians and the simultaneous IMU measurements.Ⓒ methodology is being applied [For evaluation of the validity of measured and observed RoM, the mean value of the five measurements/examinations was compared to a collection of suggestions for normal RoM values in healthy adults  were calculated to evaluate the inter-rater reliability of the measurements compared to the examiner ratings. The compared . An ICC3n et al.  or Berryn et al. , pp.40.For further analyses of inter-rater agreement between examiners and measurement tool, Bland-Altman-Plots ,31 were ∘-65 ∘) exceeded the RoM expectations of 50 ∘ while the examiners differed in their ratings (36 ∘-72 ∘). In external and internal shoulder rotation, the measurement results (92 ∘-111 ∘) and examiner ratings (88 ∘-105 ∘) exceeded the RoM expectations of 90 ∘. The measured mean elbow extension of 16 ∘ to 22 ∘ corresponding to examiner 1 exceeded expectations, while the mean examiner ratings were plausible. The measured mean elbow supination (47 ∘-76 ∘) and pronation (68 ∘-86 ∘) angles were below the expectations (90 ∘), while the examiner observed the expected values.Table ∘ to 2.86 ∘ while, in case of passive movement, the range is higher from 0.98 ∘ to 4.87 ∘. The examiners achieved a comparable low mean standard deviation below 1.23 ∘.Table Table In passive RoM examination, most measurements at shoulder, wrist, hip and knee achieved acceptable reliability (ICC: 0.71-0.96). All measured examination angles at the elbow showed slight to moderate reliability (ICC: 0.20-0.77). For extension of elbow and knee, especially on the left side, poor reliability (ICC: 0.0-0.2) was observed. Comparing the measurements and examiner ratings, in 73% of passive examination angles the measurement tool showed a higher ICC compared to the examiner ratings.Bland-Altman-Plots  allow a ∘. The examiners showed a higher systematic difference of up to 12 ∘. The LOA of the measurement results (≤11∘) were smaller compared to the examiner results (≤27∘). When inspecting the other Bland-Altman-Plots of active RoM examination, in most cases a reduction of MDiff and LOAs was observed in the measurements. Defining an absolute MDiff below 5 ∘ as an acceptable level of systematic difference, an acceptable MDiff level was achieved by 90% of measurements and 33% of examiner comparisons.Figure The agreement of passive external rotation of the left shoulder joint is depicted in Figure In this study, a measurement tool which was designed to support physicians’ medical examinations of the musculo-skeletal system in an occupational setting was evaluated and compared to the examiner ratings. We found that the overall validity and the intra-rater repeatability of the measurement tool were acceptable. Furthermore, inter-rater reliability was acceptable for active examinations, while passive examinations were associated with an influence of the respective examiner on the measurement. These results will be discussed in the following passages.∘. In external and internal shoulder rotation, examiners and measurement tool agreed in their observation of a higher RoM that would be plausible. The measured pronation and supination RoM angles of the elbow were below expectations, which would possibly be caused by soft tissue artifacts in combination with the examiner holding forearm and hand. As the IMU could not be connected rigidly to the bone structure, the sensor movement on the skin at the wrist was reduced compared to the observable hand movement. Alternatively, the pronation and supination angles were computed and analyzed using the IMU at dorsum of the hand, but showed unexpected high results based on the same effect. Holden et al. [∘ rotation at the shank during walking. While moving the subjects segment to the end of RoM, an examiner could touch and displace the IMUs on the skin leading to those soft tissue artifacts, especially at wrist and hand. The deviation in elbow extension of measurement1 could be caused by a slight systematic elbow flexion during the start of measurement by examiner1. As the algorithm computes orientation relative to the starting orientation, a deviation from the neutral posture causes a shift in the resulting measurements.A valid anatomical joint angle calculation of the measurement tool was seen in most applied examination angles, when compared to reference values from the literature, which are defined as a range of expectable RoM angles, caused by differences in the examination methods. The used measurement tools of the references were in most cases inclinometer or goniometer ,23. Devin et al.  observedAnalyzing repeatability required constant measurement conditions. This was given for the five examination movement repetitions, as the examiner, the sensor set, the sensor fixation to the human body, the sensor initialization and initial orientation were identical. Under these conditions, the repeatability of the measurements depended only on the ability of the participants, partly in combination with the examiners, to repeat the examination movements in the same way.The low deviation within the repeated measures indicated a good repeatability of the examination movement by the participants and a precise measurement of the movement by the system. In case of passive examination, a higher but also acceptable standard deviation was observed. This can be explained by having two protagonists being involved in movement execution, leading to a less accurate movement repetition. Another reason is that the examiner could touch the IMUs while handling the subject’s segment, which would lead to an additional random movement of the IMU independent of the subject’s movement.∘ scale instead of using a continuous scale, which may also lead to a reduction of the standard deviation.The examiners tended to keep their rates of the five examination repetitions constant, which would explain the low deviation within the repeated examiner rates. Furthermore most examiners used to rate the RoM angles on a 5 For inter-rater reliability analyses the previous conditions varied: three different examiners used their assigned sensor set, leading to an individual sensor fixation, initialization and initial orientation.ICC2,1) of measuring active cervical spine RoM in a range from 0.61 to 0.89. As the applied procedures and statistical models differ, a direct comparison of the results is difficult.An improvement in examination reliability under these variable conditions was observable in active RoM examination of cervical, thoracic and lumbar spine. Even though external factors  varied between the examinations, an acceptable level of reliability was achieved by the measurement tool, which was higher compared to the reliability level of the examiner ratings. In the case of active joint examination, the examiners gave only instructions on how to perform the examination movements but did not touch the subjects. For that reason they had little impact on the movement execution and the resulting measurements. Theobald et al.  evaluateICC2,1) in a range from 0.68 to 0.75. Nussbaumer et al. [ICC2,1) in a range from 0.82 to 0.95. In the examination of the elbow, acceptable reliability could not be achieved. As the analysis of validity showed deviations from expectations in the pronation, supination and extension of the elbow joint, this is also reflected in the results of the reliability analysis. Besides the aforementioned soft tissue effects and the examiner impact, the simplified anatomical calibration procedure could explain these results. Functional calibration procedures exist [In the case of passive joint examination the examiners manipulated the subjects’ body segments, while the subjects had to relax their muscles. Now the examiners had a high impact on both the neutral start position and the motion execution and finally on the resulting measurements. Under these conditions, the measurement tool achieved mostly acceptable reliability for the examination of shoulder, wrist, hip and knee. Examining active shoulder RoM, Jordan et al.  achievedr et al.  analyzedes exist ,25 that The analysis of Bland-Altman-Plots indicated an improvement of the agreement, leading to a higher level of objectivity, in active joint RoM examination of the spine by the measurement tool. In passive examination, an increase of the level of objectivity was only particularly observable for the measurement tool. In some cases the trend for over or under-estimation by the examiner was reflected in the measurements. Although the same examination procedure was applied, individual examiner impact on the execution of the examination movements would be the main reason for the lower level of agreement between measurements. Additional auxiliary aid (for example a table on which to place the arm) would help to reproduce the starting conditions for examination , which sLimitations in the presented study should be mentioned: First, for organizational reasons eight subjects needed to be examined in their normal clothing. At the lower extremities their normal clothing hindered the subjects in reaching the end of range of motion. The results of these subjects were not taken into account for analyses of the lower extremities, which is marked in the tables. A second limitation is that there was no direct comparison between active and passive examination agreement to assess the examiner’s impact on the same joint examination. As we intended to cover a whole body examination and were limited in time for all examinations, we applied the examinations either one way or the other. In further research both alternatives should be considered. Finally, a limitation not of this study but of the examination method using IMUs is the necessity to start the measurement in the correct starting position of the defined neutral-zero posture. If a subject has a functional deficit and is not able to take the zero-posture (for example a deficit in extending the elbow or knee joint), the measurement tool is not able to detect this deficit, being dependent on the starting conditions.Ⓒ physical examination methodology. With comparable low effort in cost and preparation time, the IMU-examination system measures the joint RoM while a physician applies the examination as usual without the need to operate additional measurement tools.To support the joint RoM examination in functional diagnostics we adapted an IMU system to the fokus The evaluation of the tool under conditions close to clinical practice was the objective of this study. Acceptable repeatability was observed, and most measurements showed valid results that met expectations. In active RoM examination of cervical, thoracic and lumbar spine, reliability and objectivity between independent measures was improved by the measurement tool compared with the examiner rates. In passive RoM examination of upper and lower extremities, the increase of objectivity by the measurement tool was limited for some examination angles by external factors such as the individual examiner impact on motion execution or the given joint examination conditions. Especially the elbow joint examination requires further development to achieve acceptable reliability and agreement. A modification in the examination procedure to support the reproducibility of the start postures could improve objectivity. The implementation of a more complex anatomical functional calibration procedure of the measurement tool could improve reliability in the measured passive joint RoM to be applicable on nearly the whole body. Both approaches would lead to an increase in examination effort and a reduction in the simplicity of the examination procedure that conflicts the expectations on the examination method of being fast and simple. To keep these costs of improvement low will be an important challenge for further development of the measurement tool to support joint range of motion determination in functional diagnostics.


Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different. Due to the tremendous growth in the field of information technology, one of the significant challenging issues is network security. Hence, intrusion detection system (IDS) which is an indispensable component of the network needs to be secured. The traditional IDS is unable to handle newly arising attacks. The main goal of IDSs is to identify and distinguish the normal and abnormal network connections in an accurate and quick manner which is considered as one of the main issues in intrusion detection system because of the large amount of attributes or features. To study about this aspect, data mining based network intrusion detection is widely used to identify how and where the intrusions occur. Related to achieving real-time intrusion detection, researchers have investigated several methods of performing feature selection. Reducing the number of features by selecting the important features is critical to improve the accuracy and speed of classification algorithms. Hence, selecting the differentiating features and developing the best classifier model in terms of high accuracy and detection rates are the main focus of this work.The research on machine learning or data mining considers the intrusion detection as a classification problem, implementing algorithms such as Naïve Bayes, genetic algorithm, neural networks, Support Vector Machine, decision trees. In order to improve the accuracy of an individual classifier, popular approach is to combine the classifiers. Recently, application of swarm intelligence technique for intrusion detection has gained prominence among the research community . Swarm iThe main objective of this work is (1) to select important features using two feature selection methods, namely, single feature selection method and random feature selection method and (2) to propose a hybrid optimization algorithm based on Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms for classifying intrusion detection dataset. The studies on ABC and PSO indicate that ABC has powerful global search ability but poor local search ability , while tThe rest of this paper is organized as follows. Being related to achieving real-time intrusion detection, researchers have investigated several methods of performing feature selection. Kohavi and John  describeMachine learning algorithms such as neural networks , fuzzy cKaraboga and Basturk  proposedThe following subsections provide the necessary background to understand the problem.Particle Swarm Optimization (PSO) is one of the popular heuristic technique which has been successfully applied in many different application areas, but, however, it suffers from premature convergence especially in high dimension, multimodal problems.Initialize a population of particles with randomly chosen positions and velocities.Calculate the fitness value of each particle in the population.i is better than its pbest value, then set the fitness value as a new pbest of particle i.If the fitness value of the particle  pbest is updated and it is better than the current gbest, then set gbest to the current pbest value of particle i.IfUpdate particle's velocity and position according to  and 2)..2).If the best fitness value or the maximum generation is met, then stop the process; otherwise, repeat the process from step  2.In PSO, a swarm consists of N particles in a D-dimensional searching space. The ith particle is represented as Xi = . The best previous position pbest of any particle is Pi =  and the velocity for particle i is Vi = . The global best particle in the whole swarm is denoted by Pg and it represents the fittest particle .The algorithm of the standard PSO is as follows.particle . During Each particle then moves to a new potential position as in the following equation:The Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on the intelligent foraging behaviour of honey bee swarm, proposed by Karaboga and Basturk . The ArtThe four main phases of ABC algorithm are as follows.Initialization Phase. The scout bees randomly generate the population size (SN) of food sources. The input vector xm which contains D variables represents food source where D represents the searching space dimension of the objective function to be optimized. Using  is a random number within the range .d. Using , initialEmployed Bee Phase. The employed bee finds a new food source within the region of the food source. The employed bees reminisce higher quantity of food source and share it with onlooker bees. Equation , random feature selection method (RFSM) and compared the two techniques. The proposed methods reduce the features in the datasets which aim to improve accuracy rate, reduce processing time, and improve efficiency for intrusion detection.Single feature method (SFSM) uses the one-dimensional feature vector. In the first iteration, it considers only the first attribute and is evaluated for calculating the accuracy using the Support Vector Machine classifier. In the second iteration, it considers only the corresponding attribute for evaluation. The process is repeated until all 41 features are evaluated. After calculating the entire feature's efficiency, it is sorted and vital features are selected, whose accuracy and detection rate are acc_threshold and dr_threshold values, respectively. The pseudocode of single feature selection algorithm is given in In this method, the features are removed randomly and evaluated using the classifier. In the first iteration, all the features are evaluated using SVM classifier, and then by deleting one feature, update the dataset, using the classifier efficiency. The importance of the provided feature is calculated. In the second iteration, another feature is removed randomly from the dataset and updated. The process is repeated until only one feature is left. After calculating the entire feature's efficiency, it is sorted in descending order of its accuracy. If the accuracy and detection rate are greater than the threshold value , then select those features as vital features. The pseudocode of the random feature selection algorithm is given in Tables Artificial intelligence and machine learning techniques were used to build different IDSs, but they have shown limitations in achieving high detection accuracy and fast processing time. Computational intelligence techniques, known for their ability to adapt and to exhibit fault tolerance, high computational speed, and resilience against noisy information, compensate for the limitations of these approaches . Our aimArtificial Bee Colony algorithm is a newly proposed optimization algorithm and is becoming a hot topic in computational intelligence nowadays. Because its high probability of avoiding the local optima, it can make up the disadvantage of Particle Swarm Optimization algorithm. Moreover, Particle Swarm Optimization Algorithm can help us to find out the optimal solution more easily. In such circumstances, we bring the two algorithms together so that the computation process may benefit from both of the advantages. The flowchart of the proposed hybrid MABC-EPSO is given in In this hybrid model, the colony is divided into two parts: one possesses the swarm intelligence of Artificial Bee Colony and the other one is the particle swarm intelligence. Assuming that there is cooperation between the two parts, in each iteration, one part which finds out the better solution will share its achievement with the other part. The inferior solution will be replaced by the better solution and will be substituted in the next iteration. The process of MABC-EPSO is as follows.Set the number of individuals of the swarm; set the maximum circle index of the algorithm; set the search range of the solution; set the other constants needed in both ABC and PSO.Generate a colony with a specific number of individuals. Bee colony is divided into two categories, employed foragers and unemployed foragers, according to each individual's fitness value; on the other hand, as a particle swarm, calculate the fitness value of each particle and take the best location as the global best location.fi is Rastrigin function whose value is 0 at its global minimum . This function is chosen, because it is considered to be one of the best test functions for finding the global minimum. Initialization range for the function is . This function is with cosine modulation to produce many local minima. Thus, the function is multimodal.In bee colony, to evaluate the fitness value of each solution, an employee bee is assigned using . The empvariable . The dimvariable  is impleIf the fitness value is larger than the earlier one, the bee remembers the new point and forgets the previous one; otherwise, it keeps the previous solution. Based on the shared information by employee bees, an onlooker bee calculates the shared fitness value and selects a food source with a probability value computed as in .best.An onlooker bee constructs a new solution selected among the neighbors of a previous solution. It also checks the fitness value and if this value is better than the previous one, it will substitute the old one with the new position; otherwise, it evokes the old position. The objective of scout bees is to determine new random food sources to substitute the solutions that cannot be enhanced after reaching the “limit” value. In order to obtain the best optimized solution, the algorithm goes through a predefined number of cycles (MCN). After all the choices have been made, the best solution generated in that iteration is called MABCω) [best.As there is a large effect of initial velocity in the balancing of exploration and exploitation process of swarm, in this proposed Enhanced Particle Swarm Optimization (EPSO) algorithm, inertia weight (ω)  is used a weight  as in 9ω [(8)vidbest and EPSObest is called Best and is defined asThe minimum of the value MABCIf the termination condition is satisfied, then end the process and report the best solution. Otherwise, return to Parameter Settings. The algorithms are evaluated using the two feature sets selected by SFSM and RFSM. In ABC algorithm, the parameters set are bee colony size: 40, MCN: 500, and limit: 5. In EPSO algorithm, the inertia weight ω in  are set as the maximum upper and lower bounds of xght ω in  varies fThis section provides the performance metrics that are used to assess the efficiency of the proposed approach. It also presents and analyzes the experimental results of hybrid approach and compares it with the other classifiers.The performance metrics like accuracy, sensitivity, specificity, false alarm rate, and training time are recorded for the intrusion detection dataset on applying the proposed MABC-PSO classification algorithm. Generally, sensitivity and specificity are the statistical measures used to carry out the performance of classification algorithms. Hence, sensitivity and specificity are chosen to be the parametric indices for carrying out the classification task. In intrusion detection problem, sensitivity can also be called detection rate. The number of instances predicted correctly or incorrectly by a classification model is summarized in a confusion matrix and is shown in The classification accuracy is the percentage of the overall number of connections correctly classifiedlgorithm .The main motivation is to show that the proposed hybrid method has the advantage of becoming an efficient classification algorithm based on ABC and PSO. To further prove the robustness of the proposed method, other popular machine learning algorithms  such as The performance comparison of the classifiers on accuracy rate is given in Figures χ2 = 15.716 and FF = 11.005 for SFSM and χ2 = 15.712 and FF = 10.992 for RFSM are computed. Having four datasets and six classification algorithms, distribution of FF is based on F distribution with 6 − 1 = 5 and (6 − 1)∗(4 − 1) = 15 degrees of freedom. The critical value of F for α = 0.05 is 2.9013 and P value < 0.05. So, we reject the null hypothesis, and the differences among classifiers are significant.In order to test the significance of the differences among classifiers, six classification algorithms previously mentioned over four datasets are considered and performed experiments using Friedman test and ANOVA. Tables P value and the value of F are computed. If the null hypothesis is rejected, Tukey's post hoc analysis method is applied to perform a multiple comparison which tests all means pairwise, to determine which ones are significantly different. F = 31.895 is greater than F-critical (2.773) for the significance level of 5%. Tukey's post hoc test is performed which states that significantly there are differences among MABC-EPSO and ABC with other classifiers but not among NB, j4.8, RBF, and SVM. Also, there are significant differences between ABC and MABC-EPSO; so ABC and MABC-EPSO are the best classifiers in this case. In RFSM method, there were statistically significant differences between algorithms and hence null hypothesis was rejected, as the calculated F = 48.547 is greater than F-critical (2.773) for the significance level of 5%. Tukey's posthoc test is performed and it reveals that there is a statistically significant difference among SVM, ABC, and MABC-EPSO with other classifiers but not among NB, j4.8, and RBF. However, there is no statistically significant difference between the ABC and MABC-EPSO algorithms.The means of several groups by estimating the variances among groups and within a group are compared using the ANOVA test. Here, the null hypothesis which is set as all population means are equal is tested. Also In F and F are greater than F-critical (2.773). Finally, multiple comaprison test concluded that MABC-EPSO has significant differences with all the classification algorithms with 0.05 (P = 0.05) as significance level. However, there is no statistically significant difference between the SVM and ABC algorithms.Experiment was conducted to analyze the false alarm rate and training time of each classifier using SFSM and RFSM methods. In this work, a hybrid algorithm based on ABC and PSO was proposed to classify the benchmark intrusion detection dataset using the two feature selection methods, SFSM and RFSM. A study of different machine learning algorithms was also presented. Performance comparisons amongst different classifiers were made to understand the effectiveness of the proposed method in terms of various performance metrics. The main goal of this paper was to show that the classifiers were significantly different and the proposed hybrid method outperforms other classifiers. Friedman test and ANOVA test was applied to check whether the classification algorithms were significantly different. Based on the conclusion of ANOVA test, the null hypotheses were rejected, if they were significant. Post hoc analysis using Tukey's test was applied to select which classification algorithm was significantly different from the others. The experiments also showed that the effectiveness of ABC is comparable to the proposed hybrid algorithm. In general, the proposed hybrid classifier produced best results using the features of both SFSM and RFSM methods and is also significantly different from other classification algorithms. Hence, MABC-EPSO can be considered as a preferable method for intrusion detection that outperforms its counterpart methods. In the future, we will further improve feature selection algorithm and investigate the use of bioinspired approaches as classification algorithm in the area of intrusion detection.


Parkinson’s disease (PD) is primarily associated with two dominant features: cardinal motor symptoms and the loss of cells in the substantia nigra pars compacta of the basal ganglia. Consequently, these aspects are major foci in PD-related research. However, PD is a neurodegenerative disease, which progressively affects multiple brain regions outside the basal ganglia and leads to symptoms outside the motor domain. Much less is known about the individual contribution of these secondary regions, their interplay and interaction with the basal ganglia, and the respective network dynamics in the overall manifestation of PD. These regions include classical motor structures such as the cerebellum and the supplementary motor area (SMA). However, just as the basal ganglia, these regions display a fine-grained microarchitecture, which supports sensory and sensorimotor functions. One such function is temporal processing, which has been ascribed to a network comprising all of these regions. On the one hand, pathological changes in this temporal processing network may be part and parcel of motor and non-motor symptoms in PD. On the other hand, a better understanding of the role of each network node may offer a novel perspective on compensatory mechanisms, therapeutic interventions, as well as the heterogeneity and individual differences associated with PD. We unfold this perspective by relating the neural foundations and functional implications of temporal processing to pathophysiological and neurofunctional changes characteristic of PD. The basal ganglia system is a complex of several nuclei deeply embedded into the vertebrate brain. It has extensive connections to other subcortical and cortical regions, and via these connections, the basal ganglia system contributes to a wide range of motor and non-motor behaviors –5. One oIn this context, the term “sequencing” may be broadly characterized as a function that pertains to the sequential order of motor and cognitive actions as well as to the specific temporal structure of these sequences. In other words, sequencing not only defines the order of successive actions in time but also their specific temporal relations. Motor and cognitive patterns are typically expressed as entire sequences of actions, i.e., they are “packaged as a unit ready for expression,” and monkey research indicates that accented neural activity marks the boundaries of such packages , 9. ThesHabitual behaviors unfold in the hundreds-of-milliseconds to seconds range that is central to research into interval timing, another fundamental behavioral function that has been ascribed to the basal ganglia system, and a wider network of sensorimotor regions, which includes the supplementary motor area (SMA) and the cerebellum –13. In aImpaired sequencing of motor actions is one of the hallmarks of PD, a neurodegenerative disease that leads to a loss of dopamine releasing neurons in the substantia nigra pars compacta of the basal ganglia. PD is commonly diagnosed on the basis of these primary motor symptoms, most characteristically the slowing of movements, rigidity, and resting tremor. However, PD is also a progressive disease, and these characteristic motor symptoms are asymmetric and may surface only after internal mechanisms fail to compensate for the impact of the disease . AlthougIn line with the rationale discussed above, damage to the basal ganglia system should also lead to impaired temporal processing as well as a temporally specific dysfunction of sequencing behavior. Moreover, the latter may be a consequence of the former as impaired temporal processing may factor into other motor and non-motor cognitive aspects of the disease, expressed in suboptimal timing in production and perception. The structural and functional characteristics and the interplay of brain regions that engage in temporal processing may therefore provide a novel perspective on some of the dynamic pathological and compensatory changes in regions such as the cerebellum and the SMA. Such changes have been observed in the progression of the disease but their specific role in the pathogenesis of PD remains unclear.Seminal work in the temporal processing domain has established that the cerebellum is involved in precise and automatic discrete event-based  temporal processing, whereas the basal ganglia and associated cortico-striato-thalamo-cortical circuits engage in attention-dependent interval-based (continuous-event) temporal processing, thus creating an explicit representation of the temporal relation between successive events , 14, 18.Parkinson’s disease patients display compromised performance in various temporal processing tasks spanning production and perception, which has been attributed to various components of a dysfunctional “internal clock” mechanism –23. Somede novo PD patients relative to healthy controls, most likely reflecting the contribution of the pre-SMA to the temporal sequencing of self-initiated opening and closing movements of the (right) hand at approximately every 1000 ms (Post-mortem PD brain tissue analyses have revealed a selective loss of pyramidal neurons in the pre-SMA . However 1000 ms . In the  1000 ms . However 1000 ms , as well 1000 ms . Other s 1000 ms , as well 1000 ms . While tTherefore, one goal could be to improve the efficiency and usability of interventions relying on rhythmic auditory cueing in order to provide patients with wearable devices that are customized to meet their individual therapeutic needs . ForDue to the focus on the basal ganglia, cerebellar contributions to pathological and compensatory mechanisms are often overlooked . For exaIn addition to more systematic investigations of the cerebellum and the SMA in PD by means of temporal processing tasks that are known to activate these regions, the differentiation of their primary or compensatory engagement may be addressed on the basis of recent evidence for a more fine-grained structural differentiation of cerebellar, SMA, and also basal ganglia subregions. On the one hand, the distinct functional connectivity patterns revealed in this context may provide the opportunity to dissociate between primary pathological and secondary compensatory aspects and allow further exploration of specific network functions , 44, 48.Task-dependent temporal processing characteristics may also partly explain the differential responses in specific regions to particular stimulation frequencies. For example, the right-lateralized SMA activity in temporal processing tasks in the suprasecond range may dominate the response to 1-Hz rTMS stimulation. 1-Hz stimulation has been found to impact the timing of anticipatory postural adjustments in PD patients if it is applied over the SMA but not over the dorsolateral premotor cortex, and to improve motor but not non-motor symptoms , 57. ObvAlthough the cerebellum and the SMA are affected in PD and should hence be considered in a more encompassing view of the disease, their contribution to the overall pathogenesis is a matter of debate. Perhaps most importantly, it is still unclear if changes in cerebellar and SMA activity are aspects of the primary pathology and/or secondary compensatory mechanisms . HoweverThe overlap between the sequencing and the temporal processing functions ascribed to the basal ganglia and cortico-striatal circuits offer one such direction. Moreover, the relatively specific concepts that have been developed with respect to the neural mechanisms underlying temporal processing in the basal ganglia and the cerebellum may allow improvement of existing strategies such as cueing and stimulation techniques. In this context, knowledge about the interplay of the basal ganglia with other regions engaged in temporal processing may offer a means to improve behavioral compensatory strategies via the informed manipulation of temporal structure or the identification of promising targets for interventions targeting the neural level.An interesting open question concerns the transfer of therapeutic effects from basic temporal processing tasks to more complex behavior or from motor to non-motor processing and vice versa . Such trMS and SK have written the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.