Current normative explanations of aftereffects based on scene statistics derive gain changes by (1) invoking decorrelation and linear manifold matching/equalization, or (2) using nonlinear divisive normalization obtained from parametric scene models. These principled approaches have different drawbacks: the first is not compatible with the known saturation nonlinearities in the sensors and it cannot fully accomplish information maximization due to its linear nature. In the second, gain change is almost determined a priori by the assumed parametric image model linked to divisive normalization. In this study we show that both the response changes that lead to aftereffects and the nonlinear behavior can be simultaneously derived from a single statistical framework: the Sequential Principal Curves Analysis (SPCA). As opposed to mechanistic models, SPCA is not intended to describe how physiological sensors work, but it is focused on explaining why they behave as they do. Nonparametric SPCA has two key advantages as a normative model of adaptation: (i) it is better than linear techniques as it is a flexible equalization that can be tuned for more sensible criteria other than plain decorrelation (either full information maximization or error minimization); and (ii) it makes no a priori functional assumption regarding the nonlinearity, so the saturations emerge directly from the scene data and the goal (and not from the assumed function). It turns out that the optimal responses derived from these more sensible criteria and SPCA are consistent with dysfunctional behaviors such as aftereffects.When adapted to a particular scenery our senses may fool us: colors are misinterpreted, certain spatial patterns seem to fade out, and static objects appear to move in reverse. A mere empirical description of the mechanisms tuned to color, texture, and motion may tell us where these visual illusions come from. However, such empirical models of gain control do not explain  Aftereffects are visual illusions which occur when the sensory system is put into a particular operation regime driven by the environment. After the adaptation to this environment, presented stimuli are perceived in unusual  ways.Figure In the above cases, after exposure to certain context, decoding of new stimuli in a different context (interpretation of their representation) is incorrect. Previously proposed empirical models, such as the one reviewed below, describe the phenomenology through the appropriate response change, but do not address the fundamental question: why does the system behave in this dysfunctional manner?, or more specifically, what is the goal that leads to the kind of responses observed in the sensors and the way they change with the environment?Spatio-temporal context (the presence of certain stimuli in the scene) leads to changes in the response of motion, texture, and color sensors , ac:Adaptation and masking differ in the time and location of the stimulus driving the response change. However, these gain modifications are usually described in similar ways: for instance, divisive normalization has been used both for adaptation and masking with the appropriate parameter change  activity of neighbor sensors, i.e., it is reduced by what is in its spatio-temporal context. In this introduction we only detail the classical description of the motion aftereffect based on the above model   responses associated with white noise patterns of different uniform speeds, The top panel of Figure o Heeger , given aThis straightforward decoding strategy to infer the speed from the responses, together with the inhibitory properties of the nonlinear responses, gives rise to the waterfall aftereffect. This can be seen by considering the responses elicited by a static stimulus in (1) the neutral adaptation state  interpreted as static.In the neutral adaptation state, a static stimulus leads to a balanced set of responses (center). Therefore, distance with regard to the theoretical patterns, Equation 3) . In that situation, according to the behavior shown in Figure ft = 0 plane. As a result, when the static stimulus is presented after adaptation to a moving-up (or moving-down) pattern, the nonlinear response is the one presented on the left (or right) of the third row in the Figure shifted differences in red and blue in the decoding plot. Therefore, the standard decoding procedure after adaptation implies that static patterns are (incorrectly) perceived as moving down or up, respectively.However, if this sensory system is exposed to In summary, the trends of the static motion aftereffect are robustly obtained from the divisive normalization description. The shift of the decoding distance curves in Figure somehow. However, note that the key issue is the change in the operation regime in the encoder, leading to a mismatch with the decoder, and not the particular decoding scheme. In this work we focus on the changes in the encoder and the reasons behind them (organization principles). We do not address the specific decoding mechanism: possibilities include the qualitative reasonings in Morgan et al.  following Heeger, , is convn et al. , or the n et al. , but in x is a vector with the irradiance at each pixel of an image patch, and the matrix L contains the receptive fields of local spatial frequency analyzers. According to this, c is a local-frequency transform of x . Then, the sigmoidal transform, N, modifies the output of the frequency analyzers depending on the activity of the neighbors in the immediate past. The perceived texture depends on the relative amplitude of these responses. As a result, attenuation of certain responses implies a reduction in the perceived contrast of the corresponding patterns. Similarly to the motion-tuned filters used above  prior to the computation of opponent channels . Perceptual descriptors of color are obtained from the nonlinear response, r. For instance, hue depends on the signs and the proportion of the nonlinear response of the chromatic channels. Changing the (white) adaptation point in the empirical models implies shifting the nonlinear responses  basis of the feature space. Empirical models describe how changes in the environment induce changes in the sensors that constitute the reference axes of the representation space. If reference axes change, stimuli are perceived in different  ways. Nevertheless, empirical models do not explain why reference axes change in this way.Qualitative interpretation of the response induced by a test depends on its location in a feature space, but locations depend on why these changes occur. According to his Efficient Coding Hypothesis, Horace Barlow suggested that visual aftereffects may come from a reference axes adaptation policy that tries to minimize correlation between responses thus maximizing the use of the dynamic range of the sensors  techniques, he suggests that illusions are a reflection of changes in the optimal response after the changes in the signal. Here we follow the same general idea but using a nonlinear manifold equalization method that does not suffer from the (too strong) linear and second order constraints associated with decorrelation. Following the success of statistical explanations of neural organization in fixed environments using higher order independence . The particular signal statistics together with the organization strategies in SPCA give rise to nonlinear sensors with different operation regimes depending on the environment, thus leading to the aftereffects. In Section 3 we discuss related research and stress the advantages of using flexible nonparametric learning to better state that sensible organization principles lead to aftereffects. Finally, the methodological Section 4 reviews the technical differences with conventional decorrelation and explicitly checks the ability of SPCA to obtain either nonlinear independent components or noise-robust representations for spatial textures. The accuracy of the proposed transform (and inverse) and its success in optimizing the considered design strategies confirms the validity of the results for the aftereffects.The paper is organized as follows: Section 2 analyzes the structure of the linear responses to natural signals  after stage linear + nonlinear context of Equation (1), the considered illusions come from environment-driven changes of the response after the first linear stage. There is an extensive literature that derives the linear receptive fields from the signal statistics, so we can build up from there. Therefore, in each experiment we start from a certain first linear stage , and then we derive the nonlinear behavior of the corresponding second stage from scene statistics. The linear stage  can be seen as a change of representation. The responses of the linear analyzers describe the input samples in a new (transformed) representation. We will use these transformed samples as the statistical set to train the second stage. These are the linear analyzers that we use in each experiment:In the In the motion vision case, linear Principal Component Analysis (PCA) and linear Independent Component Analysis (ICA) applied to natural movies lead to narrow band-pass filters in the spatio-temporal Fourier domain  the results consist of two parts: (1) explicit display and analysis of the scene data after the linear transforms, and (2) derivation of the different behaviors in the nonlinear responses that induce the aftereffects.Explicit display of the data after the linear transforms, which is not usual in the aftereffect literature, stresses the similarities of the three modalities analyzed here. In all three modalities the observed data distributions are similar. Therefore, if the goal of the nonlinear stage is related to equalization, the stage will present similar saturating responses in the three modalities. Here, equalization means that highly populated regions in the input space are expanded, while the low density regions are shrunk, in the response domain. Independently of the technique used to implement the equalization, the intuition obtained from the data has value in itself since it justifies the similarities of the psychophysics of the three modalities : 5 randomly-selected patches of spatial size 16 × 16 and 32 temporal frames. These patches were analyzed with the set of linear spatio-temporal sensors described in Section 1.1 that models the linear part of motion sensitive V1  natural sequences. We used the achromatic channel of 25 undistorted video clips from the publicly available VQEG and LIVE databases  caused by the high activity of neighbor sensors of the same spatial frequency but different temporal frequency. This inhibition  scenes implies specific assumptions about how the adaptation environment and the test environment are constructed. As a result, the comparison with the experimental literature on gain control, e.g., adaptation and masking  and which stimuli are presented as a pedestal for the test (masks). Therefore, adaptation and masking are distinct paradigms to study the effect of the context on the visibility of a test. In the statistical case, when gathering the samples to build the training sets one could take a literal approach: look for the required change of environment in the database and sample only from those cases . The problem with this literal approach is that it is difficult to find enough samples with the required conditions for a reliable estimation of the responses. For instance, such sudden motion changes are rare in nature.simultaneously active, e.g., because of a particular motion. Our set for the test environment consists of simpler patches in which only the sensor tuned to the test is active (the other patterns in the adaptor environment are not present in the test environment).In order to simplify obtaining the training sets, we define the adaptor and the test environments in a slightly different way, which determines the kind of gain changes these training sets can represent. We take samples disregarding which one comes first in a temporal sequence. Then, if we are interested in the response of a particular sensor tuned to a certain test, our set for the adapting environment consists of complex patches in which other linear sensors are adap subindex.As a result, we build the adaptors for aftereffects from natural environments where simultaneous stimulation of multiple sensors occur, i.e., similarly to classical masking. This adapting environment made of complex stimuli should reduce the response and lead to aftereffects since the regime in the test environment is different. In the following description (both in this motion experiment and in the texture experiment below), the stimulation in coefficients adjacent to the test in the adapting environment will be denoted using the xf, fy, ft) = , relevant for vertical motion illusion. In order to stress the generality of the result  = , for horizontal motion illusion. We will consider the response of the above two filters in the presence (or absence) of activity in other sensors of the same spatial frequency, but different temporal frequencies in the range ft = . According to the equation of the optical flow . Accordingly, we choose the illustrative case of the filter tuned to (ft = 0). The green line represents zero contrast for the ft = 0 sensor. Displacements away from the green line along the abscissa  represent non-zero contrast for the ft = 0 sensor. Positive and negative displacements along the abscissa only differ in the stimulus phase but are otherwise equivalent: higher stimulation (higher contrast) for greater distance from the green line. The ordinate axis has the same interpretation (higher stimulation for greater distance from the black solid line) but for different sensors. The different plots along the rows correspond to sensors in the ordinate axis with ft ≠ 0, where |ft| progressively increases from left to right: 2-d subspaces spanned by close or distant sensors. The top rows correspond to positive speeds and the bottom rows to negative speeds in the corresponding neighbor sensor.Figure test and adap, respectively since we are interested in the response to static stimuli (test) in different adapting environments defined by the presence of other moving stimuli. The black solid line, Cadap = 0, represents stimulation of the ft = 0 sensor for zero activity of the other sensor (isolated static stimuli). The other horizontal lines, Cadap ≠ 0, represent stimulation of the ft = 0 sensor in an environment where other moving stimuli are present. Horizontal lines with the same |Cadap| above and below the black solid line represent equivalent environments where the moving adaptor has the same contrast and speed but a different phase. Positive and negative responses come from phase coincidence or opponency between filter and stimulus. Since having one phase or its opposite is equally likely in small patches from natural scenes, different signs are equally frequent. As a result, the scatter plots are symmetrical with regard to the origin.We denote the abscissa and ordinate axes as more rounded for closer frequencies (left) and more elongated for distant frequencies (right). As a result, the conditional probabilities P(Ctest|Cadap) clearly depend on the contrast Cadap for closer frequencies, while they are more independent for distant frequencies. Greater statistical dependence between the responses of closer filters is quantitatively confirmed by the mutual information numbers (MI in bits) shown above each plot: see how the MI decreases from left to right.The scatter plots in Figure Note that these general statistical trends are the same for positive and negative phases: scatter plots are roughly symmetric with regard to the green line. The distribution of responses for different phases of the adaptor is also the same: symmetry with regard to the solid black line. Positive and negative speeds share similar properties too: the first and second rows in each panel are similar. This indicates that upward motion has the same distribution as downward motion. And the same equivalence holds for left/right motion.infomax-, and (2) error minimization. In the methodological Section 4, we explicitly show how SPCA does this job for visual textures by changing its metric parameter. For now, one merely has to know that, similarly to other unsupervised learning algorithms (such as PCA or ICA), SPCA identifies directions (curves) in the data space. These directions can be interpreted as sensors, or axes of a representation system. The response of these sensors is given by the projection of the data onto the representation axes. As discussed in more detail in Section 3, the advantage of SPCA with regard to linear techniques is that (i) the identified directions may be curves instead of straight lines, and (ii) the line element (or metric) in these curves may be non-uniform, which is equivalent to a nonlinear response.We use the responses of linear sensors to natural sequences as a set to train optimal nonlinear sensors according to biologically sensible organization criteria: (1) information maximization -d spaces, it identifies two principal curves, or two sensors. Figure Here we apply SPCA to the training data in Figure rounded-to-elongated trend in the scatter plots discussed in Figure As seen in Figure This statistically-derived behavior is similar to the empirical description in Section 1.1, and reproduces the basic trends in Morgan et al. , Mather 6 patches of spatial size 15 × 15, and analyzed their distribution after the linear stage. L was simulated using a simple 2nd order approach: PCA-based whitening . We consider two examples of low frequency first-stage sensors denoted as d subspaces formed by the DC component and the two components shown in the scatter plots. This condition is satisfied by about 1.5 · 105 samples in each 3-d subspace. As in Figure adaptors in Figure Figure P(Ctest|Cadap) clearly depend on the value of the environment Cadap. However, for very different frequencies and orientation, the conditional PDFs are relatively more independent of Cadap: note that the distribution in the case of very different frequencies is more elongated in the vertical dimension. That is Ctest and Cadap are more statistically independent when their frequencies are far away from each other. The mutual information values confirm this intuition from the scatter plots. This trend is consistent with previously reported results on dependence between image transform coefficients . Optimal SPCA sensors capture the differences with distance in frequency suggested by the distribution of natural signals in the scatter plots.infomax and error minimization strategies give rise to these qualitative trends.SPCA behavior Figure  is consiThe different design strategies lead to different saturation rates  and Yellow-Blue (or D for deuteranopic). Figure Figure Lines in the TD plane and the CIExy diagram represent the stimulation of the T and D sensors in different environments: (1) the black lines represent the stimulation of each sensor with no stimulation in the other sensor, and (2) the colored lines represent equivalent stimulation in an environment where the other linear sensor has certain average response or bias .We computed the response of SPCA sensors along the stimulation lines depicted in Figure white. A positive response in the nonlinear T channel is decoded as red, while a negative response is perceived as green. A positive response in the nonlinear D channel is decoded as yellow and a negative one is decoded as blue.Locations in the nonlinear opponent representation space are decoded as follows. A stimulus leading to zero response in both nonlinear chromatic mechanisms is interpreted as diverse reflectance world, the origin of the chromatic plane corresponds to the values where the responses of both chromatic mechanisms  have characteristic nonlinear responses that resemble those of the biological sensors. Moreover, these responses display different environment-driven operation regimes Figures , 8, 10 tH in Equation 2). In contrast, in our non-parametric case, this attenuation comes from the frequency extent of the statistical dependence in the data samples. For the color mechanisms, the current empirical appearance models  found in the different mechanisms. For example, motion sensitive mechanisms display such contrast-dependent nonlinear behavior . Similar behavior is statistically found for mechanisms sensitive to spatial texture  SPCA provide a statistical explanation of the considered motion, texture, and color behavior. In the motion perception case, the optimal behavior in an environment dominated by the presence of a high contrast moving pattern (situation 1) implies the attenuation of the neighbor sensors tuned to static stimuli Figure . In envie Figure : in prese Figure , 7, and,e Figure , 8 is coe Figure , as welle Figure . In the e Figure  with regAs a result of the different operation regimes, identified by SPCA in different contexts, illusions arise:all the wide phenomenology of the motion aftereffect. For instance, in Stocker and Simoncelli  scene. The effect is greater if the test and the adapting stimuli have similar frequency content, which is consistent with the classical reports of the illusion  actually come from sensors that try to optimize their response in each  environment. After a sudden change in the statistics, illusions appear while the sensors change from one optimal operation regime to the other, and disappear once the new optimal reference frame is set.Even though the efficient coding suggestions Barlow,  have insparametrical framework based on centering and scaling. These mechanisms are consistent with those proposed in the linear color adaptation context . We make no a-priori assumption on the way the encoder should change, i.e., we do not impose divisive normalization in any way but obtain the responses from the signal statistics through a flexible technique. We find different operation regimes depending on the environment. Therefore, the outputs of the adapted encoder are misinterpreted by a temporarily unaware decoder.The explanation of aftereffects we propose here is qualitatively inspired by the manifold equalization idea suggested, but not implemented, in Barlow  and ClifAn interesting and similar work in spirit is Bednar and Miikkulainen  since thin a certain environment.Finally, the parametric model in Carpenter and Grossberg  is worthThe proposed approach provides a principled explanation of the phenomena, i.e., it derives the behavior from the environment data and well-defined sensor organization strategies. This is a distinctive feature with regard to empirical models fitted to describe the psychophysics. Moreover, the unsupervised and nonparametric nature of the proposed learning technique makes the point stronger: the derived behavior actually comes from the scene properties and not from a-priori assumed models using specific nonlinearities.goals (and not only to redundancy reduction). In this way one can falsify alternative organization strategies. Note for instance the different sharpness in the predicted responses in Figures SPCA easily accommodates different design strategies . This is interesting since linear transforms driven by redundancy reduction certainly explain a wide range of phenomena  necessarily disregard the nonlinear nature of the visual sensors: they cannot account for the non-uniform discrimination between patterns  parametric expressions from empirical models, e.g., divisive normalization in . Temporal scales of adaptation are an important question Webster,  and somewhy the encoder behaves in that way.Another limitation of the proposed technique is that it is focused on the coding part of the coding/decoding problem. In fact, taking into account mismatches or delays between coder and decoder  of the visual system's adaptation mechanisms. In fact, in the construction of SPCA responses a number of physiologically arguable approximations are done. The first is additivity of the differential behavior. In Capilla et al.  not only for motion, but also for texture and color.Our results represent new evidences that support the classical efficient coding explanation of visual illusions, as a result, we'd like to conclude with two appropriate classical quotes. In their seminal contribution, Weiss et al.  concludeThompson  made thistatistical question: our brain may be misrepresenting reality just after sudden environment change, however, do these sudden changes really happen so often?One final http://isp.uv.es/after_effects).Certain statistical approaches to explain aftereffects stress the need of multidimensional equalization to match the data manifolds in different adaptation conditions  identified by linear unsupervised learning are just straight lines in the signal space, e.g., the Principal Components in PCA  is based on the integration of a signal-dependent Jacobian, ∇R(x), from a certain origin, xo, up to the stimulus, x, along a path made of segments of Principal Curves:The SPCA transform , and the Jacobian is made of (1) a local unfolding transform (a rotation du = ∇U(x) · dx that looks for the local decorrelated directions), and (2) a local equalization, a diagonal scaling matrix D(x) [or D(u)] whose elements depend on the marginal PDF on the unfolded coordinates: k-neighbors ball.where The important features of SPCA are (i) the PDF-dependent discrimination metric tunable according to the exponent γ, and (ii) the path made of segments of PCs, which determine the curvilinear sensors. The first property allows using SPCA with Euclidean metric (γ = 0), with metric for optimal vector quantization or for sensors with limited resolution (γ = 1∕3) . Suitable choices include those in Delicado , it is illustrative to see in which conditions SPCA reduces to PCA. This happens by (1) using Euclidean metric in the equalization along the PCs, e.g., using γ = 0, and (2) by increasing the rigidity of the principal curves so that they reduce to straight lines. Otherwise, SPCA sensors and responses will differ from those obtained through decorrelation.5 four-dimensional vectors from 2 × 2 luminance patches of natural images from the calibrated McGill database  and to the infomax SPCA. The decoded images show the practical relevance of the numerical gain achieved by the non-Euclidean γ = 1∕3 approach.The ability of SPCA for transform coding is illustrated by using sensors with limited resolution (uniform quantization in each dimension of the transformed domain) in the considered representations. In every case, resolution in each dimension was set according to standard bit allocation , but in order to minimize the error under quantization, high contrast outliers have to get relatively more resolution than in simple equalization (as is the case with γ = 1∕3).The ability of SPCA for nonlinear ICA is qualitatively and quantitatively assessed by inspecting the conditional PDFs between nonzero frequency coefficients, as in Buccigrossi and Simoncelli , Hyvärininfomax (using γ = 1) or error minimization (using γ = 1∕3). Moreover, note that the uniform quantization transform coding example (that leads to amplitude dependent noise in the PCA domain) also has biological meaning. On the one hand, amplitude-dependent noise in frequency representations of texture makes sense: psychophysical models usually assume uniform resolution after the nonlinear mechanisms (Watson and Solomon, hide more noise injected in the internal representation (Freeman and Simoncelli, These results confirm the SPCA theory in the case of visual textures: the multidimensional equalization can be tuned to optimize different perceptually meaningful criteria, either VL contributed to the development of SPCA and to the analysis of the experiments. JM conceived the work, prepared the data and code for the experiments, and contributed to the interpretation of the results.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.


This paper presents an innovative classification system for hand gestures using 2-channel surface electromyography analysis. The system developed uses the Support Vector Machine classifier, for which the kernel function and parameter optimisation are conducted additionally by the Cuckoo Search swarm algorithm. The system developed is compared with standard Support Vector Machine classifiers with various kernel functions. The average classification rate of 98.12% has been achieved for the proposed method. Surface electromyography (SEMG) is a noninvasive method of measurement of the bioelectrical activity of muscles. SEMG is used both in the diagnosis of diseases of the muscular system and in the development of man-machine interfaces. However, the accuracy of systems that translate human muscle activity into the motion of various machinery or accuracy of sophisticated analysis of human motion based on SEMG signals is still an open problem.k-Nearest Neighbors algorithm in combination with Bayesian analysis, which achieves correct classification rate of 94% [Several successful systems for EMG classification can be pointed out. Examples of systems previously developed include the use of the e of 94% . Better e of 94% , wavelete of 94% , or a coe of 94% .As it is apparent from the exemplary works presented above, there is still room for improvement, and a question is raised as to what modification should be introduced to achieve possibly even better results. This paper presents a novel classification system for SEMG signals based on the Support Vector Machine (SVM) classifier optimised by the Cuckoo Search (CS) algorithm. The SEMG signals are derived from measurements of the flexor carpi radialis and extensor carpi radialis longus muscles and are subjected beforehand to proper parametrisation in the time domain. The system was used to classify six basic hand gestures: hand closing, hand opening, wrist flexion, wrist extension, index finger straightening, and thumb straightening. As a result, a rate of 98.12% of correctly classified samples was achieved.The SEMG signal was measured using the NeuroTrac MyoPlus2 device from two locations on the forearm of a healthy man—the flexor carpi radialis and extensor carpi radialis longus muscles. Measurements were made at 16 Hz.The SEMG signal was measured while performing six types of hand gestures: hand closing (HC), hand opening (HO), wrist flexion (WF), wrist extension (WE), index finger straightening (IF), and thumb straightening (T). Proper segmentation of the SEMG signal is important in case of building systems that should react to the movement of the body. Segments that are too short lead to falsification of results, whereas too long segments cause unnecessary computational load when analysing signals in real time. There are two types of segmentation which can be applied to the SEMG signals: sliding segmentation and overlapping segmentation. An SEMG signal segment of about 200 ms contains enough information to properly classify it, and at the same time it is so short that the use of sliding segmentation does not negatively affect the operation of the system . HoweverSelection of an appropriate data representation has a significant impact on the effectiveness of a classifier. SEMG signals can be described through three types of representation: in the time, frequency, time-frequency, or time-scale domain , 8. The xi,  i ∈ {1,2,…, N}, can be found below:The following features can be distinguished in particular among the transformations in the time domain: mean absolute value (MAV), waveform length (WL), Willison amplitude (WAMP), and slope sign change (SSC). The literature proves their high efficiency in supporting the classification process, particularly when using a single coefficient (MAV or WL) or their combinations  , 9. FormA series of tests were performed to determine the best feature (or set of features) to be used for the case described. The results of these tests are presented in The Support Vector Machine (SVM) is a binary supervised classifier, the aim of which is to determine the optimal hyperplane that separates two groups (classes) of points in space , 12. TheThe problem presented is nonlinear; therefore, in order to deal with nonlinearity of the feature space particular kernel functions are used. The most commonly used functions are quadratic, polynomial, and radial basis functions.Although originally the SVM was created as a binary classifier, there are strategies, the use of which extends the capabilities of the SVM to classify multiple classes simultaneously. The strategies that can be used are one against all, everyone against everyone, and all at the same time . The strThe use of the SVM as a classifier yields good results, especially with respect to electromyographic signal , 6. HoweA group of metaheuristic optimisation methods, which are gaining increased recognition, are the swarm algorithms. Swarm algorithms are based on the concept of distributed intelligence, according to which relatively low-complex actions of many agents and their mutual interaction lead to a new and better quality. Swarm algorithms are used in a wide range of optimisation problems . The litEach cuckoo lays one or more eggs (in a randomly chosen nest) that represent coordinates of a point in the search space, being the problem solution.Some nests with the best value of fitness function are moved to the next iteration.The number of nests is fixed and at the end of each iteration a part of them is rejected with some probability.One of the most prominent swarm algorithms is the Cuckoo Search (CS) algorithm, developed by Yang and Deb . The CS Based on the above presented assumptions, an optimisation algorithm was developed. Brief description of the CS algorithms is presented in The CS algorithm proved to be efficient in several optimisation tasks , 16. MorThe CS algorithm used in the study was designed to optimise the coefficients of the SVM classifier and choose the optimal kernel function for a given class. The choice of the kernel function was made from the quadratic, polynomial, and radial basis functions.The whole dataset is divided into the main training and testing sets.The CS algorithm iterations start. Each cuckoo contains a solution, that is, the SVM kernel function and its parameters, coded as integers (the kernels) and real numbers (the parameters). Every fitness function evaluation, an SVM network is trained using the coded solution and randomly chosen training set and then the network is tested with the other part of the main training dataset, thus providing a classification rate.After performing the CS optimisation, the best solution found is used as the SVM parameters for classifying the testing set , thus providing the final result.Therefore, the optimisation algorithm uses the correct classification rate of the training set during 10-fold cross-validation as its fitness function. If the same result was obtained, each solution was compared further by the number of support vectors.The study was divided into two parts. In the first part, the quality of classification by using the SVM with various kernel functions was cross-examined with several sets of features that describe the bioelectrical activity of muscles obtained Tables , and 3. Based on the classification results collected for different features that describe the SEMG signal, it can be stated that the best of them is the characterisation made on the basis of the mean absolute value of the signal. The average correct classification rate was above 96% for each kernel function tested. However, it can be assumed that this result was affected by the small number of SEMG channels being considered and the small range of hand gestures performed. If a more complex study was performed using SEMG multichannel motion detection, the information that the other features provide may be useful. In this case, the introduction of unnecessary amount of information for the classifier probably resulted in its overfitting. It should be also noted that there is no single kernel function, for which the results are clearly the best.The results of the SVM-CS classification system developed show that the additional optimisation of SVM parameters positively affects its efficiency. In certain cases (wrist flexion and extension), the results did not exceed what was previously shown. However, the overall average correct classification rate of 98.12% distinguishes the method presented.Positive effects of the optimisation algorithm can be also seen in the number of support vectors created during the training process. Their average number per class was 5.01, which compared to the best result of the classifier without additional optimisation (22.1) is a major improvement. It should be also mentioned that the average standard deviation of the results obtained by the SVM-SC classifier amounted to 0.004, which is more than an order of magnitude smaller than the smallest standard deviation obtained with the SVM classifier without optimisation. This again confirms the reliability of the SVM-SC classifier presented.The paper presents a novel classification system developed to distinguish six types of hand gestures based on surface electromyographic signals acquired from the flexor carpi radialis and extensor carpi radialis longus muscles. The system presented is built upon the Support Vector Machine classifier and the Cuckoo Search optimisation algorithm. The purpose of the Cuckoo Search algorithm is to choose appropriate kernel function and its parameters.The system developed is compared with a standard Support Vector Machine classifier. The average correct classification rate of the new system is 98.12%, which is better by 1.31% compared to the best result obtained by the Support Vector Machine classifier without additional optimisation. Moreover, judging by the standard deviation of the results, the optimised classifier is a more reliable solution for the problem presented.It is also shown that with the set of signals provided the best feature to distinguish hand movements is the mean absolute value of the SEMG signal.Due to the use of the Cuckoo Search algorithm as an additional optimiser for the classifier's parameters, this paper presents the first such method applied to the SEMG signal analysis.


Wind energy is one of the extraordinary sources of renewable energy due to its clean character and free availability. With the increasing wind power penetration, the wind farms are directly influencing the power systems. The majority of wind farms are using variable speed wind turbines equipped with doubly fed induction generators (DFIG) due to their advantages over other wind turbine generators (WTGs). Therefore, the analysis of wind power dynamics with the DFIG wind turbines has become a very important research issue, especially during transient faults. This paper presents fuzzy logic control of doubly fed induction generator (DFIG) wind turbine in a sample power system. Fuzzy logic controller is applied to rotor side converter for active power control and voltage regulation of wind turbine. Doubly fed induction generator (DFIG) is one of the most popular wind turbines which includes an induction generator with slip ring. Wind power is the most rapidly growing one since the 20th century due to its reproducible, resourceful, and pollution-free characteristics. Wind energy has a significant impact on dynamic behavior of power system during normal operations and transient faults with larger penetration in the grid. This brings new challenges in the stability issues and, therefore, the study of influence of wind power on power system transient stability has become a very important issue nowadays. Some years ago, the most common type of generators used in the wind energy conversion system (WECS) was the squirrel cage induction generator (SCIG), a fixed speed wind turbine generator (WTG) system, which has a number of drawbacks . Most ofAs stated before, there is a global desire to institute large volume of wind power and flourish the share of energy consumption generated by the wind turbine. The interaction with the grid becomes extremely critical. Unlike conventional power plants, during and immediately following a grid failure, all the wind turbines will trip due to their inability to support the voltage and the frequency of the grid during the period. This might cause serious voltage recovery problems following a three-phase fault and begin to influence the overall power system stability . For theDynamic model of a DFIG wind turbine can be represented in terms of the equations of each of the subsystems, mainly the turbine, the drive train, the induction generator, and the control system. The detailed description of the dynamic modeling of DFIG wind turbine is out of the scope of this paper. Detailed information regarding this can be found in –10. The CP-λ-β curve demonstrates the characteristics of the aerodynamic model of a wind turbine. The mechanical torque (Tm) extracted from the wind by the wind turbine can be expressed as [ρ is the air density, A is the sweep area of the blades, Cp is the power coefficient, λ is the tip speed ratio, β is the pitch angle, VW is the wind speed, and ωt is the turbine rotor angular speed.essed as (1)Tm=ρAH is the inertia constant, ω is the rotor angular speed, and Te is the electromagnetic torque; suffixes t and g denote the turbine and the generator, respectively.Conventionally, the rotor is treated as two lumped masses; that is, turbine mass and generator mass are connected together by a shaft with certain damping and stiffness coefficient values. After simplifications of neglecting the turbine and generator self-damping, shaft stiffness, and tensional oscillations, the mathematical equation for the mechanical part can be expressed as (2)2 determined by [A synchronously rotating nd rotor , which imined by .β for temporary reduction of wind turbine mechanical power by reducing Cp when the rotor speed is over the value that sustains power system stability. It also generates the voltage command signals vr and vgc for the rotor side converter (RSC) and grid side converter (GSC), respectively, in order to control the capacitor DC voltage and the reactive power or the voltage at the grid terminals.The control system generates pitch angle command signal, A protection device, namely, “Crowbar,” is used to save the rotor circuit and power electronic converter from high rotor transient current. Crowbar is external rotor impedance, coupled via the slip rings with the generator rotor instead of the converter. When the rotor current exceeds the current rating of the crowbar, it is triggered and the RSC is blocked. As long as the crowbar is triggered, the generator behaves as a conventional squirrel cage induction generator (SCIG) .tripping of a generator or a line,sudden change of a load, including a load trip,occurrence of a fault, that is, a short circuit.Power system transient stability is the capability of a power system to return to a stable operating point after the occurrence of a disturbance that changes its topology . ExampleA and Bus D  the voltage drop across the Thevenin impedance will be larger and (2) the power transfer capability will be reduced. The transfer capability from one bus to another can be written as [V1 is the magnitude of the bus voltage at the sending end, V2 is magnitude of the bus voltage at the receiving end, X is the reactance between Buses 1 and 2, and δ is the phase angle between Buses 1 and 2. When the voltage is recovering right after the fault, the power transfer capability is decreased proportionally to the amount of voltage reduced at Bus 1 or Bus 2 or both. The voltage regulator will recover the voltages at Bus 1 and Bus 2 after some time to restore the power transfer capability. However, like the case presented here, if the network configuration changes after the fault the reactance X after the fault increases significantly thus changing the power transfer capability.It is clear that itten as , 18(5)Pδ  increase as the grid weakens (δPOST-FAULT > δPRE-FAULT). The operating δ may be reasonably small at the prefault condition. During the postfault condition, the operating δ angle increases closer to 90°, making the power system more prone to instability. The synchronous machines at two ends of the line may lose synchronism with respect to each other after a system fault, if the operating  δ  is initially large and close to the stability limit.The changes in the power-angle characteristic, representing the power transfer between the two buses, are represented in This section gives a brief highlight of the transient phenomena with grid connected DFIG wind turbines. Detailed descriptions can be found in , 11, 19.Generator rotor and stator flux decrease resulting in generator demagnetizing process. The consequent result is the reduction in the electromagnetic torque and active power of the generator. Mechanical torque gets higher as compared to the electromagnetic torque and therefore the generator starts to accelerate.High current transients appear in the stator and rotor windings. Crowbar is triggered to bypass this high current away from the RSC to prevent the converters from any kind of destructions.The GSC is not able to transfer the whole power from the rotor through the converter further to the grid. As a result, the additional energy goes into charging the dc-bus capacitor rapidly.Due to decouple control of mechanical rotor frequency from the grid frequency by means of power electronic converters, a part of potential energy stored in the rotating mass of the shaft of DFIG wind turbine cannot be supplied to the system. This fact resists the DFIG wind turbine from restoration of grid frequency .It causes exciting oscillations in the rotating mass of the shaft as well, which is severe with higher time constant and higher damping time due to manufacturing WTG with softer shaft .In the fault instant, due to short circuit, the voltage at the DFIG generator terminal drops. This has got several consequences.When the fault is cleared, the voltage cannot recover completely immediately because RSC cannot provide necessary reactive power to the generator for its magnetization process as long as it is blocked due to triggering of crowbar. The generator thus needs to absorb reactive power from the grid and this action delays the recovering process of the grid voltage. GSC successfully controls the dc-voltage back to its nominal value. When the grid voltage recovers over a certain value, the crowbar is removed. From this moment, the voltage recovers completely, the generator currents and voltages start to converge to their prefault values, and the RSC retains its control over the active and reactive power.Fault duration, strength of grid coupling, or other mechanical properties play only a minor role in fault responses if fault ride through operation continues. Only protection system setting has a major impact on fault responses and hence transient stability, only when WTG disconnection is induced by voltage or frequency deviations. If reconnection time and/or ramping time  are very long, the generated electrical power goes much lower than the extracted mechanical power (which is hardly reduced) and an unbalance between mechanical and electrical torque results both before reconnection time and during ramping time. This is how transient stability is prolonged .DFIG wind turbines are required to be disconnected from the grid to prevent from going into system instability due to the vulnerable occurrences like high transient current flow, oscillations excited to rotating mass of the drive train, rotor over speed, and dip in grid voltage due to the fault. But with more wind power penetration in the grid, WTGs are having more influences on the overall power system stability and hence “fault ride through operation” (which means remaining connected in the grid following a fault) of these WTGs is listed as requirement in the grid codes of countries with high level of wind power penetration .To sustain fault ride through operation of DFIG wind turbines, several control and power electronic devices have been implemented to mitigate the abovementioned vulnerable factors immediately after the fault so that the power system stability is retained within a few seconds after its clearance. These are discussed as follows.Installation of crowbar in the system increases the cost and hinders its reliability. Moreover, triggering of a crowbar causes RSC blocking, which results in having generator to operate as SCIG. This often forces the disconnection of WTG, which is a complete violation of grid code requirement. Limitation of rotor current has been achieved without any disconnection by converter control method. Several advanced control principles have been reported: (i) eliminating current and voltage unbalances by supplying negative sequence current –24, (ii)Oscillation excited to rotating mass of the drive train followed by a grid fault may prolong system stability. PI damping controller can damp “fast generator speed oscillations” very effectively. It produces active power reference signal for the RSC control based on the deviation of generator rotor speed from its reference. The speed reference is defined by the optimal speed curve at the incoming wind. The damping controller needs proper tuning. Insufficient tuning may lead to self-excitation of the drive train system and risk of unnecessary tripping as protection against vibration in the mechanical construction .During voltage recovery process following a transient fault, GSC cannot provide necessary reactive power due to having converter with small capacity. That is why the generator absorbs reactive power from the grid and results in voltage and frequency instability. Such instability problems can be solved by dynamic compensation of generator's reactive power demand by the following two methods: (i) using shunt flexible AC transmission system (FACTS) devices, such as the static VAR compensator (SVC) and the static synchronous compensator (STATCOM), to generate a set of balanced three-phased sinusoidal voltages at the fundamental frequency with rapidly controllable amplitude and phase angle to drive PWM converter with dc-link capacitor and transport necessary reactive power in the grid , 27, 28,At a short circuit fault, the grid voltage and then the electromagnetic torque are significantly reduced, which lead to wind turbine acceleration. The pitch angle controller therefore prevents the rotor overspeed and hence helps reestablishing the voltage at the WTG terminal by aerodynamic power shedding by the generation of pitch angle. It should be possible to reduce the power production of the wind turbine from any arbitrary operational point to below 20% of the rated power in less than 2 s . Faster This section analyzes the closed-loop behaviour of the DFIG system using indirect CMC methods. Two current regulators for the rotor-side converter are considered: the conventional regulator and a regulator intended for unbalanced grid faults. The same steps are followed in both analyses: the open-loop model of the DFIG system is first formulated; second, the current regulator is written; and, third, by combining the open-loop model and the control model, the closed-loop dynamic description of the rotor-side system is derived.d − q reference frame. A synchronously rotating reference frame is used with the direct axis oriented along the stator flux position. In this way, the reference frame is rotating with the same speed as the stator voltage. Thus, the stator and rotor voltages can be expressed as follows:The open-loop model of the DFIG system is represented, as usual, in the The inputs of the open-loop model are voltages and angular frequencies, and the outputs are currents. The stator voltage is given by the grid voltage, while the rotor voltage is supplied by the rotor-side converter. It is assumed in this study that the converters are sufficiently fast, so that they can exactly track the reference voltage. In this case, the rotor voltages are given by the rotor-side controller:The control system is based on fuzzy logic. This type of control, approaching the human reasoning that makes use of the tolerance, uncertainty, imprecision, and fuzziness in the decision-making process, manages to offer a very satisfactory performance, without the need of a detailed mathematical model of the system, just by incorporating the experts' knowledge into fuzzy rules. In addition, it has inherent abilities to deal with imprecise or noisy data; thus, it is able to extend its control capability even to those operating conditions where linear control techniques fail . This system has four main parts. First, using input membership functions, inputs are fuzzified, then, based on rule bases and inference system, outputs are produced, and finally the fuzzy outputs are defuzzified and applied to the main control system. Error of inputs from their references and error deviations in any time interval are chosen as inputs. In this paper, these parts as illustrated in Pref) power and voltage (Vref) separately using fuzzy controllers, hysteresis current controller converter, and vector control algorithm [lgorithm . Based od-axis and q-axis rotor currents, they converted to a-b-c reference frame using flux angle, rotor angle, and finally slip angle calculation and Concordia and Park transformation matrix. Then they are applied to a hysteresis current controller to be compared with actual currents and produce switching time intervals of converter.After the production of reference With the increasing wind power penetration, the wind farms (mostly equipped with increasing number of DFIG wind turbines) are directly influencing the power systems. Therefore, grid code of different countries has enlisted “fault ride through operation” of WTGs as one of the major requirements to sustain transient stability for reliable operation of power system with uninterrupted power generation and supply. It shows that the study of transient dynamics with DFIG wind turbines with its current research stand and future research options is a very important issue. Generally, DFIG wind turbine suffers from high transient current, rotor overspeed, oscillations in the rotating mass of the shaft, grid voltage, and frequency excursions while subjected to a transient fault. Research efforts throughout the world result in various advanced scientific inventions of devices like advanced converter controller, damping controller, pitch angle controller, and dynamic reactive compensator. These devices help mitigate vulnerable problems addressed by transient faults. Our exploration is to make sustenance of “fault ride through operation” of DFIG wind turbines as a means of enhancing transient stability of power systems. Extensive studies have been carried out on transient phenomena, factors influencing transient phenomena, and the impact of faults on both DFIG wind turbine and the grid. But, to our knowledge, most of them carried out qualitative research on transient stability. Most of the papers in the literature analyzed transient dynamics and stability followed by symmetrical faults on a system with constant loads. Fuzzy controllers, in contrast to the conventional PI controllers, can take care of the nonlinearity in the control law and hence are known to have better performance than PI under variable operating conditions. Moreover, the TS-fuzzy is better than the mamdani type fuzzy controllers in terms of the number of fuzzy sets for the input fuzzification, number of rules used, and the number of coefficients to be optimized. These studies may contribute to more accurate simulation results for finding out better strategies for smooth, reliable, and uninterrupted operation of power networks with the future penetration of wind energy systems.


We demonstrate the ability of Fiber Bragg Gratings (FBGs) sensors to protect large areas from unauthorized activities in railway scenarios such as stations or tunnels. We report on the technological strategy adopted to protect a specific depot, representative of a common scenario for security applications in the railway environment. One of the concerns in the protection of a railway area centers on the presence of rail-tracks, which cannot be obstructed with physical barriers. We propose an integrated optical fiber system composed of FBG strain sensors that can detect human intrusion for protection of the perimeter combined with FBG accelerometer sensors for protection of rail-track access. Several trials were carried out in indoor and outdoor environments. The results demonstrate that FBG strain sensors bonded under a ribbed rubber mat enable the detection of intruder break-in via the pressure induced on the mat, whereas the FBG accelerometers installed under the rails enable the detection of intruders walking close to the railroad tracks via the acoustic surface waves generated by footsteps. Based on a single enabling technology, this integrated system represents a valuable intrusion detection system for railway security and could be integrated with other sensing functionalities in the railway field using fiber optic technology. In particular, intrusion-sensing systems are becoming much more attractive to the transport sector in which increased protection for passengers, assets, and infrastructure is greatly needed. The usage and implementation of intrusion detection systems in railway scenarios would allow monitoring and protection of railway tunnels, level crossings, train depots, and other similar areas featuring railway scenarios. In these cases, intrusion detection systems could be used to protect properties from theft or vandalization, leading to significant cost savings.Conventional technologies for intrusion detection systems that are installed to complement human guards (and video cameras systems) typically involve microwave sensors, electric field sensors, ported coaxial cables, and infrared sensors . Among tIn contrast to these conventional technologies, optical fiber sensors can be considered as a valid option due to the intrinsic advantages associated with their use. Optical fibers sensors offer immunity to electromagnetic interference as well as high sensitivity, compactness, remote sensing ability, and stability in harsh environments. Furthermore, the optical fiber plays the dual role of sensor and communication medium, which strongly reduces the complexity of the telemetry systems.et al. demonstrated an optical fiber fence based on FBGs [et al. reported an FBG-based fiber-optic fence and developed a method based on the autocorrelation characteristics of the signals to improve the system performances by reducing the number of nuisance alarms [Among the fiber optic sensors, Fiber Bragg Grating (FBGs) sensors also offer high multiplexing capability, versatility, and simplicity of use, allowing the deployment of a cost-effective interrogation strategy. Additionally, FBGs have been employed previously for perimeter security systems. In particular, Rao  on FBGs . Successe alarms . Neverthe alarms , the useet al. [et al. demonstrated a FBG seismic sensor in a cantilever configuration for military vehicle detection [etc.) interleaved by rail tracks (for train transit) that cannot be practically obstructed with physical barriers. As an example, we refer to a specific case, i.e., the Service Area of Ente Autonomo Volturno Railway in Naples . As shown in Alternatively, Allwood et al. ,6 investetection . These retection –10. In tIn the following section, we briefly describe the principle of operation of the proposed sensing system. In the successive sections, we describe the methodology and the experimental results for indoor lab experimental activities carried out for the development of an FBG sensorized mat and outdoor experimental activities used for the development of a fiber-optic accelerometer sensing system.2.2.1.The intrusion detection system for walkable access to a protected site is composed of a mat sensorized with FBG strain sensors, as schematized in 2.2.The fiber-optic sensing system for rail track access protection is composed of two accelerometers based on FBG technology, one for each rail track. An intruder moving on the ground represents a vibration source. The seismic/acoustic surface waves propagate omni-directionally in the ground. Because the surface wave couples to the rail tracks, FBG accelerometers installed under the rail tracks are able to detect the acoustic waves generated by the footsteps of a human. Thus, a person passing through a perimeter that must be protected via the rail tracks can be detected by monitoring the Bragg wavelength shifts of the FBG-based accelerometers see .3.In this section, we report on the methodology of the experimental tests, which are divided according to strain and acceleration measurement trials.3.1.Preliminary lab activities were performed to characterize the elastic response of the sensorized mat in the presence of applied static loads and to determine the performance in terms of the sensitivity curve, response times, and required number of sensors per area unit.To maximize the strain induced by the applied load, we selected rubber as the mat material due to its low Young modulus (10–100 MPa ). The maThe “ribbed” mat allows for optical fiber placement and easy FBG sensor installation. Additionally, the possibility of damage or breakage of the optical fiber is minimized because the optical fiber is not exposed directly to the floor surface but is protected by the ribs of the mat.Once the material and geometry of the mat were identified, a 1570-nm FBG was glued onto the lower surface of the ribbed rubber mat using cyanoacrylate. The FBG wavelength shifts are detected using a commercial FBG interrogator (Micron Optics Interrogator-SM125).To characterize the elastic response of the sensorized mat, a sequence of cast iron discs (10 kg-10 kg-20 kg-20 kg) was applied on the mat one-by-one, with time intervals of five minutes corresponding to the FBG sensor. With the same time steps, all of the discs were singularly removed to recover the initial condition. During the tests, to assure uniformity of the applied pressure on the mat surface, an iron plate with a circular base, a diameter of 10 cm, and a thickness of 1 cm was positioned between the mat and the plates corresponding to the measured point. The size of the iron plate was selected such that its area was comparable with the area covered by a sole of a foot.i.e., the sensing area for a single FBG.A similar analysis was performed by applying a static load at different distances from the FBG to retrieve the influence zone of each sensor, Based on the results of the performed characterizations, we deployed the FBG sensors in a network and verified the ability of the sensing system to detect an intruder walking on the mat.Finally, we investigated the possibility of improving the performances of the sensorized mat by increasing the size of the sensing area for a single FBG (detection zone) through the use of a “rigid” plate superimposed on the rubber mat.3.2.Several trials were performed at the Maddaloni-Marcianise freight station in Italy to investigate the ability of the proposed sensing system to detect the presence of an intruder walking close to the rail tracks. The two accelerometers used in the fiber-optic sensing system for rail track access protection are both one-axis commercial accelerometers  based on FBG technology. These accelerometers feature a flat responsivity (from DC to 300 Hz) of ∼16 pm/g and a resonance frequency of ∼700 Hz.i.e., without drilling and welding to the rail tracks) and obviously without removal of the rail tracks [The accelerometers were positioned under the tracks according to the perimeter to be protected using proper packaging with the axis of measurement perpendicular to the ground plane, as shown in l tracks . For intDuring the outdoor trials, an 80-kg male (the intruder) walks near the tracks, covering a total distance of 20 m. In particular, he begins by walking parallel to the rail tracks from a distance of 10 m from the sensors, subsequently walks up to the sensors, and walks away for an additional 10 m. We repeated these tests by changing the relative position between the intruder and the rails to investigate the influence zone of the accelerometer sensors.4.The following paragraphs report a selection of results that are representative of the performed tests and are divided according to strain and acceleration measurements.4.1.versus time is shown for different applied weights in B) increases according to the positioning of each weight as it returns back to the initial position after the successive weight removal. The response times are as low as one second. In particular, for the FBG response shown in The FBG response The observed wavelength shifts are repeatable with an error of up to 15% with respect to the steady-state wavelength value. Nevertheless, the Bragg wavelength does not completely recover to the initial value after 5 min from the removal of all weights. As evident in versus the distance from the FBG, and in the same plot (on the right y-axis), we also report the Bragg wavelength shift normalized to its maximum value. As highlighted by the exponential fitting, the wavelength shift strongly decays for increasing distances and reaches less than 10% of its initial value at a distance of 10 cm. In particular, the shift is as low as 5 pm when the weight is located 25 cm from the FBG. We used this value as a threshold to establish the limit of operation of a single FBG. Consequently, we estimated that the required number of sensors per area unit required to detect a walking human using a sensorized mat is four FBG units per square meter.To this end, we applied a 60-kg static load at difference distances from the FBG. 2 and a thickness of 3 mm, as shown in Based on these results, we fixed eight FBGs equally spaced in a square array configuration on the ribbed surface of a mat with an area of 1 × 2 mWe performed several walking tests on the mat and found that the ribbed rubber mat sensorized with FBGs is always able to detect a human walking on the mat and is thus suitable to reveal “intrusion” through the protected perimeter.i.e., at the maximum distance from the FBGs, each step is easily detected by the nearest FBGs. Indeed, human footsteps can be clearly determined by observing the Bragg wavelength shifts, and each FBG response is characterized by a peak due to an intruder footstep. The maximum amplitudes of the wavelength shifts perceived by each FBG are approximately 20–50 pm, which are much higher (one order of magnitude) than the responses that we observed when we applied a 60-kg static load at a distance of 35 cm from the FBG.Actually, the load induced by a walking intruder is generally different from the previously applied static loads. Indeed, the FBG response is primarily ruled by the resulting “pressure” applied, and thus, we can expect different responses with the same applied weight if different contact surfaces occur between the foot and the mat. Basically, the static load can be viewed as a worst-case scenario because all of the weight is uniformly distributed on the iron plate in this case. In contrast, when a human walks, the weight is concentrated first on the heel, next on the entire sole, and finally, on the toe of the foot.As confirmation of this circumstance, We performed also experimental tests on the sensorized mat in which multiple intruders are accessing to the protected perimeter. By taking advantage of the spatial distribution of the sensors or the simultaneity of different FBG responses, in principle, thus it is possible to infer the presence of multiple intruders. Obviously from the collective response of the FBGs is not trivial or always possible to retrieve the intruders number as well as their direction. However, it is worth remarking that the system demonstrated again to be able to reveal the presence of an intrusion.Once the proper functioning of the sensing system is established for intrusion detection, we focused attention on the alarm generation by privileging some basic aspects specific to the proposed sensing system. Until this point, we recognized the FBG response using the Bragg wavelength shift created by intruder steps, but the Bragg wavelength shift cannot be considered as a valid observable signal. The effects of the elastic hysteresis as well as the daily thermal cycles (drifts) can affect the Bragg wavelength in a “slow” temporal regime. Therefore, to neglect these effects, a signal processing strategy must be adopted based on the comparison of the derivative of the Bragg wavelength with respect to a predetermined threshold. To exemplify this issue, These results essentially indicate that a smart data processing technique must be applied to judge (and assign) an alarm based not only on the wavelength shift but also on the derivative of the Bragg wavelength's shift. This caution is necessary in order to face specific effects, such as the elastic hysteresis and the daily thermal cycles, potentially affecting the behavior of the FBGs sensorized mat.Also the duration of the threshold exceedance can be efficiently used in order to reduce the possibility to have false alarms due to random noise. Nevertheless, in general, to reduce the possibility of false alarms, a more sophisticated signal processing strategy should be adopted to infer more detailed information on the intruder characteristics. Furthermore, dedicated studies on targets classifications are needed to improve the recognition capabilities .2 and a thickness of 1.5 mm on the rubber mat .Although significant for large areas, the beneficial effect of the reduction in the number of FBGs for unit area should obviously be evaluated by taking into account the additional costs due to the use of a rigid plate and the relative installation costs.4.2.versus time when the intruder walks between the rails of the track  and the increasing amplitude of successive peaks is due to the intruder approach.versus time, when the intruder runs between the rails of the track. Peaks due to the footsteps can be clearly appreciated and no significant amplitude variations can be seen with respect to the walking intruder. The temporal separation of the peaks instead is reduced revealing a running speed of approximately 2.5–3 m/s.We performed further intrusion tests through the rail track. Additionally, in Finally, it is worth remarking that in this configuration, the proposed sensing system does not exploit “human signatures” to discriminate among different events , but the5.In this work, we demonstrated a technological solution based on FBG sensors suitable for protecting railway areas from unauthorized access. Using the same enabling technology, we designed and developed an optical fiber sensing system composed of a network of FBG strain sensors bonded on a ribbed mat combined with FBG accelerometers sensors installed directly on the rail tracks.We carried out several experimental trials to characterize the elastic response of the sensorized mat and determine the performance in terms of the sensitivity curve, response times, and required number of sensors per area unit. The experimental results demonstrate that a network with four equally spaced FBGs per square meter enables the real-time detection of a human walking on the mat. Moreover, we demonstrated that optimization of the number of FBG per area unit is possible by covering the mat with a rigid plate that guarantees a wider distribution of the applied load.Furthermore, we installed two FBG accelerometers in proper packages under the rail tracks at the Maddaloni-Marcianise freight station, where we performed several walking tests. The accelerometer sensors detected a walking human on the rail tracks, demonstrating their ability to detect unauthorized access through the rail tracks. An intruder was also detected at 5 m of distance from the installed sensors and approximately 0.8 m away from the railroad area.During the performed tests, both subsystems demonstrated to be always able to detect a human walking through the protected perimeter, without exhibiting false negative. Additionally the experimental results indicated that the sensing system is able to provide further information on the intrusion such as the intruder speed and direction. The presence of multiple sensors, such as those installed on the mat, potentially enables also the localization of the intruder and the crossing pattern recognition.We did not carry out dedicated studies on the sensing system's response to different moving targets. Nevertheless, in principle, we expect that also our sensing system could be subject to false positive as far as an interfering event induces an excitation on the sensors similar to the signals detected in presence of human walking.In order to reduce the possibility of false alarms, high level signal processing strategies should be adopted also by exploiting and “fusing” data related to different spatial locations and time. Additionally , when the presence of false positive must be absolutely minimized, the proposed system could be used in combination with another intrusion detection system, such as a video camera system supported by automatic recognition algorithms. It is well known, indeed, that even if any intrusion detection system is subjected to its own false alarms, the combination of two systems with different technologies strongly improves the discrimination capability and reduces the false alarms .In spite of the underlined potential limitations, the proposed FBG's integrated system offers a wide variety of features and benefits. Indeed, FBG sensors do not require electrical power, offer immunity to electromagnetic interferences, and high multiplexing capability allowing the deployment of a cost-effective interrogation strategy. Other basic components, constituting the integrated system, such as the rubber mat and the accelerometers packages, are low cost materials. Consequently, the startup, operation and maintenance costs of this integrated system are competitive or lower than other security methods.Furthermore, the use of a technologically integrated solution paves the way for further functionalities integrated into the same platform for other possible security applications in the railway field. From the perspective of using a unique multifunctional technological platform, it is worth mentioning that FBG sensors have been widely used to perform smart diagnostics in such railway applications as switch monitoring, axle counting, weighing in motion, and wheel flat detection –15.


A 64-year-old man presented to the emergency department after fall with a chief complaint of left wrist pain. On physical examination, the patient was found to have swelling and tenderness of his left radial wrist. He denied any paresthesias, tingling, or numbness.What are the types of intra-articular fractures at the base of the thumb?What is the mechanism and pathoanatomy of this injury?What are some of the treatment options?What are some of the complications that can arise from treatment?Thumb metacarpal fractures can be classified as shaft or base fractures, with base fractures further classified into extra-articular or intra-articular. Intra-articular fractures are described as either Bennet-type or Rolando-type fractures and present treatment challenges secondary to the deforming forces acting at the base of the thumb.3Similar to all metacarpal base fractures, the Rolando fracture is often caused by an axial load crushing the articular surface. The base is usually split into a volar and dorsal fragment with the volar oblique ligament attached to the volar segment and the shaft pulled proximally and dorsally primarily by the abductor pollicis longus and the adductor pollicis. The patient often presents with a swollen and tender thumb base. Varus angulation of the joint may be visible. If left untreated, articular incongruity can lead to debilitating arthritis.,6,8Treatment of a Rolando fracture can be difficult due to the comminuted nature of this fracture pattern, but surgical treatment is recommended for unstable fractures. In the presence of large volar and dorsal fragments, open reduction with internal fixation is indicated. The articular surface is reconstructed with K-wires and then secured to the metacarpal shaft using a T plate. Neurovascular damage is a major complication of this approach, and care should be taken to not damage the branches of the superficial radial nerve, the lateral antebrachial cutaneous nerve, and the radial artery.5The Rolando fracture has been associated with a poorer prognosis than its counterpart, the Bennett fracture, largely due to its comminuted nature. Exact anatomic reduction of the articular surface may not be necessary to obtain a good functional result; however, reduction to 1 mm or less is generally thought to reduce the risk of radiographic arthritis.9In summary, fractures of the base of the first metacarpal are particularly common. Correct diagnosis, imaging, and treatment options must be judiciously carried out to prevent debilitating joint arthritis. The aim of treatment should be exact reduction, either by the open or by the closed method.


Typically, gene expression biomarkers are being discovered in course of high-throughput experiments, for example, RNAseq or microarray profiling. Analytic pipelines that extract so-called signatures suffer from the "Dimensionality curse": the number of genes expressed exceeds the number of patients we can enroll in the study and use to train the discriminator algorithm. Hence, problems with the reproducibility of gene signatures are more common than not; when the algorithm is executed using a different training set, the resulting diagnostic signature may turn out to be completely different.In this paper we propose an alternative novel approach which takes into account quantifiable expression levels of all genes assayed. In our analysis, the cumulative gene expression pattern of an individual patient is represented as a point in the multidimensional space formed by all gene expression profiles assayed in given system, where the clusters of "normal samples" and "affected samples" and defined. The degree of separation of the given sample from the space occupied by "normal samples" reflects the drift of the sample away from homeostasis in the course of development of the pathophysiological process that underly the disease. The outlined approach was validated using the publicly available glioma dataset deposited in Rembrandt and associated with survival data. Additionally, the applicability of the distance analysis to the classification of non-malignant sampled was tested using psoriatic lesions and non-lesional matched controls as a model.Keywords: biomarkers; clustering; human diseases; RNA The typical application of gene expression signatures for diagnosis and prediction of the course of disease is based upon an oversimplified understanding of the pathology. According to this model, there is a gene or a set of genes  that is "responsible" for a pathophysiological process within certain tissue, or a cell type, that manifests itself on an organismal level as a disease. If we see this gene over- or underexpressed, or observe a set of concerted changes in expression of a set of genes, we can diagnose the disease. We can further use the respective levels of the over- or underexpression to predict the course of the disease.per se, but with a typically limited number of already diagnosed patients we could biopsy and enter into the analysis as a training data set. As shown by simulations in   5] for bp is substantially larger than the sample size n. This property of biological datasets makes the task of distinguishing the noise from the true biological signal quite challenging, and it becomes close to impossible to obtain consistent estimator procedures [Expression levels for individual genes and other variables quantified in high- throughput biological experiments are commonly thought of as dimensions of the space on which we are collecting information. Thus the problem outlined above is known as the "curse of dimensionality" . Brieflyocedures ,10. Hencocedures ,10 or stocedures : startinocedures . Hence, ocedures . MoreoveAnother popular solution to the dimensionality curse is to use a supervised approach that relies either on the pre-selection of the feature-limiting steps or on pre-filtering the data by the strength of an association of each variable with clinical outcome, or associations between variables ,15. UnfoIn this paper we propose an alternative novel approach based on the "distances" in the multidimensional space of gene expression values. As a proof-of-principle, we show that this approach produces surprisingly good results in separation of normal and affected samples both for analysis of human malignancies and for chronic progressive conditions like psoriasis.A result of an expression experiment for a given sample is a (very long) vector that may be represented as a point in a multidimensional space. If we introduce a distance in this space, we can use the standard clustering techniques  to classThere are several ways to define the distance between two points, ssion in ). The naIt takes all components of vectors equally and does not account for any correlations between them. We can normalize the sums in equation (1) on the average vector lengthwhere the averaging denoted by angular brackets is taken over all vectors in the dataset. Then the scaled distance is defined asIn the gene expression experiments the absolute values of the vectors are not relevant since the expressions are arbitrarily normalized. Therefore, using a distance that does not take these values into account is justified. A correlation distance, or Pearson distance, is the one that has this property. It is defined asIf we have enough data to calculate covariance matrix S with the elements obtaining by the averaging over all vectors in the dataset their covariances:them we can use Mahalanobis distanceThis distance takes into account all correlations in the data. The problem with it is that to calculate correlation matrix and its inverse, many data points, i.e. many patients, are required.Therefore, in the calculations below we use, as a rule, Pearson distance (4). Note that since all components of |X| and |Y| are non-negative, this distance is always between 0 and 1.To test for the practical usefulness of the distance-based expression metrics we deployed the following strategy:http://www.ncbi.nlm.nih.gov/geo/.1 The datasets were selected from public MIAME-compliant GEO repository 2 For each subset of the samples within data set  we calculated the coordinate for the center of the space defined by points of all the vectors as the simple arithmetic mean of all the samples in the subset.3 For each point we calculated the distance to the centers of all subsets.r1 vs. the distance to another center r2 were plotted.4 The distances to one center r1, r2 and r3, each plotted to the center of the corresponding subset. In these cases we used barycentric coordinates [i dfrom the vortex i proportional toIn some datasets, three different subsets may be defined, instead of two. In these cases, each point was associated with three distances rdinates  in a equi D= 1 − di. The transformation between these two definitions is trivial:Note that most programs dealing with barycentric coordinates  use a dTo test the hypothesis that the expression profiles of primary tumors and their metastases drift away from the healthy, homeostatic state, the RNAseq dataset with GEO Series accession number GSE46622 described in  was downAccordingly we had three different subsets and three different centers of clusters. These data may be presented either as the points within a 3d cube Figure , or as bIn a practical sense, one may be interested in how far away given tumor sample departed from the center of the space occupied by normal samples. This approach allows one to draw a linear plot Figure . Intereshttp://caintegrator.nci.nih.gov/rembrandt/, which included data on 21 normal samples, 221 glioblastoma multiforme (GBMs), 145 astrocytomas, 66 oligodendrogliomas and 11 tumors of mixed origin. The raw gene expression CEL files from Affymetrix HGU133 Plus 2.0 arrays were normalized using the robust multi-array average (RMA) method [To prove this point, we downloaded data represented in publicly available Repository for Molecular Brain Neoplasia Data (Rembrandt) ) method  with defp = 3.1 × 10−6) or oligodendrogliomas (p = 0.0033). In both astrocytomas and oligodendrogliomas, the observed spreads of the distance values were quite large, possibly reflecting known heterogeneity of these tumors [The plots reflecting the distances to the center of normal samples for all studied groups of samples are shown on Figures e tumors ,23.p = 2 × 10 −9 and R2 = 0.09. This means that the dependence is statistically significant, but the degree of the drift away from the center of the normal space explains only 9% of the survival prognosis, while the remaining 91% is explained by other factors, most likely, particular chromosomal rearrangements and mutations observed within the tumor along with tumor location and other known determinants of glioma prognosis [Interestingly, patients survival were found to be negatively correlated with the distance of the tumor expression profile to the center of normal samples: the farther was the expression profile from the homeostatic center defined by normal samples, the shorter was the survival length for the patient. The corresponding plot is shown on Figure rognosis .N of paired samples = 13) and GSE11903 [N of paired samples = 15). Both datasets were created using Affymetrix Human Genome U133A Array platform. As could be seen on Figures To illustrate an applicability of the gene expression distance analysis to the classification of samples collected from patients with non-malignant chronic disease, we selected psoriasis, an autoimmune disease mainly diagnosed through the visual inspection of the lesion skin by experienced dermatologists. To analyze the properties of gene expression profiles of lesional and non-lesional psoriatic skin samples from the same patient, two datasets were downloaded from GEO, GSE6710   by a large number of reference  samples, as farther away the respective tissue will be from the well maintained, healthy state. In our study, we used publicly available datasets, to develop easily interpretable, composite measure that capable of integrating high-throughput transcriptome profiles into comprehensive, holistic metric describing the molecular homeostasis within given sample.The comprehensive distance measures account for the intrinsic heterogeneity of human tumors that plagues hight-throughput studies involving this type of the biological material  and evenImportantly, proposed composite biomarker may be suitable for a dynamic description of patients' condition. This novel concept allows one to depart from the classical two-bin prediction model (e.g. "bad prognosis/good prognosis") as it produces a continuous prognosis model, where each sample is located in the neighborhood of other samples analyzed post-hoc and associated with known survival. For each sample, this concept quantitatively describes the degree of "the drift" from the standardized phenotype that will reflect the departure of the body from homeostasis. In the concepts, the effects of each personalized intervention could be evaluated by comparing the distance metrics for samples collected before the treatment and at multiple time-points within the interventional treatment course.If proven valid, this concept might be developed into a novel type of integrative tests for the monitoring of the disease progress and the prediction of disease outcomes. The proposed distance analysis has a potential to become versatile in its application as it is equally attributable to gene expression profiles collected both by microarrays and by RNA-seq platforms, as well as, possibly, to proteome and metabolome profiles.There is no doubt that proposed computational approach requires further development and optimization, in particular, other types of correlation-based metrics have to be tested for various kinds of multiparametric datasets that comprise simultaneously measured analytes. Future studies should include an analysis of longitudinal experiments that involve either various time points in course of the therapeutic treatment that ultimately results in the normalization of the pathological condition, or gradual processes detrimental to experimental system, for example, a development of insulin resistance or an ageing.The distance analysis of molecular portraits is robust and versatile in its application as it is equally attributable to gene expression profiles collected by microarrays and by RNA-seq. The distance-based continuous predictive models depart from the classical two-bin prediction model (e.g. "bad prognosis/good prognosis") by placing each sample in the neighborhood of other samples analyzed post-hoc and associated with known survival.The authors declare that they have no competing interests.BV drafted the manuscript, provided insights concerning dimensionality curse and mathematical advice in the course of this study. AB conceived this study and developed the concept of distance metrics. LW and TC analyzed both cancer datasets and contributed to the draft of the manuscript. SB analyzed psoriasis dataset and contributed to the development of the concept of distance metrics.