Unstable sole designs have been used as functional or therapeutic tools for improving body stability during locomotion. It has been suggested that the narrow base of support under the feet generate perturbations that challenge the instability of different joints during motion, thereby forcing the body to modify its movement in order to maintain a stable gait. The purpose of the present study was to explore the correlation between the stability of the footwear-device and the magnitude of perturbation conveyed during gait.Various levels of dynamic instability were achieved using a novel foot-worn platform with two adjustable convex rubber elements attached to its sole. A total of 20 healthy male adults underwent direct in-shoe pressure measurements while walking with the footwear device. Foot center of pressure (COP) and stride to stride variability measures were extracted to examine the correlation between the magnitude of the instability and the imposed perturbations during gait.A counterintuitive but significant correlation was found between stride to stride variability and the instability of the biomechanical elements. Moreover, there was significant correlation between the instability of the elements and the perturbations found in the COP trajectory. The linear model describing this correlation was found to be statistically significant.There was significantly negative correlation between the level of instability induced by the shoe design and the amount of perturbations conveyed during gait. This suggests that the external perturbation must remain within a certain range limit. Exceeding this limit can negatively affect the treatment and probably lead to opposite results. In recent years, a novel therapeutic approach to musculoskeletal pathologies, centered on neuromuscular reeducation, has emerged and has been the focus of a vast amount of research –3. The pIt has been suggested that perturbation can generate the appropriate stimuli to improve proprioception and to adopt altered motor control strategies during gait. Footwear-generated biomechanical manipulations have been commonly utilized for this objective . Acting Several unsteady shoe designs have been developed and have been reported to produce favorable outcomes of functional activity and pain reduction –10. FindHowever, the effect of the instability of the elements on the level of perturbation generated has yet to be established. It is well accepted that perturbation manifests as stride to stride variation of gait parameters as well as in the trajectory of the foot’s COP . TherefoThe study cohort was comprised of 20 healthy male volunteers without any known history of injury or any postural or skeletal disorder which could affect normal posture or gait. All participants had an equivalent shoe size (French 43), a right dominant leg and a similar anthropometric profile. The participants’ mean ± SD age was 24 years ± 11 months, height was 176.1 ± 3.29 cm, and body mass was 68.8 ± 6.67 kg. Approval of the local Ethics Committee Ha'Emek Medical Center (Afula-Israel) was obtained and informed consent was given by all participants.The biomechanical device  utilized in this study has been previously described in detail . In briePrior to testing, participants were functionally assessed by a physiotherapist and were asked to walk several minutes with the shoe-device, in order to become accustomed to it. Then, through observations of the subject’s walk and his feedback regarding the comfort of use during walking, a qualified physiotherapist defined the “functional neutral configuration” (FNC) to which the elements were fixed throughout the whole experiment. For healthy users, FNC was defined as the position of the elements in which the apparatus exerts the least valgus, varus, dorsal or plantar torque about the ankle. This procedure has been previously described , 13. FouAll measurements were applied while the participant walked over a flat 30 m long surface, at a constant self-selected velocity. At the beginning of each experiment we have captured the typical walking speed for each participant and then fixed a metronome to that rhythm so that the walking velocity remains constant during all experiment conditions. All conditions were tested in arbitrary order on the same day. To insure uniformity of testing conditions, all subjects were provided with the same biomechanical system and all calibrations of the biomechanical device were performed by the same physiotherapist.Plantar pressure testing was applied using the Pedar-x Pressure-measurement system  which consists of 2.5 mm thickness insoles incorporating a matrix of 99 pressure-sensitive capacitive insoles which can be placed into the subject’s footwear to measure pressure during gait. The sensors were sampled at a rate of 100 Hz. Foot COP data was extracted and analyzed for the dominant leg using Matlab.Stride to stride variability (STSV) was defined for each of the testing condition as standard deviation in stance length for each testing condition and directly extracted from the Pedar system.Moreover, we defined two measures to examine if the level of instability was reflected in the foot COP trajectory. The first measure was the perturbations of the COP data and was calculated as the root mean square change in medial-lateral position of the COP during each stance phase. Mean values were calculated for each level of instability as the average value of all steps in the same trial. The second measure was defined as the mean medio-lateral location of the COP trajectory and was evaluated for four sub-phases of the stance phase: loading response (LR): 0 to 10 %; mid-stance (MS): 10 to 30 %; terminal stance (TS): 30 to 50 %; and pre-swing (PS): 50 to 60 %. Values were averaged over all steps.p-value of less than 0.05 was considered to be statistically significant. All pvalues reported are two sided.To determine if the stage of instability affected STSV and both COP measures, three linear mixed effect models were fitted. In one model, STSV was the dependent covariate with element height as fixed effect factor and patient’s effect as the random effect. The second linear model included COP perturbations as the dependent covariate and element height as fixed effect with patient’s effect as random factor. In the third model, the mean COP location was the dependent covariate with element height and gait sub-phase as fixed effect factors and patient’s effect as the random effect. A STSV = 2.45 − 0.055 × Height which means that for every mm of element height there was 0.055 mm less variability in the stance length. This is statistically significant (p = 0.048).The first linear model describing STSV as depending on the level of instability is Pert = 0.44 − 0.005 × Height. The slope is statistically significant (p = 0.005). This means that for every mm of biomechanical element height the perturbations decreases by 0.005 mm.The linear model for COP perturbations as depending on the level of instability is COP = A + B × Conv. The intercept (A) changed with gait stage being 56.28, 53.09, 46.09 and 40.89 mm for stages LR, MS, TS and PS, respectively. The Slope was −0.21 mm (±0.09) for each mm of height of the biomechanical elements. This means that for each mm of height the COP shifted medially by 0.21 mm. This was found to be statistically significant (p = 0.001). The model fit the data well (p = 0.001). In all the gait stages, the effect of the stage of instability on COP position was similar, i.e. the interaction terms were not statistically significant and were excluded from the model. The linear model for each gait stage and the COP position are presented in Fig. Mean values and standard deviations of the mean COP measured with four different levels of convexity are presented in Table less perturbations in the foot COP.Unsteady sole designs have become increasingly popular as both therapeutic and functional tools. The goal of this design is to generate perturbation during gait. The results of the current study offer a quantitative correlation between the convex design of the sole of the shoe and the magnitude of perturbation conveyed. In contrast to the study hypothesis, a negative association was found between the height of the biomechanical element and the perturbations induced by the shoe. Our results showed that, with increasing the height of the elements by 1 mm, there was 0.055 mm less stance to stance variability, and 0.005 mm Analysis of the COP locus, on the other hand, demonstrated that, with the same increase in the convexity of the elements, a 0.21 mm medial shift in the COP trajectory occurred. We speculate that this change of trajectory location may explain the above results and indicate that coping with increased unsteadiness occurs via inversion of the ankle joint, which results in a medial COP shift and diminished perturbations.A similar finding was reported by Han et al. who investigated the effect of high-heeled shoes on planter pressure data and found that the COP locus was inwardly  displaced during the stance phase of high-heeled gait compared to flat heeled gait . They alIt should be noted that the velocity was set to the normal walking pace of each patient and was constant during the whole experiment in all four testing conditions, so that the effect of velocity on COP and spatiotemporal measures can be excluded.Putting the results together, we conclude that increasing the instability of the shoe results in less perturbations in the COP trajectory, less stride to stride variability, and a medially locked COP. If it is correct to assume that higher convexity causes more instability when worn during gait then, our speculation is that, at some point, the external manipulation becomes extreme so that, in order to maintain stable and safe progression, the body is forced to make modifications that result in a mechanical stop of the ankle joint at the eversion position. Increased activity of the foot evertors takes the foot away from its medial plane and results in a medially displaced COP .Attending to the outcomes of this study, external foot-based manipulations should be used in moderation. Extreme steep designs can lead  to increReduced ability to control balance during gait  is expected to affect these relations and, therefore, should be further studied. Moreover, Nigg et al.investigated gender differences in lower extremity gait kinematics and kinetics when using unstable shoes and reported that gender effects should be taken into consideration if functional or therapeutic effects of unstable shoes are assessed. Their results suggest that women and men use different strategies to control the ankle joint when walking in unstable shoes and, therefore, are expected to affect COP patterns .It should be noted that the results of the current study are valid only for individuals with characteristics similar to those of the study cohort . Further studies are required to validate these findings in other populations.It should also be noted that the biomechanical testing was performed shortly after the device was first used by the participants. Continued usage of such an apparatus may lead to substantial gait adaptations and could influence the outcome of these interventions.Finally, it should be noted that the apparatus was employed at neutral position as a base condition for the experiment. This was set by a trained physiotherapist who watched the patients walk and moved the elements untill a minimum varus/valgus moment was achieved. We are aware of the limitations of this method, however, it has been previously used . The posFuture studies considering the interaction effect between the convexity and the location of these elements are needed for better understanding of the foot COP and its relation to the apparatus utilized. Another limitation of the apparatus shoe design which should be pointed out, is that it provides ankle stability that could affect the motion and the joints and alter our results. Nevertheless, the current study is unique in that it offers a quantitative correlation between the instability (height) of the elements and the amount of perturbation produced during gait and brings a whole new aspect for the use of unstable therapeutic devices that can be extremely important for practitioners.The current study examines the correlation between the instability of the shoe design and the amount of perturbations produced during gait.Understanding the effect of the shoe instability on the generated perturbatios is cruitial for understanding the strategy of utilizing such devices and the expected compliance of users. The findings of this research could therefore, have great implications on the apparatus device and practice.


Follicular lymphoma (FL) is one of the most common lymphoid malignancies in the western world. FL cases are stratified into three histological grades based on the average centroblast count per high power field (HPF). The centroblast count is performed manually by the pathologist using an optical microscope and hematoxylin and eosin (H&E) stained tissue section. Although this is the current clinical practice, it suffers from high inter- and intra-observer variability and is vulnerable to sampling bias.In this paper, we present a system, called Follicular Lymphoma Grading System (FLAGS), to assist the pathologist in grading FL cases. We also assess the effect of FLAGS on accuracy of expert and inexperienced readers. FLAGS automatically identifies possible HPFs for examination by analyzing H&E and CD20 stains, before classifying them into low or high risk categories. The pathologist is first asked to review the slides according to the current routine clinical practice, before being presented with FLAGS classification via color-coded map. The accuracy of the readers with and without FLAGS assistance is measured.FLAGS was used by four experts (board-certified hematopathologists) and seven pathology residents on 20 FL slides. Access to FLAGS improved overall reader accuracy with the biggest improvement seen among residents. An average AUC value of 0.75 was observed which generally indicates “acceptable” diagnostic performance.The results of this study show that FLAGS can be useful in increasing the pathologists’ accuracy in grading the tissue. To the best of our knowledge, this study measure, for the first time, the effect of computerized image analysis on pathologists’ grading of follicular lymphoma. When fully developed, such systems have the potential to reduce sampling bias by examining an increased proportion of HPFs within follicle regions, as well as to reduce inter- and intra-reader variability.The online version of this article (doi:10.1186/s12911-015-0235-6) contains supplementary material, which is available to authorized users. The average CB count per HPF is based on ten randomly selected high power fields within the tissue. The average count stratifies follicular lymphoma cases into three histological grades: Grade I (0–5 CB/HPF), Grade II (6–15 CB/HPF) and Grade III (>15 CB/HPF). Grades I and II are considered a low-risk category while grade III is considered a high-risk category.Follicular lymphoma (FL) is a cancer of lymph system, and is the second most common lymphoid malignancy in the western world. It is a mature B lymphocyte malignancy of follicular center cell origin. Its diagnosis is based on specific morphologic, immunophenotypic and cytogenetic findings in lymph node/tissue biopsy specimens. Currently the most commonly used risk stratification method is the histological grading (HG) system adopted by the World Health Organization . The HG The histological grading system, however, suffers from several drawbacks. Firstly, the CB count is performed manually by the pathologist using an optical microscope on hematoxylin and eosin (H&E) stained tissue sections, which leads to subjectivity. It is well documented that the grading system is subject to high inter- and intra-observer variability , 3 even From our observation, there could be between 250 to 1650 high power fields (approximately 2165 × 1365 pixels) that can fit into the tissue section of the FL images. Out of these, 100 to 800 fields may be useful for examination (i.e. within the follicle area). Compared to the ten randomly selected HPFs that are used for grading a typical follicular lymphoma slide, this represents a vast amount of unused data. While it is unrealistic to expect the pathologists to examine such a large number of HPFs, a computer system can do this efficiently and consistently. The proposed system will display all the detected HPFs within the tissue alongside its class (low or high risk). The pathologists can then use this as a second opinion to finalize their grading from the ten randomly sampled HPFs in the HG system. It is important to stress that the system is not intended to replace the pathologists in grading, but rather to provide a second opinion for them to improve their accuracy by reducing the sampling bias. While our previous work documented the positive effect of automatically choosing the HPFs on inter-reader variability, to the best of our knowledge, this is the first work in documenting the effect of computer-aided classification to pathologist grading.The rest of this paper is organized as follows: Sections 2 explains the image analysis strategies and methods used in detecting the HPFs and classifying them into low and high risk categories, followed by description of the FLAGS system. Section 3 discusses the effect of the proposed system on the expert and inexperienced pathologists involved in the project. Finally, the last section concludes the paper with a summary of the work as well as future directions.When pathologists are presented with an FL H&E case, they must select ten HPFs and analyze these HPFs to determine the grade. Selection of the HPFs are left to the pathologists who typically select these regions in areas of follicles while trying to capture as much of the heterogeneity as possible. Considering that there are thousands of potentials HPFs in a given slide, this is not an easy task. Due to the limited time they can allocate reviewing each case, they are limited to these ten HPFs for their decision making.In most cases, follicles in a FL case are easy to distinguish from the other structures in a slide. However, our previous studies demonstrated that it is easier for the computer algorithms to detect the follicles in immunohistochemically (IHC) stained slides –10. TherFigure A local thresholding operation is carried out on the registered CD20 images to separate the tissue samples from the background as well as the follicle regions from the non-follicle regions. The local regions were selected to be of size 400 × 400 pixels within HPF blocks of size 1365 × 2165 pixels at 40× magnification. The mean saturation value within each local block is calculated and compared to the threshold value. The threshold value is determined as the value for which the histogram has the peak. In the absence of a peak in the histogram, the default value of 0.5 is used. The output of the local thresholding is a binary image in which the potential follicle regions are marked as positive (i.e. 1). The binary image is then divided into non-overlapping blocks of 1365 × 2165 pixels, and the blocks with more than 75 % positive pixels are deemed suitable high power fields for examination. The choice of 75 % as the threshold was selected after consultation with pathologists, who recommended that at least three quarters of the HPF be within the follicle. Experimental results on 20 randomly selected FL test cases (10 high risks and 10 low risks) suggest that 75 % is a good threshold choice.Additional file Most of the previous work in the classification of follicular lymphoma was based on the segmentation and recognition of the centroblasts –19. In tk-nearest neighbor classification is carried out based on the 3771 centroblast and 4000 non-centroblast training blocks. A block is then classified according to the majority class of its k neighbors. An HPF is classified as a high centroblast field if its number of sub-blocks classified as centroblasts exceeds a certain threshold p. Experimentally, the best classification was achieved when k = 49, and p = 115. At the tissue level, the tissue will be classified as high risks (grade III) if the number of detected HPFs classified as high exceeds 50 %. Table Figure Experiments on the 20 FL test cases gives an accuracy of 80 % compared against consensus ground truth. Table All 20 cases were used in a study of the influence of the proposed computer detection and classification of FL tissues on pathologist accuracy. As shown in Table For each case, the ground truth was generated by extensive reading by two experienced pathologists, where each and every centroblast in the selected HPFs was identified and counted. Eleven pathologists (four experts and seven residents) were involved in the experiments, and each pathologist was asked to grade twenty cases of H&E stained follicular lymphoma slides. The confidence scores were then used to perform Receiver Operating Characteristic (ROC) analyses comparing reader accuracy before and after using the computer.A:Pathologist selects a case to be examined. For this phase of the review, the pathologist is instructed to review the case in a standard clinical setting; i.e., using glass slides and under the microscope.Pathologist will review the case under a microscope to count the number of centroblasts in ten randomly selected high power fields, as in standard clinical practice.The counts are recorded into the computer using the interface on the left hand panel of Additional file Once the ten counts have been entered, the system will show the grade based on World Health Organization (WHO) guidelines. Again, this is for convenience for the pathologists, the computer simply calculates the average count so that they do not need to use a calculator to carry out this step. Up to this point, the pathologist carries out the reading in the conventional way.Conventional reading:B:5.The computer will ask the pathologist to provide a score in the range: 0–100, reflecting how confident they are that the case is high risk (Grade III).6.For the whole tissue sample, the computer will identify suitable high power fields and classify them into low (grade I and II) or high (grade III) category regions. The pathologist can view and verify these high power fields by clicking on the colored boxes  to 100 (very likely).The second part of the system shows the pathologists the computer detected HPFs as well as classification for each HPF according to the classifier explained in Section 3. The classification was presented in color code so that high grade HPF regions were marked in green outline and low grade regions in red. To understand the computer’s classification decisions, the pathologists were given the option to click on any of the detected high power field regions to view the area at high resolution (i.e. 40x). After showing the classification, the system proceeded to ask “After seeing the computer’s output, how likely is this Grade III?” To answer this question, the pathologists have the options to maintain or change their previously entered likelihood score. All the pathologists’ ten CB counts, average CB count, initial likelihood score and final likelihood score were recorded for analysis.It is important to point out that, as this study was intended as a proof of concept, our readers had to go through extra steps to enable our extensive statistical analysis. Once these studies are completed and the system is redesigned to be deployed clinically, several of the steps in the current system  will not be necessary and the whole process will be very streamlined. The FLAGS system was designed to guide pathologists towards the most informative areas on the slide. The final classification decision is that of the pathologist. Pathologists with experience in FL diagnosis have a choice of agreeing or disagreeing with computer marking of centroblasts by re-reviewing the areas of interest highlighted by the computer as centroblast rich.Since pathologists will likely review only a small number of HPFs with computer aid, the additional time is kept to a minimum. Once the computer identifies the HPFs, it is up to the pathologist to select which computer-classified HPFs they want to inspect more closely. We preferred this approach because it gives more flexibility to the pathologist in terms of whether they prefer computer assistance, and if they do, the extent of the assistance. This is a more realistic scenario as in regards to how such a system might be used in practice, as opposed to pre-specifying which HPFs the pathologist should inspect more closely with the computer aid.Two pathologists were considered to agree on a case if they both rated the case as high grade (score > =50), or if they both rated the case as low grade (score < 50). It is important to point out that from the experiment, even expert pathologists disagree on the grading for some of the cases. Out of the 20 cases, only nine cases were unanimously agreed upon by the expert pathologists. The interrater agreement improved to ten cases upon seeing the computers’ output. Thus, the agreement—when taking into account resident pathologists reading as well—is worse: only a single case registered unanimous agreement, which improved to four cases upon seeing the computer’s output. Additional file We used a threshold of 50 on pathologists’ scores to define agreement with ground truth. Thus, if the ground truth for a case was high risk, a pathologist was considered to be in agreement with the ground truth if his/her score was equal to or larger than 50. If the ground truth for a case was low risk, a pathologist was considered to be in agreement with the ground truth if his/her score was less than 50. Overall, out of 220 readings (20 cases × 11 pathologists), only 125 initial readings were in agreement with the ground truth (56.8 %). Agreement improved to 148 (67.3 %) after the pathologists reviewed the computer’s output.Comparing expert pathologists to residents, 53.8 % (43 out of 80) of the experts’ readings were initially in agreement with the ground truth, while 58.6 % (82 out of 140) of the residents’ readings were initially in agreement. These numbers improved to 62.5 % and 70.0 %, respectively upon reviewing the computer’s output. While the initial reading was more or less similar between the experts and inexperienced readers, it is interesting to note that residents were more inclined to change their score after seeing the computer results.We analyzed the agreement data using the Obuchowski-Rockett (OR) method  with HilAnother analysis that can be made is on the agreement to the computer’s output, instead of to the ground truth. Overall 153 initial readings (69.6 %) are in agreement with the computer classification, which increases to 176 readings (80.0 %) after consulting the computer output. From these numbers, the experts registered an initial 68.8 % agreement which later improved to 77.5 %, while the residents recorded an initial 70.0 % agreement that subsequently improved to 81.4 %. As in the comparison against the ground truth, the agreement increases after seeing the computer’s output, and the resident pathologists are more willing to alter their confidence score.In terms of overall changes in the agreement between the pathologists and the ground truth, 25 out of the 220 readings (8 from the experts and 17 from the residents) recorded a change towards computer results, with only two cases (one from an expert and one from a resident) recorded the change in the opposite direction. This indicates that although the pathologists were not informed about the performance level of the computer system, both experts and residents developed a trust for the computer system for their interpretation.We used a threshold of 50 on the computer scores to define correctly and incorrectly classified cases by the computer system. Thus, if the ground truth for a case was high risk, it was considered to be correctly classified if the computer score was equal to or larger than 50. The 220 readings can be subclassified into 88 readings of correctly classified high grade cases (8 high grade cases × 11 pathologists), 88 readings of correctly classified low grade cases (8 low grade cases × 11 pathologists), and 44 readings of incorrectly classified cases (4 incorrectly classified cases × 11 pathologists). It was observed that the pathologists performed better in grading the correctly classified high grade cases compared to the correctly classified low grade cases, for both initial and final grading. Before computer results were presented, 69 out of the 88 correctly classified high grade readings (78.4 %) were in agreement with the ground truth, while only 48 of the 88 low grade readings (54.6 %) were. These numbers improved to 78 and 62 cases respectively (88.6 % and 70.5 %) after consulting the computer results. Contrarily, only 8 of the 44 initial readings of incorrectly classified cases (18.2 %) were in agreement with the ground truth, which remained the same for the final grading.The agreement against the computer‘s output shows more or less the same improvement between initial and final confidence scores as in the agreement against the ground truth. In terms of actual confidence scores, for correctly classified high grade cases, the average final confidence score was 82.2 % compared to 73.1 % initially. For the correctly classified low grade cases, the average final confidence score was 28.8 % compared to 43.1 % initially. As expected, the average scores for the incorrectly classified cases approximated the 50 % mark .Stand-alone computer ratings of the likelihood that a case was Grade III  were compared to the ground truth by calculating the Area Under the ROC Curve (AUC) using the trapezoidal rule and, by viewing the AUC as a binomial proportion , an exacThe Obuchowski-Rockett (OR) method  with HilAdditional file Overall, the convergence of the final grading towards the ground truth and computer results, as well as the improvement of the confidence scores (towards 100 % for high risk cases and towards 0 % for low risk cases) suggests that with proper image analysis methods that can yield acceptable accuracy, the computer detection and classification does help in increasing the confidence of the pathologists in their grading ability.k-nearest neighbor classifier of potential centroblast regions within sub-blocks of the HPF regions followed by rule-based classification at the block, HPF and tissue levels. The proposed classification methods are able to achieve an accuracy of 80 % for this data set. The detected and classified HPFs are then presented as an HPF classification map for pathologists’ review and verification. To the best of our knowledge, for the first time, we measure the effect of computer-detection on the decisions of experts and inexperienced readers in grading follicular lymphoma. The average AUC value of the 11 readers increased from 0.65 to 0.75 when they used the computer system, which is generally considered acceptable diagnostic performance. Residents were found more likely to change their scores after using the computer which could explain why their readings were more accurate than the pathologist readings.We have proposed a system to assist pathologists in grading follicular lymphoma cases. The system first identifies potential high power fields for examination by analyzing accompanying CD20 images. Even at relatively small false positive rates, the number of computer selected high power fields is much higher than 10  and represents only a tiny fraction of all available high power fields in a given slide. These high power fields are then classified into low or high grades based on the The current study is performed on set of cases with classical follicular pattern that is predominant in FL clinical cases, thus non-follicular patterns in follicular lymphoma may pose problem for our system as it is deployed right now. Recognizing the important issue of FL with diffuse pattern and fused follicles, we have been working on an algorithmic solution that will recognize characteristic staining for follicular dendric cells networks (FDCS)  to help with this problem. We believe that digital computer triangulation of CD20 and FDCS positive areas will accurately guide our algorithm towards proper diagnostic areas in FL cases with diffuse pattern and with fused follicles.Finally, due to the limited number of case samples available to us at the time of the study, some parameters of the computer system were selected based on the regions of interest extracted from the twenty cases that were used in the study with the pathologists. This may have optimistically biased the performance of the computer system, which had an AUC value of 0.87, higher than any of the pathologists who participated in the study. In the future, we intend to apply the designed computer system to a completely independent, randomly selected data set of tissue samples, and investigate if the computer performance on the current data set is biased. However, regardless of the potential bias, we demonstrated for the first time that a computer system with the characteristics and performance described in this study has the potential to increase both the percent agreement with the ground truth and the AUC value of the pathologists in the task of follicular lymphoma grading. When fully developed, the proposed system has the potential to reduce sampling bias, thus decreasing potential errors. Our future work will focus on further improving the classification accuracy of both the detection and classification algorithms, as well as expanding the study on a larger number of cases and more pathologists.


Note taking while attending a PPT requires high activity of memory and writing process which ultimately leads to what is called “death by power point” referring to boredom and fatigue.  To overcome this we planned to evaluate the impact of utilisation of uncompleted handouts given prior to PPT presentations.Final year MBBS students were divided in 2 batches, batch A and batch B.  For a set of lectures one batch was provided with handouts before lecture while the other batch was given lectures only. Crossover was done to avoid bias, all the lectures being given by the same presenter.  At the end of each lecture, a short questionnaire of 10 Multiple Choice Question (MCQ) was provided to the students. Mean scores were calculated for lectures with handouts and without handouts.For a set of lectures, when batch A was provided with handouts, the mean score was 28.2; for batch B to which no handouts were given the mean score was 23.4. Similarly, for batch B when provided with handouts the mean score was 29.1, for batch A which was not provided with handouts the mean score was 24. There was an average increase of 4.2 marks. Actual gain when handouts were provided was 1.2 marks per lecture.  It was more for the batch comprising of repeater students as compared to the batch of fresher students. Increase in attendance was also noted.Providing uncompleted handouts before a didactic lecture definitely results in increase in knowledge gain; repeater students benefit more with uncompleted handouts, Power point presentation is the preferred method of visual aid in lecture halls .It is thThere are different types of classroom handouts; one type is providing a copy of the slides as it is. This type of handout is inflexible. Another type of handout is a structured outline of the lecture. In this type of handout plenty of space for note taking is provided, but the limiting factor is speed of delivery of the lecture.  The third type is uncompleted handouts with diagrams and figures that students will complete with lecture. Advantage of this handout is keeping the students involved in lecture and saving their time since they don’t have to constantly write as the presenter talks. Another important aspect is when to provide handouts. Generally, they are provided at the start or end of a lecture. Handouts at the start of lectures are preferred .  In this study we evaluated the impact of utilisation of uncompleted handouts given one day prior to the actual didactic lecture to the final year MBBS students of a deemed university. We evaluated the impact by calculating the score of a short questionnaire of Multiple Choice Questions from the same topic. The questionnaire was given at the end of the lecture.  The study was conducted in a Rural medical college set up. Students of final year bachelor of medicine and bachelor of surgery (MBBS) were the participants of the study. Prior to the study, the institutional ethical committee approval was taken. The study was carried out during the didactic lectures on orthopaedics based on the undergraduate curriculum of the university. The study was done with the aim to analyse the impact of using uncompleted handouts prior to didactic lecture (power point presentation).th semester were divided into two groups: A and B. This division was actually done according to the college undergraduate lecture roaster.  Group A consisted of 77 students  and group B consisted of 103 students (77 fresher + 26 repeaters).The design of study was interventional, using 2 groups and post tests.  It was a prospective study. 180 students of 7Before starting the study, uncompleted handouts of the didactic lectures were prepared in consultation with peers. The finalised handouts were printed. Similarly, a short questionnaire of 10 questions (MCQs) was prepared on each topic, reviewed by peers.  Hard prints of the questionnaire were prepared. The batch representatives were given the responsibility of distributing handouts to the students a day prior to the actual lecture. In   the university undergraduate curriculum, one orthopaedic lecture was scheduled every week. Two presenters were assigned to each one of the two batches for a single topic at two different lecture halls. In order to avoid bias, all the lectures under study were taken by a single presenter. The lectures were delivered using power point presentations (PPT).  Each week one batch was involved in the study and the other batch was not. Another presenter delivered lectures as per university curriculum to the non-involved group. Hence, the study did not interfere with the schedule of the lectures.   The lecture duration was one hour out of which nearly 52 minutes were allocated to actual lecture and 8 minutes to recording the attendance of the students.  During this attendance time students were asked to complete the short questionnaire; hence, extra time was not needed. nd batch on the 2nd and 4th weeks.  In the first week, batch B was selected by draw of lots and they were not provided with handouts. After the PPT, at the end of the session the questionnaires were distributed and after completion by the students, they were collected.  In next week, batch A was provided with handouts a day prior to PPT   and at the end of the session the same questionnaires were distributed and collected .This batch (batch A)   was not provided with handout on the 3rd week. In the fourth week, the other batch (batch B) was provided with handout a day prior to the lecture. By this we have achieved crossover as well as confidentiality of our handouts. The same presenter delivered the same lecture (PPT) on the same topic to both batches. The investigator checked the questionnaires, and calculated the average score for each lecture.In order to avoid intervention bias, power point presentations were delivered by the same presenter. Hence, one batch was given a lecture on one topic this week and next week the other batch was given the lecture on the same topic by the same presenter. To avoid sampling bias, for the first week intervention batch was selected randomly by draw of lots. There was a possibility that students may exchange handouts to the other batch which may adversely affect the study. Therefore, hand outs were provided to the 2This cycle was repeated for all 16 lectures covering 8 topics over four months.  That means out of 8 lectures for each batch, four were with handouts and four were without handouts. For the mean score of each batch, with or without handouts see . Intra-bThe total average score for (PPT ONLY) was 23.7. The total average score for (UNCOMPLETED HANDOUTS + PPT) was 28.7. By unpaired t-test, for the group with handouts the mean was 7.163±0.453 and for group with no handout the mean was 5.925±0.417. The two-tailed P-value was less than 0.0001 and this difference is considered to be extremely statistically significant. The average gain of both batches for 4 cycles comprising of 8 lectures when handouts were provided was 4.8 .  Hence,During a didactic lecture by power point presentation, the instructor should actively engage the students in the process of conveying information. One strategy is providing handouts. In this approach, students are involved in note taking which requires more focus. This strategy ultimately leads to improved learning outcomes -11. GeorThere are various types of handouts. One type is the direct print of the power point slides. The advantage of this kind of handout is, that it is easy to prepare. The disadvantage is that the student is getting a complete print of the slides and this may adversely affect his/her class attendance.  Another type of handout is a structured outline of the lecture in which a plenty of space is kept for note taking. Advantage of this type of handout is that, it is structured and the disadvantage is that students still have to write a lot.  The third type of handout is uncompleted handouts; this type of handout has notes, diagrams and figures and student should complete them as the lecture proceeds. The most important advantage of this handout is that the students are more involved during the class since they do not have to write continuously .  With uncompleted handouts, students need to write only minimal notes and hence they can focus more on the content of PPT. Also they have more time to listen and focus on the educational content. If the concept is ambiguous, the student can record brief explanation. Taking notes by each student differs from the others since each student’s requirements are different. Moreover, students’ personal notes help them for subsequent recall. A study by Larsen in 2009 indicated that proper use with a perfect design of the handout increases short time recall . HandoutIn a study by N. Hephzibah Kirubamani, he concluded that since handouts are portable it will improve recall of information.  Students give positive feedback for effectiveness of the handouts.  He also stressed that much care in needed in preparing the handout. A handout which is rich in content and information level makes the students learn from handout later on their own and better than students’ notes. In his study 94% of the students expressed that they could use handouts as a guide for future learning . Brown &Moreover these handouts could give the students a general overview of the educational material. With handouts in hand, students at any time can return to the previous slides thus preventing missing the past concepts during presentation. Jannis Mitkits in a research paper “The Use of Classroom Handouts” concluded that handouts are very useful for the learning process . At the In the present study when for a set of lectures batch A was provided with handouts prior to the lecture, the average score was 28.2 as compared to batch B which was not provided with handouts, for whom the score was 23.4.  After crossover for the other set of lectures where batch B was provided with handouts the average score was 29.1 as compared to batch A which was 24 without handouts. The total average score for PPT ONLY was 23.7 and for (UNCOMPLETED HANDOUTS + PPT) was 28.7. There was a net gain of 4.8 marks, for 4 episodes comprising of 8 lectures. When the scores were compared between the fresher and repeater students, the gain was more for batch B (5.7) as compared to batch A (4.2). The probable explanation for this could be that batch B was comprised of fresher and repeater students as compared to batch A which included fresher students only.  Providing handouts may actually have helped the repeater students to gain more knowledge. The improvement in attendance was also observed during the study. Since this is short term study we cannot authoritatively say that this improvement was due to the innovation in the teaching method. The present study proposes that providing uncompleted handouts before didactic lectures definitely results in increase in knowledge gain. Also this is more helpful for the repeater students; it also helps in increasing the attendance of the class.This strategy (using uncompleted handouts) could be very useful in developing countries where there are limited medical teachers and the teachers need to handle many tasks at the same time apart from teaching. This could be of extreme help to the medical teachers who are physicians, too.LimitationsThis study was of short duration and was limited to only one medical college and that to a single subject. The present study was also of short outcome in indicator matrix, so for further intermediate outcomes, we would like to expand the study to other subjects and other colleges of the university for longer duration. From this study it can be concluded that providing uncompleted handouts before didactic lectures definitely results in increase in knowledge gain. Repeater students benefit more with uncompleted handouts. The association between improvement in attendance and uncompleted handouts could not be established conclusively.


In 1972–1973 Wilson and Cowan introduced a mathematical model of the population dynamics of synaptically coupled excitatory and inhibitory neurons in the neocortex. The model dealt only with the mean numbers of activated and quiescent excitatory and inhibitory neurons, and said nothing about fluctuations and correlations of such activity. However, in 1997 Ohira and Cowan, and then in 2007–2009 Buice and Cowan introduced Markov models of such activity that included fluctuation and correlation effects. Here we show how both models can be used to provide a quantitative account of the population dynamics of neocortical activity.directed percolation.We first describe how the Markov models account for many recent measurements of the resting or spontaneous activity of the neocortex. In particular we show that the power spectrum of large-scale neocortical activity has a Brownian motion baseline, and that the statistical structure of the random bursts of spiking activity found near the resting state indicates that such a state can be represented as a percolation process on a random graph, called Other data indicate that resting cortex exhibits pair correlations between neighboring populations of cells, the amplitudes of which decay slowly with distance, whereas stimulated cortex exhibits pair correlations which decay rapidly with distance. Here we show how the Markov model can account for the behavior of the pair correlations.Finally we show how the 1972–1973 Wilson–Cowan equations can account for recent data which indicates that there are at least two distinct modes of cortical responses to stimuli. In mode 1 a low intensity stimulus triggers a wave that propagates at a velocity of about 0.3 m/s, with an amplitude that decays exponentially. In mode 2 a high intensity stimulus triggers a larger response that remains local and does not propagate to neighboring regions. The analysis of large-scale brain activity is a difficult problem. There are about 50 billion neurons in the cortex of the human brain: 80 % are excitatory, whereas the remaining 20 % are inhibitory. Each neuron has about seven thousand axon terminals from other neurons, but there is some redundancy in the connectivity so that it has effective connections from about 80 other neurons, mostly nearest neighbors. Each neuron is actually a complex switching device, but in this review, we introduce only the simplest cellular model, that neurons are binary switches, either quiescent or activated. It follows that there are approximately mentclass2pt{minimspontaneous or resting activity, or stimulus-driven evoked activity.There is a large body of data on large-scale brain activity, including electroencephalographic (EEG) recordings with large electrodes from the surface of the scalp, functional magnetic resonance (fMRI) measurements of blood flow in different brain regions , local field potentials (LFP) recorded with smaller electrodes, microelectrode recordings from or near individual neurons, or (currently) microelectrode arrays which can record the simultaneous activity of many neighboring neurons. Currently there are also new techniques for forming optical images of local brain activity, using voltage sensitive dyes (VSD). All such recordings can be classified as either We first consider the resting brain activity of unanesthetized animals first observed in animals by Caton in 1875 , and in alpha rhythm, seen in awake relaxed humans, mainly in the occipital region of the brain which processes visual signals from the eyes. Figure beta rhythm of occipital EEG activity. Interestingly if the contributions of such peaks are eliminated, what is left can be fitted with the function a and b are constants, and f is the frequency in Hz. Figure desynchronized and random. It will be seen that there are intermittent bursts of 10 Hz oscillations in the scalp activity. These oscillations comprise the Other measurements of resting brain activity have been carried out on lightly anesthetized animals using local field potential recordings of spiking neuron activity, or else via fMRI measurements of blood flows in the brain that accompany unanesthetized brain activity. Figure bursts of propagating activity from a number of randomly occurring sites. Any variation of the level of anesthesia, either up or down, abolished the activity.But the most detailed studies, and the most information about the nature of spontaneous activity, has been obtained from studies of isolated neocortical slabs. The first detailed studies were carried out in the early 1950s by DeLisle Burns, on isolated slabs of parietal neocortex , 8. The However, it was not until 2003 that a systematic study of such burst activity was carried out by Beggs and Plenz  using isavalanches defined as follows: the configuration of active electrodes in the array during one time bin of width Δt is termed a frame, and a sequence of frames preceded and followed by blank frames is called an avalanche. However, successive frames are not highly correlated, so the activity is not wave-like: it is in fact self-similar, and in addition, the avalanche size distribution follows the power law α is approximately −1.5. This is the mean-field exponent of a critical branching process Beurle introduced the update equation q and By assuming that d out in .at least threshold excitation, as was first noted by Uttley [The update equation is incorrect. y Uttley .This proportion can be expressed  as: 2\dosigmoid form, This implies that the function In Cowan  the logi neurons . This inThe corrected version of Beurle’s equation takes the form Beurle’s formulation does not explicitly incorporate a role for inhibitory neurons.There are three points to note here. Wilson and Cowan corrected and extended Beurle’s work and introduced equations for the population dynamics of a spatially homogeneous population of coupled excitatory and inhibitory binary neurons , and itsNote that d in Eq. , such thNote also that the variables from Eq.  that \dofocus. In fact by varying the synaptic weights A major feature of Eq.  is that Fitzhugh , but firFitzhugh –31 in a Fitzhugh  and HoppFitzhugh  have grea and b, and is therefore of codimension 2. In such a bifurcation an equilibrium point can simultaneously become a marginally stable saddle and an Andronov–Hopf point. So at the bifurcation point the eigenvalues of its stability matrix have zero real parts. In addition the right panel of Fig. E–I phase space before diverging. As we will later discuss, this property of the nullclines is closely connected with the existence of a balance between excitatory and inhibitory currents in the network described by the Wilson–Cowan equations, and therefore with the existence of avalanches in stochastic Wilson–Cowan equations [The left panel of Fig. quations . Fig. 11N excitatory binary neurons. Such neurons transition from a quiescent state q to an activated state a at the rate f and back again to the quiescent state q at the rate α, as shown in Fig. To develop such equations we need to reformulate neural population dynamics as a Markov process. We first consider the representation of the dynamics of a cortical sheet or slab comprising a single spatially homogeneous network of n activated neurons, each becoming quiescent at the rate α. This produces a flow out of the state n at rate α, proportional to n from the state The first step is to formulate a master equation describing the evolution of the probability distribution of neural activity n, each prepared to spike at rate Now consider the n from the state The flow into the state It follows that the probability m of Eq.  for a simentclasspt{minimadensity of active neurons at the cortical location x at time t, and the total input current Before proceeding we note that these equations can be extended to cover the situation introduced in Eq.  which inSince about tend Eq.  to inclun et al.  for a deTo analyze such effects we need to look more closely at the attractor dynamics of Eq. . There aN, the total number of neurons in the network, and standard distribution proportional to The system-size expansion was introduced by van Kampen  to analyA is a constant and The deterministic term satisfies Eq. , the randt are random variables dependent upon the network state. The simulation is carried out for a network in which certain symmetry conditions are introduced. These conditions are An early version of this application of the system-size expansion can be found in Ohira and Cowan . The extIt should be evident from a study of these figures that the location of the fixed point of Eq.  remains It will be seen that the simulations described above, in which the network symmetry represented in Eq.  is preseWe first note the experimental data provided by DeLisle Burns  and Beggn of Eq.  on a 2-dWe turn now to the neuro-dynamics underlying random bursting. We first note that the fixed point of the dynamics remains unchanged as l by Eq.  that \doequences , 39. Mosmentclasspt{minimaNow introduce the change of variables that Eq.  transford Millar , and used Millar . But it d Millar  in a stud Millar .decoupled, with the unique stable solution Σ and Δ, the fixed point current is Σ, and effective feed-forward system exhibiting a balance between excitatory and inhibitory currents, and a balanced amplification of a stimulus h.The important point about the transformed equations is that they are lled Eq.  an effecWe can now perform a system-size expansion of the associated master equations , to obtaA lies close to the matrix B̄ is the signature of the normal form of the Bogdanov–Takens bifurcation [The Jacobian matrix urcation . Thus thfluctuation-driven region near the marginally stable fixed point [We now turn to case 2, in which the network dynamics is at a marginally stable fixed point. As we showed earlier, such a fixed point is a Bogdanov–Takens point. We cannot use the system-size expansion at such a point, but we can use the methodology and formalism of statistical field theory –45. Howeed point . Thus weDirected Percolation phase transition, or DP [absorbing state, corresponding to the neural population state in which all neurons are quiescent, so that the mean number of activated states or order parameteractivated state. At a critical point the quiescent state becomes marginally stable and is driven by fluctuations into the activated state.The basic result is that the stochastic equivalent of the Bogdanov–Takens bifurcation is the critical point of a n, or DP . In DP tcorrelations are large, and pair correlations extend over significant cortical distances [What is important for the present study is that in the neighborhood of such a critical point, i.e. in the fluctuation-driven regime, there are two significant features of the activity which relate to the experimental data we have described: (a) the resting behavior shows random burst behavior whose statistical signature is consistent with DP, i.e., the distribution of bursts follows a power law with slope exponent −1.5, which is the slope of several forms of random percolation, including what is called mean-field DP , 10; (b)istances .E–I master equation described earlier. The conclusion we reach is that in the case that there is a balance between excitation and inhibition, so that the network is at weakly stable node, or possible a focus, then random burst behavior with a power law slope exponent close to −1.5 is seen [Assuming that the resting state occurs in the neighborhood of a weakly stable node or focus, to start with we can use the results of the system-size expansion of the  is seen . This is is seen , and wit is seen .As to pair correlations associated with resting or spontaneous activity, we refer to Fig. δ-correlated Gaussian noise to the equations. The resulting pair-correlation function for resting activity is shown in the left panel of Fig. (a) We first make use of Eq. , the meaSuch simulations of the behavior of Wilson–Cowan equations replicate very accurately, the pair-correlation behavior shown in Fig. We now consider the results reported by Carandini et al. –14, of tnot propagate very far and remains localized. This property was actually reported in Wilson and Cowan’s 1973 paper [The other result reported by Carandini et al. is that for strong stimuli the resulting LFP does 73 paper ! The botIt is evident that there are big differences between the effects produced by weak and strong stimuli. What is the cause of such differences? Given that the only parameter in the Wilson–Cowan equations that is varied in the two cases is the stimulus intensity, this suggests that the property which causes the different responses is the level of inhibition. It must therefore be the case that the threshold for inhibitory activity is set high enough that weak stimuli do not trigger inhibitory effects, whereas strong enough stimuli do trigger such effects. Indeed this is one of the possibilities suggested by Carandini et al. in their papers. Thus inhibition blocks LFP (and VSD) propagation.This possibility is also consistent with the effects of stimuli on pair correlations. We predict that the pair-correlation function should falloff more slowly in the case of resting or weakly driven activity, than in the case of stronger stimuli. Such a result would be consistent with the suggestions of Churchland et al. that one effect of stimuli is to lower noise levels.attractor dynamics. Further work by Ermentrout and Cowan [The main results described in this article concern the use of the Wilson–Cowan equations to analyze the dynamics of large populations of interconnected neurons. Early workers, including Shimbel and Rapaport  and Beurnd Cowan –31 and bnd Cowan , and Hopnd Cowan , 34 usedavalanches. In addition the avalanche distribution was that of a power law, with a slope exponent The problem of going beyond the mean-field regime proved to be very difficult. Some progress was made by Ohira and Cowan  formulatThere remained the problem of developing a statistical theory for the fluctuations about a marginally stable critical point, such as a Bogdanov–Takens point. This problem was formulated by Cowan  and solvHowever, although the statistical theory is relevant to the pair-correlation problem, it is the mean-field Wilson–Cowan equations that proved to be necessary and sufficient to analyze neocortical responses to brief stimuli, both weak and strong. In our opinion the close fit between the data and the simulations of the Wilson–Cowan equations with fixed parameters is quite remarkable, especially given the fact that these equations were formulated some 45 to 50 years ago! More detailed papers dealing with these and other results on neocortical responses to stimuli are in preparation.


This paper presents relative orientation and position detection methods for jumping sensor nodes (JSNs) recycling. The methods are based on motion captures of the JSNs by an RGB-D sensor mounted on a carrier robot and the dynamic cooperation between the carrier and the JSNs. A disc-like label with two different colored sides is mounted on the top of the JSNs. The RGB-D sensor can detect the motion of the label to calculate the orientations and positions of the JSNs and the carrier relative to each other. After the orientations and positions have been detected, the JSNs jump into a cabin mounted on the carrier in dynamic cooperation with the carrier for recycling. The performances of the proposed methods are tested with a prototype system. The results show that the carrier can detect a JSN from up to 2 m away and sense its relative orientation and position successfully. The errors of the JSN’s orientation and position detections relative to the carrier could be reduced to the values smaller than 1° and 1 cm, respectively, by using the dynamic cooperation strategies. The proposed methods in this paper could also be used for other kinds of mobile sensor nodes and multi-robot systems. Wireless sensor networks (WSNs) can provide quick construction and easy utilization for measurement systems. WSNs also reduce the operation and maintenance costs of the systems. Wireless sensor nodes have been integrated into various application systems such as home automation , bridge In order to optimize the performance of a WSN, mobile robots are added into the WSN as mobile sink or sensor nodes , which fThe mobile robots in a MWSN are usually small sized ,15,16,17Because the energy of the MSNs is usually supplied by batteries, they cannot traverse a very long distance for self-deployment. The sensor nodes in WSN and MWSN are usually transported and deployed by humans or by airplanes . HoweverThe difficulties during recycling include relative orientation and position detections between the MSNs and the carrier, and the docking method design. The compass can be used for the orientation detection . HoweverThe docking method design for MSNs recycling could adopt two main approaches. The first one is that the MSNs are grasped by the grasper of a manipulator mounted on the carrier . The carIn this paper, we present JSNs recycling by a wheeled carrier robot, including short-distance orientation and position detection methods. The detection methods are based on an RGB-D sensor and dynamic cooperation strategies. The dynamic cooperation between the JSN and the carrier can improve orientation and position detection precisions and reduce the difficulties during docking. The rest of this paper is organized as follows. The components of the proposed JSNs recycling system include a wheeled carrier and several miniature JSNs, as illustrated in We assume that the carrier can detect the position and battery information of the JSNs periodically and save them in its database during long-distance localization. The carrier is able to move to a JSN for recycling it when its power is lower than a threshold, which is the most common problem that may happen to JSNs. The carrier can also navigate to a JSN using the latest position information of the JSN to replace it if it is damaged suddenly. Because we focus the short-distance relative orientation and position detections in this paper, we assume that the initial distance between the JSN and the carrier is within 2 m after the carrier moves to the JSN using long-distance localization and navigation methods. The working procedure of a JSN recycling is shown in cjO) relative to the JSN and judges if the JSN is in the center of the field of view. The carrier steers and calculates cjO until the JSN is in the center of the field of view. Then the carrier stops steering and detects the orientation jcO of the JSN relative to the carrier. The JSN steers if needed until it faces to the carrier. Next, the depth sensor is used to detect the distance between the carrier and the JSN. The carrier adjusts its location in order to make the cabin enter one jump range of the JSN. Finally, the JSN jumps into the cabin to finish the JSN recycling process. The flow chart of the relative orientation and position detections is shown in U-O-V (a) and image plane coordinate frame X-P-Y (b) are shown in cjO, we define the azimuth coefficient ζ as follows:u is the pixel coordinate of the center of the label in the horizontal direction, w is the total pixels of the image in the horizontal direction. The color camera of the Kinect is used to record objects around the carrier. The pixel coordinate frame ζ and orientation cjO calculations is shown in cjO and pitch angle cjP are as follows:d is the distance between the JSN and the carrier, which is detected by the Kinect. x and y are the coordinates of the center of the label in the image plane coordinate frame as follows:v is the pixel coordinate of the center of the label in vertical direction; xR and yR are the resolutions of the images in U and V directions, respectively; and α and β are the angles of field of view of the Kinect in horizontal and vertical directions, respectively. ζ is used to decide the relative orientation roughly, while cjO is adopted to determine the exact relative orientation. Before jcO detecting, the carrier steers and calculates ζ and cjO until ζ ≈ 0 and cjO ≈ 0. The diagram of the azimuth coefficient jcO of the JSN relative to the carrier, the two sides of the label are designed as red and green colors, respectively. In the beginning, the red side of the label faces to the front of the JSN. There are eight conditions for the jcO detection as illustrated in jcO detection and adjustment is shown in After the carrier has detected and adjusted its orientation, the JSN is in the center of the images of the Kinect. In order to decide the orientation jcO) detecting is the color camera detects the horizontal pixels a, the vertical pixels b, and the color of the label which are the inputs of the algorithm. Then, the carrier judges the relationship between a and b, and the color to decide the conditions shown in a ≈ b and the color is red, which is the condition shown in jcO is 0° and the JSN does not need to adjust its orientation. If a ≈ b and the color is green, which is the condition illustrated in jcO is 180° and the JSN needs to adjust its orientation 180° clockwise. If a ≈ 0, then the JSN controls its label to rotate 45° clockwise and the carrier judges the relationship between a and b, and the color of the label again. If a < b and the color is red, which is the condition shown in jcO is −90°, and the JSN needs to steer 90° clockwise. If a < b and the color is green, which is the condition shown in jcO is 90°, and the JSN has to steer 90° anticlockwise. The first step for orientation  we can obtain the detection error ojcE of jcO in the range of  when there is one pixel detection error of the label in the images:xp < (b − 1) is the real horizontal pixel of the label in the images, a = xp + 1 or a = xp − 1. Because a and xp decrease with the increase of jcO, as illustrated in ojcE increases with the increase of xp as shown in ojcE decreases with the increase of jcO. This means that jcO detection has higher precision when it is closer to 90°. The dynamic cooperation strategy is that the label rotates to a proper angle that is easier for the Kinect to detect the jcO. This will improve the precision of the orientation detection. The rotational angle of the label is recorded. The label rotates to its initial orientation after detecting jcO. The third one is that the detection of The 3D model and prototype of the JSN are shown in jA is about 39.6° if the distance d is 0.5 m as shown in The 3D model and prototype of the carrier are shown in The fundamental performances and influence factors on the detection precisions of the proposed relative orientation and position detection methods were tested firstly. The influence factors are shown in d, different real orientations of cjO, and different orientations of jcO with a diameter D = 5 cm and surface color C = red label when ambient illumination A = 42 lux, resolution of the color camera R = 640 × 480, and the background was a white board. The orientation detection of the carrier was tested at different distances d increased from 0.5 m to 1.0 m at the step of 0.1 m. The recorded images and calculated ζ and cjO are shown in ζ increases slightly from 0.553 to 0.569. The ζ is larger than 0 and smaller than 1, which indicates that the label is in the right side of the images. The detected orientation cjdO increases from 17.1° to 17.6°. The results show that the highest detection precision can be obtained at distance of 1.0 m and the detection error is within 2.9°. ζ and cjO were detected when the real orientation cjrO increased from ‒24° to 24° at the step of 6°. The results are shown in ζ increases linearly with cjO. The ζ is utilized to decide the relative orientation roughly at different cjrO. The largest error of cjO detection is only about 2.22° when |cjrO| = 24°. The results show that the proposed method can detect the orientation cjO with a high precision for JSNs recycling. The jcrO of the JSN relative to the carrier increased from 0° to 76.8° at the step of 9.6° when the real orientation cjrO was set as 0°. The results are shown in cjO detection has highest precision when jcrO is 0° and 9.6°. The precision decreases with the increase of the jcrO. The largest error is ‒0.19°. The carrier cannot detect the JSN when the jcrO is close to 90°. In this test, orientation d, D, C, A and R are shown in Because the orientation detection of the JSN was based on the horizontal and vertical pixels sensing of the label in the images, the influence factors shown in jcrO and the detected orientation jcdO at different d are shown in d mainly happen when the real orientation is near 9.6°. The results show that the proposed method can detect the orientation of the label with an acceptable precision at different distances. The detection precision is higher at smaller distance and when the real orientation jcrO is close to 90°. The real orientation D of the labels are 5 cm and 10 cm as shown in The largest errors are −2.6° and 2.69° when the diameters The red and green labels were tested. The largest errors for the orientation detections of the red and green labels are −2.6° and 3.5° as shown in A = 42 lux and A = 333 lux as detected by a light sensor TSL2550. The results are shown in The ambient illumination was changed with different number of lights in the indoor environment. The tests were performed at R tests. The results are shown in The resolution of the camera was set as 640 × 480 and 1280 × 960 for influence factor The position of the JSN relative to the carrier could be decided after the orientations and distance between the JSN and carrier are detected. The distance detection tests were conducted at different real distances, sizes and colors of the label, and ambient illuminations when the Kinect faced to the JSN. rD are shown in dD are zeros when the rD are 40 cm and 50 cm. This indicates that the depth sensor of the Kinect cannot detect too small distances. The largest error is −3.1 cm when the rD is 200 cm. The test results at different D, C, and A when the real distance rD = 70 cm are shown in The test results at different real distances The results of distance detection show that the maximum error is only about 3 cm within 200 cm detection range, and the detection precision is higher when the real distance is smaller. The size and color of the label and the ambient illumination do not have serious influences on the distance detection precision. The errors are within 1 cm. cD are shown in cD of 140 cm. The initial distance between the JSN and the carrier was set as 200 cm. The carrier moved to the JSN at a step of 10 cm and detected the label periodically. The carrier recorded the distance between it and the JSN at every step. The real displacement of the carrier was measured. The depth images at different controlled displacements cD, the real displacement rD, and the displacement control error dcE are shown in dcE has the trend of increase with the displacement of the carrier. The maximum error is about 1.7 cm. The detected distance dD and the detection error ddE are shown in ddE decreases with displacement of the carrier. The absolute value of ddE is reduced from 1.9 cm to about 0.1 cm. This indicates that the distance detection precision could be improved by using the dynamic cooperation strategy. The controlled displacement d is 100 cm and 200 cm. This capability improves the JSN detection success rate for recycling. Several circle and ellipse like labels with the similar size and same color of the label of the JSN were stuck on the white board to imitate the situation that there were objects or other JSNs in the background of the JSN needed to be recycled. The board was put behind the JSN. The carrier steered to detect the labels. The carrier stopped steering and recorded all the sizes, shapes, and colors of the labels when it found the labels. Then it sent a control command to the JSN. The label on the JSN rotated a proper angle to adjust its shape on the images of the Kinect. The carrier detected the labels again and calculated the differences between the images recorded before and after the rotation of the label to distinguish the label of JSN from other labels. The recorded and processed images are shown in jcrO, the detected orientation jcdO, and the orientation detection error ojcE are shown in The real orientation of the JSN was controlled to increase from 2.4° to 64.8° at the step of 2.4°. The carrier detected the orientation of the JSN at every step and calculated the detection error. The real orientation ζ and the orientation cjO step by step. When the ζ and cjO were close to zero, the carrier stopped to steer and began to move to the JSN. The carrier stopped in front of the JSN at the distance about 30 cm and began to detect the orientation jcO of the JSN. The detected jcO was about 20.2°. The carrier sent the jcO to the JSN. The JSN steered angle jcO when it faced to the carrier. The carrier moved to the JSN again and stopped when the distance d was about 20 cm. Then the JSN could jump into the cabin for recycling. The performance of the prototype system for JSN automatic detection was tested finally. In the beginning of the test, the JSN was put behind the carrier on the right 1.6 m far, and the headings of the carrier and JSN were east. The carrier ran the detection algorithm and dynamic cooperated with the JSN. The video sequences of the orientations and position detection process are shown in jA (39.6°), so the JSN could jump into the cabin easily. The JSN automatic detection test results show that the carrier can detect and move to the JSN successfully and the JSN could be recycled after it jumps into the cabin. We propose short-distance relative orientation and position detection methods between a carrier robot and jumping sensor nodes during JSNs recycling. The methods are based on the RGB-D sensor and the dynamic cooperation strategies. The system components, the recycling procedure, and the detection methods are introduced, respectively. A prototype system including a carrier and a JSN are designed and fabricated for validating the proposed methods. The orientations and position of the carrier and the JSN relative each other are tested at different situations. The results show that the orientation detection of the carrier relative to the JSN has largest errors about 3° at different test distances, different real headings, and different orientation of the JSN. The orientation detection tests of the JSN show that higher precision can be obtained at smaller distance. The size and color of the label do not have serious influences on the detection precision. But the precision decreases when the ambient illumination increases. This is because the preset thresholds of the RGB values are static during the tests while the ambient illumination has effects on the RGB values of the images. The higher detection precision of this orientation could be obtained when the Kinect is set at higher resolution. The distance detection results show that the maximum error is only about 3 cm within 200 cm detection range, and the detection precision is higher when the real distance is smaller. The dynamic cooperation strategies test results show that the distance detection error could be reduced from several centimeters to several millimeters. This error is far smaller than the jumping range of the JSN. The carrier is able to distinguish the JSN from the interferences successfully when the distance between them is as far as 2 m, which improves the success rate of the JSN detection for recycling. The results also show that the orientation detection of the JSN has higher precision when the real orientation is close to 90°. The detection error could be reduced from 6.6° to about 1°, which is far smaller than angle The proposed detection methods combined with the dynamic cooperation strategies have high detection precisions for JSNs recycling. The proposed methods in this paper could not only be used for JSNs, but also be adopted for other kinds of mobile sensor nodes and multi-robot systems. The limitations include that the visual based detection methods could be affected by the ambient illumination and the performance of the infrared-based depth sensor of the Kinect could significantly be affected by the sunlight in outdoor application environment. Future work includes four main aspects. Firstly, the factors that influence visual based orientation detection method will be dynamically compensated using light sensor to improve the detection precision. Furthermore, the ultrasonic sensor or laser sensor could be combined with infrared distance sensor for application in outdoor environment with strong sunlight. Secondly, the long-distance localization methods and multisensor data fusion will be investigated for the JSNs recycling. Thirdly, we will try to design docking method for the suddenly damaged JSNs, which cannot jump into the cabin of the carrier. A simple and low cost manipulator combined with the visual detection method could be a solution for this problem. Finally, the universality of the proposed methods for JSNs recycling will be tested on other kinds of miniature robotic mobile sensor nodes.


In the present work, weintroduce an improved version of the hyperspheres path tracking method adapted for piecewise linear (PWL) circuits. This enhanced version takes advantage of the PWL characteristics from the homotopic curve, achieving faster path tracking and improving the performance of the homotopy continuation method (HCM). Faster computing time allows the study of complex circuits with higher complexity; the proposed method also decrease, significantly, the probability of having a diverging problem when using the Newton–Raphson method because it is applied just twice per linear region on the homotopic path. Equilibrium equations of the studied circuits are obtained applying the modified nodal analysis; this method allows to propose an algorithm for nonlinear circuit analysis. Besides, a starting point criteria is proposed to obtain better performance of the HCM and a technique for avoiding the reversion phenomenon is also proposed. To prove the efficiency of the path tracking method, several cases study with bipolar (BJT) and CMOS transistors are provided. Simulation results show that the proposed approach can be up to twelve times faster than the original path tracking method and also helps to avoid several reversion cases that appears when original hyperspheres path tracking scheme was employed. Circuit simulation is an important phase during the development of new electronic circuits. As for the integrated circuit design, this phase is vital and requires advanced tools capable to perform faster and accurate analysis. The DC analysis, also known as operating point calculation, commonly is the first step in circuit analysis. For a nonlinear circuit, the DC analysis provides a nonlinear algebraic equation system (NAES). The NAES, usually, is solved using the Newton–Raphson Method (NRM). Nevertheless, this method occasionally fails, leading to oscillations or diverging to infinity; another shortcoming of the method is its inefficiency to find multiple operating points. The NRM method performs in such a way that once a solution is found, stops and no further operations are performed. As an alternative, the homotopy continuation method (HCM)   than the highest positive voltage supply or lower  than the lowest negative voltage supply; the values for current variables should be in a higher range than the possible normal operating currents for the circuit, for instance, flip-flops typically work in the range of milliamperes so setting the values in the range of amperes would be suitable for current variables.Test 2 This work proposes circuits modeled by devices of type u outside the bounded region by the lower breakpoint ,,8), placAs shown in Fig. In order to avoid a possible oscillation of the NRM or iterations with complex numbers, when it approaches the empty region, a limit in the number of iterations is established. When the limit is reached, the NRM is executed with a different starting point as described in Torres-Munoz et al. . EmpiricThis work proposes a modification to the path tracking method presented in Vazquez-Leal et al. . It is cThe Newton Homotopy should be formulated based on . As showOnce the homotopic system is defined, the SP should be arranged in a way to accomplish both Test 1 and Test 2  w w11) wilAs reported in Vazquez-Leal et al. , if the culating  using thIn this section we introduce an algorithm for the SHPT method.The general procedure work as follows: first, the system of equations is generated as shown in (14). Second, the user provides a starting point. This starting point has to fulfill Test 1 and Test 2 already introduced in the previous section. For the case when starting point does not fulfill any of the tests, the user is requested to provide another starting point and the verification process is performed until a valid point is achieved. Then, the process continues by generating the Homotopic formulation for the system. Afterwards, the hypersphere formulation is given. It is important to mention that, at this point, the inverse hypersphere formulation is also generated to avoid the reversion phenomenon. Notice that the hypersphere and inverse hypersphere formulation will be updated after every iteration by adjusting its center. Once all necessary equations are already provided and a valid starting point is given, the main loop is started. It begins with a hypersphere iteration, the result of the iteration and the starting point allows the calculation of another point that allows calculation of a predictive straight line in the next block. Once this step is done, the Straight Line Iteration block is performed as explained in the corresponding section. As iteration continues, detection for crossing at see Fig. . When \dsee Fig.  is appliIn this section we provide five circuits to be analysed using the proposed path tracking method. All the proposed SP for the circuits fulfils the Test 1 and Test 2 starting point criteria. Also, we compare the proposed path tacking method against the proposed in Vazquez-Leal et al. , this coThe circuit in Fig. Figure Applying the MNA method to the circuit, the equilibrium system of equations are obtained ask and n represent the diodes nodes; the second column shows that every variable is greater than or equal to the value of the voltage source 18) to seUsing the SP shown in Table 2 for every transistor, except for T12 and T4, for both transistors it was set to 2. The model was simulated in SPICE using the parameter values given in Example 4, except for T12 and T4, their parameters were set as follows: W = 51u, L = 2u, and W = 102u, L = 1u, respectively.The circuit shown in Fig. The SP for the circuit is shown in Table This section presents a performance comparison between the SHPT method proposed in this work and the MSA proposed in Vazquez-Leal et al. . Also, tTables Example 1 and example 2 did not show any sign of the reversion phenomenon in any of the simulations performed. Nevertheless, the rest of the simulations using the MSA2 method showed reversion. No simulation using the MSA1 method showed reversion. It is important to make notice of the fact that the homotopic path is the same for the three types of simulation; this is because they are based on the same homotopy formulation, and the difference lies in the applied path tracking method. The results prove that the efficiency is improved using the inverted sphere technique proposed in this work, as it helped to avoid the revision phenomenon and allowed to perform the path tracking without issues. The cases where reversion phenomenon were noticed are marked with an asterisk (*), the computing time for these cases was not possible to be calculated because the method locked and no further calculations were possible.As shown in Tables Computing time increases as the density of breakpoints grows. Nevertheless, for the worst case scenario, a curve with high density of breakpoints or a non-PWL curve, the proposed method would only spend the same computing time as the method proposed in Vazquez-Leal et al. . The comThis work introduced a path algorithm for analysis of PWL circuits using the HCM, this algorithm exhibited improvements in the computing time compared to the algorithm proposed in Vazquez-Leal et al. . Also, a