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Although high dynamic range (HDR) display has become popular recently, the legacy content such as standard dynamic range (SDR) video is still in service and needs to be properly converted on HDR display devices. Therefore, it is desirable for HDR TV sets to have the capability of automatically converting input SDR video into HDR video, which is called reverse tone mapping (RTM). In this paper, we propose a novel learning-based low-complexity RTM scheme that not only expands the suppressed dynamic ranges (DR) of the SDR videos (or images), but also effectively restores lost detail in the SDR videos. Most existing conventional RTM schemes have focused on how to expand the DR of global contrast, resulting in limitations in recovering lost detail of SDR videos. On the other hand, the recent convolutional neural network-based approaches show promising results, but they are too complex to be applied on the users’ devices in practice. In this paper, our learning-based RTM scheme is computationally simple but effective in recovering lost detail. To learn the SDR-to-HDR relation, training “SDR-HDR” images are first separated into their base layer components and detail layer components by applying a guided filter. The detail layer components of the “SDR-HDR” pairs are used to train the SDR-to-HDR mapping. The mapping matrices are computed based on kernel ridge regression. In the meantime, the global contrast of the base layers is expanded by a nonlinear function that suppresses darker regions and amplifies brighter regions to fit the full DR of a target HDR display. To verify the effectiveness of our learning-based RTM scheme, we performed subjective quality assessment for images and videos. The experimental results show that our RTM scheme outperforms the existing RTM scheme with the successful restoration of lost detail in SDR images.

In swarm robotic systems, task allocation is a challenging problem aiming to decompose complex tasks into a series of subtasks. We propose a self-organizing method to allocate a swarm of robots to perform a foraging task consisting of sequentially dependent subtasks. The method regulates the proportion of robots to meet the task demands for given tasks. Our proposed method is based on the response threshold model, mapping the intensity of task demands to the probability of responding to candidate tasks depending on the response threshold. Each robot is suitable for all tasks but some robots have higher probability of taking certain tasks and lower probability of taking others. In our task allocation method, each robot updates its response threshold depending on the associated task demand as well as the number of neighbouring robots performing the task. It relies neither on a centralized mechanism nor on information exchange amongst robots. Repetitive and continuous task allocations lead to the desired task distribution at a swarm level. We also analyzed the mathematical convergence of the task distribution among a swarm of robots. We demonstrate that the method is effective and robust for a foraging task under various conditions on the number of robots, the number of tasks and the size of the arena. Our simulation results may support the hypothesis that social insects use a task allocation method to handle the foraging task required for a colony’s survival.

Purpose The aim of this study is to find how the pyruvate intake and aerobic exercise effect on the body composition, exercise performance ability, blood factor and obesity related hormone, and to verify the effect of pyruvate intake and aerobic exercise as an effective substance for obesity improvement. Methods This study selected 20 obese men in their twenties who has more BMI than 25kg/m2, and are applicable in 25% of the body fat, and randomly sampled group of 10 people for pyruvate intake and aerobic exercise (PYA), and 10 people for placebo intake and aerobic exercise (PLA). Intake of pyruvate and placebo was implemented for 10 weeks, 6 g a day, and aerobic exercise, treadmill exercise in the intensity of 50 ~ 60%’s target heart rate, was conducted for 10 weeks, 3 times a week, 60 minutes a day. To demonstrate the effect of pyruvate intake and aerobic exercise, all of the body composition, exercise performance ability, Lactate, and blood factor and hormone related to obesity were measured before and after the test in the same manner. Results The main results from this study are as follow; 1) In the case of body composition, in PYA, weight(p < .01), BMI(p < .05), body fat percentage(p < .01), and body weight without fat(p < .001) are reduced meaningfully. 2) In the exercise performance ability increased significantly in both PYA(p < .01) and PLA(p < .001) for VO2max, Also, in the case of distance during the exercise, PYA(p < .01) and PLA(p < .05) increased significantly in 15 minutes and PYA(p < .01) and PLA​(p < .05) 30 minutes, but only in PYA from 45 minutes(p < .01) to 60 minutes(p < .05) 3) In case of Lactate, the significant decrease in PYA during stabilization and the significant increase in PYA after 30 minutes of exercise was not seen after 45 minutes 4) In the case of blood factor, HDL-C showed a meaningful decrease in PLA(p < .05) and Leptin showed a meaningful decrease in PYA(p < .001). Conclusions To sum up these results, it was more effective for the group of PYA which ingested pyruvate in improving obesity, even when the same aerobic exercise is conducted.

The task allocation problem has been a challenging issue in swarm robotic systems. In this paper, we handle dynamic task allocation in the object foraging task, where multiple robots are supposed to collect various objects in parallel during a given time span. We propose a decentralized strategy of the response threshold model without any communication among robots. Here, we introduce a task selection probability function for each robot to balance the task demands with robots working on tasks. The method can produce varying tendencies to tasks by changing the response thresholds given in the task selection probability function. With this property, each robot can select its own task among all available tasks, by regulating the response threshold values. This ultimately promotes a desired task distribution in a group level and reduces the number of task changes. Our approach suggests that the response threshold of robot is updated based on what objects have been observed recently and what tasks have been done by its neighboring robots observed in the local surrounding area. We provide a convergence analysis that the system can stochastically converge to the equilibrium of the desired task distribution. The method is effective even when only local information about the environment is given to individual robots. The suggested method is tested with a simulation of multiple robots taking the foraging task, and a dynamic task allocation process is demonstrated.

Background The risk for obesity-related diseases increases with the prevalence of obesity. In obesity, adipokines secreted from adipose tissue induce inflammation, causing adverse effects. Recently, adipokines such as apelin, visfatin, and chemerin have been studied. Long-term resistance training improves health in middle-aged women by improving metabolic risk factors, body composition, and muscle strength. However, there is still a lack of evidence on the association of apelin concentration with different exercise types in middle-aged obese women This study aimed to investigate the effects of 8 weeks of aerobic and resistance exercises on apelin-12 and apelin-36 levels and thereby verify the effects of different exercise types in obese, middle-aged women. Methods Participants were middle-aged women aged 50–61 years, with no experience of systematic exercise in the last 6 months, and met the WHO obesity criteria for the Asia-Pacific region of waist circumference ≥ 80 cm and body fat percentage ≥ 30%. Subjects were selected and allocated to the aerobic exercise, resistance exercise, or no exercise group by block randomization. Body weight, body fat, and body mass index were measured by bioelectrical impedance analysis. Analysis of variance, the t-test, and Tukey’s post-hoc test were performed. Results A total of 24 participants were selected with eight participants in each group. Both aerobic and resistance exercises were effective in altering the physical composition, showing significant decreases in weight, waist circumference, BMI, and body fat. The aerobic and resistance exercise group showed a significant, positive change in apelin-12 levels. Conclusions In obese individuals, aerobic and resistance exercise were effective in improving obesity and reducing blood apelin-12 concentration, which is closely correlated with indicators of metabolic syndrome. Future research should focus on comparing the response of apelin to exercise in obese subjects treated with only dietary control and the response in the obese subjects of different ages and sex. Trial registration No. 1040917–201,506-BR-153-04, Clinical Research Information Service (CRIS), Republic of Korea (05 October 2018, retrospectively registered).

Background: Aging societies lead to higher demand for gait rehabilitation as age-related neurological disorders such as stroke increase. Since conventional methods for gait rehabilitation are physically and economically burdensome, robotic gait training systems have been studied and commercialized, many of which provided movements confined in the sagittal plane. For better outcomes of gait rehabilitation with more natural gait patterns, however, it is desirable to provide pelvic movements in the transverse plane. In this study, a robotic gait training system capable of pelvic motions in the transverse plane was used to evaluated the effect of the pelvic motions on stroke patients. Method: Healbot T, which is a robotic gait training system and capable of providing pelvic movements in the transverse plane as well as flexion/extension of the hip and knee joints and adduction/abduction of the hip joints, is introduced and used to evaluate the effect of the pelvic movement on gait training of stroke patients.Experiment: 23 stroke patients with hemiparesis participated in this study and were assigned into two groups. Pelvis-on group was provided with pelvic motions whereas no pelvic movement was allowed for pelvis-off group during 10 sessions of gait trainings in Healbot T. EMG signals and interaction forces as well as the joint angles of the robot were measured. Gait parameters such as stride length, gait period, cadence, and walking speed were measured after gait training. Result: 37.5 % lower interaction forces of pelvis were observed in the pelvis-on group than the pelvis-off group. Furthermore, the interaction forces at the thighs and calves of both groups showed significant decrease. The EMG signals of gluteus medius of the pelvis-on group increased by 77.2 %. Furthermore, statistically significant increases in various muscles were measured in the pelvis-on group during the stance phase. Conclusion: Gait training using a robotic gait training system with pelvic movements was conducted to study the effects of lateral and rotational pelvic movements in gait training of stroke patients. The pelvic movements made gait training less interfered by the exoskeleton while stimulating the voluntary muscle activation during the stance phase. Clinical trial registration: KCT0003762, 2018-1254, Registered 28 October 2018, https://cris.nih.go.kr/cris/search/search_result_st01_kren.jsp?seq=14310

This study prognoses the remaining useful life of a turbofan engine using a deep learning model, which is essential for the health management of an engine. The proposed deep learning model affords a significantly improved accuracy by organizing networks with a one-dimensional convolutional neural network, long short-term memory, and bidirectional long short-term memory. In particular, this paper investigates two practical and crucial issues in applying the deep learning model for system prognosis. The first is the requirement of numerous sensors for different components, i.e., the curse of dimensionality. Second, the deep neural network cannot identify the problematic component of the turbofan engine due to its &ldquo, black box&rdquo, property. This study thus employs dimensionality reduction and Shapley additive explanation (SHAP) techniques. Dimensionality reduction in the model reduces the complexity and prevents overfitting, while maintaining high accuracy. SHAP analyzes and visualizes the black box to identify the sensors. The experimental results demonstrate the high accuracy and efficiency of the proposed model with dimensionality reduction and show that SHAP enhances the explainability in a conventional deep learning model for system prognosis.

A 3D touchless display is created where a human finger position is visualized in structural colors of a soft photonic crystal., The development of a lightweight, low-power, user-interactive three-dimensional (3D) touchless display in which a human stimulus can be detected and simultaneously visualized in noncontact mode is of great interest. Here, we present a user-interactive 3D touchless sensing display based on multiorder reflection structural colors (SCs) of a thin, solid-state block copolymer (BCP) photonic crystal (PC). Full-visible-range SCs are developed in a BCP PC consisting of alternating lamellae, one of which contains a chemically cross-linked, interpenetrated hydrogel network. The absorption of a nonvolatile ionic liquid into the domains of the interpenetrated network allows for further manipulation of SC by using multiple-order photonic reflections, giving rise to unprecedented visible SCs arising from reflective color mixing. Furthermore, by using a hygroscopic ionic liquid ink, a printable 3D touchless interactive display is created where 3D position of a human finger is efficiently visualized in different SCs as a function of finger-to-display distance.

Highly stretchable fiber strain sensors are one of the most important components for various applications in wearable electronics, electronic textiles, and biomedical electronics. Herein, we present a facile approach for fabricating highly stretchable and sensitive fiber strain sensors by embedding Ag nanoparticles into a stretchable fiber with a multifilament structure. The multifilament structure and Ag-rich shells of the fiber strain sensor enable the sensor to simultaneously achieve both a high sensitivity and largely wide sensing range despite its simple fabrication process and components. The fiber strain sensor simultaneously exhibits ultrahigh gauge factors (∼9.3 × 105 and ∼659 in the first stretching and subsequent stretching, respectively), a very broad strain-sensing range (450 and 200% for the first and subsequent stretching, respectively), and high durability for more than 10 000 stretching cycles. The fiber strain sensors can also be readily integrated into a glove to control a hand robot an...

For local homing navigation, a mobile robot is supposed to return home using snapshots of the surrounding environment. It basically follows the snapshot model, comparing the home snapshot and the current view to determine the homing direction. In this paper, we suggest a high-order moment potential to describe the landmark feature distribution for local homing navigation. The moment potential function calculates the sum of products of the feature and the distance of landmark particles as a holistic view, allowing a high order of the distance. It effectively combines the range sensor values of landmarks in the current view and the visual features. By analogy with the moment in physics, the center of the moment is estimated as the reference point, which is the unique convergence point in the convex moment potential from any view, and using the property, the gradient of moment potential at the current position and home location can derive the homing vector. We provide a proof of convergence for any moment potential with order greater than or equal to one. Also, we demonstrate homing performances with various moment models in real environments to validate our models. The suggested moment models combining both landmark distance and visual feature have better performances than the visual information alone, and high-order moment potentials can be searched to obtain a better description of landmark distribution for a given environment.

Visual homing navigation has been a challenging issue in indoor localization and navigation. Inspired by insect navigation, the snapshot model was introduced for homing navigation, where a pair of snapshots at the current location and at the nest are compared to guide the homing direction. We investigate Haar-like features in vision to extract visual cues, based on the snapshot model. The Haar-like features consist of masks randomly generated over the snapshot image at the home location, and later, their matching scores at the snapshot available at the current location are calculated for the correspondence measure. We draw landmark vectors using the correspondence measure of Haar-like features at their angular positions. Interestingly, a collection of Haar-like features provide visual characteristics to reflect a pair of snapshot images, which can determine the homing direction. In this paper, we propose two types of homing methods based on the image difference using Haar-like features, the Haar-like landmark vector model and the Haar-like image distance model. We demonstrate the effectiveness of the methods in several environments.

In the present work, continuous carbon fibre (CCF) reinforced polyamide 6 (PA6) composites were fabricated using the Fused Filament Fabrication (FFF) 3D printing technique. The sliding friction and wear behaviour of the printed CCF/PA6 composites was investigated as a function of varying fibre orientations (i.e. normal, parallel, and anti-parallel) and layer deposition directions (i.e. parallel and perpendicular) with respect to the sliding direction. It emerged that fibre orientation played a key role in determining the wear resistance of 3D printed composites. Severe fibre breakage was observed when the orientation of fibre was in normal direction against the sliding direction, associated with a higher wear rate than that obtained in parallel or anti-parallel direction. To better understand the effects of voids on wear mechanism, tribo-tests were further carried out with the specimens that underwent compression moulding (CM) post-treatment process. The results confirmed that the internal defects could cause adverse impacts on wear performance, depending on the fibre orientations. In particular, it was found that fibre distribution, as well as the fibre/matrix interface within the filament, was the crucial parameter affecting the overall tribological performance of the printed structures. The findings add to our understanding of designing and fabricating wear-resistant polymer composites using 3D printing technologies.

Since its introduction, the 3D printing technology has been widely used in fields such as design, rapid prototyping, and biomedical devices, owing to its advantages of inexpensive, facile embodiment of computer 3D files into physical objects. Later, 4D printing was introduced by adding the temporal dimension to 3D. Stimuli such as heat, humidity, pH, and light trigger the actuation of printed objects without motors or wires. Smart materials that respond to external stimuli are good candidates for 4D printing. In this paper, we review the recent research on 4D printing, and categorize it with respect to the activating stimuli. The mechanical properties of 4D printing materials are mentioned as well. Finally, the future of 4D printing is discussed.

Intelligent imaging and analysis have been studied in various research fields, including medical imaging, biomedical applications, computer vision, visual inspection and robot systems [...]

While inverse tone mapping (ITM) was frequently used for graphics rendering in the high dynamic range (HDR) space, the advent of HDR TVs and the consequent need for HDR multimedia contents open up new horizons for the consumption of ultra-high quality video contents. Unfortunately, previous methods are not appropriate for HDR TVs, and their inverse-tone-mapped results are not visually pleasing with noise amplification or lack of details. In this paper, we first present the ITM problem for HDR TVs and propose a CNN-based architecture, called ITM-CNN, which restores lost details and local contrast with its training strategy for enhancing the performance based on image decomposition using the guided filter. We demonstrate the benefits of decomposing the image by experimenting with various architectures and also compare the performance for different training strategies. Our ITM-CNN is a powerful means to solve the lack of HDR video contents with legacy LDR videos.

Rapidly aging society faces with increases in neurological disorders including stroke. Hemiplegia, which is one of the common sequelae due to stroke, causes difficulties in activities of daily living. As the number of stroke patients grows, demands for gait training increases, where robotic gait training systems are necessary. A robotic gait training system, called "COWALK-I," is designed to provide pelvic motion on the transverse plane as well as leg motions in the sagittal plane during gait training sessions. The pelvic motion allows weight-shifting as well as more natural gait patterns during gait training. In this research, effect of the pelvic motion during waking in the COWALK-I system is studied. Interaction force between the healthy subjects and the COWALK-I and electromyography(EMG) sensor data are measured. The average interaction forces did not show significant difference while each subject exhibited diverse patterns. The EMG signals shows that more activation of rectus femoris and less activation of gastrocnemius and gluteus medius.

In this paper, we suggest a phase difference algorithm inspired by weakly electric fish. Weakly electric fish is a fish which generates electric field though its electric organ in the tail. The weakly electric fish search for prey and detect an object by using electrolocation. The weakly electric fish have Jamming Avoidance Response (JAR) to avoid jamming signal. One of pulse-type weakly electric fish Gymnotus carapo also have JAR to reduce the probability of coincidence of pulses. We analyze this response signal and design the phase difference algorithm. We expect that simple algorithm inspired by weakly electric fish can be used in many engineering fields.

Noble metal-free mesoscopic perovskite solar cell based on low-cost molybdenum (Mo) cathode has been developed for the first time. By optimizing the thickness of Mo electrode and combination with high quality perovskite layer, the Mo cathode-based perovskite solar cell exhibited a best power conversion efficiency as high as 15.06% with invariance of current density–voltage curves at different scan rates and very small hysteresis according to scan direction. In addition to photovoltaic performances, importance of mechanical durability of a cathode material for perovskite solar cell was highlighted and results from nano-indentation and scratch test indicated that Mo electrode has superior resistance and restoration capability against external scratch or deformation compared to Au electrode. The Mo cathode-based perovskite solar cells have great potential for realizing low cost and high performance as well as mechanical durability.

Organic and polymeric electroluminescent (EL) devices working under alternating current (AC) electricity have drawn technological attention due to their light‐emitting principles and have great potential for applications. In spite of recent advances in AC EL devices, mechanically robust, patternable full‐color emission layers with high brightness have rarely been demonstrated. In this manuscript, we report high‐performance full‐color AC EL devices with nonradiating polymers solution blended in fluorescent polymer emissive layers. Conventional nonradiating polymers such as poly(styrene) (PS) and poly(α‐methyl styrene) in an emissive layer enhanced the brightness of individual red (R), green (G), and blue (B) colors to several thousand cd m⁻². Systematic investigation revealed bi‐functional roles of PS not only as a diluting agent but also as an electron capturer. This resulted in the hole and electron carriers being balanced in the emissive layer, leading to improved power and current efficiency. Furthermore, our blended emission film consisting of 83 vol % PS is mechanically robust with excellent surface adhesion as well as uniformity, when combined with scratch‐tolerant AC device architecture, not only resulted in large area cell operation but also allowed for a solution‐based pattern‐mask process, giving rise to well‐defined R, G, and B cells individually addressable in a single device platform. © 2015 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2015, 53, 1629–1640

This paper presents a distributed coordination methodology for multi-robot systems, based on nearest-neighbor interactions. Among many interesting tasks that may be performed using swarm robots, we propose a biologically-inspired control law for a shepherding task, whereby a group of external agents drives another group of agents to a desired location. First, we generated sheep-like robots that act like a flock. We assume that each agent is capable of measuring the relative location and velocity to each of its neighbors within a limited sensing area. Then, we designed a control strategy for shepherd-like robots that have information regarding where to go and a steering ability to control the flock, according to the robots’ position relative to the flock. We define several independent behavior rules; each agent calculates to what extent it will move by summarizing each rule. The flocking sheep agents detect the steering agents and try to avoid them; this tendency leads to movement of the flock. Each steering agent only needs to focus on guiding the nearest flocking agent to the desired location. Without centralized coordination, multiple steering agents produce an arc formation to control the flock effectively. In addition, we propose a new rule for collecting behavior, whereby a scattered flock or multiple flocks are consolidated. From simulation results with multiple robots, we show that each robot performs actions for the shepherding behavior, and only a few steering agents are needed to control the whole flock. The results are displayed in maps that trace the paths of the flock and steering robots. Performance is evaluated via time cost and path accuracy to demonstrate the effectiveness of this approach.

A number of landmark-based navigation algorithms have been studied using feature extraction over the visual information. In this paper, we apply the distance information of the surrounding environment in a landmark navigation model. We mount a depth sensor on a mobile robot, in order to obtain omnidirectional distance information. The surrounding environment is represented as a circular form of landmark vectors, which forms a snapshot. The depth snapshots at the current position and the target position are compared to determine the homing direction, inspired by the snapshot model. Here, we suggest a holistic view of panoramic depth information for homing navigation where each sample point is taken as a landmark. The results are shown in a vector map of homing vectors. The performance of the suggested method is evaluated based on the angular errors and the homing success rate. Omnidirectional depth information about the surrounding environment can be a promising source of landmark homing navigation. We demonstrate the results that a holistic approach with omnidirectional depth information shows effective homing navigation.

This paper focuses on task allocation problem in multi-agent system and we consider the solution for regulating the proportion of groups performing tasks equally to the proportion of task demands. The response threshold model inspired by division of labor observed in insect societies is applied. Each agent converts the information from surrounding environment to the spike and decides its task based on the response threshold model. The decision of an agent has partial control over its environment, but the overall system shows a desired performance. The algorithm is implemented using simulated robots and demonstrates its adaptivity with changes for task demands and the number of agents in a group.

Dynamic task allocation is a necessity in a group of robots. Each member should decide its own task such that it is most commensurate with its current state in the overall system. In this work, the response threshold model is applied to a dynamic foraging task. Each robot employs a task switching function based on the local task demand obtained from the surrounding environment, and no communication occurs between the robots. Each individual member has a constant-sized task demand history that reflects the global demand. In addition, it has response threshold values for all of the tasks and manages the task switching process depending on the stimuli of the task demands. The robot then determines the task to be executed to regulate the overall division of labor. This task selection induces a specialized tendency for performing a specific task and regulates the division of labor. In particular, maintaining a history of the task demands is very effective for the dynamic foraging task. Various experiments are performed using a simulation with multiple robots, and the results show that the proposed algorithm is more effective as compared to the conventional model.

Fabrication strategies that pursue “simplicity” for the production process and “functionality” for a device, in general, are mutually exclusive. Therefore, strategies that are less expensive, less equipment-intensive, and consequently, more accessible to researchers for the realization of omnipresent electronics are required. Here, this study presents a conceptually different approach that utilizes the inartificial design of the surface roughness of paper to realize a capacitive pressure sensor with high performance compared with sensors produced using costly microfabrication processes. This study utilizes a writing activity with a pencil and paper, which enables the construction of a fundamental capacitor that can be used as a flexible capacitive pressure sensor with high pressure sensitivity and short response time and that it can be inexpensively fabricated over large areas. Furthermore, the paper-based pressure sensors are integrated into a fully functional 3D touch-pad device, which is a step toward the realization of omnipresent electronics.

During the last several years, legged locomotive mechanism has been considered as one of the main self-propelling mechanisms for future endoscopic microrobots due to its superior propulsion efficiency of an endoscopic microrobot inside the intestinal track. Nevertheless, its clinical application has been largely limited since the legged locomotive mechanism utilizes an end effector which has a sharp tip to generate sufficient traction by physically penetrating and interlocking with the intestinal tissue. This can cause excessive physical tissue damage or even complete perforation of the intestinal wall that can lead to abdominal inflammation. Hence, in this work two types of new end effectors, penetration-limited end effector (PLEE) and bi-material structured end effector (BMEE) were specially designed to acquire high medical safety as well as effective traction generation performance. The microscopic end effector specimens were fabricated with micro-wire electric discharge machining process. Traction generation performance of the end effectors was evaluated by direct measurement of resistance forces during contact-sliding tests using a custom-built contact-sliding tester. The safety of the end effector design was evaluated by examination of microscopic intestinal tissue damage using a scanning electron microscope (SEM). Physical damage characteristics of the intestinal tissue and related contact physics of the end effectors were discussed. From the results, the end effectors were evaluated with respect to their prospects in future medical applications as safe end effectors as well as micro-surgical tools.

Mobile robots and their applications are involved with many research fields including electrical engineering, mechanical engineering, computer science, artificial intelligence and cognitive science [...]

Purpose Locally advanced esophageal cancers are generally treated with neoadjuvant chemoradiotherapy, followed by surgery in operable candidates. However, even if the patients were diagnosed as operable disease, surgery could not be performed on patients with poor condition or other comorbidity. In this case, definitive chemoradiotherapy (dCRT) is the other option for localized esophageal cancer. Therefore, the purpose of this study was to evaluate the efficacy and clinical prognostic factors for dCRT in locally advanced esophageal cancer. Materials and methods We conducted a review of patients who received dCRT for locally advanced squamous esophageal cancer from 2004 to 2010, focusing on stages III and IVa. All patients received at least two cycles of platinum-based chemotherapy during radiation, and all tumor burdens were included in the radiation field. The treatment results were analyzed for patterns of failure and prognostic factors associated with survival. Results In total, 63 patients were enrolled in this study. The overall response rate was 84.1%. Relief from dysphagia after dCRT was achieved in 48 patients. The most frequent failure was local recurrence. The median overall survival (OS) was 23.0 months, and the 2-year survival rate was 45.4%. Similar results were observed for elderly study patients. Significant prognostic factors for OS were duration of smoking, high grade of dysphagia (score of 3 or 4), and shorter duration of progression-free and dysphagia-free survival. Maintenance chemotherapy after dCRT did not influence OS. However, "good risk" patients receiving maintenance chemotherapy showed better OS than those who did not receive maintenance chemotherapy (30.4 months vs. 12.0 months, p=0.002). Conclusion dCRT has a major role in improving survival and palliation of dysphagia in inoperable advanced esophageal cancer, even in elderly patients. Maintenance chemotherapy after dCRT may be effective in prolonging survival in "good risk" patients.

Visual navigation in robotics is one of the challenging issues, and many navigation approaches are based on localization of a mobile robot in the environment. The snapshot model is a biologically inspired model of insect behaviour to return home and it shows a simple algorithm to compare the snapshot images at the current position and the destination, instead of complex localization process. Here, we propose a new homing navigation method based on a moment measure to characterize the snapshot image efficiently. The method uses range values or pixel values of surrounding landmarks. Then it defines a moment measure to evaluate the environmental features, or landmark distributions, and the measure forms a convex shape of landscape with respect to robot positions in the environment. Based on the landscape, the mobile robot can return home successfully. Range sensors or image sensors can sufficiently provide the landscape information. Our experimental results demonstrate that the method is effective even in real environments.

Homing navigation is an important aspect in navigation behaviours of animals. There has been many types of navigation but we focus on the vision-based landmark navigation to return home. Visual navigation is involved with image matching process over snapshot images. Landmark vector methods simplify the environmental information into a set of landmark vectors, and then compare the landmark vectors obtained from each snapshot. In this paper, we encode landmark vectors using the gray-colored values as the length of vectors. Then we apply the landmark arrangement method to those landmark vectors. Using the gray-colored information, we can estimate the homing direction at a given position. We show that the suggested method is effective in homing navigation.

Task allocation problem has been an issue in multi-agent systems. Among many interesting tasks, we focus on an algorithm for the proportional regulation of population where the swarm is divided into groups depending on task demands. We take the response threshold model inspired by division of labor in several social insects. In our approach, the member proportion of each sub-group is regulated proportional to the external task demands and local social interactions among agents. Here, the interactions control the response thresholds for given tasks. The proposed algorithm was applied to simulation experiments of robots, and the experimental results show that the proposed method has adaptive and robust responses under dynamically changing environments.

Concurrent chemoradiotherapy (CRT) is a main treatment option for patients with advanced oesophageal cancer. However, improvement of survival outcomes and toxicities according to the selected treatment is still needed. This phase II trial was conducted to assess the efficacy and safety of concurrent CRT with weekly docetaxel and cisplatin in advanced oesophageal cancer.Patients with unresectable oesophageal cancer due to advanced stage or patients medically unfit for surgery were enrolled. Patients received 20 mg/m(2) docetaxel and 25 mg/m(2) cisplatin in weeks 1, 2, 3, 5, 6, and 7 with concurrent 54-Gy radiotherapy at 200 cGy/day.Thirty-six patients with oesophageal squamous cell carcinoma were enrolled from December 2007 to December 2009. Among them, the toxicity and response rate of 35 were evaluated. Thirty-five patients completed radiotherapy as planned, and 33 completed chemotherapy as planned. Grade 3 or 4 toxicity during CRT included leucopenia (5.7 %), febrile neutropenia (2.9 %), oesophagitis (22.9 %), and tracheo-oesophageal fistula (5.7 %). After CRT, 8 patients (22.9 %) had a complete response, 22 (62.9 %) had a partial response, 4 (11.4 %) had stable disease, and 1 (2.9 %) had progressive disease. Improvement of dysphagia was observed in 85.3 %. At a median follow-up of 26.7 months, the median time to progression was 13.5 months, and median overall survival was 26.9 months. The 3-year progression-free survival rate was 16.7 %, and survival rate was 27.8 %.Concurrent CRT with weekly docetaxel and cisplatin was well tolerated and is a convenient combination with promising efficacy. This result indicated favourable activity in terms of both tumour and symptom control.

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Combination chemotherapy is a standard treatment approach in advanced gastric cancer. However, combination chemotherapy for advanced gastric cancer is often associated with severe treatment-related toxicities and most oncologists are reluctant to perform combination chemotherapy in patients with a poor clinical condition. We retrospectively investigated the efficacy and tolerability of single-agent chemotherapy in patients with recurrent or metastatic gastric cancer with poor performance status (PS). We reviewed advanced gastric adenocarcinoma patients who received first-line single-agent palliative chemotherapy due to poor PS between June 2006 and December 2010. A total of 125 patients with Eastern Cooperative Oncology Group (ECOG) PS 2-3, whose general condition did not allow combination chemotherapy, were enrolled. Four single agents were used: TS-1 (n=63), paclitaxel (n=42), irinotecan (n=15) and capecitabine (n=5). The median age was 66 years, with a range of 25-81 years. The percent response rate and rate of stable disease (SD) were 19.2 and 35.2%, respectively, giving a disease control rate of 54.4%. The median progression-free survival (PFS) was 3.9 months (95% CI, 2.73-5.06). The median overall survival (OS) was 9.1 months (95% CI, 7.70-10.56) with a 1-year survival rate of 31.2%. Multivariate analysis demonstrated that the independent prognostic factors for OS were chemotherapy regimen (capecitabine) [reference: TS-1, hazard ratio (HR), 5.00; 95% CI, 1.81-13.81; P=0.002], no second-line chemotherapy (HR, 2.3; 95% CI, 1.48-3.57; P=0.001), bone metastasis (HR, 2.73; 95% CI, 1.22-6.09; P=0.014), ECOG PS 3 (HR, 38.10; 95% CI, 13.72-105.78; P=0.001), Glasgow prognostic score (GPS) ≥1 (HR, 1.88; 95% CI, 1.24-2.85; P=0.003) and chemotherapy response [SD + progressive disease (PD) + not evaluable (NE); HR, 2.37; 95% CI, 1.39-4.05; P=0.002)]. First-line single-agent palliative chemotherapy demonstrated a relatively good clinical efficacy for recurrent or metastatic gastric cancer patients with poor PS.

There are several ways to estimate the distance to an object based on visual information. Many robotic systems compute object distances based on disparity values or motion information. In addition to a disparity cue or image motion, research has shown that animals can possibly estimate the distance using the image size as well as the azimuth and elevation angles in their visual field. In this paper, inspired by the vigilance behavior of fiddler crabs, we suggest distance-estimation methods with several visual cues for engineering applications. A foraging fiddler crab can estimate the distance of an intruder from the burrow entrance. If the intruder is close to the burrow, the crab rushes back home to maintain possession of the burrow. In this study, we investigated burrow-centered distance-estimation methods based on visual information such as the retinal position, that is, the azimuth and elevation angles in an omnidirectional view as well as the image size and retinal speed of a target object. The methods show potential for engineering applications to the surveillance problem.

The development of an autonomous navigating robot is a challenging task. Motivated by the performance of insects successfully returning to the nest, researchers have studied bio-inspired navigation algorithms for their potential use in mobile robots. In this paper, we analyze landmark-based approaches, especially Distance Estimated Landmark Vector (DELV), Average Correctional Vector and Average Landmark Vector methods, that use landmark vectors for visible environmental landmarks. We evaluated the homing performance of various landmark vector methods with surrounding landmarks under occlusion and found that the occluded or missing landmarks have a significant influence on the performance. We also developed a landmark vector algorithm with a visual compass that uses only retinal images without a reference compass. From our experimental results, we conclude that the DELV shows robust homing navigation performance with missing or occluded landmarks among landmark vector methods.

It is known that big brown bats can distinguish echo of a prey at various angles. In this paper, we suggest a new object localization strategy using ultrasonic echolocation. We calculate the relative energy ratio between a high frequency component of ultrasound signal and a low frequency component of ultrasound signal for a target object. We found the measure depends on bearing angle of the object in space. We also tested energy ratio of echoed FM ultrasound signals depending on frequency, based on cross-correlation. It can determine the relative angular position of objects even though the reflected signals are congested form each object.

Weakly electric fish use self-generated electric fields to identify prey, explore their surroundings, and communicate with conspecifics. They have electroreceptors over their entire skin surfaces, and readings from these electroreceptors form electric images. The characteristics of electric images include important information such as the locations, shapes, and electrical properties of target objects. The lateral distance to a target object can be estimated using the rate between maximal slope and maximal amplitude, or relative slope, and provides a direct cue to localize target objects, irrespective of size and electrical properties. Sensor readings acquired from distributions of electroreceptors are interpreted as spatial electric images. Temporal electric images can be assessed using a single electroreceptor by back-and-forth swimming. Some species of weakly electric fish engage in back-and-forth swimming behavior during foraging and can use spatiotemporal information to localize objects. With active body movement and forward swimming, a single electroreceptor can scan the time course of potential perturbation caused by a target object. If there is more than one target object, electric images may be distorted by interactions between individual images. In this paper, we suggest that temporal relative slope is an effective measure for the electrolocation of multiple objects by robotic systems. We investigate the characteristics of temporal relative slopes based on back-and-forth sweeping motions to localize multiple spherical objects via modeling experiments. This method can be applied to biomimetic robotic systems in the underwater environment.

There have been many homing navigation algorithms for robotic system. In this paper, we suggest a bio-inspired navigation model. It builds path integration based on optical flow. We consider two factors on robot movements, translational movement and rotational movement. For each movement, we found distinguishable optical flows. Based on optical flow, we estimate ego-centric robot movement and integrate the path optimally. We can determine the homing direction and distance. We test this algorithm and evaluate the performance of homing navigation for robotic system.

Many animals can return home accurately after exploring for food using their own homing navigation algorithm. Path integration plays a critical role in homing navigation. It is believed that animals are able to recognize their relative location from the nest by accumulating both distance and direction experienced during their travel. We tested possible patterns of neuronal organization for a path integration mechanism. The neural networks consisted of a circular array of neurons, following population coding. We describe here a neural model of path integration involving a relatively small number of neurons and discuss how well the model operates for homing navigation. Robotic simulations suggest that a neural structure with only a few sensor neurons can successfully handle the path integration needed for homing navigation.

Many insects and animals exploit their own navigation systems to navigate in space. Biologically-inspired methods have been introduced for landmark-based navigation algorithms of a mobile robot. The methods determine the movement direction based on a home snapshot image and another snapshot from the current position. In this paper, we suggest a new landmark-based matching method for robotic homing navigation that first computes the distance to each landmark based on ego-motion and estimates the landmark arrangement in the snapshot image. Then, landmark vectors are used to localize the robotic agent in the environment and to choose the appropriate direction to return home. As a result, this method has a higher success rate for returning home from an arbitrary position than do the conventional image-matching algorithms.

Autonomous navigating algorithms for mobile robots have been proved to be a difficult task. Based on the excellent homing performance shown by many insects, bio-inspired navigation algorithms for robotic experiments have been widely researched and applied to the design of navigational strategies for mobile robots. In this paper, among them, we analyze two simple landmark navigation methods their strengths and limits. We investigate the effect of the occlusion problem mainly, which is an important yet tough problem in many landmark navigation algorithms. In the point of view of the error of homing vector and the performance of the homing paths in the environment with artificial occlusions, we investigate the effect of occlusion problem in both methods in order to further study on solutions.

Weakly electric fish use electric organ discharge (EOD) and their electroreceptors to identify prey, explore their surroundings, and communicate with other members of the same species. They are specialized in active electrolocation using a self-generated electric field, and they can sense distortion of their self-generated electric field caused by a target object. Electric fish have many electrosensors on the surface of their body, and the sensor readings from the electroreceptors form an electric image. A correlation exists between features of the electric images and characteristics of a target object. In estimating the location of a target object, the intensity, width, and slope of the electric image must be considered. In this article, we suggest that periodic EOD signals are helpful to extract localization features from noisy electrosensory signals. Cross-correlation between an efference copy signal and sensory signals in the waveform can produce filtered signals in the temporal domain. For a biomimetic fish robot, we can use two-phase filtering: noise-filtering with cross-correlation in the temporal axis and additional filtering in the rostrocaudal spatial axis. This spatiotemporal filtering can effectively remove noise, thus making it possible to obtain accurate localization features of a target object in an underwater environment.

In this paper, we suggest a method for discretized frequency modulations of ultrasonic signals. A continuous sweep of frequency modulation signals can be modelled with fine levels of discretization. If the ultrasound signals are modulated with monotonically decreasing frequencies, then the cross-correlation between an emitted signal and received signal can be used to identify the distance of multiple target objects. For the discretized frequency synthesis, CF ultrasounds with different frequencies are serially ordered. The auto-correlation test with the signal shows effective results for distance estimation. The discretized frequency syntheses have better distance resolution than CF ultrasound signals and the resolution depends on the number of the combined ultrasound frequencies.