Abstract. In this paper, we present Sinkhorn multidimensional scaling (Sinkhorn MDS) as a method for visualizing shape functionals in shape spaces. This approach uses the Sinkhorn divergence to map these infinite-dimensional spaces into lower-dimensional Euclidean spaces. We establish error estimates for the embedding generated by Sinkhorn MDS compared to the unregularized case. Moreover, we validate the method through numerical experiments, including visualizations of the classical Dido’s problem and the newly introduced double-well, triple-well, and Sinkhorn cone-type shape functionals. Our results demonstrate that Sinkhorn MDS effectively captures and visualizes shapes of shape functionals.

As adaptive bitrate streaming services have spread, it has become more important for video streaming providers to control video quality and prevent viewing abandonments. However, since viewing abandonments are caused not only by quality degradations but also by a lack of users' interest in contents, it will first be necessary to clarify how quality and/or content affect viewing abandonments. To investigate this, we conducted an adaptive bitrate streaming experiment and developed a viewing-abandonment-reason-classification model that classifies abandonment reasons into quality or content. Using training data, we developed four models (logistic regression, classification tree, random forests, and support vector machine) where feature variables related to application quality, users' operation behaviors, and the attributes of viewed contents were used as explanatory variables. These four models were validated by using validation data. From the results, the support vector machine model was considered to be the best since it obtained relatively good validation results and did not appear to be over-trained.

Bitrate-selection algorithms are key to improving the quality of experience (QoE) of adaptive video streaming. Although current bitrate selection algorithms maximize the QoE, video consumers are concerned with QoE and traffic-volume usage due to the pay-per-use or data-capped plans. To balance between the QoE and traffic volume, some commercial video-streaming services enable users to set the upper limit of the selectable bitrate. However, it is difficult for users to set an appropriate limit to obtain sufficient QoE. We propose BANQUET, a novel bitrate-selection algorithm that enables users to control intuitively the balance between the QoE and traffic volume. Assuming a user-set target QoE as a balancing parameter, BANQUET selects the bitrate that minimizes the traffic volume while maintaining the estimated mean opinion score (MOS) above the target QoE. BANQUET calculates the appropriate bitrate based on estimations of the throughput and butter transition. A trace-based simulation shows that BANQUET reduces the traffic volume by up to 47.0% compared to a baseline while maintaining the same average estimated MOS.

In this paper, the development of a two-degrees-of-freedom (DOF) human-powered robot using regenerative clutches and constant-force springs is described. A regenerative clutch can control a transmitted torque precisely and it includes a regenerative brake and a differential gear mechanism. The single-clutch human-powered joint-drive mechanism developed in this study is a system that can output forward and reverse torques and, to control the joint angle, performs servo control of the joint with one regenerative clutch by offsetting the torque using a constant-force spring. A crank-speed-sensitive spring-locking system has also been developed to implement a safe emergency stop function when the operator stops supplying power. The two-DOF human-powered robot using our systems realized planar trajectory tracking, suggesting that the position control of the end point of a multi-DOF human-powered robot is possible.

Adaptive bitrate streaming services for mobile terminals have drastically spread in recent years, and it is becoming more important for service providers to increase users' satisfaction by understanding users' viewing behaviors (e.g., how long users watch videos, and why users quit viewing videos) and taking measures such as appropriately designing the quality levels of the videos to be placed on their distribution servers. To investigate the impacts of quality factors on users' viewing behaviors in adaptive bitrate streaming, we conducted an experiment in which participants could freely search and watch videos on smartphones under various network conditions. Through 800 10-minute tests, 1,449 valid views were collected, and the collected dataset was analyzed to characterize the impacts of the initial loading delay, average bitrate, and stalling events on the cumulative quit rate (CQR) of users. Furthermore, the impacts of the average bitrate and stalling events were evaluated quantitatively, using the 2-sample Anderson-Darling test, as well as the combined impact of these two quality factors. The characteristics of the impacts of the above quality factors indicate the possibility of applying survival analysis for our dataset, and suggest that the average bitrate, the number of stalling events, and the average stalling duration should be considered as the external covariates when building a model to estimate the users' viewing time.

The University of São Paulo is the largest public university in Brasil, with 11 campuses where the campus in the city of São Paulo alone has an area of more than 3 million square meters. Security in the university is an issue that is being prioritized in the past years. A mobile app was introduced two years ago with which users in any campus can report security and maintenance events. Security events are monitored by the Campus Security Guard that has the capacity to dispatch agents immediately upon report of an event by the app. Last year an Electronic Monitoring System (EMS) composed of more than 300 cameras was deployed in the campus of the city of São Paulo together with analytics software that greatly enhances its functionality. Integrating the mobile app and the EMS is a great challenge that is being addressed in this paper. Here we present a description of both systems: the mobile application and the EMS. Then we present the Artificial Intelligence Surveillance system (AISUSP), which integrates both other systems using machine learning for automated scene analysis, an impossible task for human operators in an environment with more than 300 cameras. The ongoing result will lead to the prediction of emergency situations using a historical data base and making intelligent decisions related to the university's users safety.

In this work, the evaluation of user engagement's characteristics in adaptive bitrate video streaming is addressed. To this aim, the viewing time and its relation with video quality is studied in two carefully designed subjective tests. Video quality and viewing time were addressed in distinct experiments. In case of viewing time, users were allowed to stop watching the videos when they desired. It was found that for low-quality videos, the number of users dropping the video increases logarithmically as a function of time. In addition, when a stalling event occurs, users start dropping video playback after a waiting period of 5 seconds. Then, when the stalling ends, the dropping rate returns to its baseline rate (which depends on video quality). The number of users stopping watching video after a stalling event was found to be a function of stalling position, stalling duration, and the quality affected by coding. A baseline model considering only stalling features is defined. Finally, a model for predicting the video completion rate is proposed that achieves a Pearson correlation of 0.96 and a root-mean-square error (RMSE) of 0.064.

In this paper, a development of a single DOF joint drive mechanism using regenerative servo clutches is described. The regenerative servo clutch is a kind of a clutch developed by the authors which consists of a regenerative brake and a differential gear mechanism, and it can control the transmitted torque by controlling the regenerative brake torque. The joint drive mechanism developed in this study is the system which can perform the servo control of the angle of the output shaft using the externally provided constant-speed power directly, by controlling the transmitted torque by using the two regenerative servo clutches for normal and reverse rotation. Through the experimental results, the effectiveness of the developed mechanism and controller was confirmed.

In this paper, formal representations for user preferences will be provided, which also take fairness in the achievement of probably conflicting goals into account. The analysis of the Bottleneck Flow Control (BFC) algorithm for congestion avoidance in traffic networks will serve as a base to specify extensions of relations among preference sets. In particular, these relations are the lexicographic minimum, the maxmin fairness, and the newly introduced ordered-ordered weighted averaging operator. All of them are shown to become, in a relational sense, maximized by the BFC algorithm. Their further expansion by introducing fuzzy fusion operators in their formal definitions than establishes a comprehensive sets of relations. The application of these relations to the Multiple Relation Analysis (MRA) of subjective video quality evaluation data is demonstrated and gives the conclusion that also fairness criterions are present in such evaluations, at least as a secondary preference criterion.

In this paper, a traveling wave type miniature ultrasonic motor using a foil type stator is proposed. The developed motor has a simple mechanism without being a piezoelectric vibrator in itself. Traveling waves from the vibrator source propagate to the foil type stator through the long-length waveguide. Unlike an ordinary ultrasonic motor, the developed motor rotates in the same direction as the traveling wave. To clarify the reverse rotational phenomenon, transient response analysis was executed. The result showed that the foil type motor driving principal was different from that of the general ultrasonic motor which was driven by elliptical motion of the elastic body surface, the main driving force was the generated symmetric lamb wave which rotates the coil surface forward direction. A rotation experiment using the developed prototype motor was executed. Rotational speed was found to be 6,380 rpm and starting torque was 698.8 µNm when the ideal gap between rotor and stator was realized.

An EMG controlled intelligent orthosis was developed to support the rollover movement of cancer bone metastasis patients. In this paper, the validation of the developed signal processing algorithm to recognize the rollover was focused. Firstly, the ElectroMechanicalDelay of the internal oblique muscle was measured as the about 65 (msec). Secondly, it was confirmed that the rollover movement was recognized about 65 (msec) before the movement started. Therefore, the developed Micro Macro Neural Network (MMNN) recognized the rollover movement using the EMG signal as quick as possible. Finally, the robustness of the developed MMNN was discussed by conducting the experiment to discriminate between the rollover and turning out. We proposed and developed the original algorithm in which the logical XOR operation was added to the MMNN, because the MMNN which learned the characteristics of the only rollover recognized the turning out movement as the rollover movement perfectly. When the proposed algorithm that combined the MMNN and XOR operator was used, the rollover and turning out movements were discriminated 83%.

Many motion support robots of the elderly and disable were studies all over the world. We have developed the rollover support system, which is one of the ADL. Our ultimate goal is to develop an effective rollover support system for patients with cancer bone metastasis. The core of this system is a pneumatic rubber muscle that is operated by EMG signals from the trunk muscle. A Time Delay Neural Network (TDNN) is the traditional method for recognizing EMG signals. However, response delay and false recognition are the problem of the traditional neural network. We previously proposed a new neural network, called the Micro-Macro Neural Network (MMNN), to recognize the rollover movement earlier and with more accuracy than is possible with TDNN. MMNN is composed of a Micro Part, which detects rapid changes in the strength of the EMG signal, and a Macro Part, which detects the tendency of the EMG signal to continually increase or decrease. However, the methodology to determine the structure of the MMNN was not established. In this paper, the optimal structure of the MMNN is determined. A comparison of each of the 360 sets of test times of MMNN versus TDNN was done. These results showed that recognition using MMNN is 40 (msec) (S.D. 49) faster than recognition using TDNN. Additionally, the number of false recognitions using MMNN is one-third of that using TDNN. By comparing the output using only the Micro part and Macro part in MMNN, it was found that the combination of quick response of the Micro part and stable recognition of the Macro part are advantages of MMNN. In the future, we plan to test the effectiveness of the total system in clinical tests with cancer patients in terminal care.

IPTV services are attracting attention as an influential application of the next-generation network (NGN). Managing in-service communication quality based on users' quality of experience (QoE) is very important for improving customer satisfaction. For this, an accurate and efficient quality- assessment technology is desired. This is called objective quality assessment and has been studied for a long time. However, there are hardly any studies on methods that can be applied to quality monitoring at a user's premise. In this paper, we first overview the quality management approaches discussed in International Telecommunication Union (ITU). Next, we review available technologies from the viewpoint of the applicability to quality monitoring. Then, we propose a new objective quality-assessment approach to solve the problem current technologies have. To verify the validity of the new approach, we show a preliminary evaluation result of the proposed approach.

A mechanism using a pneumatic artificial rubber muscle (PARM) to restrict trunk twisting range of motion was proposed and an intelligent corset was developed to support the rollover movement of cancer bone metastasis patients. In this research, the specifications of the intelligent corset were deteminated so that it could perform in the same way as a traditional hard corset. Furthermore, based on the permitted trunk twist angle derived from interviews with hospital nurses, systems were evaluated for detecting the intention of rollover, using a neural network to recognize rollover, and using a PARM to restrict trunk range of motion (ROM). Through experimentation, the effectiveness of the intelligent corset was confirmed in ULPT and LLPT rollover movements. However, in 11% of the LLFT rollover trials, the twisting angle was higher than the permitted angle. Additionally, it was found that the intelligent corset solved the problem of the hard corset that patients feel pressure because of the constant restriction. In future, the intelligent corset will be evaluated in the clinical test.

Currently, over seven million people die of cancer each year; some of whom suffer from pain caused by bone metastasis. In their final stages of life, the pain is such that they cannot even roll over, one of the activities of daily life. With this in mind, in this research, we aim to develop equipment for patients with cancer bone metastasis to support roll-overs in terminal care. Specifically, the EMG signal, which is the input signal of the equipment, is discussed in this paper. The activities of four muscles affecting trunk movement were monitored during the process of rolling over. Following an EMG experiment with four subjects, the internal abdominal oblique (IO) muscle was seen to be active in the early stages of roll-over motion, although IO muscle was relatively prevalent in previous works. If we support roll-over at this point, the patient feels no pain, because the timing is detected before the strongest burden movement, which involves buoying the trunk and rotating the pelvis. Hence, the EMG signal of the IO muscle is suitable for the input of the signal to support roll-overs.

Hip joint surgeries are commonplace in our aging society. Robotic hip joint surgery is performed to cut bone precisely and is expected to be performed as minimally invasive treatment. Minimally invasive hip joint surgery needs operating space between muscle tissues and the bone around hip joint. In this paper a prototype of an articulated muscle-retracting robot for minimally invasive RAO (rotational acetabular osteotomy and one of the hip joint surgeries) is described. The muscle-retracting robot's role is to create an operating space between muscles and the surface of the bone for the other robot, whose role is to cut bone. The mechanical structure is very thin to follow a narrow path and tough enough to scrape and retract muscles around a hip joint. The prototype is designed based on the required specification from the previous experiments data. The prototype has 9 DOF, in which 3 DOF arms are capable of controlling the tip position and the force between muscle tissues and the surface of the bone. Evaluation of the prototype was done by using a compliant control as scraping method of muscle tissues from surface of the bone. The capability is revealed as a scraping length on a phantom of 140 mm around human hip joint model and on the living tissues of 27 mm in the crest of the ilium by magnitude of maximum force of 25 N. The scraped living tissues were a narrow part around the hip joint. We shall continue to research it and establish a stable control method to scrape tissues.

Medical procedures such as RFA and cryosurgery require the insertion of a needle into the target area within the body. Needle insertion is difficult because it can easily result in organs being deformed and displaced. This research aims to produce an intelligent robot for needle insertion, incorporating the following three techniques, A: visual feedback B: organ-model base control C: force control. This research uses a robot model and a liver for the target object to evaluate organ-model base control. For the purpose of organ-model base control, three experiments were conducted to evaluate the physical properties of the liver for robot control. A dynamic viscoelastic test was then carried out to show the dynamic properties of the liver in the form of a differential equation. The nonlinearity of the liver was supported by the creep test while an axial needle insertion test was also carried out to model the force and the extent of deformation experienced during needle insertion. In addition, an intelligent needle insertion robot was developed for the purpose of the experimentation concerning intelligent control of the needle. The experimental result displayed the margin error between the needle tip and target marker when the needle reaches the target position was around 1[mm] and a result led to positive findings.

Minimally invasive surgery has recently become a key word in medical engineering. In this operation, to introduce surgical instruments, spatulas which push tissues aside and retain the approach path to the affected area as well as workspace for the insertion of such instruments are necessary. Therefore, a new type of hydraulically-driven flexible manipulator for neurosurgery has been developed. With an attached balloon and using only physiological saline for the drive system, safety of the brain tissue, especially in terms of pressure, is assured as is the simplicity of the mechanism. In addition, this provides the advantage of MRI compatibility. From the results of several evaluations, this manipulator has been proved effective as a new type of medical device.

This paper proposes the planning and navigation of a surgical robot to perform safe and effective operations using finite element analysis results based on physical models of the brain. The physical brain models were proposed based on the results of physical property tests. Finite element analyses of virtual tension tests were carried out under the same conditions as the actual tension tests, and the results of the analysis coincided with those of the test. The finite element analyses of robotic brain surgery were carried out using a two-dimensional structure, which consisted of the proposed physical models. Assuming two types of robot models are used (the brain spatula and capsule type robot respectively), stress distribution in the brain, caused by each robot model, was simulated in the analyses. The preoperative planning was considered based on the analysis results, and the safe velocity of the robot’s movement was estimated from the same. The results of this research showed that organ modeling was necessary for preoperative planning in order to implement safe surgery.

Medical procedures such as RFA and cryosurgery require inserting a needle into the target location inside the body. Needle insertion is difficult because it is easy for organs to be deformed and displaced. Modeling the target object is essential in analyzing and developing a control system. Thus, a precise model is required to develop an intelligent robot that is able to insert the needle into the target area. This research uses a robot model and a liver for the target object. Four experiments were conducted to show the physical properties of the liver for robot control. A dynamic viscoelastic test was carried out to show the dynamic properties of the liver as a differential equation. The nonlinearity of the liver was supported by the creep test. In addition, the liver model was validated using a constant strain rate test. A needle insertion test was also carried out to validate the model.

To extend applicable area of minimally invasive neurosurgery, multi-DOF brain retract manipulator is developed. By inserting this manipulator deeper into the brain as an conductor and then other micromanipulator afterwards, it will be possible to realize new minimally invasive neurosurgery. The brain retract manipulator system is controlled by new safety method with brain retraction pressure monitoring system, Tendon-driven unit which controls lock or free status of each joint of multi-joint spatula, Passive-hybrid control system that assure the system a suitable safety. In evaluation test with a hog, insertion of the multi-joint spatula as deep as the cerebral base was realized, and the possibility of securing the space between the temporal lobe and the cerebral base is verified.