Traditional baseline-subtraction methods in calculating pupillary response have short-comings in assessing workloads. The enhanced algorithms based on pupil oscillation, such as the Index of Cognitive Activity (ICA), Index of Pupillary Activity (IPA), and the enhanced version, Low/High Index of Pupillary Activity (LHIPA), utilizing wavelet analysis, have been developed to measure cognitive loads in scenarios where the gaze is relatively static. The applicability of these algorithms in motor tasks, where eye-hand coordination plays a significant role, has not been sufficiently studied. This research explores the effectiveness of LHIPA in measuring task workloads of users in a simulated surgical task. The task’s complexity was determined using Fitts’ index of difficulty, which varied by target size and distance. We found that LHIPA effectively differentiates task workloads across various levels of task difficulties in the surgical task. Results are crucial for creating objective methods to assess task workloads using pupil-based metrics during goal-oriented movements.

Research has found that changes in mental workload during both cognitive and motor tasks can be indicated by pupil dilation. To measure cognitive workload, researchers have developed tools such as the wavelet analysis-based index of cognitive activity (ICA) and index of pupil activity (IPA). However, it is still unclear whether these tools can accurately measure workload during motor tasks. This study aims to investigate whether the IPA can differentiate task workload during a motor task involving aiming in a tele-operation setting, where the task requirements were quantified using Fitts’ index of difficulty(ID) based on target size and distance. The study found that the IPA can differentiate between different levels of motor task workload, provided the proper window is used, namely the period before the tool touches the targets. This finding is significant as it can aid in developing objective methods to evaluate task workload using pupil parameters during goal-directed movements.

Blinks have been widely studied in various fields including medical and human computer interactions, and in driver fatigue. Automatic detection of blinks has valuable practical importance. While existing deep neural networks excel in extracting spatial features from images and demonstrate impressive performance in visual object recognition, their application for blink detection in videos on a frame-by-frame basis is suboptimal, as they only consider spatial features from single images. In this paper, we developed a spatial-temporal-aware deep learning framework that capitalizes on the rapid advancements of the existing state-of-the-art visual object recognition networks, aiming to enhance their performance specifically in blink detection. Our framework takes consecutive frames as input to extract spatial and temporal features simultaneously for better detection of eye movements. We also propose a sliding window re-sampling strategy to mitigate overfitting on training data. Extensive experimental evaluations and comparisons demonstrate the feasibility of the proposed algorithm, which delivers excellent performance for detecting blinks.

Surgery is a team effort. In this video, we display how we record two surgeons' eye motions during a simulated surgical operation, then performed Cross Recurrence Analysis (CRA) on the dual eye-tracking data to develop a valid technology to assess shared cognition. Twenty-two dyad teams were recruited to perform object transportation task using laparoscopic techniques. Outputs from CRA, including overlapping, recurrence rate and phase delay were correlated with team performance measured by the task time, errors made, and movement de-synchronization. Gaze behaviors between the two team members recorded in the surgical videos correlated positively with team performance. Elite teams were overlapping gaze more with higher recurrence rate than the poor teams. Dual eye-tracking analysis can be a useful tool for assessing team cognition and evaluating the team training.

Pupil size is known to correlate with the changes of cognitive task workloads, but how the pupil responds to requirements of basic goal-directed motor tasks involved in human-machine interactions is not yet clear. This work conducted a user study to investigate the pupil dilations during aiming in a tele-operation setting, with the purpose of better understanding how the changes in task requirements are reflected by the changes of pupil size. The task requirements, managed by Fitts' index of difficulty (ID), i.e. the size and distance apart of the targets, were varied between tasks, and pupil responses to different task IDs were recorded. The results showed that pupil diameter can be employed as an indicator of task requirements in goal-directed movements-higher task difficulty evoked higher valley to peak pupil dilation, and the peak pupil dilation occurred after a longer delay. These findings contribute to the foundation for developing methods to objectively evaluate interactive task requirements using pupil parameters during goal-directed movements in HCI.

Pupil size is known to correlate with changes of cognitive task workloads, but the pupillary response to requirements of basic goal-directed motor tasks is not yet clear, although pointing with tools is a ubiquitous human task. This work describes a user study to investigate the pupil dilations during aiming in two tele-operation tasks with different target settings, one aiming at targets with different sizes located at constant distance apart, and the other aiming at targets varying in different distances. The task requirements in each task were defined by Fitts' index of difficulty (ID). The purpose of this work is to further explore how the changes in task requirements are reflected by the changes of pupil size, i.e., whether the pupil responds to either target size or target distance, or to both of them. Pupil responses to different task IDs were recorded in each task. The results showed that the pupil responds to the changes of ID, not just to the change of target size. This implies that pupil diameter can be employed as an indicator of task requirement in goal-directed movements, because higher task difficulty evoked higher peak pupil dilation which occurred with longer delay. These findings can be used for detailed understanding of eye-hand coordination mechanisms in interactive systems and contribute to the foundation for developing methods to objectively evaluate interactive task requirements using pupil parameters during goal-directed movements.

Novices were trained to perform a unimanual peg transport task in a laparoscopic training box with an illuminated interior displayed on a monitor. Subjects were divided into two groups; one group was verbally instructed to direct their gaze at distant targets, while the other group had their gaze behaviour implicitly manipulated using distant target illumination. Both groups achieved similar task completion times post-training and developed peripheral vision strategies leading to delayed foveation on targets until the instrument was closer to its destination, although the ability to focus on targets earlier during manual movements as done by an expert surgeon was quickly regained by the verbal instruction group post-training. This suggests that care should be taken when employing visual attention cuing methods such as target highlighting for training eye-hand coordination skills, as simple verbal instruction may be sufficient to help trainees to adopt more expert-like gaze behaviours.

Irregular streaks are important clues for Melanoma (a potentially fatal form of skin cancer) diagnosis using dermoscopy images. This paper extends our previous algorithm to identify the absence or presence of streaks in a skin lesions, by further analyzing the appearance of detected streak lines, and performing a three-way classification for streaks, Absent, Regular, and Irregular, in a pigmented skin lesion. In addition, the directional pattern of detected lines is analyzed to extract their orientation features in order to detect the underlying pattern. The method uses a graphical representation to model the geometric pattern of valid streaks and the distribution and coverage of the structure. Using these proposed features of the valid streaks along with the color and texture features of the entire lesion, an accuracy of 76.1% and weighted average area under ROC curve (AUC) of 85% is achieved for classifying dermoscopy images into streaks Absent, Regular, or Irregular on 945 images compiled from atlases and the internet without any exclusion criteria. This challenging dataset is the largest validation dataset for streaks detection and classification published to date. The data set has also been applied to the two-class sub-problems of Absent/Present classification (accuracy of 78.3% with AUC of 83.2%) and to Regular/Irregular classification (accuracy 83.6% with AUC of 88.9%). When the method was tested on a cleaned subset of 300 images randomly selected from the 945 images, the AUC increased to 91.8%, 93.2% and 90.9% for the Absent/Regular/Irregular, Absent/Present, and Regular/Irregular problems, respectively.

To observe whether there is a difference in eye gaze between doing a task, and watching a video of the task, we recorded the gaze of 17 subjects performing a simple surgical eye-hand coordination task. We also recorded eye gaze of the same subjects later while they were watching videos of their performance.

For gaze-based training in surgery to be meaningful, the similarity between a trainee's gaze and an expert's gaze during performance of surgical tasks must be assessed. As it is difficult to record two people's gaze simultaneously, we produced task videos made by experts, and measured the amount of overlap between the gaze path of the expert surgeon and third-party observers while watching the videos. For this investigation, we developed a new, simple method for displaying and summarizing the proportion of time during which two observers' points of gaze on a common stimulus were separated by no more than a specified visual angle.

We present a general model using supervised learning and MAP estimation that is capable of performing many common tasks in automated skin lesion diagnosis. We apply our model to segment skin lesions, detect occluding hair, and identify the dermoscopic structure pigment network. Quantitative results are presented for segmentation and hair detection and are competitive when compared to other specialized methods. Additionally, we leverage the probabilistic nature of the model to produce receiver operating characteristic curves, show compelling visualizations of pigment networks, and provide confidence intervals on segmentations.

The study of surgeons' eye movements is an innovative way of assessing skill and situation awareness, in that a comparison of eye movement strategies between expert surgeons and novices may show differences that can be used in training.

A real-time eyegaze selection interface was implemented using a Tobii eyegaze tracking monitor. A hierarchical button menu was displayed on the screen and specified selections were made by eyegaze fixations and glances on the menu widgets. The initial version tested three different spatial layouts of the menu widgets and employed a dwell + glance method of selection. Results from the pilot interface led to usability improvements in the second version of the interface. Selections were activated using a glance + dwell method. The usability of the second study interface received a positive response from all 8 participants. Each selection gained more than a 100% speed increase using the revised interface.

Visual realism has been a major objective of computer graphics since the inception of the field. However, the perception of visual realism is not a well-understood process and is usually attributed to a combination of visual cues and image features that are difficult to define or measure. For highly complex images, the problem is even more involved. The purpose of this paper is to present a study based on eye tracking for investigating the perception of visual realism of static images with different visual qualities. The eye-fixation clusters helped to define salient image features corresponding to 3D surface details and light transfer properties that attract observers' attention. This enabled the definition and categorization of image attributes affecting the perception of photorealism. The dynamics of the visual behavior of different observer groups were examined by analyzing saccadic eye movements. We also demonstrated how the different image categories used in the experiments were perceived with varying degrees of visual realism. The results presented can be used as a basis for investigating the impact of individual image features on the perception of visual realism. This study suggests that post-recall or simple abstraction of visual experience is not accurate and the use of eye tracking provides an effective way of determining relevant features that affect visual realism, thus allowing for improved rendering techniques that target these features.

Clinical prediction rules play an important role in medical practice. They expedite diagnosis and limit unnecessary tests. However, the rule creation process is time consuming and expensive. With the current developments of efficient data mining algorithms and growing accessibility to medical data, the creation of clinical rules can be supported by automated rule induction from data. A data-driven method based on the reuse of previously collected medical records and clinical trial statistics is cost-effective; however, it requires well defined and intelligent methods for data analysis. This paper presents a new framework for knowledge representation for secondary data analysis and for generation of a new typicality measure, which integrates medical knowledge into statistical analysis. The framework is based on a semiotic approach for contextual knowledge and fuzzy logic for approximate knowledge. This semio-fuzzy framework has been applied to the analysis of predictors for the diagnosis of obstructive sleep apnea. This approach was tested on two clinical data sets. Medical knowledge was represented by a set of facts and fuzzy rules, and used to perform statistical analysis. Statistical methods provided several candidate outliers. Our new typicality measure identified those, which were medically significant, in the sense that the removal of those important outliers improved the descriptive model. This is a critical preprocessing step towards automated induction of predictive rules from data. These experimental results demonstrate that knowledge-based methods integrated with statistical approaches provide a practical framework to support the generation of clinical prediction rules.

Researchers are using emerging technologies to develop novel play environments, while established computer and console game markets continue to grow rapidly. Even so, evaluating the success of interactive play environments is still an open research challenge. Both subjective and objective techniques fall short due to limited evaluative bandwidth; there remains no corollary in play environments to task performance with productivity systems. This paper presents a method of modeling user emotional state, based on a user's physiology, for users interacting with play technologies. Modeled emotions are powerful because they capture usability and playability through metrics relevant to ludic experience; account for user emotion; are quantitative and objective; and are represented continuously over a session. Furthermore, our modeled emotions show the same trends as reported emotions for fun, boredom, and excitement; however, the modeled emotions revealed differences between three play conditions, while the differences between the subjective reports failed to reach significance.

Two studies evaluated linear and hierarchy+elision small‐screen display formats for clinical reasoning tasks. A controlled, quantitative study with 28 medically naive participants using a task abstracted from clinical use of laboratory results found that both display formats supported rapid and accurate decision making. Distribution of the search targets significantly affected speed, with decisions in linear format made 13% faster (4.7 sec) when all targets could be viewed on a single screen than when targets required scrolling between several screens and in hierarchical format 15% faster (5.1 sec) when all the targets were confined within one category. Performance was equivalent regardless of the relative order of the target results and data in the laboratory report. In a qualitative study, 7 physicians used the displays to perform a realistic diagnosis. Physicians were comfortable with both display formats, but preference varied with clinical experience. The 5 less experienced clinicians favored hierarchy+elision, whereas the 2 highly experienced clinicians tended to prefer the linear display.

The goal of this research is to use eyegaze tracking data to provide insights into designing radiology workstations. We designed a look-alike radiology task with artificial stimuli. The task involved a comparative visual search of two side-by-side images, using two different interaction techniques. We tracked the eyegaze of four radiologists while they performed the task and measured the duration of the fixations on the controls, the left and right images, and on the artificial targets. Response time differences between the two interaction techniques exceeded the differences of fixations on the controls. Fixations on the left-side images are longer than the right-side images, and the search for multifeatured targets occurs in two phases: first a regular scan path search phase for a likely target and then a confirmation phase of several fixations on the target in each side-by-side image. We conclude that eyegaze tracking shows that disruption of visual search leads to cognitive disruption; subjects use the left image as a reference image and multiple saccades between left and right side images are necessary, because of the limitations of the visual working memory.

Magnetic resonance imaging (MRI) reconstruction techniques are often validated with signal-to-noise ratio (SNR), contrast-to-noise ratio, and mean-to-standard-deviation ratio measured on example images. We present human and model observers as a novel approach to evaluating reconstructions for low-SNR magnetic resonance (MR) images. We measured human and channelized Hotelling observers in a two-alternative forced-choice signal-known-exactly detection task on synthetic MR images. We compared three reconstructions: magnitude, wavelet-based denoising, and phase-corrected real. Human observers performed approximately equally using all three reconstructions. The model observer showed very close agreement with the humans over the range of images. These results contradict previous predictions in the literature based on SNR. Thus, we propose that human observer studies are important for validating MRI reconstructions. The model's performance indicates that it may provide an alternative to human studies.

We describe a series of experiments that compare 2D displays, 3D displays, and combined 2D/3D displays (orientation icon, ExoVis, and clip planes) for relative position estimation, orientation, and volume of interest tasks. Our results indicate that 3D displays can be very effective for approximate navigation and relative positioning when appropriate cues, such as shadows, are present. However, 3D displays are not effective for precise navigation and positioning except possibly in specific circumstances, for instance, when good viewing angles or measurement tools are available. For precise tasks in other situations, orientation icon and ExoVis displays were better than strict 2D or 3D displays (displays consisting exclusively of 2D or 3D views). The combined displays had as good or better performance, inspired higher confidence, and allowed natural, integrated navigation. Clip plane displays were not effective for 3D orientation because users could not easily view more than one 2D slice at a time and had to frequently change the visibility of individual slices. Major factors contributing to display preference and usability were task characteristics, orientation cues, occlusion, and spatial proximity of views that were used together.

Displays combining both 2D and 3D views have been shown to support higher performance on certain visualization tasks. However, it is not clear how best to arrange a combination of 2D and 3D views spatially in a display. In this study, we analyzed the eyegaze strategies of participants using two arrangements of 2D and 3D views to estimate the relative position of objects in a 3D scene. Our results show that the 3D view was used significantly more often than individual 2D views in both displays, indicating the importance of the 3D view for successful task completion. However, viewing patterns were significantly different between the two displays: transitions through centrally-placed views were always more frequent, and users avoided saccades between views that were far apart. Although the change in viewing strategy did not result in significant performance differences, error analysis indicates that a 3D overview in the center may reduce the number of serious errors compared to a 3D overview placed off to the side.

We compare 2D/3D combination displays to displays with 2D and 3D views alone. Combination displays we consider are: orientation icon (i.e., side-by-side), in-place methods (e.g., clip planes), and a new method called ExoVis. We specifically analyze performance differences (i.e., time and accuracy) for 3D orientation and relative position tasks. Empirical results show that 3D displays are effective for approximate navigation and relative positioning whereas 2D/3D combination displays (orientation icon and ExoVis) are useful for precise orientation and position tasks. Combination 2D/3D displays had as good or better performance as 2D displays. Clip planes were not effective for a 3D orientation task, but may be useful when only one slice is needed.

Visual information is important in surgeons' manipulative performance especially in laparoscopic surgery where tactual feedback is less than in open surgery. The study of surgeons' eye movements is an innovative way of assessing skill, in that a comparison of the eye movement strategies between expert surgeons and novices may show important differences that could be used in training. We conducted a preliminary study comparing the eye movements of 5 experts and 5 novices performing a one-handed aiming task on a computer-based laparoscopic surgery simulator. The performance results showed that experts were quicker and generally committed fewer errors than novices. We investigated eye movements as a possible factor for experts performing better than novices. The results from eye gaze analysis showed that novices needed more visual feedback of the tool position to complete the task than did experts. In addition, the experts tended to maintain eye gaze on the target while manipulating the tool, whereas novices were more varied in their behaviours. For example, we found that on some trials, novices tracked the movement of the tool until it reached the target.

In clinical applications where structural asymmetries between homologous shapes have been correlated with pathology, the questions of definition and quantification of "asymmetry" arise naturally. When not only the degree but the position of deformity is thought relevant, asymmetry localization must also be addressed. Asymmetries between paired shapes have already been formulated in terms of (nonrigid) diffeomorphisms between the shapes. For the infinity of such maps possible for a given pair, we define optimality as the minimization of deviation from isometry under the constraint of piecewise deformation homogeneity. We propose a novel variational formulation for segmenting asymmetric regions from surface pairs based on the minimization of a functional of both the deformation map and the segmentation boundary, which defines the regions within which the homogeneity constraint is to be enforced. The functional minimization is achieved via a quasi-simultaneous evolution of the map and the segmenting curve, conducted on and between two-dimensional surface parametric domains. We present examples using both synthetic data and pairs of left and right hippocampal structures and demonstrate the relevance of the extracted features through a clinical epilepsy classification analysis.

We present the problem of visualizing time-varying medical data. Two medical imaging modalities are compared-MRI and dynamic SPECT. For each modality, we examine several derived scalar and vector quantities such as the change in intensity over time, the spatial gradient, and the change of the gradient over time. We compare several methods for presenting the data, including isosurfaces, direct volume rendering, and vector visualization using glyphs. These techniques may provide more information and context than methods currently used in practice; thus it is easier to discover temporal changes and abnormalities in a data set.

This paper describes the compression of grayscale medical ultrasound images using a recent compression technique, i.e., space-frequency segmentation (SFS). This method finds the rate-distortion optimal representation of an image from a large set of possible space-frequency partitions and quantizer combinations and is especially effective when the images to code are statistically inhomogeneous, which is the case for medical ultrasound images. We implemented a compression application based on this method and tested the algorithm on representative ultrasound images. The result is an effective technique that performs better than a leading wavelet-transform coding algorithm, i.e., set partitioning in hierarchical trees (SPIHT), using standard objective distortion measures. To determine the subjective qualitative performance, an expert viewer study was run by presenting ultrasound radiologists with images compressed using both SFS and SPIHT. The results confirmed the objective performance rankings. Finally, the performance sensitivity of the space-frequency codec is shown with respect to several parameters, and the characteristic space-frequency partitions found for ultrasound images are discussed.

Joint space-frequency segmentation is a relatively new image compression technique that finds the rate-distortion optimal representation of an image from a large set of possible space-frequency partitions and quantizer combinations. As such, the method is especially effective when the images to code are statistically inhomogeneous, which is certainly the case in the ultrasound modality. Unfortunately, however, the original paper on space-frequency segmentation neglected to use an actual entropy coder, but instead relied upon the zeroth-order entropy to guide the algorithm. In this work, we fill this gap by comparing actual entropy-coding strategies and their effect on both the resulting segmentations as well as the rate-distortion performance. We then apply the resulting "complete" algorithm to representative ultrasound images. The result is an effective technique that performs significantly better than SPIHT using both objective and subjective measures.

Medical image analysis is shifting from current film oriented light screen environments to computer environments that involve viewing and analyzing large sets of images on a computer screen. Magnetic resonance imaging (MRI) studies, in particular, can involve many images. The paper examines how best to meet the needs of radiologists in a computational environment. To this end, a field study was conducted to observe radiologists' interactions during MRI analysis in the traditional light screen environment. Key issues uncovered involve control over focus and context, dynamic grouping of images and retrieval of images and image groups. To address the problem of focus and context, existing layout adjustment and magnification techniques are explored to provide the most appropriate solution. Our interest is in combining the methodologies of human computer interaction studies with computational presentation possibilities to design a visual environment for the crucial field of medical image analysis.

A robust fully automatic method for segmenting the brain from head magnetic resonance (MR) images has been developed, which works even in the presence of radio frequency (RF) inhomogeneities. It has been successful in segmenting the brain in every slice from head images acquired from several different MRI scanners, using different-resolution images and different echo sequences. The method uses an integrated approach which employs image processing techniques based on anisotropic filters and "snakes" contouring techniques, and a priori knowledge, which is used to remove the eyes, which are tricky to remove based on image intensity alone. It is a multistage process, involving first removal of the background noise leaving a head mask, then finding a rough outline of the brain, then refinement of the rough brain outline to a final mask. The paper describes the main features of the method, and gives results for some brain studies.

To segment brain tissues in magnetic resonance images of the brain, the authors have implemented a stochastic relaxation method which utilizes partial volume analysis for every brain voxel, and operates on fully three-dimensional (3-D) data. However, there are still problems with automatically or semi-automatically segmenting thick magnetic resonance (MR) slices, particularly when trying to segment the small lesions present in MR images of multiple sclerosis patients. To improve lesion segmentation the authors have extended their method of stochastic relaxation by both pre- and post-processing the MR images. The preprocessing step involves image enhancement using homomorphic filtering to correct for nonhomogeneities in the coil and magnet. Because approximately 95% of all multiple sclerosis lesions occur in the white matter of the brain, the post-processing step involves application of morphological processing and thresholding techniques to the intermediate segmentation in order to develop a mask image containing only white matter and Multiple Sclerosis (MS) lesion. This white/lesion masked image is then segmented by again applying the authors' stochastic relaxation technique. The process has been applied to multispectral MRI scans of multiple sclerosis patients and the results compare favorably to manual segmentations of the same scans obtained independently by radiology health professionals.

This article compares four widely utilized, yet fundamentally different, approaches for registering medical scans of the head. Comparisons are made on the basis of method, accuracy, robustness, computer requirements, and usability. This examination is intended to provide a means for determining an appropriate method for any given application. These approaches are: 1) an iterative method based on the repeated manual selection of 1-2 corresponding points, 2) an approach using the manual selection of 9-15 corresponding points, 3) an automatic surface matching method, and 4) an automatic approach based on voxel similarity. The methods are tested both on simulated data to provide a gold standard of accuracy, and on real data. All registrations are performed in the same visualization environment created for multipurpose image processing. Simulated data tests provided mean transformation errors and time requirements for the different methods, as well as the displacement errors for a set of anatomical landmarks. These results show all of the methods provide good accuracy when the data is not highly distorted and has a large amount of overlap. From the tests using real data both transformations and time requirements are tabulated for comparison. All of the techniques successfully aligned the real data with the exception of surface matching, which failed on the PET-MRI. Each method exhibits strengths and weaknesses that should be understood in order to utilize the most appropriate technique for a given problem. Based on the authors' examination, the voxel-similarity approach proved in general to be the method of choice.

In many distributed systems concurrent access is required to a shared object, where abstract object servers may incorporate type-specific properties to define consistency requirements. Each operation and its outcome is treated as an event, and conflicts may occur between different event types. Hence concurrency control and synchronization are required at the granularity of conflicting event types. With such a fine granularity of locking, the occurrence of conflicts is likely to be lower than with whole-object locking, so optimistic techniques become more attractive. This work describes the design, implementation, and performance of servers for a shared atomic object, a semiqueue, where each server employs either pessimistic or optimistic locking techniques on each conflicting event type. We compare the performance of a purely optimistic server, a purely pessimistic server, and a hybrid server which treats certain event types optimistically and others pessimistically, to demonstrate the most appropriate environment for using pessimistic, optimistic, or hybrid control. We show that the advantages of low overhead on optimistic locking at low conflict levels is offset at higher conflict levels by the wasted work done by aborted transactions. To achieve optimum performance over the whole range of conflict levels, an adaptable server is required, whereby the treatment of conflicting event types can be changed dynamically between optimistic and pessimistic, according to various criteria depending on the expected frequency of conflict. We describe our implementations of adaptable servers which may allocate concurrency control strategy on the basis of state information, the history of conflicts encountered, or by using preset transaction priorities. We show that the adaptable servers perform almost as well as the best of the purely optimistic, pessimistic, or hybrid servers under the whole range of conflict levels, showing the versatility and efficiency of the dynamic servers. Finally we outline a general design methodology for implementing adaptable concurrency control in servers for atomic objects, illustrated using an atomic shared B-tree.

Multicast communication in a distributed system connected by a local area network can increase parallelism, and it can also provide a greater functionality than one-to-one communication. In the authors' multicast protocol, the sender directs a message to a named group of receivers, which can be specified by function without requiring the sender to know the specific members of the group. Each host's kernel in the network can respond to every group message sent, providing various levels of reliability. It was found that the overhead of providing dependable multicast over a single local area network was very small, mainly because the protocol operates at the kernel level rather than the user level. Several forms of this multicast communication, expressed as simple message-passing communication primitives, are described, and the effectiveness of the protocol is evaluated using an example of a distributed algorithm. Performance analyses and actual performance data for the protocol are presented.>

The design of a VME-based parallel-processing data-acquisition system for the proposed TRIUMF/UBC positron emission tomographs is described. Event records, consisting of eight bytes of data, must be acquired and digitally processed at rates of 250000 to 500000 events per second, corresponding to a data acquisition rate of up to 4 Mb/s. Real-time processing is necessary to reduce the data storage required by half to about 80-90 Mb for a typical patient run, and it also reduces the average data flow and directly reduces the time required for image reconstruction. Real-time data acquisition and processing requirements can be met by about 40 20-MHz Motorola 6820/68881 processors operating asynchronously and in parallel. The authors present the system architecture, the software design, and the hardware design that realizes this very high performance.>

A $70 toy robot has been successfully used in Computer Science undergraduate laboratory courses in real-time programming and advanced operating systems to provide students with hands on experience.

Abstract High‐level language primitives for concurrent programming exist in languages such as Ada and Modula‐2. However, each of these languages provides only a single means for specifying multitasking and synchronization, essential in the implementation of concurrent systems. The SR language provides several mechanisms for specifying multi‐tasking and synchronization, so it can be used to explore the performance of various communication techniques. This paper presents performance results for SR's multi‐tasking and synchronization mechanisms and discusses the effects of the generated code, the run‐time support and the hardware on these results. These results are compared with those for similar mechanisms in other languages, leading to some general conclusions about the performance of process communication primitives. These performance results can be used by programmers to make design choices that allow systems programs written in high‐level languages to meet real‐time performance specifications.

This paper explores program designs for layered systems such as communication protocols and server/client systems that do not exhibit a strict hierarchy in their control flow. Clark has proposed structuring such systems, where both upward and downward control flow are required, to use efficient synchronous procedure calls between the layers whenever possible. The termupcallis used by Clark to describe this synchronous upward communication from server to client. Several techniques are possible for structuring such programs using upcalls. Comparisons are made by implementing a communication protocol described by Clark in three different ways. The first method implements all the protocol routines in a single large module. The second method structures the routines into modules occupying vertical slices of the protocol layers, and the third method structures the routines into modules corresponding to the protocol layers. Comparisons are made on two fronts: Preservation of modularity, in order to determine which method shows fault-tolerance and ease of programming, and program performance, which is a key motivation for the upcalls programming style. We conclude that the vertically layered protocol design is to be preferred unless there are many shared variables between the send-side and receive-side, as it is very efficient and provides the best protection of clients from each other. The horizontally layered design is the least efficient, but it is the easiest to program.

This paper describes structures used for exception handling in multi-process implementations of the X.25 and X.29 protocols. Each protocol layer is implemented as a collection of cooperating processes. These are structured in one of two alternative ways, reflecting differences in the type of exceptional situations that may occur.