Spatial and temporal disparities in aurally aided visual search

Research over the last decade has shown that auditorily cuing the location of visual targets reduces the time taken to locate and identify targets for both free-field and virtually presented sounds. The first study conducted for this thesis confirmed these findings over an extensive region of free-field space. However, the number of sound locations that are measured and stored in the data library of most 3-D audio spatial systems is limited, so that there is often a discrepancy in position between the cued and physical location of the target. Sampling limitations in the systems also produce temporal delays in which the stored data can be conveyed to operators. To investigate the effects of spatial and temporal disparities in audio cuing of visual search, and to provide evidence to alleviate concerns that psychological research lags behind the capabilities to design and implement synthetic interfaces, experiments were conducted to examine (a) the magnitude of spatial separation, and (b) the duration of temporal delay that intervened between auditory spatial cues and visual targets to alter response times to locate targets and discriminate their shape, relative to when the stimuli were spatially aligned, and temporally synchronised, respectively. Participants listened to free-field sound localisation cues that were presented with a single, highly visible target that could appear anywhere across 360° of azimuthal space on the vertical mid-line (spatial separation), or extended to 45° above and below the vertical mid-line (temporal delay). A vertical or horizontal spatial separation of 40° between the stimuli significantly increased response times, while separations of 30° or less did not reach significance. Response times were slowed at most target locations when auditory cues occurred 770 msecs prior to the appearance of targets, but not with similar durations of temporal delay (i.e., 440 msecs or less). When sounds followed the appearance of targets, the stimulus onset asynchrony that affected response times was dependent on target location, and ranged from 440 msecs at higher elevations and rearward of participants, to 1,100 msecs on the vertical mid-line. If targets appeared in the frontal field of view, no delay of acoustical stimulation affected performance. Finally, when conditions of spatial separation and temporal delay were combined, visual search times were degraded with a shorter stimulus onset asynchrony than when only the temporal relationship between the stimuli was varied, but responses to spatial separation were unaffected. The implications of the results for the development of synthetic audio spatial systems to aid visual search tasks was discussed.

Writing public space : design, philosophy, art

Among the factors explaining the emotional poverty of contemporary urban spaces is the dissociation of those who think, design and adorn them. Using his experience in designing high profile public artworks in Australia, notably Relay (Homebush Bay, Sydney, 2000 Olympics), Nearamnew (Federation Square, Melbourne) and Solution (Docklands, Melbourne), Paul Carter argues that a new dialogue between designers, philosophers and artists is urgently needed. The basis of this dialogue will be an expanded notion of graphicality, a new engagement with the discursive character of public space, and the evolution of postrepresentationalist art practices that make surface the psychic violence and cultural waste involved in the provision of new functionallydefined “places”. This paper traverses a number of projects: Relay (1998‑2000), Nearamnew (1998‑2003), Solution (2002), Save the Wall (2004‑) Golden Grove (2004‑).

Diversions with John Gough : Blokus in space

The article evaluates the board game Blokus from Sekkoïa.

Tomorrow’s car – for today’s people : can tilting three wheeled vehicles be a solution for the problems of today and the future?

The current automotive industry and todays car drivers are faced with every increasing challenges, not previously experienced. Climate Change, financial issues, rising fuel prices, increased traffic congestion and reduced parking space in cities are all leading to changes in consumer preferences and the requirements of modern passenger vehicles. However, despite the shift in the industry dynamics, the principal layout of a car hasn’t changed since its invention. The design of a ’conventional’ vehicle is still principally a matchbox with four wheels, one at each corner. The concept has served its purpose well for over 100 years, but such a layout is not suited to solving today’s problems. To address the range of problems faced by the industry, a number of alternative commuting vehicles have been developed. Yet the commercialization of these ‘alternative’ vehicles has yet to be successful. This is largely due failure of these vehicles to meet the changing demands of the industry and the limited understanding of consumer behaviour, motivation and attitudes. Deakin University’s Tomorrow’s Car concept tackles all of these problems. The vehicle is a novel three-wheeler cross over concept between a car and a motorbike that combines the best of both worlds. The vehicle combines the low cost, small size and ‘fun’ factor of a motorbike together with the safety, comfort and easy to drive features of a car produce a vehicle with a fuel efficiency better than either car or scooter. Intensive market research has been conducted for various major potential markets of alternative vehicles including India, China and Australia. The research analysed consumer attitudes in relation to narrow tilting vehicles, and in particular towards Deakin’s Tomorrow’s Car (TC). The study revealed that a relatively large percentage of consumers find such a concept very appealing. For the other consumers, the overall appearance and perception of safety and not the actual safety performance were found to be the most impeding factors of such vehicles. By addressing these issues and marketing the vehicle accordingly the successful commercialization of Tomorrow’s Car can be ensured.

Performance optimization problem in speculative prefetching

Speculative prefetching has been proposed to improve the response time of network access. Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper investigates a complementary area which has been largely ignored, that of performance modeling. We analyze the performance of a prefetcher that has uncertain knowledge about future accesses. Our performance metric is the improvement in access time, for which we derive a formula in terms of resource parameters (time available and time required for prefetehing) and speculative parameters (probabilities for next access). We develop a prefetch algorithm to maximize the improvement in access time. The algorithm is based on finding the best solution to a stretch knapsack problem, using theoretically proven apparatus to reduce the search space. An integration between speculative prefetching and caching is also investigated.

Efficient algorithms for subwindow search in object detection and localization

Recently, a simple yet powerful branch-and-bound method called Efficient Subwindow Search (ESS) was developed to speed up sliding window search in object detection. A major drawback of ESS is that its computational complexity varies widely from O(n²) to O(n⁴) for n × n matrices. Our experimental experience shows that the ESS's performance is highly related to the optimal confidence levels which indicate the probability of the object's presence. In particular, when the object is not in the image, the optimal subwindow scores low and ESS may take a large amount of iterations to converge to the optimal solution and so perform very slow. Addressing this problem, we present two significantly faster methods based on the linear-time Kadane's Algorithm for 1D maximum subarray search. The first algorithm is a novel, computationally superior branchand- bound method where the worst case complexity is reduced to O(n³). Experiments on the PASCAL VOC 2006 data set demonstrate that this method is significantly and consistently faster (approximately 30 times faster on average) than the original ESS. Our second algorithm is an approximate algorithm based on alternating search, whose computational complexity is typically O(n²). Experiments shows that (on average) it is 30 times faster again than our first algorithm, or 900 times faster than ESS. It is thus wellsuited for real time object detection.

Solving the median shortest path problem in the planning and design of urban transportation networks using a vector labeling algorithm

This paper proposes an alternative algorithm to solve the median shortest path problem (MSPP) in the planning and design of urban transportation networks. The proposed vector labeling algorithm is based on the labeling of each node in terms of a multiple and conflicting vector of objectives which deletes cyclic, infeasible and extreme-dominated paths in the criteria space imposing cyclic break (CB), path cost constraint (PCC) and access cost parameter (ACP) respectively. The output of the algorithm is a set of Pareto optimal paths (POP) with an objective vector from predetermined origin to destination nodes. Thus, this paper formulates an algorithm to identify a non-inferior solution set of POP based on a non-dominated set of objective vectors that leaves the ultimate decision to decision-makers. A numerical experiment is conducted using an artificial transportation network in order to validate and compare results. Sensitivity analysis has shown that the proposed algorithm is more efficient and advantageous over existing solutions in terms of computing execution time and memory space used.

Solving a multiobjective job shop scheduling problem using Pareto Archived Cuckoo Search

This paper investigates a new approach for solving the multiobjective job shop scheduling problem, namely the Cuckoo Search ( CS) approach. The requirement is to schedule jobs on a single machine so that the total material waste is minimised as well as the total tardiness time. The material waste is quantified in terms of saving factors to show the reduction in material that can be achieved when producing two jobs with the same materials in sequence. The estimated saving factor is used to calculate a cost savings for each job based on its material type. A formulation of multiobjective optimisation problems is adopted to generate the set of schedules that maximise the overall cost savings and minimise the total tardiness time. where all trade-offs are considered for the two conflicting objectives. A Pareto Archived Multiobjective Cuckoo Search (PAMOCS) is developed to find the set ofnondominated Pareto optimal solutions. The solution accuracy of PAMOCS is shown by comparing the closeness of the obtained solutions to the true Pareto front generated by the complete enumeration methad. Results shaw that CS is a very effective and promising technique to solve job shop scheduling problems.

Neural network training by hybrid accelerated cuckoo particle swarm optimization algorithm

Metaheuristic algorithm is one of the most popular methods in solving many optimization problems. This paper presents a new hybrid approach comprising of two natures inspired metaheuristic algorithms i.e. Cuckoo Search (CS) and Accelerated Particle Swarm Optimization (APSO) for training Artificial Neural Networks (ANN). In order to increase the probability of the egg’s survival, the cuckoo bird migrates by traversing more search space. It can successfully search better solutions by performing levy flight with APSO. In the proposed Hybrid Accelerated Cuckoo Particle Swarm Optimization (HACPSO) algorithm, the communication ability for the cuckoo birds have been provided by APSO, thus making cuckoo bird capable of searching for the best nest with better solution. Experimental results are carried-out on benchmarked datasets, and the performance of the proposed hybrid algorithm is compared with Artificial Bee Colony (ABC) and similar hybrid variants. The results show that the proposed HACPSO algorithm performs better than other algorithms in terms of convergence and accuracy.

A new color space based on K-medoids clustering for fire detection

Pixel color has proven to be a useful and robust cue for detection of most objects of interest like fire. In this paper, a hybrid intelligent algorithm is proposed to detect fire pixels in the background of an image. The proposed algorithm is introduced by the combination of a computational search method based on a swarm intelligence technique and the Kemdoids clustering method in order to form a Fire-based Color Space (FCS), in fact, the new technique converts RGB color system to FCS through a 3*3 matrix. This algorithm consists of five main stages:(1) extracting fire and non-fire pixels manually from the original image. (2) using K-medoids clustering to find a Cost function to minimize the error value. (3) applying Particle Swarm Optimization (PSO) to search and find the best W components in order to minimize the fitness function. (4) reporting the best matrix including feature weights, and utilizing this matrix to convert the all original images in the database to the new color space. (5) using Otsu threshold technique to binarize the final images. As compared with some state-of-the-art techniques, the experimental results show the ability and efficiency of the new method to detect fire pixels in color images.

Deducing the reachable space from fingertip positions

The reachable space of the hand has received significant interests in the past from relevant medical researchers and health professionals. The reachable space was often computed from the joint angles acquired from a motion capture system such as gloves or markers attached to each bone of the finger. However, the contact between the hand and device can cause difficulties particularly for hand with injuries, burns or experiencing certain dermatological conditions. This paper introduces an approach to find the reachable space of the hand in a non-contact measurement form utilizing the Leap Motion Controller. The approach is based on the analysis of each position in the motion path of the fingertip acquired by the Leap Motion Controller. For each position of the fingertip, the inverse kinematic problem was solved under the physiological multiple constraints of the human hand to find a set of all possible configurations of three finger joints. Subsequently, all the sets are unified to form a set of all possible configurations specific for that motion. Finally, a reachable space is computed from the configuration corresponding to the complete extension and the complete flexion of the finger joint angles in this set.