Automatic Control and Systems Engineering

When a mixture of particles with dierent attributes under-
goes vibration, a segregation pattern is often observed. For
example, in muesli cereal packs, the largest particles|the
Brazil nuts|tend to end up at the top. For this reason, the
phenomenon is known as the Brazil nut eect. In previous
research, an algorithm inspired by this eect was designed to
produce segregation patterns in swarms of simulated agents
that move on a horizontal plane.
In this paper, we adapt this algorithm for implementation
on robots with directional vision. We use the e-puck robot
as a platform to test our implementation. In a swarm of e-
pucks, dierent robots mimic disks of dierent sizes (larger
than their physical dimensions). The motion of every robot
is governed by a combination of three components: (i) at-
traction towards a point, which emulates the eect of a grav-
itational pull, (ii) random motion, which emulates the eect
of vibration, and (iii) repulsion from nearby robots, which
emulates the eect of collisions between disks. The algo-
rithm does not require robots to discriminate between other
robots; yet, it is capable of forming annular structures where
the robots in each annulus represent disks of identical size.
We report on a set of experiments performed with a group
of 20 physical e-pucks. The results obtained in 100 trials of
20 minutes each show that the percentage of incorrectly-
ordered pairs of disks from dierent groups decreases as the
size ratio of disks in dierent groups is increased. In our
experiments, this percentage was, on average, below 0.5% for
size ratios from 3.0 to 5.0. Moreover, for these size ratios, all
segregation errors observed were due to mechanical failures
that caused robots to stop moving.

This paper investigates a non-traditional sensing trade-off in swarm robotics: one in which each robot has a relatively long sensing range, but processes a minimal amount of information. Aggregation is used as a case study, where randomly-placed robots are required to meet at a common location without using environmental cues. The binary sensor used only lets a robot know whether or not there is another robot in its direct line of sight.

This paper treats the effects of EMG signal sampling frequency and the pass-band frequency on neuromuscular signal recognition which are of a great importance. It improves the optimisation of the choice of those parameter values that guarantees the best performance. The considered EMG signal sampling frequency and pass-band frequency have a significant importance in viewpoint of the extracted information from this signal. It will be shown that this information depends on both sampling and pass-band frequency values. The signal information do not have the same pass-band frequency location and also do not have the same sampling frequency value for all the features, but these parameters are then self depending on the type of the feature. In this case the classification of the EMG signals is done by using only the beginning part of the signal, which is equal in our case to 256ms. The classification is done using two intelligent computational methods: Radial Basis Function network (RBF) and Fuzzy Subtractive Clustering network (FSC). Each method has been applied using four different values of spread and radius values corresponding to RBF and FSC respectively.

With help of exploitation of myoelectric signals, amputee persons can have a chance to improve their life with myoelectric prosthesis which are able to function with the amputee's muscle movements. The myoelectric signal (MES) is the electrical manifestation of muscular contraction. This signal recorded at the surface of the skin of the forearm has been exploited to provide the recognition of the movement of the hand and finger Movements of healthy subject. The objective of the paper is first to describe the identification procedure, based on EMG patterns of forearm activity using Fuzzy logic and Neural Networks methods. Second to show the advantage of using features in Time-frequency domain, in comparison to those in time domain. Suitable features in time-frequency domain give high classification rates. Third is to compare between different intelligent computational methods of identification, which are used in this work: Multi-Layer Perceptron (MLP), Radial Basis Function Networks (RBF) and Learning Vector Quantization network (LVQ) as supervised methods and fuzzy Subtractive Clustering (FSC) as unsupervised method.

Evolution provided biological systems with a number of adaptive and error-tolerating solutions for the detection and identification of audiovisual events. Some of these solutions seem to rely on specific neuronal mechanism operating at the level of the neocortex. In this report we first present findings from rodents (Mongolian gerbils) on cortical dynamics during the transfer of knowledge from the auditory to the visual modality. In a second part we investigate the influences of auditory and visual stimuli, in animals passively listening to auditory stimuli or passively seeing visual stimuli, on the neuronal interactions between auditory and visual cortex.

Blindsight is the residual visuo-motor ability without subjective awareness observed after lesions of the primary visual cortex (V1). Various visual functions are retained, however, instrumental visual associative learning remains to be investigated. Here we examined the secondary reinforcing properties of visual cues presented to the hemianopic field of macaque monkeys with unilateral V1 lesions. Our aim was to test the potential role of visual pathways bypassing V1 in reinforcing visual instrumental learning. When learning the location of a hidden area in an oculomotor search task, conditioned visual cues presented to the lesion-affected hemifield operated as an effective secondary reinforcer. We noted that not only the hidden area location, but also the vector of the saccade entering the target area was reinforced. Importantly, when the visual reinforcement signal was presented in the lesion-affected field, the monkeys continued searching, as opposed to stopping when the cue was ...

In this article is proposed a visualisation algorithm for classification of different types of movements of a human hand, based on myo-electric signals in real time. The visualisation is a natural continuation of the previous researches of the German and Bulgarian scientific team and it can be successfully applied in medicine and other areas using humanmachine interface.

In this paper, we propose a new architecture that combines prediction and decision-making in the form of a hybrid framework aimed at providing clinicians with transparent and accurate maps, or charts, to guide and to support treatment decisions, and to interrogate the clinical patients' course as it develops. These maps should be patient-specific, with options displayed of possible treatment pathways. They would suggest the optimal care pathways, and the shortest routes to the most efficient care, by predicting clinical progress, testing the ensuing suggestions against the developing clinical state and patient condition, and suggesting new options as necessary. These maps should also mine an extensive database of accumulated patient data, modelled diseases, and modelled patient-responses based on expert-derived rules. These individualized hierarchical targets, which are implemented in order to prevent life-threatening illnesses, will also have to "adapt" to the patient's altering clinical condition. Therapies that support one system can destabilize others and selecting which specific support to prioritize is an uncertain process, the prioritization of which can vary between clinical experts. Whilst clinical therapeutic decisions can be made with some degree of anticipation of the "likely" outcome (based on the experts' opinion and judgment), treatment is essentially rooted in the present, and is dependent on analyzing the current clinical condition and available data. The recursive learning approach presented in this paper, allows decision rules to predict the possible future course, and reflects back derived information from such projections to the present time and thus support proactive clinical care rather than reactive clinical care. The proposed framework for such a patient map supports and enables an optimized choice from available options and also ensures that decisions are based on both the available evidence and a database of best clinical practice. Preliminary results are encouraging and it is hoped to validate the approach clinically in the near future.

The process of pollutant adsorption from industrial wastewaters is a multivariate problem. This process is affected by many factors including the contact time (T), pH, adsorbent weight (m), and solution concentration (ppm). The main target of this work is to model and evaluate the process of pollutant adsorption from industrial wastewaters using the non-linear multivariate regression and intelligent computation techniques. In order to achieve this goal, 54 industrial wastewater samples gathered by Institute of Color Science & Technology of Iran (ICSTI) were studied. Based on the laboratory conditions, the data was divided into 4 groups (A-1, A-2, A-3, and A-4). For each group, a non-linear regression model was made. The statistical results obtained showed that two developed equations from the A-3 and A-4 groups were the best models with R 2 being 0.84 and 0.74. In these models, the contact time and solution concentration were the main effective factors influencing the adsorption process. The extracted models were validated using the t-test and F-value test. The hybrid PSO-based ANN model (particle swarm optimization and artificial neural network algorithms) was constructed for modelling the pollutant adsorption process under different laboratory conditions. Based on this hybrid modeling, the performance indices were estimated. The hybrid model results showed that the best value belonged to the data group A-4 with R 2 of 0.91. Both the non-linear regression and hybrid PSO-ANN models were found to be helpful tools for modeling the process of pollutant adsorption from industrial wastewaters.

The specific aim of this thesis, which considers the biological system "human movement", is presented in two parts. The first part considers the recognition (classification) of measured Electro-myography (EMG) signals of forearm muscles corresponding to hand movements. The second part treats the musculoskeletal system, which is considered by Knee-joint dynamics under non-isometric conditions, in terms of its measured angle between thigh and shank as response for Functional Electrical Stimulation (FES) impulses. This procedure is known as system-identification. Understanding human movement functions is of a great importance in the domain of neuromusculoskeletal systems and biomechanics. Whole-body movement is achieved with help of the interaction between the neuromuscular control signal and musculoskeletal dynamics system. Many elements of the neuromusculoskeletal system interact to enable smooth and coordinated movements. The skeletal system, composed of bones and joint co...

The processing of incoming sensory information can be differentially affected by varying levels of α-power in the electroencephalogram (EEG). A prominent hypothesis is that relatively low prestimulus α-power is associated with improved perceptual performance. However, there are studies in the literature that do not fit easily into this picture, and the reasons for this are poorly understood and rarely discussed. To evaluate the robustness of previous findings and to better understand the overall mixed results, we used a spatial TOJ task in which we presented auditory and visual stimulus pairs in random order while recording EEG. For veridical and non-veridical TOJs, we calculated the power spectral density (PSD) for 3 frequencies (5 Hz steps: 10, 15, and 20 Hz). We found on the group level: (1) Veridical auditory TOJs, relative to non-veridical, were associated with higher β-band (20 Hz) power over central electrodes. (2) Veridical visual TOJs showed higher β-band (10, 15 Hz) power ...

The need for a reliable communication network within a university campus is not a luxury; it is a necessity. Currently, no such network exists at the Federal University of Technology Owerri (FUTO) in Nigeria. This project addresses the situation by using a top-down approach to design a campus network of adequate network performance to support bandwidth-intensive applications such as teleconferencing. The campus network design, carried out after analysis of the user and technical network requirements, was partitioned into logical and physical domains. Network simulation was carried out with the aid of the Cisco Packet Tracer 5.3 software. SIP-based client software, FUTO Messenger, was developed to run on the network. The software supports text, voice and video chats. A prototype network system was implemented to test the network design and also to verify the functionality of the developed software. The system had 6 workstations comprising 2 teleconferencing PCs, 2 non-teleconferencin...

This paper presents a framework for creating neural field models from electrophysiological data. The Wilson and Cowan or Amari style neural field equations are used to form a parametric model, where the parameters are estimated from data. To illustrate the estimation framework, data is generated using the neural field equations incorporating modeled sensors enabling a comparison between the estimated and true parameters. To facilitate state and parameter estimation, we introduce a method to reduce the continuum neural field model using a basis function decomposition to form a finite-dimensional state-space model. Spatial frequency analysis methods are introduced that systematically specify the basis function configuration required to capture the dominant characteristics of the neural field. The estimation procedure consists of a two-stage iterative algorithm incorporating the unscented Rauch–Tung–Striebel smoother for state estimation and a least squares algorithm for parameter estimation. The results show that it is theoretically possible to reconstruct the neural field and estimate intracortical connectivity structure and synaptic dynamics with the proposed framework.►We derive a model-based, data-driven estimator of continuum neural field parameters. ►Systematic field decomposition enables a finite-dimensional state-space model. ►We show how to infer intracortical connectivity and synaptic dynamics from data. ►Our framework provides a new link between theoretical and experimental neuroscience.

A small-scale package, CADMVS, has been developed for the analysis and desi9n of multivariable systems. The package runs on a BBC microcomputer with additional memory, either in the form of a 32 k shadow RAM boar, or a second processor. The package consists of two main programs. One program employs frequencydomain techniques; Rosenbrock's Inverse Nyquist Array (INA). The other program offers facilities for analysis in the time domain; data transformations between statespace and transfer function matrix descriptions, time response simulations and a pole assignment technique.

Problems of vibrations are common in engineering applications, and need to be studied so that effective control/vibration suppression techniques can be developed. Part of the solution to problems of this nature is to simulate the systems under consideration. These are essentially governed by partial differential equations (PDE) that require an excessive computational effort when solved on a digital computer. We show how the infinite order of the system modes can be reduced to a finite number thus making the simulation easier. Since this modal type of analysis can lead to inaccuracies in the resonant frequencies it is imperative that any online vibration control system, includes a strategy for tuning the parameters. We show that a suitable solution can be obtained by using a Doppler shift technique to reduce the error between the model and system modal frequencies.

The paper deals with two computationally intensive control problems in the area of aerospace systems. The first of these considers automatic flight control for aircraft, and the second is related to vibration suppression in flexible structures such as aircraft wings. It is shown that, by functionally decomposing the computational tasks and using a suitably configured transputer system, it is possible to achieve real-time performance in both cases.

The paper considers the use of transputer systems programmed in occam 2 to solve, in realtime, the problem of vibration suppression in flexible structure beam systems. A reducedorder state-space model is used for the design of suitable control laws that can be applied to the system and actively dampen the unwanted oscillations. Simulations are presented demonstrating the viability of the approach.

This project aimed to compare the use of and resultant errors when Measurement Fusion (Plot Fusion) and Track Fusion were used to combine data from various sensors in a simulated environment analogous to the Singaporean environment. The environment and analysis was done wholly using a program executed by MATLAB 6.1, and results showed that Measurement Fusion was more accurate when tracking objects following a path with many turns. However, the major source of error was not the fusion algorithm, but the inclusion algorithm.