Non-adaptive Systems Research Articles

Temporal uncoupling between demographic and genetic metrics in fisheries assessment: the European hake case study

An authentic ecological transition for fisheries sustainability requires the integration of all relevant biological criteria in assessment models. Current analyses aim to dissect the evolution of some demographic and genetic metrics in the overharvested Southern European hake fishery in the last four decades. Demographic data were drawn from the official records of ICES, while genetic data were obtained from a lustrum-based sampling series between 1975 and 2014. Opposite to Non-Adaptive Polymorphic Systems (NAPS) microsatellites, Adaptive Polymorphic Systems (APS) microsatellites succeeded at detecting genetic erosion at the maximum population overharvest in lustrum 1996–2000, a phenomenon also confirmed with the molecular variation of the mtDNA cytochrome b gene. We show that the maximum demographic mortality Z_NSSB (1986–1990) predated both the maximum genetic effective mortality Z_LDNe (1991–1995) as well as the maximum cohort-based mortality Z¯_ICES (1996–2000). Such temporal uncoupling between demographic and genetic metrics suggests that official cohort analyses are far from assessing the real biological status of the Southern European hake fishery to achieve its sustainability-oriented advice.

NSERC USRA Research Paper

This research paper discusses the comprehensive exploration of adaptive and non-adaptive control systems utilizing Quanser's QCar platform. The study covers real-time computational capabilities, real-time computer vision techniques, and the educational potential of Quanser's QCar. The resulting systems – Adaptive Lane Following, Sign Detection, Traffic Light Detection, and Adaptive Cruise Control – showcase their effectiveness across diverse driving scenarios and environmental conditions. The research methodology involves distinct phases, starting with an Initial Onboarding Process. Familiarity with software platforms such as Simulink, ROS, Python, and more was essential. Understanding the QCar platform was facilitated through User Manuals and predeveloped Simulink documentation. In-depth studies in Autonomous Technology and Computer Vision informed system development. A structured approach encompassing Research, Planning, Development & Testing stages was followed. Research gathered insights from repositories, videos, research papers, and websites. This also involved creating final testing environments to simulate various lighting conditions which were called daytime, afternoon, and nighttime environments. This allowed for the QCar’s performance to be effectively evaluated and compared against each other. The Adaptive Lane Following system tackles fluctuating lighting conditions using adaptive thresholding for consistent lane tracking. The Sign Detection System identifies stop signs, serving as a foundation for more complex systems. The accurate Traffic Light Detection system enables safe navigation. The ACC and Object Detection system enhances QCar's safety in dynamic traffic. The study effectively combines theory with practical implementation, exploring both adaptive and non-adaptive control systems. The developed systems showcase capabilities across different scenarios. Additional potential research directions include Python threading, audio features, GPS Track Network, and ROS optimization. This research mainly contributes to educational autonomous vehicle innovation and development.

Continuum limits for adaptive network dynamics

Adaptive (or co-evolutionary) network dynamics, i.e., when changes of the network/graph topology are coupled with changes in the node/vertex dynamics, can give rise to rich and complex dynamical behavior. Even though adaptivity can improve the modelling of collective phenomena, it often complicates the analysis of the corresponding mathematical models significantly. For non-adaptive systems, a possible way to tackle this problem is by passing to so-called continuum or mean-field limits, which describe the system in the limit of infinitely many nodes. Although fully adaptive network dynamic models have been used a lot in recent years in applications, we are still lacking a detailed mathematical theory for large-scale adaptive network limits. For example, continuum limits for static or temporal networks are already established in the literature for certain models, yet the continuum limit of fully adaptive networks has been open so far. In this paper we introduce and rigorously justify continuum limits for sequences of adaptive Kuramoto-type network models. The resulting integro-differential equations allow us to incorporate a large class of co-evolving graphs with high density. Furthermore, we use a very general measure-theoretical framework in our proof for representing the (infinite) graph limits, thereby also providing a structural basis to tackle even larger classes of graph limits. As an application of our theory, we consider the continuum limit of an adaptive Kuramoto model directly motivated from neuroscience and studied by Berner et al.~in recent years using numerical techniques and formal stability analysis.

Adaptive and personalized user behavior modeling in complex event processing platforms for remote health monitoring systems

Taking care of people who need constant care is essential and its cost is rising every day. Many intelligent remote health monitoring systems have been developed from the past till now. Intelligent systems explainability has become a necessity after the worldwide adoption of such systems, especially in the health domain to explain and justify decisions made by intelligent systems. Rule-based techniques are among the best in terms of explainability. However, there are several challenges associated with remote health monitoring systems in general and rule-based techniques, specifically. In this research, an adaptive platform based on Complex Event Processing (CEP) has been proposed for user behavior modeling to provide adaptive and personalized remote health monitoring. This system can manage a massive amount of data in real-time utilizing the CEP engine. It can also avoid human errors in setting rules thresholds by extracting thresholds from previous data using JRip rule-based classifier. Moreover, a feature selection method is proposed to decrease the high number of features while maintaining accuracy. Additionally, a rule adaption method has been proposed to cope with changes over time. Additionally, a personalized rule adaption method is proposed to address the need for responsiveness of the system to the special requirements of each user. The experimental results on both hospital and activity data sets showed that the proposed rule adaption method improves the accuracy by about 15 % compared to non-adaptive systems. Additionally, the proposed personalized rule adaption method has an accuracy improvement of about 3 % to 6 % on both mentioned datasets.

Problems of Setting Robust Control Systems

The issues of expediency of using adaptation tools in robust control systems are discussed. It is stated that purely robust systems without the use of adaptation tools, in principle, cannot provide high efficiency and versatility in solving practical problems. Robust control systems were originally conceived as non-adaptive systems (passive adaptive systems). In view of the emerging problems in solving practical problems, then a mutation occurred and works appeared under the name "Adaptive-robust systems", "Combined robust systems", etc., partially using adaptation algorithms. Setting the problem of control synthesis under conditions of uncertainty without elements of adaptation is figuratively speaking similar to "search for a black cat in a dark room, especially if it is not there" (Confucius). The most adequate from the point of view of compliance with the fundamental principles of the theory of automatic control is an approach based on an increase in the gain of an open loop. Nevertheless, here too a problem arises — an increase in the gain violates the stability of a closed system. All known research is concentrated around the solution of this problem. An integrated gain self-tuning algorithm and the corresponding circuitry and Simulink implementation scheme have been developed. The reliability of theoretical reasoning was verified by simulating a limiting robust system with self-tuning and a parametrically indeterminate object — "peak gyroscope (parametric pendulum)". Computer studies have made it possible to draw a number of positive conclusions that are of great practical importance.

Neural network technologies in control systems of cargo movement mechanisms

THE PURPOSE. Development and analysis of control systems for cargo movement mechanisms that do not contain a speed sensor in their structure. The use of intelligent devices in the implementation of sensorless control systems. The study of the proposed solutions in closed-type systems in order to identify the most optimal option that provides the best performance according to the criteria, in this case, the accuracy of speed testing.METHODS. It is possible to achieve these goals through the use of mathematical modeling carried out in the Matlab Simulink simulation environment.RESULTS. In the study, the analysis of systems containing various kinds of velocity observers in their structure was carried out. The stability of the work of the observers under consideration was evaluated taking into account external disturbing influences – the inter-turn closure mode was considered.CONCLUSION. The use of control systems that do not have sensors in their structure is in demand on mechanisms installed in rooms with a small area, on objects with elevated ambient temperatures and with increased pollution. The study compared systems with a speed sensor, a system containing a non-adaptive observer and systems with neural network observers. Optimal indicators were obtained in a system containing a NARMA-L2 neurocontroller. A combined structure is proposed containing several neuroregulators that are trained for dynamic engine parameters and monitored dangerous modes that may occur in dynamics.

Optimizing aid activation in adaptive and non-adaptive aiding systems: A framework for design and validation

Development of adaptive aids to support human performance in complex systems is a cornerstone of human factors. Research in this area has led to a diversity of ideas regarding potential activation methods. However, little guidance has been provided on how to select among aid activation methods, and this lack of guidance could hinder adaptive aid development and deployment. Within the current paper, we review available methods of aid activation and describe a process for developing and validating adaptive aiding systems. We focus on supporting system designers who wish to select the ideal aid activation method for an intended application. The process that we recommend is an empirical approach to evaluate the feasibility, costs, and benefits of various potential methods of aid activation. This methodological framework will support practitioners making critical decisions about the design of aiding systems.

Effects of adaptive optics on bit error rate of M-ary PPM oceanic optical wireless communication systems with aperture averaging in strong turbulence

Scintillation is the result of oceanic turbulence reducing the bit error rate (BER) performance of oceanic optical wireless communication (OWC) systems. The scintillation, also known as intensity fluctuations, occurs due to the turbulence-induced wavefront deformations. The correction of deformations by adaptive optics (AO) reduces the scintillation effect of turbulence and results in improved BER performance. In this paper, an oceanic OWC (OOWC) system that has a Gaussian laser beam at the transmitter, finite-sized circular aperture at the receiver, employing M-ary pulse position modulation (PPM) and operating in strong oceanic turbulence, is considered. Improvement in the BER performance of the OOWC system is examined with the implementation of AO correction. Comparison of BER performances between the AO and non-adaptive optics OOWC systems is shown by calculating the metric defined. BER of M-ary PPM OOWC links is evaluated over gamma–gamma fading channels. The modified Rytov theory together with the Zernike filter functions is used to find the AO corrected aperture averaged scintillation index where extended Huygens–Fresnel technique is used to obtain the average received signal power.

Frequency-Based Optimal Radar Waveform Design for Classification Performance Maximization Using Multiclass Fisher Analysis

In this article, we propose new waveform design procedures and classification schemes to improve target classification performance nonadaptively in radar systems. The new designs and schemes are all inspired by 2-class and multiclass Fisher discriminant analysis. The proposed system does not require as much computational capability as adaptive waveform design systems while also overcoming: 1) angular uncertainty in classifying high fidelity targets and 2) drops in performance experienced by nonadaptive systems when classification is extended to more than two targets. The waveform design procedure is based on an optimization problem to find the waveform that maximizes the objective function inspired by Fisher analysis under constant energy constraint. We also derive two closed-form solutions for the optimization problems under certain conditions for the 2-class and multiclass cases. All the methods are tested using synthetic and real data to show the performance of the proposed methods against the average Mahalanobis distance (AMD) nonadaptive waveform design and classifier.

Mate choice in a changing world

Human activities by altering environmental conditions are influencing the mate choice of animals. This is by impacts on: (i) the production and expression of traits evaluated by mate choosers; (ii) the transmission of information about potential mates to choosers; (iii) the reception and processing of the information by choosers; and (iv) the final mate choice. Here, I first discuss how these four stages of the mate-choice process can be altered by environmental change, and how these alterations, in turn, can influence individuals, populations, and communities. Much evidence exists for human-induced environmental changes influencing mate choice, but the consequences for the fitness of courters and choosers are less well known, and even less is known about the impact on population dynamics, species interactions and community composition. More evidence exists for altered mate-choice systems influencing interspecific matings and thereby community composition and biodiversity. I then consider whether plastic adjustments and evolutionary changes can rescue adaptive mate-choice systems, and reflect on the possibility of non-adaptive mate-choice systems becoming less maladaptive under environmental change. Much evidence exists for plastic adjustments of mate-choice systems, but whether these are adaptive is seldom known, as is the contribution of genetic changes. Finally, I contemplate the possibility of mate-choice systems rescuing populations from decline in changing environments. I explain how this is context dependent with both positive and negative outcomes possible. In summary, while much evidence exists for human-induced environmental changes influencing mate-choice systems, less is known about the consequences for ecological and evolutionary processes. Considering the importance that mate choice plays in determining individual fitness and population viability, the effects of environmental change on mate-choice systems should be considered in studies on the ecological and evolutionary consequences of human disturbances to habitats.

Trust and Acceptance of Adaptive and Conventional Collision Avoidance Systems

A simulator driving experiment was conducted to test the implementation of an adaptive driver support system for avoiding collisions caused by hazardous lane changes. Conventional (nonadaptive) collision avoidance systems with fixed function allocation might be effective from a safety perspective, but they are not always desirable from human factors perspective due to problems related to performance, trust, and acceptance. We therefore propose an adaptive collision avoidance system to provide support depending on the situation in consideration of the driver’s ability to handle the situation and the time to collision. Four driving modes were compared: no support, a haptic shared steering control system with an adaptive haptic authority based on time to collision, a cooperative steering control system that decoupled the driver’s steering input and controlled the vehicle’s direction temporarily, and an adaptive steering control system in which both haptic and cooperative control systems were integrated to provide different automated interventions at the steering wheel depending on the situation. The simulation participants were subjected to various potentially hazardous lane change maneuvers that provided significant differences in their abilities to perceive the risk and handle the situation. Compared to the no-support condition, all systems were more effective in accident prevention. The adaptive system was more effective than the haptic system and less effective than the cooperative system in terms of safety. However, driver performance, trust, and acceptability were significantly improved with the adaptive system compared to the nonadaptive systems.

Performance of adaptive subcarrier modulated MIMO wireless optical communications in Malaga turbulence

Abstract Space diversity of multiple-input multiple-output (MIMO) system combined with the adaptive subcarrier modulation technique can effectively reduce the atmospheric channel fading effects. Malaga turbulent probability density function is obtained based on the MeijerG function. The asymptotic expression of average bit error rate in non-adaptive and adaptive modulated MIMO wireless optical systems are derived respectively, and M-ary phase shift keying (MPSK) and M-ary differential phase shift keying (MDPSK) modulation are used in these two kinds of systems. Under moderate turbulence conditions, error performances are analyzed with numerical simulations between these two systems. The results show that the average bit error performance in the adaptive modulated system can be significantly better than the non-adaptive modulated system. But under the same conditions of modulation order and target bit error rate, error performance of non-adaptive modulation is easier to be affected by transmit and receive aperture number than adaptive modulation in MIMO system.

Cost function design for the fully adaptive radar framework

By emulating the neuropsychological processes underpinning animal cognition, the field of cognitive radar seeks to improve performance compared to non-adaptive systems. The fully adaptive radar (FAR) framework is an application agnostic means of implementing the perception-action cycle in radars. This work proposes a method of designing the FAR framework's component cost functions inspired by the field of multi-objective optimisation. As an illustration, the general cost functions were used to implement waveform adaptation for single target tracking. Both simulated and experimental results demonstrated how altering the cost functions can tailor the FAR performance to specific radar operating modes.

Conformal Beam-Steering Antenna Controlled by a Raspberry Pi for Sustained High-Throughput Applications

A complete autonomous system consisting of a beam-steerable hemispherical square-loop antenna (HSLA) controlled by a Raspberry Pi is presented for optimizing the throughput in a scattered and a poor signal-to-noise ratio environment. Four different indoor communication configurations at various distances were analyzed in the presence of interferences. In three configurations, HSLA performance was also compared to that of a standard monopole antenna link. It was found that HSLA can offer up to 1450% higher throughput and can withstand much higher interference levels before the system breaks. In terms of quality, this means sustaining compressed high-definition communications. In effect, it improves the system throughput for the test 2.4 GHz (802.11b/g/n) WiFi band. The uniqueness about the system is that it only uses single antenna for both sensing and communication. The algorithm works at application layer that controls the RF switch and antenna patterns at physical layer. Thus, the entire middle protocol layers are untouched. The system can easily be retrofitted to the existing nonadaptive communication systems.

A Mobile Channel Model for VLC and Application to Adaptive System Design

In this letter, we propose a realistic channel model for visible light communication (VLC) assuming a mobile user. Based on non-sequential ray tracing, we first obtain channel impulse responses for each point over the user movement trajectories, and then express path loss and delay spread as a function of distance through curve fitting. Our results demonstrate large variations in received power. In system design, this necessitates the use of adaptive schemes, where transmission parameters can be selected according to channel conditions. To demonstrate the benefits of link adaptation over a mobile VLC channel, we propose an adaptive system with luminary selection and demonstrate improvements in spectral efficiency over non-adaptive systems.

Application of Adaptive Coded Modulation Technology in UAV Data Link

UAV data link has been considered as an important part of UAV communication system, through which the UAV could communicate with warships. However, constant coding and modulation scheme that UAV adopts does not make full use of the channel capacity when UAV communicates with warships in a good channel environment. In order to improve channel capacity and spectral efficiency, adaptive coded modulation technology is studied. Based on maritime channel model, SNR estimation technology and adaptive threshold determination technology, the simulation of UAV data link communication is carried out in this paper. Theoretic analysis and simulation results show that according to changes in maritime channel state, UAV can dynamically adjust the adaptive coded modulation scheme on the condition of meeting target Bit-Error-Rate (BER), the maximum amount of data transfer is non-adaptive systems three times.

Preliminary results for an adaptive pattern recognition system for novel users using a powered lower limb prosthesis.

Powered prosthetic legs are capable of improving the gait of lower limb amputees. Pattern recognition systems for these devices allow amputees to transition between different locomotion modes in a way that is seamless and transparent to the user. However, the potential of these systems is diminished because they require large amounts of training data that is burdensome to collect. To reduce the effort required to acquire these data, we developed an adaptive pattern recognition system that automatically learns from subject-specific data as the user is ambulating. We tested our proposed system with two able-bodied subjects ambulating with a powered knee and ankle prosthesis. Each subject initially ambulated with a pattern recognition system that was not trained with any data from that subject (making each subject a novel user). Initially, the pattern recognition system made frequent errors. With the adaptive algorithm, the error rate decreased over time as more subject-specific data were incorporated. When compared to a non-adaptive system, the adaptive system reduced the number of errors by 32.9% [8.6%], mean [standard deviation]. This study demonstrates the potential improvements of an adaptive pattern recognition system over non-adaptive systems presented in prior research.

A zSlices-based general type-2 fuzzy logic system for users-centric adaptive learning in large-scale e-learning platforms

Sophisticated educational technologies are evolving rapidly, and online courses are becoming more easily available, generating interest in innovating lightweight data-driven adaptive approaches that foster responsive teaching and improving the overall learning experience. However, in most existing adaptive educational systems, the black-box modeling of learner and instructional models based on the views of a few designers or experts tended to drive the adaptation of learning content. However, different sources of uncertainty could affect these views, including how accurately the proposed adaptive educational methods actually assess student responses and the corresponding uncertainties associated with how students receive and comprehend the resulting instruction. E-learning environments contain high levels of linguistic uncertainties, whereby students can interpret and act on the same terms, words, or methods (e.g., course difficulty, length of study time, or preferred learning style) in various ways according to varying levels of motivation, pre-knowledge, cognition, and future plans. Thus, one adaptive instructional model does not fit the needs of all students. Basing the instruction model on determining learners’ interactions within the learning environment in interpretable and easily read white-box models is crucial for adapting the model to students’ needs and understanding how learning is realized. This paper presents a new zSlices-based type-2 fuzzy-logic-based system that can learn students’ preferred knowledge delivery needs based on their characteristics and current levels of knowledge to generate an adaptive learning environment. We have evaluated the proposed system’s efficiency through various large-scale, real-world experiments involving 1871 students from King Abdulaziz University. These experiments demonstrate the proposed zSlices type-2 fuzzy-logic-based system’s capability for handling linguistic uncertainties to produce better performance, particularly in terms of enhanced student performance and improved success rates compared with interval type-2 fuzzy logic, type-1 fuzzy systems, adaptive, instructor-led systems, and non-adaptive systems.

Users-Centric Adaptive Learning System Based on Interval Type-2 Fuzzy Logic for Massively Crowded E-Learning Platforms

Abstract Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learner reception of instruction. High linguistic uncertainty levels in e-learning settings result in different user interpretations and responses to the same techniques, words, or terms according to their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching models must be targeted to individual learners’ needs. Thus, developing a teaching model based on the knowledge of how learners interact with the learning environment in readable and interpretable white box models is critical in the guidance of the adaptation approach for learners’ needs as well as understanding the way learning is achieved. This paper presents a novel interval type-2 fuzzy logic-based system which is capable of identifying learners’ preferred learning strategies and knowledge delivery needs that revolves around characteristics of learners and the existing knowledge level in generating an adaptive learning environment. We have conducted a large scale evaluation of the proposed system via real-word experiments on 1458 students within a massively crowded e-learning platform. Such evaluations have shown the proposed interval type-2 fuzzy logic system’s capability of handling the encountered uncertainties which enabled to achieve superior performance with regard to better completion and success rates as well as enhanced learning compared to the non-adaptive systems, adaptive system versions led by the teacher, and type-1-based fuzzy based counterparts.

PERANCANGAN PENGAMANAN SERVER SECARA OTOMATIS MENGGUNAKAN METODE ADAM (AUTOMATIC EVENT DETECTION AND ACTIVITY MONITORING)

In an era like today's global, Internet-based information system security is a must to be considered, because of the public nature of the internet network and global is not safe. Basically the threat is coming from someone who wishes mempuyai gain illegal access to a computer network. Whenever there is a threat encountered on the server such as port scanning, the attacker IP addresses will be captured. Next will be used method Automatic Event Detection And Activity Monitoring (ADAM) to process security. ADAM will carry out retaliatory attacks in the form of a computer virus that was sent to the attacker. For this reason when a computer network is attacked by the intruder, then ADAM server will detect this type of attack is done, then asked for help from another server to strike back. Security server by applying the method ADAM able to do the blocking of port scanning the attacker did not end there ADAM will then send the file is a virus automatically. In terms of time efficiency, the method of securing the ADAM automatically faster than if all phases of the security is done manually. ADAM test non-adaptive systems this takes 4.4 minutes, while the time taken by the ADAM system to immobilize the attacker system only 1 minute 03:59 seconds, so the method ADAM work faster. Traffic normal state (RX 232B, 144B TX), but when encountered in the form of port scanning attacks become 21.46Kib TX RX 25.83Kib, and after working as RX 219B ADAM TX127B, resulting in a significant reduction in traffic.