AROMA: Automatic Generation of Radio Maps for Localization Systems

WLAN localization has become an active research field recently. Due to the wide WLAN deployment, WLAN localization provides ubiquitous coverage and adds to the value of the wireless network by providing the location of its users without using any additional hardware. However, WLAN localization systems usually require constructing a radio map, which is a major barrier of WLAN localization systems' deployment. The radio map stores information about the signal strength from different signal strength streams at selected locations in the site of interest. Typical construction of a radio map involves measurements and calibrations making it a tedious and time-consuming operation. In this paper, we present the AROMA system that automatically constructs accurate active and passive radio maps for both device-based and device-free WLAN localization systems. AROMA has three main goals: high accuracy, low computational requirements, and minimum user overhead. To achieve high accuracy, AROMA uses 3D ray tracing enhanced with the uniform theory of diffraction (UTD) to model the electric field behavior and the human shadowing effect. AROMA also automates a number of routine tasks, such as importing building models and automatic sampling of the area of interest, to reduce the user's overhead. Finally, AROMA uses a number of optimization techniques to reduce the computational requirements. We present our system architecture and describe the details of its different components that allow AROMA to achieve its goals. We evaluate AROMA in two different testbeds. Our experiments show that the predicted signal strength differs from the measurements by a maximum average absolute error of 3.18 dBm achieving a maximum localization error of 2.44m for both the device-based and device-free cases.Comment: 14 pages, 17 figure

Modelling and Optimization of an Off-Grid Hybrid Power System for Supplying Unmanned Offshore Installations in Eastern Malaysia

In this project, investigation about the potential utilization of hybrid power system in the Malaysian offshore environment is examined. An unmanned offshore installation was selected for the study. The solar and wind energy potentials in Malaysia were investigated and the feasibility study of the project was proved by using meteorological data from the project location at Southeast China Sea. In addition to that, the installation power demand was estimated based on the load profile from Cutter Platform, World first unmanned offshore platform to be operated totally by renewable energy in the North Sea. Moreover, a hybrid power system topology was proposed to supply the loads on-board the offshore installation

The Nature of Word-Accent in English With Special Reference to Duration and Perception

This is mainly a study of accent-based durational differences in syllables in British English. A framework for the study of accent is described. It is characterized by considering the two often separated domains (i. e. one-word utterances and longer utterances) as a single domain. The durational manifestations of the different degrees of accent are then studied. The method adopted is that of comparisons of the durations of syllables with identical syntagmatic and paradigmatic structures, with the average margins of difference being assessed in terms of significance against a reference duration of 40 msec. The condition of identicality in syntagmatic and paradigmatic structures is sometimes abandoned, however, for the sake of widening the scope of the material analysed or studying factors modulating the accent-duration relationship (e. g. speech-rate). The factor of syllable-position is occasionally used as a variable that affects this relationship. The hierarchy proposed for accentual degrees is found to be consistently manifested by duration in a directly proportional relation unless other variables are operative. On the basis of syllable-durations, the dissociation of so-called "word-accent" and "sentence-accent" has been found to be implausible. Comparisons of the durations of syllable-tokens in one-word and longer utterances have been found to produce significant durational variations only when one of two factors is involved: final lengthening, and the change from primary tonic to primary non-tonic accent and vice versa. Both factors are known to operate in both domains. The results of various Tests confirm on the basis of syllable-durations the inconsistency in the marking of secondary accents in the English Pronouncing Dictionary (EPD). It is proposed that further studies of other parameters in relation to accent would find it worthwhile to keep the syntagmatic and paradigmatic structures of syllables constant. Various tenets and theories in the field of perception are then reviewed with respect to accent in the light of the results of a Perception Test. The results of the three Groups of judging informants (i. e. native linguists, native and phonetically naive, and non-native) were found to bear positively on the motor theory of speech perception. Familiarity with linguistic concepts was also found to be one of the factors that positively induced correct judgements. The advantage of native speakers of English over non-native ones was found to be maintained both in terms of the average percentage of correct judgement and of the patterns of incorrect judgement (e. g. opting for another prominent syllable in the word or for a non-prominent one). The deviation of the scores of correct judgements and the patterns of incorrect judgements in the case of given types of word (e. g. deliberately misaccented words and compound words) from the general percentages and patterns were also individually accounted for

Characteristics of the mechanical impedance of the hand-arm system

The mechanical impedance of the human hand-arm system was measured within the frequency range of 5--1000 Hz. A handle specially designed for such measurements, was used. The studies were carried out on ten healthy male subjects during different experimental conditions defined by, three different vibration amplitudes (0.01, 0.005, and 0.001 m/s) different combinations of push (0--75 N) and grip (25--50 N) forces, and two different methods of handle mass subtraction (mathematical and electronic). The effect of test subjects\u27 weight on the results was also studied. The outcome shows that the mechanical impedance of the hand-arm system depends on the frequency of the vibration stimuLi Impedance was found to increase rapidly with the increase of frequency starting from 80 Hz (for the 0.001 m/s vibration amplitude) and from 200Hz (for the 0.01 and 0.005 m/s amplitudes) to reach a maximum of about 950 Ns/m at 1000Hz. (Abstract shortened by UMI.)

Smart-Insect Monitoring System Integration and Interaction via AI Cloud Deployment and GPT

The Insect Detection Server was developed to explore the deployment and integration of an Artificial Intelligence model for Computer Vision in the context of insect detection. The model was developed to accurately identify insects from images taken by camera systems installed on farms. The goal is to integrate the model into an easily accessible, cloud-based application that allows farmers to analyze automatically uploaded images containing groups of insects found on their farms. The application returns the bounding boxes and the detected classes of insects whenever an image is captured on-site, enabling farmers to take appropriate actions to address the issue of the insects\u27 presence. To extend the capabilities of the application, the server is linked to a GPT-3.5 API. This will allow users to ask questions about the bugs detected on their farms, creating an online expert -like feature. Python, C++, and Computer Vision libraries were used for the detection model, while the OpenAI API was used for GPT-3.5\u27s integration. By combining these technologies, farmers can more effectively and efficiently manage pests on their farms than current alternatives. This Generative Pre-trained Transformer (GPT) aspect of the project can be leveraged to enable the emulation of agricultural experts for users/farmers. The large language model (LLM) neural network can be fine-tuned using prompt engineering to generate natural language responses to user queries. This will enable farmers to get expert advice and guidance on pest management without having to consult with a human expert. The integration of GPT-3.5 API will also allow the application to provide personalized recommendations based on each farm\u27s specific needs and circumstances. This added feature will give the farmers a more comprehensive and tailored approach to pest management, further increasing the efficiency and effectiveness of their pest control strategies. The significance of this research lies in the development of a practical and accessible tool for farmers to manage pests on their farms. Using Computer Vision and Artificial Intelligence, farmers can quickly and accurately identify insects, leading to more efficient and effective pest management. This could help farmers reduce the use of pesticides and other forms of pest management, leading to improved crop yields and reduced environmental impacts. The potential benefits of this technology extend beyond the agricultural industry, as the techniques used in this research can be applied to a wide range of computer vision and user-facing data analytic applications. For example, the developed techniques could be applied to other fields, such as surveillance, security, and medical imaging