Tracking Framework for Object Localization Using Arduino

Paljudel inimestel on kodust lahkudes kaasas isiklikud asjad, nagu näiteks rahakott, koduukse võtmed või mobiiltelefon. Kui vahel mõni neist koju ununeb, siis see tekitab ebameeldivusi. Selle vältimiseks oleks vaja esemetel paremini silma peal hoida. Käesolev bakalaureusetöö pakub välja lahenduse, mis aitab objekte jälgida ja vajadusel nende asukohta kindlaks määrata. Süsteem põhineb Arduino mikrokontrolleril (Jälgija) ja Bluetooth'i tehnoloogial. Jälgitavaks objektiks (Objekt) võib olla näiteks võtmehoidja, milles on Bluetooth'i moodul. Objektiks sobib ka Bluetooth'iga varustatud mobiiltelefon. Jälgija kinnitatakse kasutajale vööle, seejärel ühendab kasutaja jälgitavad objektid üle Bluetooth'i ühenduse Jälgija külge. Kui mõni Objektidest Jälgijast liiga kaugele liigub, siis käivitub alarm. Samas on võimalik Jälgijast saata signaal Objektile ja helina järgi üles leida näiteks diivani padja alla ununenud mobiiltelefon. Lahendusena valmisid prototüübid Jälgijast ja Objektist. Jälgijana kasutati Arduino BT (Bluetooth) mikrokontrollerit ning Objektina Android operatsioonisüsteemil põhinevat nutitelefoni. Töös antakse ülevaade kasutatud tehnoloogiatest. Seejärel kirjeldatakse erinevaid objekti asukoha määramise ja jälgimise tehnoloogiaid ning tuuakse välja nende eelised ja puudused. Töös vaadeldakse ka valminud lahenduse nii riistvaralist kui ka tarkvaralist arhitektuuri. Valminud prototüüpidega tehtud eksperimendid näitasid, et Bluetooth'i signaalitugevust on võimalik kasutada seadme kauguse hindamiseks. Elektrienergia tarbimise katses veenduti, et Jälgija prototüüp on võimeline ka tavaliste Alkaline patareidega edukalt töötama.

Localization System Supporting People with Cognitive Impairment and Their Caregivers

Localization systems are an important componentof Ambient and Assisted Living platforms supporting personswith cognitive impairments. The paper presents a positioningsystem being a part of the platform developed within the IONISEuropean project. The system’s main function is providing theplatform with data on user mobility and localization, whichwould be used to analyze his/her behavior and detect dementiawandering symptoms. An additional function of the system islocalization of items, which are frequently misplaced by dementiasufferers.The paper includes a brief description of system’s architecture,design of anchor nodes and tags and exchange of data betweendevices. both localization algorithms for user and item positioningare also presented. Exemplary results illustrating the system’scapabilities are also included

Feasibility of LoRa for Smart Home Indoor Localization

With the advancement of low-power and low-cost wireless technologies in the past few years, the Internet of Things (IoT) has been growing rapidly in numerous areas of Industry 4.0 and smart homes. With the development of many applications for the IoT, indoor localization, i.e., the capability to determine the physical location of people or devices, has become an important component of smart homes. Various wireless technologies have been used for indoor localization includingWiFi, ultra-wideband (UWB), Bluetooth low energy (BLE), radio-frequency identification (RFID), and LoRa. The ability of low-cost long range (LoRa) radios for low-power and long-range communication has made this radio technology a suitable candidate for many indoor and outdoor IoT applications. Additionally, research studies have shown the feasibility of localization with LoRa radios. However, indoor localization with LoRa is not adequately explored at the home level, where the localization area is relatively smaller than offices and corporate buildings. In this study, we first explore the feasibility of ranging with LoRa. Then, we conduct experiments to demonstrate the capability of LoRa for accurate and precise indoor localization in a typical apartment setting. Our experimental results show that LoRa-based indoor localization has an accuracy better than 1.6 m in line-of-sight scenario and 3.2 m in extreme non-line-of-sight scenario with a precision better than 25 cm in all cases, without using any data filtering on the location estimates

Mental Imagery and Chunks: Empirical and Computational Findings

To investigate experts’ imagery in chess, players were required to recall briefly-presented positions in which the pieces were placed on the intersections between squares (intersection positions). Position types ranged from game positions to positions where both the piece distribution and location were randomized. Simulations were run with the CHREST model (Gobet & Simon, 2000). The simulations assumed that pieces had to be centered back one by one to the middle of the squares in the mind’s eye before chunks could be recognized. Consistent with CHREST’s predictions, chess players (N = 36), ranging from weak amateurs to grandmasters, exhibited much poorer recall on intersection positions than on standard positions (pieces placed on centers of squares). On the intersection positions, the skill difference in recall was larger on game positions than on the randomized positions. Participants recalled bishops better than knights, suggesting that Stroop-like interference impairs recall of the latter. The data supported both the time parameter in CHREST for shifting pieces in the mind’s eye (125 ms per piece) and the seriality assumption. In general, the study reinforces the plausibility of CHREST as a model of cognition

Location Finding of Wireless Beacons

Some objects, especially relatively small objects such as a keyring, wallet, etc., can be difficult to find when misplaced. It is desirable to provide a system that can assist in locating misplaced items

Unpacking People's Understandings of Bluetooth Beacon Systems - A Location-Based IoT Technology

Bluetooth beacon technology is an emerging location-based Internet of Things (IoT) technology, designed to transform proximity-based services in various domains such as retail. Beacons are part of the IoT infrastructure, but people rarely interact with them directly and yet they could still pose privacy risks to users. However, little is known about people's understandings of how beacon-based systems work. This is an important question since it can influence people's perceptions, adoption, and usage of this emerging technology. Drawing from 22 semi-structured interviews, we studied people's understandings of how beacon-based systems work and identified several factors that shaped their understandings or misunderstandings, such as how information flows among the components and who owns the beacons. These understandings and misunderstandings can potentially pose significant privacy risks to beacon users