RFID-based smart shelving storage systems

In recent years, RFID systems that are widely applied for the identification of objects and people in radio frequency, are also going to be applied used for localization purposes. In indoor applications (apartments, shopping malls, airports), conventional solutions can use a number of signal parameters: instant of arrival ( Time of Arrival , Time Difference of Arrival, TOA, TDOA), angle information (Angle of Arrival, AOA), phase information (Phase Difference of Arrival , PDOA ) or the amplitude of the received signal (Received Signal Strength Indicator, RSSI). There are also some scenarios with small dimensions where the location can be extremely useful. For example, in a hospital, a better service could be offered through RFID technology, as it can add more control to prevent human errors. Indeed, RFID technology can be useful for correct patient drug supply, dose recording, accurate dispensing, anti-counterfeiting as well as replenishment ordering; besides, it simplifies the information transfer between doctors and nurses (e.g. allergic reactions or drug treatment). In retail industry, real-time inventory based on RFID allows to monitor actual customer demand for products, to prevent an out-of-stock situation by timely replenishing orders, to increase sales through additional services (e.g. fitting rooms with smart mirrors providing suggestions to the customer). In food and restaurant industry, RFID technology allows for a better food control, as for example avoiding expired products sale. In this framework, RFID-based smart shelves, smart freezers, and proximity point readers have been developed in libraries, hospitals and retail industries. In Chapter I, a brief introduction on RFID systems will be presented, in particular describing the main components involved and the principle of operation. It will be described what is proposed in literature about RFID smart shelf and localization algorithms, with particular attention to the methods exploiting the RSSI information. In Chapter II, an exhaustive experimental study by using off-the-shelf reader, antennas and tags, will be presented with reference to a wooden drawer filled with drug boxes. The LDA algorithm (supervised classifier) will be compared with the K-Means clustering algorithm (unsupervised classifier), to validate the proposed method. The procedure to get several RSSI average samples during the drawer sliding actions is described, and classification performance is investigated. First of all, an RSSI analysis is described with reference to a static configuration of the drawer (not sliding). Then, two classification algorithms are compared by considering a different number of drawer sub-regions. In the second part, the algorithm exploiting the drawer sliding is described and system performance is illustrated to verify the classification capability in a two-region drawer. In Chapter III, a localization technique for smart bookshelves based on UHF-RFID systems is presented. Two off-the-shelf reader antennas attached to the bookshelf columns, one in front of the other, are used as an alternative to large-area thin planar antennas integrated onto the shelf top. Two scenarios were considered: the first one with a shelf that is D = 97 cm long, and the second one with D = 150 cm. Exploiting RSSI data acquired by the two antennas, a clustering algorithm is implemented to classify tagged books within one of the regions the shelf has been subdivided into. Preliminary results of the system performance analysis have been compared with simulations to demonstrate that is possible to create an interference region in different sectors on the shelf, through a proper phase shift between the feed currents of the two antennas. The system requires a power divider, a switch, variable phase shifters and finally a fixed or variable power attenuator based on the size of the shelf. In Chapter IV the algorithm implemented will be described and then the performance results (in terms of normalized confusion matrix) will be presented and discussed. Finally, a preliminary analysis has been presented considering different tags, even if it is still under developing

Intelligent sticky notes

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008.Page 96 blank.Includes bibliographical references (p. 81-82).This thesis introduces 'Quickies', an attempt to bring one of the most useful inventions of the 20th century into the digital age: the ubiquitous sticky notes. Sticky notes help us manage our to-do lists, tag our objects and documents and capture short reminders or information that we may need in the near future. 'Quickies' enrich the experience of using sticky notes by allowing them to be tracked and managed more effectively. Quickies are sticky notes that have a digital duplicate and the ability to remind us about the task we ought to perform or to provide us at the right time with the information we captured in the past. The thesis explores how the use of Artificial Intelligence (AI), Natural Language Processing (NLP), RFID, and ink recognition technologies can make it possible to create intelligent sticky notes that can be searched, located, can send reminders and messages, and more broadly, can help us to seamlessly connect our physical and digital information worlds.by Pranav Mistry.S.M

Reducing localization ambiguity of immobile passive UHF RFID tagged physical objects

Location sensing of physical objects is one of critical issues in many applications. Passive UHF Radio Frequency Identification (RFID) technique provides an efficient solution because of its low cost for installation and easy identification of the tagged physical objects. In this paper, we research on the localization problem using passive UHF RFID systems. We discuss theoretical and practical characteristics of a passive UHF RFID system. We propose novel algorithm to minimize the number of ambiguous tag points against a single RSSI value from a target tag and increase accuracy of location estimation in 2D grid space. Because of a single Received Signal Strength Indicator (RSSI) can be related to multiple points in the monitoring area due to signal propagation when we use the RSSI based localization technique. According to the analysis of our experiment results, our proposed approach shows over 50% improvement compared with the conventional k-Nearest Neighbor algorithm in a test frame, and 23.24cm of estimation error with a high granularity for localization of box level items with 4 different positions in the immobile applied application such as a smart shelf. © 2011 IEEE

Eulerian-Lagrangian definition of coarse bed-load transport: Theory and verification with low-cost inertial measurement units

Fluvial sediment transport is controlled by hydraulics, sediment properties and arrangement, and flow history across a range of time scales. This physical complexity has led to ambiguous definition of the reference frame (Lagrangian or Eulerian) in which sediment transport is analysed. A general Eulerian-Lagrangian approach accounts for inertial characteristics of particles in a Lagrangian (particle fixed) frame, and for the hydrodynamics in an independent Eulerian frame. The necessary Eulerian-Lagrangian transformations are simplified under the assumption of an ideal Inertial Measurement Unit (IMU), rigidly attached at the centre of the mass of a sediment particle. Real, commercially available IMU sensors can provide high frequency data on accelerations and angular velocities (hence forces and energy) experienced by grains during entrainment and motion, if adequately customized. IMUs are subjected to significant error accu- mulation but they can be used for statistical parametrisation of an Eulerian-Lagrangian model, for coarse sediment particles and over the temporal scale of individual entrainment events. In this thesis an Eulerian-Lagrangian model is introduced and evaluated experimentally. Absolute inertial accelerations were recorded at a 4 Hz frequency from a spherical instrumented particle (111 mm diameter and 2383 kg/m3 density) in a series of entrainment threshold experiments on a fixed idealised bed. The grain-top inertial acceleration entrainment threshold was approximated at 44 and 51 mg for slopes 0.026 and 0.037 respectively. The saddle inertial acceleration entrainment threshold was at 32 and 25 mg for slopes 0.044 and 0.057 respectively. For the evaluation of the complete Eulerian-Lagrangian model two prototype sensors are presented: an idealised (spherical) with a diameter of 90 mm and an ellipsoidal with axes 100, 70 and 30 mm. Both are instrumented with a complete IMU, capable of sampling 3D inertial accelerations and 3D angular velocities at 50 Hz. After signal analysis, the results can be used to parametrize sediment movement but they do not contain positional information. The two sensors (spherical and ellipsoidal) were tested in a series of entrainment experiments, similar to the evaluation of the 111 mm prototype, for a slope of 0.02. The spherical sensor entrained at discharges of 24.8 ± 1.8 l/s while the same threshold for the ellipsoidal sensor was 45.2 ± 2.2 l/s. Kinetic energy calculations were used to quantify the particle-bed energy exchange under fluvial (discharge at 30 l/s) and non-fluvial conditions. All the experiments suggest that the effect of the inertial characteristics of coarse sediments on their motion is comparable to the effect hydrodynamic forces. The coupling of IMU sensors with advanced telemetric systems can lead to the tracking of Lagrangian particle trajectories, at a frequency and accuracy that will permit the testing of diffusion/dispersion models across the range of particle diameters

Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Collected Papers (on Physics, Artificial Intelligence, Health Issues, Decision Making, Economics, Statistics), Volume XI

This eleventh volume of Collected Papers includes 90 papers comprising 988 pages on Physics, Artificial Intelligence, Health Issues, Decision Making, Economics, Statistics, written between 2001-2022 by the author alone or in collaboration with the following 84 co-authors (alphabetically ordered) from 19 countries: Abhijit Saha, Abu Sufian, Jack Allen, Shahbaz Ali, Ali Safaa Sadiq, Aliya Fahmi, Atiqa Fakhar, Atiqa Firdous, Sukanto Bhattacharya, Robert N. Boyd, Victor Chang, Victor Christianto, V. Christy, Dao The Son, Debjit Dutta, Azeddine Elhassouny, Fazal Ghani, Fazli Amin, Anirudha Ghosha, Nasruddin Hassan, Hoang Viet Long, Jhulaneswar Baidya, Jin Kim, Jun Ye, Darjan Karabašević, Vasilios N. Katsikis, Ieva Meidutė-Kavaliauskienė, F. Kaymarm, Nour Eldeen M. Khalifa, Madad Khan, Qaisar Khan, M. Khoshnevisan, Kifayat Ullah,, Volodymyr Krasnoholovets, Mukesh Kumar, Le Hoang Son, Luong Thi Hong Lan, Tahir Mahmood, Mahmoud Ismail, Mohamed Abdel-Basset, Siti Nurul Fitriah Mohamad, Mohamed Loey, Mai Mohamed, K. Mohana, Kalyan Mondal, Muhammad Gulfam, Muhammad Khalid Mahmood, Muhammad Jamil, Muhammad Yaqub Khan, Muhammad Riaz, Nguyen Dinh Hoa, Cu Nguyen Giap, Nguyen Tho Thong, Peide Liu, Pham Huy Thong, Gabrijela Popović‬‬‬‬‬‬‬‬‬‬, Surapati Pramanik, Dmitri Rabounski, Roslan Hasni, Rumi Roy, Tapan Kumar Roy, Said Broumi, Saleem Abdullah, Muzafer Saračević, Ganeshsree Selvachandran, Shariful Alam, Shyamal Dalapati, Housila P. Singh, R. Singh, Rajesh Singh, Predrag S. Stanimirović, Kasan Susilo, Dragiša Stanujkić, Alexandra Şandru, Ovidiu Ilie Şandru, Zenonas Turskis, Yunita Umniyati, Alptekin Ulutaș, Maikel Yelandi Leyva Vázquez, Binyamin Yusoff, Edmundas Kazimieras Zavadskas, Zhao Loon Wang.‬‬‬