2,692 research outputs found
Where Does the Density Localize? Convergent Behavior for Global Hybrids, Range Separation, and DFT+U
Approximate density functional theory (DFT) suffers from many-electron self-
interaction error, otherwise known as delocalization error, that may be
diagnosed and then corrected through elimination of the deviation from exact
piecewise linear behavior between integer electron numbers. Although paths to
correction of energetic delocalization error are well- established, the impact
of these corrections on the electron density is less well-studied. Here, we
compare the effect on density delocalization of DFT+U, global hybrid tuning,
and range- separated hybrid tuning on a diverse test set of 32 transition metal
complexes and observe the three methods to have qualitatively equivalent
effects on the ground state density. Regardless of valence orbital diffuseness
(i.e., from 2p to 5p), ligand electronegativity (i.e., from Al to O), basis set
(i.e., plane wave versus localized basis set), metal (i.e., Ti, Fe, Ni) and
spin state, or tuning method, we consistently observe substantial charge loss
at the metal and gain at ligand atoms (ca. 0.3-0.5 e or more). This charge loss
at the metal is preferentially from the minority spin, leading to increasing
magnetic moment as well. Using accurate wavefunction theory references, we
observe that a minimum error in partial charges and magnetic moments occur at
higher tuning parameters than typically employed to eliminate energetic
delocalization error. These observations motivate the need to develop
multi-faceted approximate-DFT error correction approaches that separately treat
density delocalization and energetic errors in order to recover both correct
density and magnetization properties.Comment: 34 pages, 11 figure
Forecast-Driven Enhancement of Received Signal Strength (RSS)-Based Localization Systems
Real-time user localization in indoor environments is an important issue in
ambient assisted living (AAL). In this context, localization based on received signal strength
(RSS) has received considerable interest in the recent literature, due to its low cost and energy
consumption and to its availability on all wireless communication hardware. On the other
hand, the RSS-based localization is characterized by a greater error with respect to other
technologies. Restricting the problem to localization of AAL users in indoor environments,
we demonstrate that forecasting with a little user movement advance (for example, when
the user is about to leave a room) provides significant benefits to the accuracy of RSS-based
localization systems. Specifically, we exploit echo state networks (ESNs) fed with RSS
measurements and trained to recognize patterns of user’s movements to feed back to the
RSS-based localization syste
Generalizable Deep-Learning-Based Wireless Indoor Localization
The growing interest in indoor localization has been driven by its wide range of applications in areas such as smart homes, industrial automation, and healthcare. With the increasing reliance on wireless devices for location-based services, accurate estimation of device positions within indoor environments has become crucial. Deep learning approaches have shown promise in leveraging wireless parameters like Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) to achieve precise localization. However, despite their success in achieving high accuracy, these deep learning models suffer from limited generalizability, making them unsuitable for deployment in new or dynamic environments without retraining. To address the generalizability challenge faced by conventionally trained deep learning localization models, we propose the use of meta-learning-based approaches. By leveraging meta-learning, we aim to improve the models\u27 ability to adapt to new environments without extensive retraining. Additionally, since meta-learning algorithms typically require diverse datasets from various scenarios, which can be difficult to collect specifically for localization tasks, we introduce a novel meta-learning algorithm called TB-MAML (Task Biased Model Agnostic Meta Learning). This algorithm is specifically designed to enhance generalization when dealing with limited datasets. Finally, we conduct an evaluation to compare the performance of TB-MAML-based localization with conventionally trained localization models and other meta-learning algorithms in the context of indoor localization
Wi-Fi Fingerprinting for Indoor Positioning
Wireless Fidelity (Wi-Fi) Fingerprinting is a remarkable approach developed by modern science to detect the user’s location efficiently. Today, the Global Positioning System (GPS) is used to keep track of our current location for outdoor positioning. In GPS technology, satellite signals cannot reach indoor environments as they are shielded from obstructions so that indoor environments with a lack of Line of Sight (LoS) do not provide enough satellite signal accuracy. Since indoor environments are very difficult to track, thus, a wide variety of techniques for dealing with them have been suggested. The best way to offer an indoor positioning service with the current technology is Wi-Fi since the most commercial infrastructure is well equipped with Wi-Fi routers. For indoor positioning systems (IPS), Wi-Fi fingerprinting approaches are being extremely popular. In this paper, all the approaches for Wi-Fi fingerprinting have been reviewed for indoor position localization. Related to Wi-Fi fingerprinting, most of the algorithms have been interpreted and the previous works of other researchers have been critically analyzed in this paper to get a clear view of the Wi-Fi fingerprinting process
Wi-Fi Fingerprinting for Indoor Positioning
Wireless Fidelity (Wi-Fi) Fingerprinting is a remarkable approach developed by modern science to detect the user’s location efficiently. Today, the Global Positioning System (GPS) is used to keep track of our current location for outdoor positioning. In GPS technology, satellite signals cannot reach indoor environments as they are shielded from obstructions so that indoor environments with a lack of Line of Sight (LoS) do not provide enough satellite signal accuracy. Since indoor environments are very difficult to track, thus, a wide variety of techniques for dealing with them have been suggested. The best way to offer an indoor positioning service with the current technology is Wi-Fi since the most commercial infrastructure is well equipped with Wi-Fi routers. For indoor positioning systems (IPS), Wi-Fi fingerprinting approaches are being extremely popular. In this paper, all the approaches for Wi-Fi fingerprinting have been reviewed for indoor position localization. Related to Wi-Fi fingerprinting, most of the algorithms have been interpreted and the previous works of other researchers have been critically analyzed in this paper to get a clear view of the Wi-Fi fingerprinting process
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