/pdf/shyw-wrd-hd-tzryq-khwn-dr-bymrn-bstry-dr-11-bymrstn-thrn-w-2vik9rycr0.pdf

شیوع عوارض حاد تزریق خون در بیماران بستری در 11 بیمارستان تهران و مازندران

The past years have witnessed the rapid conceptualization and development of wireless sensing based on Channel State Information (CSI) with commodity WiFi devices. Recent studies have demonstrated the vast potential of WiFi sensing in detection, recognition, and estimation applications. However, the widespread deployment of WiFi sensing systems still faces a significant challenge: how to ensure the sensing performance when exposing a pre-trained sensing system to new domains, such as new environments, different configurations, and unseen users, without data collection and system retraining. This survey provides a comprehensive review of recent research efforts on cross-domain WiFi Sensing. We first introduce the mathematical model of CSI and explore the impact of different domains on CSI. Then we present a general workflow of cross-domain WiFi sensing systems, which consists of signal processing and cross-domain sensing. Five cross-domain sensing algorithms, including domain-invariant feature extraction, virtual sample generation, transfer learning, few-shot learning and big data solution, are summarized to show how they achieve high sensing accuracy when encountering new domains. The advantages and limitations of each algorithm are also summarized and the performance comparison is made based on different applications. Finally, we discuss the remaining challenges to further promote the practical usability of cross-domain WiFi sensing systems.

Cross-Domain WiFi Sensing with Channel State Information: A Survey

Spin transfer torque (STT) switching-based magnetic random access memory (MRAM) is gaining significant industrial and academic attention due to its potential application as a non-volatile memory device in computing devices, smartphones, and so on. STT-MRAM devices with in-plane magnetization configuration have been marketed as niche products. Devices with out-of-plane magnetization are being considered for embedded memory as the first step. Aggressive goals include replacing dynamic random access memory (DRAM) with STT-MRAM. This review article introduces the basics of STT-MRAM and describes the recent progress in overcoming certain challenges such as reduction of power consumption and increase of storage density, which will make it a strong competitor for DRAM.

Recent Developments in Spin Transfer Torque MRAM

While decade-long research has clearly demonstrated the vast potential of radio frequency (RF) for many human sensing tasks, scaling this technology to large scenarios remained problematic with conventional approaches. Recently, researchers have successfully applied deep learning to take radio-based sensing to a new level. Many different types of deep learning models have been proposed to achieve high sensing accuracy over a large population and activity set, as well as in unseen environments. Deep learning has also enabled detection of novel human sensing phenomena that were previously not possible. In this survey, we provide a comprehensive review and taxonomy of recent research efforts on deep learning based RF sensing. We also identify and compare several publicly released labeled RF sensing datasets that can facilitate such deep learning research. Finally, we summarize the lessons learned and discuss the current limitations and future directions of deep learning based RF sensing.

Deep Learning for Radio-Based Human Sensing: Recent Advances and Future Directions

We present a brief pedagogical review of theoretical Green's function methods applicable to open quantum systems out of equilibrium, in general, and single molecule junctions, in particular. We briefly describe experimental advances in molecular electronics and then discuss different theoretical approaches. We then focus on Green's function methods. Two characteristic energy scales governing the physics are many-body interactions within the junctions and molecule-contact coupling. We, therefore, discuss weak interactions and weak coupling as two limits that can be conveniently treated within, respectively, the standard nonequilibrium Green's function (NEGF) method and its many-body flavors (pseudoparticle and Hubbard NEGF). We argue that the intermediate regime, where the two energy scales are comparable, can in many cases be efficiently treated within the recently introduced superperturbation dual fermion approach. Finally, we review approaches for going beyond these analytically accessible limits, as embodied by recent developments in numerically exact methods based on Green's functions.

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Green's function methods for single molecule junctions.

To fully support the emerging location-aware applications, location information with meter-level resolution (or even higher) is required anytime and anywhere. Unfortunately, most of the current location sources (e.g., GPS and check-in data) either are unavailable indoor or provide only house-level resolutions. To fill the gap, this paper utilizes the ubiquitous WiFi signals to establish a (sub)meter-level localization system, which employs WiFi propagation characteristics as location fingerprints. However, an unsolved issue of these WiFi fingerprints lies in their inconsistency across different users. In other words, WiFi fingerprints collected from one user may not be used to localize another user. To address this issue, we propose a WiFi-based Domain-adaptive system FiDo, which is able to localize many different users with labelled data from only one or two example users. FiDo contains two modules: 1) a data augmenter that introduces data diversity using a Variational Autoencoder (VAE); and 2) a domain-adaptive classifier that adjusts itself to newly collected unlabelled data using a joint classification-reconstruction structure. Compared to the state of the art, FiDo increases average F1 score by 11.8% and improves the worst-case accuracy by 20.2%.

FiDo: Ubiquitous Fine-Grained WiFi-based Localization for Unlabelled Users via Domain Adaptation

Emerging Internet of Things (IoT) applications, such as cashier-less shopping, mobile ads targeting, and geo-based augmented reality (AR), are expected to bring us much more convenience and infotainment. To realize this amazing future, we need to feed these applications with user locations of (sub)meter-level resolution anytime and anywhere. Unfortunately, many widely used location sources are either unavailable indoor (e.g., global positioning system) or coarse grained (e.g., user check-ins). In order to provide ubiquitous localization services, the widespread WiFi signals are being leveraged to establish (sub)meter-level localization systems. Fine-grained WiFi propagation characteristics, which are sensitive to human body locations, have been employed to create location fingerprints. However, these WiFi characteristics are also sensitive to: 1) the body shapes of different users and 2) the objects in the background environment. Consequently, systems based on WiFi fingerprints are vulnerable in the presence of: 1) new users with different body shapes and 2) daily changes of the environment, e.g., opening/closing doors. To tackle this issue, this article proposes a WiFi-based localization system based on domain-adaptation with cluster assumption, named Fidora. Fidora is able to: 1) localize different users with labeled data from only one or two example users and 2) localize the same user in a changed environment without labeling any new data. To achieve these, Fidora integrates two major modules. It first adopts a data augmenter that introduces data diversity using a variational autoencoder (VAE). It then trains a domain-adaptive classifier that adjusts itself to newly collected unlabeled data using a joint classification-reconstruction structure. We conducted real-world experiments to evaluate Fidora against the state of the art. It is demonstrated that when tested on an unlabeled user, Fidora increases the average $F1$ score by 17.8% and improves the worst case accuracy by 20.2%. Moreover, when applied in a varied environment, Fidora outperforms the state of the art by 23.1%.

Fidora: Robust WiFi-Based Indoor Localization via Unsupervised Domain Adaptation

We present a real-space theoretical formalism and its numerical implementation for investigating nonequilibrium quantum Coulomb drag between parallel two-terminal transport structures in quasi-one-dimension. In addition to the Coulomb interaction and the finite external potential bias, our formalism takes into account the effects of impurity disorder. The theory is formulated in the nonequilibrium Green's function formalism, with the long-range Coulomb interaction treated at the many-body GW approximation level and the disorder-average carried out at the coherent potential approximation (CPA) level. The coupled GW and CPA equations are solved self-consistently so that the fundamental conservation laws are ensured. The effects related to the electron-hole symmetry in the Coulomb drag physics have been generalized to the nonlinear transport regime. A set of symmetry-induced relations linking physical quantities with the particle distribution were established on generic footing and, remarkably, they are found robust against uniformly distributed impurities. As an application, the theoretical formalism is employed to analyze the Coulomb drag transport physics in quasi-one-dimensional systems. The dependencies of the drag current on external bias, chemical potential, temperature, and the system sizes are predicted.

Coulomb drag between quantum wires: A nonequilibrium many-body approach

Electronic structures and thermoelectric properties of La or Ce-doped Bi2Te3 alloys from first principles calculations

The phenomenon of spin transfer torque (STT) has attracted a great deal of interest due to its promising prospects in practical spintronic devices. In this paper, we report a theoretical investigation of STT in a noncollinear magnetic tunnel junction under ac modulation based on the nonequilibrium Green's-function formalism, and we derive a closed formulation for predicting the time-averaged STT. Using this formulation, the ac STT of a carbon-nanotube-based magnetic tunnel junction is analyzed. Under ac modulation, the low-bias linear (quadratic) dependence of the in-plane (out-of-plane) torque on bias still holds, and the $sin\ensuremath{\theta}$ dependence on the noncollinear angle is maintained. By photon-assisted tunneling, the bias-induced components of the in-plane and out-of-plane torques can be enhanced significantly, about 12 and 75 times, respectively. Our analysis reveals the condition for achieving optimized STT enhancement and suggests that ac modulation is a very effective way for electrical manipulation of STT.

Chenyi Zhou

Papers

FiDo: Ubiquitous Fine-Grained WiFi-based Localization for Unlabelled Users via Domain Adaptation

Fidora: Robust WiFi-Based Indoor Localization via Unsupervised Domain Adaptation

Coulomb drag between quantum wires: A nonequilibrium many-body approach

Electronic structures and thermoelectric properties of La or Ce-doped Bi2Te3 alloys from first principles calculations

Enhancing the spin transfer torque in magnetic tunnel junctions by ac modulation