SignFi

With the development of the internet of things, localization and activity recognition based on WIFI signals have received much attentions. In order to improve the performance, in this paper, a convolutional neural network (CNN) based localization and activity recognition algorithm using channel state information (CSI) measurements and decision fusion is proposed. In the off-line phase, the original CSI measurements obtained from multiple receivers are preprocessed to remove the outliers and noise by Hampel filter and Gaussian filter. Then the normalized CSI measurements are rendered into RGB images. At last, at each receiver, the CNN and distributed training is used for classification learning of localization and activity recognition, respectively. In the on-line phase, the decision-level fusion is used to fuse the intermediate estimation and obtain the final localization and activity recognition results. Experiment results show the better performance of the proposed algorithm.

Radio frequency (RF) fingerprinting techniques provide a promising supplement to cryptography-based approaches but rely on dedicated equipment to capture in-phase and quadrature (IQ) samples, hindering their wide adoption. Recent advances advocate easily obtainable channel state information (CSI) by commercial WiFi devices for lightweight RF fingerprinting, while falling short in addressing the challenges of coarse granularity of CSI measurements in an open-world setting. In this paper, we propose CSI<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>Q, a novel CSI fingerprinting system that achieves comparable performance to IQ-based approaches. Instead of extracting fingerprints directly from raw CSI measurements, CSI<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>Q first transforms frequency-domain CSI measurements into time-domain signals that share the same feature space with IQ samples. Then, we employ a deep auxiliary learning strategy to transfer useful knowledge from an IQ fingerprinting model to the CSI counterpart. Finally, the trained CSI model is combined with an OpenMax function to estimate the likelihood of unknown ones. We evaluate CSI<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>Q on one synthetic CSI dataset involving 85 devices and two real CSI datasets, including 10 and 25 WiFi routers, respectively. Our system achieves accuracy increases of at least 16% on the synthetic CSI dataset, 20% on the in-lab CSI dataset, and 17% on the in-the-wild CSI dataset.

In recent years, channel state information (CSI) in WiFi 802.11n has been increasingly used to collect data pertaining to human activity. Such raw data are then used to enhance human activity recognition. Activities such as lying down, falling, walking, running, sitting down, and standing up can now be detected with the use of information collected through CSI. Human activity recognition has a multitude of applications, such as home monitoring of patients. Four deep learning models are presented in this paper, namely: a convolution neural network (CNN) with a Gated Recurrent Unit (GRU); a CNN with a GRU and attention; a CNN with a GRU and a second CNN, and a CNN with Long Short-Term Memory (LSTM) and a second CNN. Those models were trained to perform Human Activity Recognition (HAR) using CSI amplitude data collected by a CSI tool. Experiments conducted to test the efficacy of these models showed superior results compared with other recent approaches. This enhanced performance of our models may be attributable the ability of our models to make full use of available data and to extract all data features, including high dimensionality and time sequence. The highest average recognition accuracy reached by the proposed models was achieved by the CNN-GRU, and the CNN-GRU with attention models, standing at 99.31% and 99.16%, respectively. In addition, the performance of the models was evaluated for unseen CSI data by training our models using a random split-of-dataset method (70% training and 30% testing). Our models achieved impressive results with accuracies reaching 100% for nearly all activities. For the lying down activity, accuracy obtained from the CNN-GRU model stood at 99.46%; slightly higher than the 99.05% achieved by the CNN-GRU with attention model. This confirmed the robustness of our models against environmental changes.

Indoor occupancy detection (IOD) via Wi-Fi sensing capitalizes on the varying patterns in CSI (Channel State Information) to estimate the number of people in a given area. However, the precision of such systems heavily depends on the quality of the CSI data, which can be degraded by noise and environmental factors. To address this issue, In this paper, we present a CSI preprocessing method to improve the accuracy of IOD systems using Wi-Fi sensing. Unlike existing preprocessing methods that use computationally complex signal processing or statistical techniques, we expand the dimension of CSI amplitude data into a three-channel vector through nonlinear transformation to amplify subtle differences between CSI data belonging to a different number of people. By drawing clearer boundaries between CSI data distributions belonging to a different number of people in a monitored area, our method improves the people-counting accuracy of a Wi-Fi sensing system. To ensure temporal consistency and improve data quality, we discretize the CSI measurements based on their transmission periods and aggregate consecutive measurements over a given time interval. These samples are then fed into a Convolutional Neural Network (CNN) specifically trained for the IOD task. Experimental results in diverse real-world scenarios verify that compared to the traditional methods, the enhanced feature representation capability of our approach leads to more accurate and robust sensing outcomes even in the most resource-constrained environment, where a commercial off-the-shelf CSI capture machine with only one antenna is used when a Wi-Fi sender with one transmit antenna sends packets periodically to the channel with the smallest Wi-Fi channel bandwidth.

A transmitted packet that fails to reach its intended destination may be correctly received by neighbor nodes due to the broadcast nature of the wireless medium. In a cooperative retransmission scheme, these neighbor nodes, known as relays, can retransmit the failed packet on behalf of the original source node. The challenge is that multiple concurrent transmissions may lead to collision at the destination, and thus the problem is to decide which relay should help in retransmitting the failed packet so that the destination can successfully receive it. This paper proposes a decentralized partially observable Markov decision process (DEC-POMDP) model for selecting the relays to perform the cooperative retransmission. The proposed DEC-POMDP model does not require global channel state information (CSI). In addition, it is robust to noise in CSI measurements. Furthermore, the proposed DEC-POMDP scheme utilizes the gradient descent learning method to eliminate the need for a wireless channel model. We show that the proposed learning method based on the DEC-POMDP model can perform near optimally in the absence of a channel model and despite its implementation simplicity.

Location-Based Service (LBS) has been widely deployed. One of the key components of LBS is the positioning algorithm. For outdoor environments, the Global Positioning System (GPS) has been used as the default positioning scheme. However, GPS requires the line of sight to the satellites. When the line of sight is blocked, GPS simply stops working. To tackle the problem with GPS, varied WiFi-based positioning schemes have been proposed. However, the positioning precision of the existing methods is not satisfactory. In this paper, we present a high-precision positioning scheme named Deep Learning based Positioning (DLP). Technically, DLP utilizes both Received Signal Strength Indicator (RSSI) and Channel State Information (CSI) to improve the positioning precision. In detail, a deep neural network is used to model the received RSSI and CSI measurements, which leads to satisfactory positioning accuracy. Our experimental results acquired from a large-scale testbed indicate that DLP outperforms the existing positioning schemes in terms of positioning precision.

Identification of crops is an important topic in the agricultural domain. Hyperspectral remote sensing data are very useful for crop feature extraction and classification. Remote sensing data is an unstructured data and Convolutional Neural Network (CNN) can work on unstructured data efficiently. This paper presents an evaluation of CNN for crop classification using the Indian Pines standard dataset obtained from the AVIRIS sensor and the study area dataset obtained from the EO-1hyperion sensor. Optimized CNN has been tuned by training the model on different parameters. It has been compared with two classification algorithms: Deep Neural Network (DNN) and Convolutional Autoencoder. According to the test results, the proposed optimized CNN model provided better results as compared to the other two methods. CNN has given 97 ± 0.58% overall accuracy for the Indian Pines standard dataset and 78 ± 2.43% for our study area dataset.

Acquiring accurate channel state information (CSI) is essential for enabling reliable and efficient wireless transmission and reception. However, CSI is inherently stochastic, high-dimensional, and time-varying, which makes its acquisition particularly challenging. Motivated by the success of deep learning (DL) across many data modalities, this thesis explores DL-based solutions for CSI estimation, compression, and prediction.First, we study CSI estimation in full-duplex (FD) multiple-input multiple-output (MIMO) systems, where strong self-interference (SI) complicates channel acquisition. To reduce the pilot and computational burden of estimating both SI and user channels, we propose a pilot-sharing strategy together with a convolutional neural network that jointly estimates these channels.We further introduce a neural mapping that enables CSI acquisition at the transmit chain.Second, we investigate DL–based CSI compression and its limited robustness under distribution shifts. To address this issue, we adopt a full-model fine-tuning while explicitly accounting for model update signaling overhead. Specifically, we employ a spike-and-slab prior to promote sparsity in the model updates and fine-tune the pretrained network using a rate–distortion objective regularized by the update bit rate.Third, we tackle CSI prediction using a diffusion-based generative framework. The method consists of a temporal encoder that extracts latent features from past CSI and a diffusion generator that synthesizes future CSI. We also study a simplified encoder-free design to reduce latency, compare autoregressive and sequence-to-sequence inference, and explore multiple architectures for both temporal encoding and diffusion generation.

Channel state information (CSI) estimation is one of the most fundamental problems in wireless communication systems. Various methods, so far, have been developed to conduct CSI estimation. However, they usually require high computational complexity, which makes them unsuitable for 5G wireless communications due to employing many new techniques (e.g., massive MIMO, OFDM, and millimeter-Wave (mmWave)). In this paper, we propose an efficient online CSI prediction scheme, called OCEAN, for predicting CSI from historical data in 5G wireless communication systems. Specifically, we first identify several important features affecting the CSI of a radio link and a data sample consists of the information of the features and the CSI. We then design a learning framework that is an integration of a CNN (convolutional neural network) and a long short term with memory (LSTM) network. We also further develop an offline-online two-step training mechanism, enabling the prediction results to be more stable when applying it to practical 5G wireless communication systems. To validate OCEAN's efficacy, we consider four typical case studies, and conduct extensive experiments in the four scenarios, i.e., two outdoor and two indoor scenarios. The experiment results show that OCEAN not only obtains the predicted CSI values very quickly but also achieves highly accurate CSI prediction with up to 2.650-3.457 percent average difference ratio (ADR) between the predicted and measured CSI.

Person re-identification (Re-ID) is a challenging task that tries to recognize a person across different cameras, and that can prove useful in video surveillance as well as in forensics and security applications. However, traditional Re-ID systems analyzing image or video sequences suffer from well-known issues such as illumination changes, occlusions, background clutter, and long-term re-identification. To simultaneously address all these difficult problems, we explore a Re-ID solution based on an alternative medium that is inherently not affected by them, i.e., the Wi-Fi technology. The latter, due to the widespread use of wireless communications, has grown rapidly and is already enabling the development of Wi-Fi sensing applications, such as human localization or counting. These sensing procedures generally exploit Wi-Fi signals variations that are a direct consequence, among other things, of human presence, and which can be observed through the channel state information (CSI) of Wi-Fi access points. Following this rationale, in this paper, for the first time in literature, we show how the pervasive Wi-Fi technology can also be directly exploited for person Re-ID. More accurately, Wi-Fi signals amplitude and phase are extracted from CSI measurements and analyzed through a two-branch deep neural network working in a siamese-like fashion. The designed pipeline can extract meaningful features from signals, i.e., radio biometric signatures, that ultimately allow the person Re-ID. The effectiveness of the proposed system is evaluated on a specifically collected dataset, where remarkable performances are obtained; suggesting that Wi-Fi signal variations differ between different people and can consequently be used for their re-identification.

With the ubiquity of commodity WiFi devices and the rapid development of Internet of Things (IoT), there are increasingly intelligent sensing applications emerging by utilizing fine-grained channel state information (CSI) from WiFi signals, which can realize contactless human-computer interaction (HCI) for smart home. However, most of CSI-based activity recognition approaches are vulnerable to random noises derived from indoor environments. In this paper, we present a multimodal fusion-AdaBoost based human activity recognition scheme on WiFi platform. For this purpose, we develop two theoretical underpinnings, including a sensing model and a recognition model. The sensing model is firstly established to investigate the impact of human activities on propagation properties of WiFi signals. To this end, Fresnel zone model is used to quantify the correlation between CSI dynamics and human activities. Moreover, the recognition model is used to exploit physical activity-induced signal changes to infer potential activity information. In the process, we firstly construct a CSI tensor through integrating all CSI information at each WiFi receiver. Then, a CANDECAMP/PARAFAC (CP) decomposition method is applied to this CSI tensor for obtaining representative features. Finally, based on the features extracted, human activities are able to be recognized by combining multimodal fusion and AdaBoost. We implement the proposed scheme on a set of WiFi devices and evaluate it in both laboratory and corridor environments. The experimental results confirm that the proposed scheme can achieve average recognition accuracies of 96% and 95% in two indoor scenarios, respectively.

Convolutional neural networks (CNN) have provided great advances for the task of sign language recognition (SLR). However, recurrent neural networks (RNN) in the form of long–short-term memory (LSTM) have become a means for providing solutions to problems involving sequential data. This research proposes the development of a sign language translation system that converts Panamanian Sign Language (PSL) signs into text in Spanish using an LSTM model that, among many things, makes it possible to work with non-static signs (as sequential data). The deep learning model presented focuses on action detection, in this case, the execution of the signs. This involves processing in a precise manner the frames in which a sign language gesture is made. The proposal is a holistic solution that considers, in addition to the seeking of the hands of the speaker, the face and pose determinants. These were added due to the fact that when communicating through sign languages, other visual characteristics matter beyond hand gestures. For the training of this system, a data set of 330 videos (of 30 frames each) for five possible classes (different signs considered) was created. The model was tested having an accuracy of 98.8%, making this a valuable base system for effective communication between PSL users and Spanish speakers. In conclusion, this work provides an improvement of the state of the art for PSL–Spanish translation by using the possibilities of translatable signs via deep learning.

Hand gesture is a visual input of human-computer interaction for providing different applications in smart homes, healthcare, and eldercare. Most deep learning-based techniques adopt standard convolution neural networks (CNNs) which require a large number of model parameters with high computational complexity; thus, it is not suitable for application in devices with limited computational resources. However, fewer model parameters can reduce the system accuracy. To address this challenge, we propose a lightweight heterogeneous deep learning-based gesture recognition system, coined CSI-DeepNet. The CSI-DeepNet comprises four steps: i) data collection, ii) data processing, iii) feature extraction, and iv) classification. We utilize a low-power system-on-chip (SoC), ESP-32, for the first time to collect alphanumeric hand gesture datasets using channel state information (CSI) with 1,800 trials of 20 gestures, including the steady-state data of ten people. A Butterworth low-pass filter with Gaussian smoothing is applied to remove noise; subsequently, the data is split into windows with sufficient dimensions in the data processing step before feeding to the model. The feature extraction section utilizes a depthwise separable convolutional neural network (DS-Conv) with a feature attention (FA) block and residual block (RB) to extract fine-grained features while reducing the complexity using fewer model parameters. Finally, the extracted refined features are classified in the classification section. The proposed system achieves an average accuracy of 96.31% with much less computational complexity, which is better than the results obtained using state-of-the-art pre-trained CNNs and two deep learning models using CSI data.

As the technique that determines the position of a target device based on wireless measurements, Wi-Fi localization is attracting increasing attention due to its numerous applications and the widespread deployment of Wi-Fi infrastructure. In this paper, we propose ConFi, the first convolutional neural network (CNN)-based Wi-Fi localization algorithm. Channel state information (CSI), which contains more position related information than traditional received signal strength, is organized into a time-frequency matrix that resembles image and utilized as the feature for localization. The ConFi models localization as a classification problem and addresses it with a five layer CNN that consists of three convolutional layers and two fully connected layers. The ConFi has a training stage and a localization stage. In the training stage, the CSI is collected at a number of reference points (RPs) and used to train the CNN via stochastic gradient descent algorithm. In the localization stage, the CSI of the target device is fed to the CNN and the localization result is calculated as the weighted centroid of the RPs with high output value. Extensive experiments are conducted to select appropriate parameters for the CNN and demonstrate the superior performance of the ConFi over existing methods.

BackgroundThe aging of the world population poses a major health challenge, and brain–computer interface (BCI) technology has the potential to provide assistance and rehabilitation for the elderly.ObjectivesThis study aimed to investigate the electroencephalogram (EEG) characteristics during motor imagery by comparing young and elderly, and study Convolutional Neural Networks (CNNs) classification for the elderly population in terms of fatigue analysis in both frontal and parietal regions.MethodsA total of 20 healthy individuals participated in the study, including 10 young and 10 older adults. All participants completed the left- and right-hand motor imagery experiment. The energy changes in the motor imagery process were analyzed using time–frequency graphs and quantified event-related desynchronization (ERD) values. The fatigue level of the motor imagery was assessed by two indicators: (θ + α)/β and θ/β, and fatigue-sensitive channels were distinguished from the parietal region of the brain. Then, rhythm entropy was introduced to analyze the complexity of the cognitive activity. The phase-lock values related to the parietal and frontal lobes were calculated, and their temporal synchronization was discussed. Finally, the motor imagery EEG data was classified by CNNs, and the accuracy was discussed based on the analysis results.ResultFor the young and elderly, ERD was observed in C3 and C4 channels, and their fatigue-sensitive channels in the parietal region were slightly different. During the experiment, the rhythm entropy of the frontal lobe showed a decreasing trend with time for most of the young subjects, while there was an increasing trend for most of the older ones. Using the CNN classification method, the elderly achieved around 70% of the average classification accuracy, which is almost the same for the young adults.ConclusionCompared with the young adults, the elderly are less affected by the level of cognitive fatigue during motor imagery, but the classification accuracy of motor imagery data in the elderly may be slightly lower than that in young persons. At the same time, the deep learning method also provides a potentially feasible option for the application of motor-imagery BCI (MI-BCI) in the elderly by considering the ERD and fatigue phenomenon together.