With the rapid development of Internet Things (IoT) and rise 5G communication networks automatic driving, millimeter wave (mmWave) sensing is emerging starts impacting our life workspace. mmWave can sense humans objects in a contactless way, providing fine-grained ability. In past few years, many techniques have been proposed applied various human applications (e.g., localization, gesture recognition, vital monitoring). We discover need comprehensive survey to summarize technology, platforms...

Deep Learning plays an increasingly important role in device-free WiFi Sensing for human activity recognition (HAR). Despite its strong potential, significant challenges exist and are associated with the fact that one may require a large amount of samples training, trained network cannot be easily adapted to new environment. To address these challenges, we develop novel scheme using matching enhanced channel state information (MatNet-eCSI) facilitate one-shot learning HAR. We propose CSI...

Deep learning has demonstrated its great potential in channel state information (CSI)-based human activity recognition (HAR), and hence attracted increasing attention both the industry academic communities. While promising, most existing high-accuracy methodologies require to retrain their models when applying previous-trained ones a new/unseen environment. This issue limited practical usabilities. In order overcome this challenge, article proposes an innovative scheme, which combines...

Spectrum sensing plays a critical role in dynamic spectrum sharing, promising technology to address the radio shortage. In particular, of orthogonal frequency division multiplexing (OFDM) signals, widely accepted multi-carrier transmission paradigm, has received paramount interest. Despite various efforts, noise uncertainty, timing delay and carrier offset (CFO) still remain as challenging problems, significantly degrading performance. this work, we develop two novel OFDM frameworks...

Facial expression recognition plays a vital role to enable emotional awareness in multimedia Internet of Things applications. Traditional camera or wearable sensor based approaches may compromise user privacy cause discomfort. Recent device-free open promising direction by exploring Wi-Fi ultrasound signals reflected from facial muscle movements, but limitations exist such as poor performance presence body motions and not being able detect multiple targets. To bridge the gap, we propose...

Channel State Information (CSI) is widely used for device free human activity recognition. Feature extraction remains as one of the most challenging tasks in a dynamic and complex environment. In this paper, we propose recognition scheme using Deep Learning Networks with enhanced information (DLN-eCSI). We develop CSI feature enhancement (CFES), including two modules background reduction correlation enhancement, preprocessing data input to DLN. After cleaning compressing signals CFES, apply...

Blood pressure (BP) measurement is an indispensable tool in diagnosing and treating many diseases such as cardiovascular failure stroke. Traditional direct can be invasive, wearable-based methods may have limitations of discomfort inconvenience. Contact-free BP has been recently advocated a promising alternative. In particular, Millimetre-wave (mmWave) sensing demonstrated its potential, however it confronted with several challenges including noise vulnerability to human's tiny motions which...

Deep learning has shown a strong potential in device-free human activity recognition (HAR). However, fundamental challenge is ensuring accuracy, without re-training, when exposing previously trained architecture to new or unseen environment. To overcome the aforementioned challenge, this paper proposes an environment-robust channel state information (CSI) based HAR by leveraging properties of matching network (MatNet) and enhanced features (HAR-MN-EF). improve CSI quality, we propose...

Device free WiFi Sensing using channel state information (CSI) has been shown great potentials for human activity recognition (HAR). However, extracting reliable and concise feature signals remains as a challenging problem, especially in dynamic complex environment. In this paper, we propose novel scheme CSI-based HAR deep learning network (CH-DLN), with an innovative CSI correlation extraction (CCFE) method. The CCFE method pre-processes the input to DLN two steps. Firstly, it uses...

The Cognitive Radio Sensor Network (CRSN) is considered as a viable solution to enhance various aspects of the electric power grid and realize smart grid. However, several challenges for CRSNs are generated due harsh wireless environment in As result, throughput reliability become critical issues. On other hand, spectrum aggregation technique expected play an important role By using aggregation, can be improved efficiently, so address unique In this regard, we proposed Spectrum Aggregation...

The relay satellite scheduling is a main content in Tracking and Data Relay Satellite System (TDRSS). How to build solve the models of key problem. In this paper, based on artificial bee colony algorithm proposed. Firstly, model daily task proposed which NP-hard as one combinatorial optimization problems. Then (ABC) given used Finally, some simulation results are presented. comparison with other swarm intelligence algorithms, provides better fits

Spectrum sensing technology plays an increasingly important role in cognitive radio networks. Consequently, several spectrum algorithms have been proposed the literature. In this paper, we present a new algorithm "Differential Characteristics-Based OFDM (DC-OFDM)" for detecting signal on account of differential characteristics. We put primary value channel gain θ around zero to detect presence user. Furthermore, utilizing same method operation, improve two traditional (cyclic prefix and...

Abstract Spectrum sensing is an important aspect of (interweave) cognitive radio network. In the particular case orthogonal frequency division multiplexing (OFDM) transmission, many previous spectrum algorithms have utilized unique correlation properties provided by cyclic prefix (CP). However, they also had to both estimate and compensate for inherent timing offset a practical system. This because will affect test statistic threshold, inaccurate estimation lead poor performance. So in this...

Spectrum sensing plays a critical role in dynamic spectrum sharing, promising technology to address the radio shortage. In particular, of Orthogonal frequency division multiplexing (OFDM) signals, widely accepted multi-carrier transmission paradigm, has received paramount interest. Despite various efforts, most conventional OFDM methods suffer from noise uncertainty, timing delay and carrier offset (CFO) that significantly degrade accuracy. To these challenges, this work develops two novel...

Deep learning (DL)-based signal detection techniques have demonstrated significantly superior performance than the conventional methods in orthogonal time frequency space (OTFS) systems. Despite effectiveness, existing using DL are environment-specific. For instance, a model trained one environment may become ineffective if environmental changes occur, e.g., user scheduling, inter-user interference and network scheduling. A re-training process is required to refine numerous samples from...

Although spectrum sensing, a key technique in dynamic access, has been widely investigated, conventional methods suffer from carrier frequency offset (CFO), timing delay and noise uncertainty, which can significantly degrade the sensing performance. In this paper, we aim to tackle those challenging issues by developing stacked autoencoder based approach (SAE-SS). The SAE architecture is employed effectively learn useful hidden information original received signals. Compared existing methods,...

Device-free WiFi sensing utilizing channel state information (CSI) is attractive for human activity recognition (HAR). However, several challenging problems are yet to be resolved, e.g., difficulty in extracting proper features from input signals, susceptibility the phase shift of CSI and identifying similar behaviors (e.g., lying standing). In this paper, we aim tackle these by proposing a novel scheme CSI-based HAR that uses filter-based deep learning network (HAR-AF-DLN) with enhanced...

In this paper, the decision-level data fusion techniques are extended to multiuser detection (MUD) field. Then two novel MUD algorithms, that is chairman arbitrating criterion (CA-DFC) based algorithm and veto logic (VL-DFC) algorithm, proposed for DS-UWB communication systems. CA-DFC method, can make his arbitration among preliminary decisions from sub-optimal detectors by own rule. VL-DFC undetermined bits in these considered construct a simplified solution space, then final decision...

This paper firstly analyzes the comparative advantages of carpooling under mobile Internet and traditional travel modes, including buses, private cars taxis, as well differences between carpooling, so to obtain factors that affect travelers’ mode choices. Secondly, mixed logit model is used describe choice behavior, which effectively avoids limitations IIA characteristics preference randomness model. Finally, we conducted an SP survey on 1077 samples online offline. After eliminating some...

Accelerator mass spectrometry (AMS) radiocarbon dating a continuous core from Lake Gun Nur, northern Mongolia, shows period between 10 and 8 ka BP that could not be dated accurately. Further on alkali-insoluble residue humic acid the same samples in Nur suggest this AMS 14 C date anomaly is neither analytical nor material related. We hypothesize may derived increasing production rates of caused by diminished solar activity, low CO 2 / ratio atmosphere, or an unstable flux lower atmosphere...

Applying blocking technology to the computation of big dense matrices can make a better use computer's memory hierarchies and increase computing efficiency. The blocked algorithm for LU factorization is studied in this paper. Efficient algorithms are designed different matrix operations involved algorithm. Optimization techniques including transposing loop unrolling used implementation computations. Experimental results show that block runs much faster than standard factorization. A speedup...

Waveform Division Multiple Access (WDMA) based on the orthogonal wavelet function is recently presented due to its admirable characteristics. However, communication performance of existing waveform system breaks down drastically as number users increases. In order meet requirement for WDMA-UWB with massive users, a new design pulse proposed in this paper. The correlation property and Bit Error Rate (BER) raised apparently close that single-user system.