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To further enhance the detection sensitivity of the bio-radar, a 94 GHz asymmetric antenna radar is used to acquire human speech signal in this paper. However, the radar speech signal are often degraded by sources of noise that include radar harmonic noise, electrocircuit noise, and ambient noise. Thus, this paper proposes a new algorithm, which uses a perceputal wavelet packet, teager energy operator and a modified thresholding function to suppress the noise of the radar speech signal. The spectrogram results show that this method can reduce the noise and significantly improve the performance of radar speech.

Objective. Although various driving fatigue detection strategies have been introduced, the limited practicability is still an obstacle for the real application of these technologies. This study is based on the newly proposed non-hair-bearing (NHB) method to achieve practical driving fatigue detection with fewer channels from NHB areas and more efficient electroencephalogram (EEG) features. Approach. EEG data were recorded from 20 healthy subjects (15 males, age = 22.2 ± 3.2 years) in a 90 min simulated driving task using a remote wireless cap. Behaviorally, subjects demonstrated a salient fatigue effect, as reflected by a monotonic increase in reaction time. Using a sliding-window approach, we determined the vigilant and fatigued states at individual level to reduce the inter-subject differences in behavioral impairment and brain activity. Multiple EEG features, including power-spectrum density (PSD), functional connectivity (FC), and entropy, were estimated in a pairwise manner, which were set as input for fatigue classification. Main results. Intriguingly, this data-driven approach showed that the best classification performance was achieved using three EEG channel pairs located in the NHB area. The mixed features of the frontal NHB area lead to the high within-subject detection rate of driving fatigue (92.7% ± 0.92%) with satisfactory generalizability for fatigue classification across different subjects (77.13% ± 0.85%). Moreover, we found the most prominent contributing features were PSD of different frequency bands within the frontal NHB area and FC within the frontal NHB area and between frontal and parietal areas. Significance. In summary, the current work provided objective evidence to support the effectiveness of the NHB method and further improved the performance, thereby moving a step forward towards practical driving fatigue detection in real-world scenarios.

The quantitative characterization of movement disorders and their related neurophysiological signals is important for the management of Parkinson&rsquo, s disease (PD). The aim of this study is to develop a novel wearable system enabling the simultaneous measurement of both motion and other neurophysiological signals in PD patients. We designed a wearable system that consists of five motion sensors and three electrophysiology sensors to measure the motion signals of the body, electroencephalogram, electrocardiogram, and electromyography, respectively. The data captured by the sensors are transferred wirelessly in real time, and the outcomes are analyzed and uploaded to the cloud-based server automatically. We completed pilot studies to (1) test its validity by comparing outcomes to the commercialized systems, and (2) evaluate the deep brain stimulation (DBS) treatment effects in seven PD patients. Our results showed: (1) the motion and neurophysiological signals measured by this wearable system were strongly correlated with those measured by the commercialized systems (r &gt, 0.94, p &lt, 0.001), and (2) by completing the clinical supination and pronation frequency test, the frequency of motion as measured by this system increased when DBS was turned on. The results demonstrated that this multi-sensor wearable system can be utilized to quantitatively characterize and monitor motion and neurophysiological PD.

Accumulating efforts have been made to discover effective solutions for fatigue recovery with the ultimate aim of reducing adverse consequences of mental fatigue in real life. The previously-reported behavioral benefits of physical exercise on mental fatigue recovery prompted us to investigate the restorative effect and reveal the underlying neural mechanisms. Specifically, we introduced an empirical method to investigate the beneficial effect of physical exercise on the reorganization of EEG functional connectivity (FC) in a two-session experiment where one session including a successive 30-min psychomotor vigilance task (PVT) ( No-intervention session ) compared to an insertion of a mid-task 15-min cycling exercise ( Intervention session ). EEG FC was obtained from 21 participants and quantitatively assessed via graph theoretical analysis and a classification framework. The findings demonstrated the effectiveness of exercise intervention on behavioral performance as shown in improved reaction time and response accuracy. Although we found significantly altered network alterations towards the end of experiment in both sessions, no significant differences between the two sessions and no interaction between session and time were found in EEG network topology. Further interrogation of functional connectivity through classification analysis showed decreased FC in distributed brain areas, which may lead to the significant reduction of network efficiency in both sessions. Moreover, we showed distinct patterns of FC alterations between the two sessions, indicating different information processing strategies adopted in the intervention session . In sum, these results provide some of the first quantitative insights into the complex neural mechanism of exercise intervention for fatigue recovery and lead a new direction for further application research in real-world situations.

The general (α,t)-path connectivity index of a molecular graph originates from many practical problems such as three-dimensional quantitative structure-activity (3D QSAR) and molecular chirality. It is defined as Rtα(G)=∑Pt=vi1vi2⋯vit+1⊆G[d(vi1)d(vi2)⋯d(vit+1)]α, where the summation is taken over all possible paths of length t of G and we do not distinguish between the paths vi1vi2⋯vit+1 and vit+1⋯vi2vi1. In this paper, we focus on the structures of Polycyclic Aromatic Hydrocarbons (PAHn), which play a role in organic materials and medical sciences. We try to compute the exact general (α,3)-path connectivity indices of this family of hydrocarbon structures. Furthermore, we exactly derive the monotonicity and the extremal values of R3α(PAHn) for any real number α. These valuable results could produce strong guiding significance to these applied sciences.

After chemical or nuclear leakage or explosions, finding survivors is a huge challenge. Although human bodies can be found by smart vehicles and drones equipped with cameras, it is difficult to verify if the person is alive or dead this way. This paper describes a continuous wave radar sensor for remotely sensing the vital signs of human subjects. Firstly, a compact and portable 24 GHz Doppler radar system is designed to conduct non-contact detection of respiration signal. Secondly, in order to improve the quality of the respiration signals, the self-correlation and adaptive line enhancer (ALE) methods are proposed to minimize the interferences of any moving objects around the human subject. Finally, the detection capabilities of the radar system and the signal processing method are verified through experiments which show that human respiration signals can be extracted when the subject is 7 m away outdoors. The method provided in this paper will be a promising way to search for human subjects outdoors.

Changes in intestinal flora in obese patients and intravascular C-reactive protein (CRP), interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) and coronary heart disease (CHD) were analyzed. A total of 75 cases of obese patients were divided into obesity (OB) alone (n=40) and OB with CHD group (n=35). There was no statistically significant difference in age, sex, pre-existing basic diabetes, history of hypertension, and body mass index (P>0.05). Results showed that total bacterial load of CHD was obviously higher than that of OB group. The uric acid decomposed by intestinal flora (IFUA) and blood uric acid levels in CHD were higher than those in OB group, but the fecal uric acid level was lower than that of OB group (P

Traditional errors-in-variables (EIV) models are widely adopted in applied sciences. In practice, some of the columns of the coefficient matrix in EIV models may be known exactly. EIV models with exactly known columns are called mixed EIV (MEIV) models. Similar to EIV models, the MEIV models can be highly biased by gross error. This paper focuses on robust estimation in MEIV models. A new class of robust estimators, called robust weighted mixed total least squared (RWMTLS) estimators for the MEIV models by M-estimator and Lagrange multiplier method, is introduced. A simulated example is carried out to demonstrate the performance of the presented RWMTLS algorithm. The result shows that the RWMTLS algorithm can indeed resist gross error to achieve the reliable solution.

In this paper, we analyze lithospheric density distribution of China and surrounding regions on the basis of 30′ × 30′ gravity data and 1° × 1° P-wave velocity data. Firstly, we used the empirical equation between the density and the P-wave velocity difference as the base of the initial model of the Asian lithospheric density. Secondly, we calculated the gravity anomaly, caused by the Moho discontinuity and the sedimentary layer discontinuity, by the Parker formula. Thirdly, the gravity anomaly of the spherical harmonics with 2–40 order for the anomalous body below the lithosphere is calculated based on the model of EGM96. Finally, by using Algebra Reconstruction Techniques (ART), the inversion of 30′ × 30′ residual lithospheric Bouguer gravity anomaly caused by the lithosphere yields a rather detailed structural model. The results show that the lithospheric density distribution of China and surrounding regions has a certain connection with the tectonic structure. The density is relatively high in the Philippine Sea plate, Japan Sea, the Indian plate, the Kazakhstan shield and the Western Siberia plain, whereas the Tibetan Plateau has low-density characteristics. The minimum value of density lies in the north of Philippines, in the Taiwan province and in the Ryukyu island arc.

Judgment and early danger warning of obstructive sleep apnea (OSA) is meaningful to the diagnosis of sleep illness. This paper proposed a novel method based on wavelet information entropy spectrum to make an apnea judgment of the OSA respiratory signal detected by bio-radar in wavelet domain. It makes full use of the features of strong irregularity and disorder of respiratory signal resulting from the brain stimulation by real, low airflow during apnea. The experimental results demonstrated that the proposed method is effective for detecting the occurrence of sleep apnea and is also able to detect some apnea cases that the energy spectrum method cannot. Ultimately, the comprehensive judgment accuracy resulting from 10 groups of OSA data is 93.1%, which is promising for the non-contact aided-diagnosis of the OSA.

A millimeter wave (MMW) radar sensor is employed in our laboratory to detect human speech because it provides a new non-contact speech acquisition method that is suitable for various applications. However, the speech detected by the radar sensor is often degraded by combined noise. This paper proposes a new perceptual wavelet packet method that is able to enhance the speech acquired using a 94 GHz MMW radar system by suppressing the noise. The process is as follows. First, the radar speech signal is decomposed using a perceptual wavelet packet. Then, an adaptive wavelet threshold and new modified thresholding function are employed to remove the noise from the detected speech. The results obtained from the speech spectrograms, listening tests and objective evaluation show that the new method significantly improves the performance of the detected speech.

It is a major challenge to search for survivors after chemical or nuclear leakage or explosions. At present, biological radar can be used to achieve this goal by detecting the survivor’s respiration signal. However, owing to the random posture of an injured person at a rescue site, the radar wave may directly irradiate the person’s head or feet, in which it is difficult to detect the respiration signal. This paper describes a multichannel-based antenna array technology, which forms an omnidirectional detection system via 24-GHz Doppler biological radar, to address the random positioning relative to the antenna of an object to be detected. Furthermore, since the survivors often have random body movement such as struggling and twitching, the slight movements of the body caused by breathing are obscured by these movements. Therefore, a method is proposed to identify random human-body movement by utilizing multichannel information to calculate the background variance of the environment in combination with a constant-false-alarm-rate detector. The conducted outdoor experiments indicate that the system can realize the omnidirectional detection of random human-body movement and distinguish body movement from environmental interference such as movement of leaves and grass. The methods proposed in this paper will be a promising way to search for survivors outdoors.

Physiological monitoring devices in modern clinical area are basically used electrodes or sensors directly touching the surface of human subject body, which will increase physiological and psychological load of the subjects. In order to realize non-contact monitoring of respiration and heartbeat, firstly, the micro bioradar was used to detect human body motion signal. Then, the respiration signal and heartbeat signal was extracted from the body-motion signal by using signal and conditioning circuits, digital filter and signal processing. Finally, the results of heart rate and breathing rate was wirelessly transmitted. The experimental results showed that the device for non-contact monitoring of respiration and heartbeat waveforms has advantages of small volume, low power consumption, which can realize the monitoring of physiological parameters in real time.

In order to improve the speech acquisition ability of a non-contact method, a 94 GHz millimeter wave (MMW) radar sensor was employed to detect speech signals. This novel non-contact speech acquisition method was shown to have high directional sensitivity, and to be immune to strong acoustical disturbance. However, MMW radar speech is often degraded by combined sources of noise, which mainly include harmonic, electrical circuit and channel noise. In this paper, an algorithm combining empirical mode decomposition (EMD) and mutual information entropy (MIE) was proposed for enhancing the perceptibility and intelligibility of radar speech. Firstly, the radar speech signal was adaptively decomposed into oscillatory components called intrinsic mode functions (IMFs) by EMD. Secondly, MIE was used to determine the number of reconstructive components, and then an adaptive threshold was employed to remove the noise from the radar speech. The experimental results show that human speech can be effectively acquired by a 94 GHz MMW radar sensor when the detection distance is 20 m. Moreover, the noise of the radar speech is greatly suppressed and the speech sounds become more pleasant to human listeners after being enhanced by the proposed algorithm, suggesting that this novel speech acquisition and enhancement method will provide a promising alternative for various applications associated with speech detection.

Doppler radar has been used to search survivors buried under earthquake rubble by contact-free detection of respiration signals. However, the respiration signals were minute and easy to be polluted by noise from moving-object around the survivor such as swing of flowers, leaves and bushes. In order to restrain the noise and improve the signal-to-noise ratio (SNR), the Adaptive Line Enhancer (ALE) was used to detect the respiration signals. Simulation and experiment results showed that the SNR was significantly improved.

This study exploits and tests the circuit self-repairing based on the evolution technology using Virtual Reconfigurable Circuit technology in order to further improve the viability of electronic system under a severe environment. First of all, this paper builds a gate circuit model for the digital circuit evolution and designs a binary chromosome encoding according to Virtual Reconfigurable Circuit technology. Afterwards, it puts forwards the working process to realize circuit self-repair and discusses ways to diagnose and repair circuit faults. In the simulation that a single particle in the space burns the logic gate in the DC brushless motor control circuit, common SA faults are produced, leading to abnormal operation of the motor. Finally, by means of online evolution method, the motor may rotate again by skipping wrong units. This experiment demonstrates that the evolution method may effectively solve SA faults in circuits, thus improving the reliability of circuits.

This chapter designs an evolvable hardware IP (intellectual property) core for system call in order to solve problems of lacking flexibility in hardware system and difficulties in expending the general scale. The VRC IP core is realized by the description of bottom functions of VRC in VHDL and the organization of functional elements with SCH file. The core is characterized with reuse like the traditional IP core. However, compared with the traditional FPGA core, the interior circuit of the core may be evolved dynamically. Besides, the core may be used by any FPGA since it is designed in VHDL. Finally, the IP core evolves a motor control system in Spartan-3E FPGA and the evolved IP core is transplanted into a Virtex5 FPGA with high property. The experimental results show that the IP is provided with good portability and flexible connectivity.

The through-wall detection and classification of human activities are critical for anti-terrorism, security, and disaster rescue operations. An effective through-wall detection and classification technology is proposed for finer-grained human activities such as piaffe, picking up an object, waving, jumping, standing with random micro-shakes, and breathing while sitting. A stepped-frequency continuous wave (SFCW) bio-radar sensor is first used to conduct through-wall detection of finer-grained human activities; Then, a comprehensive range accumulation time-frequency transform (CRATFR) based on inverse weight coefficients is proposed, which aims to strengthen the micro-Doppler features of finer activity signals. Finally, in combination with the effective eigenvalues extracted from the CRATFR spectrum, an optimal self-adaption support vector machine (OS-SVM) based on prior human position information is introduced to classify different finer-grained activities. At a fixed position (3 m) behind a wall, the classification accuracies of six activities performed by eight individuals were 98.78% and 93.23%, respectively, for the two scenarios defined in this paper. In the position-changing experiment, an average classification accuracy of 86.67% was obtained for five finer-grained activities (excluding breathing) of eight individuals within 6 m behind the wall for the most practical scenario, a significant improvement over the 79% accuracy of the current method.