| 1. |
- Bao, Nan, et al.
(author)
-
Wi-Breath : A WiFi-based Contactless and Real-time Respiration Monitoring Scheme for Remote Healthcare
- 2023
-
In: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208. ; 27:5, s. 2276-2285
-
Journal article (peer-reviewed)abstract
- Respiration rate is an important healthcare indicator, and it has become a popular research topic in remote healthcare applications with Internet of Things. Existing respiration monitoring systems have limitations in terms of convenience, comfort, and privacy, etc. This paper presents a contactless and real-time respiration monitoring system, the so-called Wi-Breath, based on off-the-shelf WiFi devices. The system monitors respiration with both the amplitude and phase difference of the WiFi channel state information (CSI), which is sensitive to human body micro movement. The phase information of the CSI signal is considered and both the amplitude and phase difference are used. For better respiration detection accuracy, a signal selection method is proposed to select an appropriate signal from the amplitude and phase difference based on a support vector machine (SVM) algorithm. Experimental results demonstrate that the Wi-Breath achieves an accuracy of 91.2% for respiration detection, and has a 17.0% reduction in average error in comparison with state-of-the-art counterparts.
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
- Deng, Min, et al.
(author)
-
Genome-wide association analyses in Han Chinese identify two new susceptibility loci for amyotrophic lateral sclerosis
- 2013
-
In: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 45:6, s. 697-
-
Journal article (peer-reviewed)abstract
- To identify susceptibility genes for amyotrophic lateral sclerosis (ALS), we conducted a genome-wide association study (GWAS) in 506 individuals with sporadic ALS and 1,859 controls of Han Chinese ancestry. Ninety top SNPs suggested by the current GWAS and 6 SNPs identified by previous GWAS were analyzed in an independent cohort of 706 individuals with ALS and 1,777 controls of Han Chinese ancestry. We discovered two new susceptibility loci for ALS at 1q32 (CAMK1G, rs6703183, P-combined = 2.92 x 10(-8), odds ratio (OR) = 1.31) and 22p11 (CABIN1 and SUSD2, rs8141797, P-combined = 2.35 x 10(-9), OR = 1.52). These two loci explain 12.48% of the overall variance in disease risk in the Han Chinese population. We found no association evidence for the previously reported loci in the Han Chinese population, suggesting genetic heterogeneity of disease susceptibility for ALS between ancestry groups. Our study identifies two new susceptibility loci and suggests new pathogenic mechanisms of ALS.
|
|
| 5. |
|
|
| 6. |
- Feng, Zhiwei, et al.
(author)
-
An Efficient UAV Hijacking Detection Method Using Onboard Inertial Measurement Unit
- 2019
-
In: ACM Transactions on Embedded Computing Systems. - : ASSOC COMPUTING MACHINERY. - 1539-9087 .- 1558-3465. ; 17:6
-
Journal article (peer-reviewed)abstract
- With the fast growth of civil drones, their security problems meet significant challenges. A commercial drone may be hijacked by a GPS-spoofing attack for illegal activities, such as terrorist attacks. The target of this article is to develop a technique that only uses onboard gyroscopes to determine whether a drone has been hijacked. Ideally, GPS data and the angular velocities measured by gyroscopes can be used to estimate the acceleration of a drone, which can be further compared with the measurement of the accelerometer to detect whether a drone has been hijacked. However, the detection results may not always be accurate due to some calculation and measurement errors, especially when no hijacking occurs in curve trajectory situations. To overcome this, in this article, we propose a novel and simple method to detect hijacking only based on gyroscopes' measurements and GPS data, without using any accelerometer in the detection procedure. The computational complexity of our method is very low, which is suitable to be implemented in the drones with micro-controllers. On the other hand, the proposed method does not rely on any accelerometer to detect attacks, which means it receives less information in the detection procedure and may reduce the results accuracy in some special situations. While the previous method can compensate for this flaw, the high detection results also can be guaranteed by using the above two methods. Experiments with a quad-rotor drone are conducted to show the effectiveness of the proposed method and the combination method.
|
|
| 7. |
- Feng, Zhiwei, et al.
(author)
-
Efficient Drone Hijacking Detection using Onboard Motion Sensors
- 2017
-
In: Proceedings Of The 2017 Design, Automation & Test In Europe Conference & Exhibition (DATE). - : IEEE. - 9783981537093 ; , s. 1414-1419
-
Conference paper (peer-reviewed)abstract
- The fast growth of civil drones raises significant security challenges. A legitimate drone may be hijacked by GPS spoofing for illegal activities, such as terrorist attacks. The target of this paper is to develop techniques to let drones detect whether they have been hijacked using onboard motion sensors (accelerometers and gyroscopes). Ideally, the linear acceleration and angular velocity measured by motion sensors can be used to estimate the position of a drone, which can be compared with the position reported by GPS to detect whether the drone has been hijacked. However, the position estimation by motion sensors is very inaccurate due to the significant error accumulation over time. In this paper, we propose a novel method to detect hijacking based on motion sensors measurements and GPS, which overcomes the accumulative error problem. The computational complexity of our method is very low, and thus is suitable to be implemented in the micro-controllers of drones. Experiments with a quad-rotor drone are conducted to show the effectiveness of the proposed method.
|
|
| 8. |
- Feng, Zhiwei, et al.
(author)
-
Efficient drone hijacking detection using two-step GA-XGBoost
- 2020
-
In: Journal of systems architecture. - : ELSEVIER. - 1383-7621 .- 1873-6165. ; 103
-
Journal article (peer-reviewed)abstract
- With the fast growth of civilian drones, their security problems meet significant challenges. A commercial drone may be hijacked by Global Positioning System (GPS)-spoofing attacks for illegal activities, such as terrorist attacks. Ideally, comparing positions respectively estimated by GPS and Inertial Navigation System (INS) can detect such attacks, while the results may always get fault because of the accumulated errors over time in INS. Therefore, in this paper, we propose a two-step GA-XGBoost method to detect GPS-spoofing attacks that just uses GPS and Inertial Measurement Unit (IMU) data. However, tunning the proper values of XGBoost parameters directly on the drone to achieve high prediction results consumes lots of resources which would influence the real-time performance of the drone. The proposed method separates the training phase into offboard step and onboard step. In offboard step, model is first trained by flight logs, and the training parameter values are automatically tuned by Genetic Algorithm (GA). Once the offboard model is trained, it could be uploaded to drones. To adapt our method to drones with different types of sensors and improve the correctness of prediction results, in onboard step, the model is further trained when a drone starts a mission. After onboard training finishes, the proposed method switches to the prediction mode. Besides, our method does not require any extra onboard hardware. The experiments with a real quadrotor drone also show the detection correctness is 96.3% and 100% in hijacked and non-hijacked cases at each sampling time respectively. Moreover, our method can achieve 100% detection correctness just within 1 s just after the attacks start.
|
|
| 9. |
|
|
| 10. |
- Guan, Nan, et al.
(author)
-
WCET Analysis with MRU Cache : Challenging LRU for Predictability
- 2014
-
In: ACM Transactions on Embedded Computing Systems. - : Association for Computing Machinery (ACM). - 1539-9087 .- 1558-3465. ; 13:4s
-
Journal article (peer-reviewed)abstract
- Most previous work on cache analysis for WCET estimation assumes a particular replacement policy called LRU. In contrast, much less work has been done for non-LRU policies, since they are generally considered to be very unpredictable. However, most commercial processors are actually equipped with these non-LRU policies, since they are more efficient in terms of hardware cost, power consumption and thermal output, while still maintaining almost as good average-case performance as LRU. In this work, we study the analysis of MRU, a non-LRU replacement policy employed in mainstream processor architectures like Intel Nehalem. Our work shows that the predictability of MRU has been significantly underestimated before, mainly because the existing cache analysis techniques and metrics do not match MRU well. As our main technical contribution, we propose a new cache hit/miss classification, k-Miss, to better capture the MRU behavior, and develop formal conditions and efficient techniques to decide k-Miss memory accesses. A remarkable feature of our analysis is that the k-Miss classifications under MRU are derived by the analysis result of the same program under LRU. Therefore, our approach inherits the advantages in efficiency and precision of the state-of-the-art LRU analysis techniques based on abstract interpretation. Experiments with instruction caches show that our proposed MRU analysis has both good precision and high efficiency, and the obtained estimated WCET is rather close to (typically 1%similar to 8% more than) that obtained by the state-of-the-art LRU analysis, which indicates that MRU is also a good candidate for cache replacement policies in real-time systems.
|
|
