Physiologically, the thoracic (THO) and abdominal (ABD) movement signals, captured using wearable piezo-electric bands, provide information about various types of apnea, including central sleep apnea (CSA) and obstructive sleep apnea (OSA). However, the use of piezo-electric wearables in detecting sleep apnea events has been seldom explored in the literature. This study explored the possibility of identifying sleep apnea events, including OSA and CSA, by solely analyzing one or both the THO and ABD signals. An adaptive non-harmonic model was introduced to model the THO and ABD signals, which allows us to design features for sleep apnea events. To confirm the suitability of the extracted features, a support vector machine was applied to classify three categories - normal and hypopnea, OSA, and CSA. According to a database of 34 subjects, the overall classification accuracies were on average 75.9%±11.7% and 73.8%±4.4%, respectively, based on the cross validation. When the features determined from the THO and ABD signals were combined, the overall classification accuracy became 81.8%±9.4%. These features were applied for designing a state machine for online apnea event detection. Two event-byevent accuracy indices, S and I, were proposed for evaluating the performance of the state machine. For the same database, the S index was 84.01%±9.06%, and the I index was 77.21%±19.01%. The results indicate the considerable potential of applying the proposed algorithm to clinical examinations for both screening and homecare purposes.
This paper presents an ultra-wideband (UWB) impulse-radio radar signal processing platform used to analyze human respiratory features. Conventional radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory signal information is available that has not been explored using radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB signal processing platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB radar signal processing platform including a radar front-end chip and an FPGA chip. The proposed radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.
Cooperative spectrum sensing has recently become an important research topic for cognitive radio systems because it solves the hidden terminal problem in single-user spectrum sensing. However, idle cognitive users must consume massive spectrum sensing energy for one operating cognitive user. This characteristic reduces the attraction of the cooperative spectrum sensing technique in practical cognitive radio systems. Therefore, this paper develops a partial spectrum sensing algorithm with decision result prediction (DRP) and decision result modification (DRM) techniques to reduce the cooperative spectrum sensing energy. This study also designs and implements an energy-saving spectrum sensing processor for cognitive radio systems. The proposed cooperative spectrum sensing chip reduces energy consumption by about 64% for one fast Fourier transform (FFT) spectrum sensing calculation. For any given specified spectrum detection time, the proposed chip could also improve the detection performance compared to the traditional FFT spectrum sensing.Index Terms-Cognitive radio, cooperative spectrum sensing, integrated circuit, partial fast Fourier transform (FFT).
This paper presents a modified interpolation-based QR decomposition algorithm for the grouped-ordering multipleinput multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Based on the original research that integrates the calculations of the frequency-domain channel estimation and the QR decomposition for the MIMO-OFDM system, this study proposes a modified algorithm that possesses a scalable property to save the power consumption for interpolation-based QR decomposition in the variable-rank MIMO scheme. Furthermore, we also develop the general equations and a timing scheduling method for the hardware design of the proposed QR decomposition processor for the higher-dimension MIMO system. Based on the proposed algorithm, a configurable interpolation-based QR decomposition and channel estimation processor was designed and implemented using a 90-nm one-poly nine-metal CMOS technology. The processor supports 2 2, 2 4 and 4 4 QR-based MIMO detection for the 3GPP-LTE MIMO-OFDM system and achieves the throughput of 35.16 MQRD/s at its maximum clock rate 140.65 MHz.
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