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Using DNA as the medium to store information has recently been recognized as a promising solution for long-term data storage. While several system prototypes have been demonstrated, the error characteristics in DNA data storage are discussed with limited content. Due to the data and process variations from experiment to experiment, the error variation and its effect on data recovery remain to be uncovered. To close the gap, we systematically investigate the storage channel, i.e., error characteristics in the storage process. In this work, we first propose a new concept named sequence corruption to unify the error characteristics into the sequence level, easing the channel analysis. Then we derived the formulations of the data imperfection at the decoder including both sequence loss and sequence corruption, revealing the decoding demand and monitoring the data recovery. Furthermore, we extensively explored several data-dependent unevenness observed in the base error patterns and studied a few potential factors and their impacts on the data imperfection at the decoder both theoretically and experimentally. The results presented here introduce a more comprehensive channel model and offer a new angle towards the data recovery issue in DNA data storage by further elucidating the error characteristics of the storage process.

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DNA has become an attractive medium for long-term data archiving due to its extremely high storage density and longevity. Short single-stranded DNAs, called oligonucleotides (oligos), have been designed and synthesized to store digital data. Previous works designed the oligos with a pair of primer binding sites (PBSs) (each with a length of around 200) attached at the two ends of each basic readable data block. The addition of PBSs decreases the data density significantly because in the current DNA synthesis, the maximum length of a synthesized oligo in good quality is around 200. Furthermore, the maximum homopolymer run allowed by the existing experiments has been reported to be three nucleotides. In this work, to increase the data density, we have devised and tested an oligo design for DNA-based storage with the basic readable data block appended by a single PBS at one end only, while allowing the maximum homopolymer run to be increased to 4. We also present an oligo assembly algorithm that can reconstruct oligos with a single PBS from the error-prone raw readouts obtained from the sequencing process. We have conducted a wet lab experiment to validate the proposed design, where we tested with 398KB of data stored into 10,750 oligos. The experimental results show that it is possible to recover over 99 percent of the oligo sequences without error, which proves that one PBS is sufficient for implementing a DNA-based data storage system with maximum homopolymer run relaxed to 4. The use of single PBS leads to a significant data density gain from 14.3 to 140.2 percent over the existing short-strand DNA data storage schemes by reserving more nucleotides for storing information bits.

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BACKGROUND: With the inherent high density and durable preservation, DNA has been recently recognized as a distinguished medium to store enormous data over millennia. To overcome the limitations existing in a recently reported high-capacity DNA data storage while achieving a competitive information capacity, we are inspired to explore a new coding system that facilitates the practical implementation of DNA data storage with high capacity. RESULT: In this work, we devised and implemented a DNA data storage scheme with variable-length oligonucleotides (oligos), where a hybrid DNA mapping scheme that converts digital data to DNA records is introduced. The encoded DNA oligos stores 1.98 bits per nucleotide (bits/nt) on average (approaching the upper bound of 2 bits/nt), while conforming to the biochemical constraints. Beyond that, an oligo-level repeat-accumulate coding scheme is employed for addressing data loss and corruption in the biochemical processes. With a wet-lab experiment, an error-free retrieval of 379.1 KB data with a minimum coverage of 10x is achieved, validating the error resilience of the proposed coding scheme. Along with that, the theoretical analysis shows that the proposed scheme exhibits a net information density (user bits per nucleotide) of 1.67 bits/nt while achieving 91% of the information capacity. CONCLUSION: To advance towards practical implementations of DNA storage, we proposed and tested a DNA data storage system enabling high potential mapping (bits to nucleotide conversion) scheme and low redundancy but highly efficient error correction code design. The advancement reported would move us closer to achieving a practical high-capacity DNA data storage system.

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Light-regulated modules offer unprecedented new ways to control cellular behavior in precise spatial and temporal resolution. The availability of such tools may dramatically accelerate the progression of synthetic biology applications. Nonetheless, current optogenetic toolbox of prokaryotes has potential issues such as lack of rapid and switchable control, less portable, low dynamic expression and limited parts. To address these shortcomings, we have engineered a novel bidirectional promoter system for Escherichia coli that can be induced or repressed rapidly and reversibly using the blue light dependent DNA-binding protein EL222. We demonstrated that by modulating the dosage of light pulses or intensity we could control the level of gene expression precisely. We show that both light-inducible and repressible system can function in parallel with high spatial precision in a single cell and can be switched stably between ON- and OFF-states by repetitive pulses of blue light. In addition, the light-inducible and repressible expression kinetics were quantitatively analysed using a mathematical model. We further apply the system, for the first time, to optogenetically synchronize two receiver cells performing different logic behaviors over time using blue light as a molecular clock signal. Overall, our modular approach layers a transformative platform for next-generation light-controllable synthetic biology systems in prokaryotes.

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This paper presents a new highly accurate gait phase detection system using wearable wireless ultrasonic sensors, which can be used in gait analysis or rehabilitation applications. The gait phase detection system uses the foot displacement information during walking to extract the following gait phases: heel-strike, heel-off, toe-off, and mid-swing. The displacement of foot-mounted ultrasonic sensor is obtained from several passive anchors placed at known locations by employing local spherical positioning technique, which is further enhanced by the combination of recursive Newton-Gauss method and Kalman Filter. The algorithm performance is examined by comparing with a commercial optical motion tracking system with ten healthy subjects and two foot injured subjects. Accurate estimates of gait cycle (with an error of -0.02 ±0.01 s), stance phase(with an error of 0.04±0.03 s), and swing phase (with an error of -0.05±0.03 s) compared to the reference system are obtained. We have also investigated the influence of walking velocities on the performance of the proposed gait phase detection algorithm. Statistical analysis shows that there is no significant difference between both systems during different walking speeds. Moreover, we have tested and discussed the possibility of the proposed system for clinical applications by analyzing the experimental results for both healthy and injured subjects. The experiments show that the estimated gait phases have the potential to become indicators for sports and rehabilitation engineering.

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Owing to its efficiency and cost effectiveness, auscultation using a stethoscope is the primary diagnosis method preferred by most medical examiners for cardiovascular disease. Phonocardiography (PCG) is the next level of auscultation and an effective analysis technique in cardiology. This paper presents a method to quantify Mitral Regurgitation (MR) using PCG. MR is one of the most common cardiovascular diseases associated with a defect in mitral valve. We analyzed the multifractal nature of heart sounds with murmur followed by classification and clustering using Hurst exponents. Thus, we were able to project the difference between severities of MR patients by correlating the complexity of heart sounds from four major auscultation sites, using a complexity analysis tool called singularity spectra. The simulation results show that the method can quantify the severity of MR using PCG.

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In order to assist cardiac diagnosis by phonocardiography, the automated identification of fundamental heart sounds for heart beat segmentation in a cardiac cycle plays a significant role in signal processing. Recent advancements in signal processing have also shown the potential of multifractality in biomedical applications. Hence, in this paper, the multifractal property of heart sounds is utilized to identify first and second heart sounds. The root mean square (rms) fluctuation used to obtain multifractal/singularity spectrum is used to decompose the heart sound into its own fractally-important components in time domain along with simultaneous Gaussianity test to filter out fundamental components. The performance is evaluated on an experimental database of 23 different heart sounds and 6 patients' recordings done in a real clinical environment. Simulation results have shown that it is a promising approach in Heart Sound Segmentation (HSS).

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This paper presents an unrestrained measurement system based on a wearable wireless ultrasonic sensor network to track the lower extremity joint and trunk kinematics during a squat exercise with only one ultrasonic sensor attached to the trunk. The system consists of an ultrasound transmitter (mobile) and multiple receivers (anchors) whose positions are known. The proposed system measures the horizontal and vertical displacement, together with known joint constraints, to estimate joint flexion/extension angles using an inverse kinematic model based on the damped least-squares technique. The performance of the proposed ultrasonic measurement system was validated against a camera-based tracking system on eight healthy subjects performing a planar squat exercise. Joint angles estimated from the ultrasonic system showed a root mean square error (RMSE) of 2.85° ± 0.57° with the reference system. Statistical analysis indicated great agreements between these two systems with a Pearson's correlation coefficient (PCC) value larger than 0.99 for all joint angles' estimation. These results show that the proposed ultrasonic measurement system is useful for applications, such as rehabilitation and sports.

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Distributed transmission rate tuning is important for a wide variety of IEEE 802.15.4 network applications such as industrial network control systems. Such systems often require each node to sustain certain throughput demand in order to guarantee the system performance. It is thus essential to determine a proper transmission rate that can meet the application requirement and compensate for network imperfections (e.g., packet loss). Such a tuning in a heterogeneous network is difficult due to the lack of modeling techniques that can deal with the heterogeneity of the network as well as the network traffic changes. In this paper, a distributed transmission rate tuning algorithm in a heterogeneous IEEE 802.15.4 CSMA/CA network is proposed. Each node uses the results of clear channel assessment (CCA) to estimate the busy channel probability. Then a mathematical framework is developed to estimate the on-going heterogeneous traffics using the busy channel probability at runtime. Finally a distributed algorithm is derived to tune the transmission rate of each node to accurately meet the throughput requirement. The algorithm does not require modifications on IEEE 802.15.4 MAC layer and it has been experimentally implemented and extensively tested using TelosB nodes with the TinyOS protocol stack. The results reveal that the algorithm is accurate and can satisfy the throughput demand. Compared with existing techniques, the algorithm is fully distributed and thus does not require any central coordination. With this property, it is able to adapt to traffic changes and re-adjust the transmission rate to the desired level, which cannot be achieved using the traditional modeling techniques.

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Techniques that could be used to monitor human motion precisely are helpful in various applications such as rehabilitation, gait analysis, and athletic performance analysis. This paper focuses on the 3-D foot trajectory measurements based on a wearable wireless ultrasonic sensor network. The system consists of an ultrasonic transmitter (mobile) and several receivers (anchors) with fixed known positions. In order not to restrict the movement of subjects, a radio frequency (RF) module is used for wireless data transmission. The RF module also provides the synchronization clock between mobile and anchors. The proposed system measures the time-of-arrival (TOA) of the ultrasonic signal from mobile to anchors. Together with the knowledge of the anchor's position, the absolute distance that the signal travels can be computed. Then, the range information defines a circle centered at this anchor with radius equal to the measured distance, and the mobile resides within the intersections of several such circles. Based on the TOA-based tracking technique, the 3-D foot trajectories are validated against a camera-based motion capture system for ten healthy subjects walking on a treadmill at slow, normal, and fast speeds. The experimental results have shown that the ultrasonic system has sufficient accuracy of net root-mean-square error ( 4.2 cm) for 3-D displacement, especially for foot clearance with accuracy and standard deviation ( 0.62 ±7.48 mm) compared to the camera-based motion capture system. The small form factor and lightweight feature of the proposed system make it easy to use. Such a system is also much lower in cost compared to the camera-based tracking system.

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Energy is scarce in mobile computing devices including wearable and implantable devices in a wireless body area network. In this paper, an adaptive routing protocol is developed and analyzed which minimizes the energy cost per bit of information by using the channel information to choose the best strategy to route data. In this approach, the source node will switch between direct and relayed communication based on the quality of the link and will use the relay only if the channel quality is below a certain threshold. The mathematical model is then validated through simulations which shows that the adaptive routing strategy can improve energy efficiency significantly compared with existing methods.

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In this paper, a low-cost motion analysis system using a wireless ultrasonic sensor network is proposed and investigated. A methodology has been developed to extract spatial-temporal gait parameters including stride length, stride duration, stride velocity, stride cadence, and stride symmetry from 3D foot displacements estimated by the combination of spherical positioning technique and unscented Kalman filter. The performance of this system is validated against a camera-based system in the laboratory with 10 healthy volunteers. Numerical results show the feasibility of the proposed system with average error of 2.7% for all the estimated gait parameters. The influence of walking speed on the measurement accuracy of proposed system is also evaluated. Statistical analysis demonstrates its capability of being used as a gait assessment tool for some medical applications.

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In this paper, a new method for measuring and monitoring human body joint angles, which uses wearable ultrawideband (UWB) transceivers mounted on body segments, is proposed and investigated. The model is based on providing a high ranging accuracy (intersensor distance) between a pair of transceivers placed on the adjacent segments of the joint center of rotation. The measured distance is then used to compute the joint angles based on the law of cosines. The performance of the method was compared with a flexible goniometer by simultaneously measuring joint flexion-extension angles at different angular velocities, ranging between 8 and 90(°) /s. The measurement errors were evaluated by the average differences between two sets of data (ranging from 0.8(°) for slow movement to 2.8(°) for fast movement), by standard deviation (ranging from 1.2(°) to 4.2(°) for various movement speeds) and by the Pearson correlation coefficient (greater than 0.99) which demonstrates the very good performance of the UWB-based approach. The experimental results have shown that the system has sufficient accuracy for clinical applications, such as rehabilitation.

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This paper introduces a novel method for arm flexion/extension angles measurement using wireless ultrasonic sensor network. The approach uses unscented Kalman filter and D-H kinematical chain model to retrieve the joint angles. This method was experimentally validated by calculating the 2-dimensional wrist displacements from one mobile, placed on the point of subject's wrist, and four anchors. The performance of the proposed ultrasonic motion analysis system was bench-marked by commercial camera motion capture system. The experimental results demonstrate that a favorable performance of the proposed system in the estimation of upper limb motion. The proposed system is wireless, easy to wear, to use and much cheaper than current camera system. Thus, it has the potential to become a new and useful tool for routine clinical assessment of human motion.

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In this paper, an ultrasonic-based system for foot parameters measurement has been proposed and investigated. An extended Kalman filtering-based methodology has been developed to extract foot parameters including step length, stride length and cycle time from horizontal displacement during walking. The system comprises of one ultrasonic transmitter (mobile) and four ultrasonic receivers (anchors) with fixed known positions. A Radio Frequency (RF) module is used in our system not only to provide synchronization clock between the mobile and anchors, but also to transmit collected data wirelessly to reduce the wires used. To evaluate the performance of the proposed system, the 2-dimensional foot displacement and the foot parameters were measured and validated against the reference camera motion capture system. These experiment results demonstrate the capability of the proposed system being used as a gait analysis tool for rehabilitation and other medical applications.

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Foot clearance above ground is a key factor for a better understanding of the complicated relationship between falls and gait. This paper proposes a wearable system using UWB transceivers to monitor the vertical heel/toe clearance during walking. First, a pair of very small and light antennas is placed on a point approximating to the heel/toe of the foot, acting as a transmitter and receiver. Then, the reflected signal from ground is captured and propagation delay is detected using noise suppressed Modified-Phase-Only-Correlator (MPOC). The performance of the UWB-based system was compared with an ultrasound system for stationary movements. The experimental results show that an overall mean difference between these two systems is about 0.634mm with correlation coefficient value of 0.9604. The UWB-based system is then used to measure foot clearance during walking which shows promising results for gait events detection.

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This paper proposes a wearable system using UWB transceivers to measure the knee flexion/extension angle parameter, who is known to be of clinical importance. First, a pair of very small and light antennas is placed on the adjacent segments of knee joint. Then, the range data between these two antennas is acquired using Time of Arrival (TOA) estimator. We further use the measured distance to compute the flexion/extension angle using the law of cosines. The performance of the method was compared with a flexible goniometer by simultaneously measuring knee flexion-extension angle. The experimental results show that the system has reasonable performance and has sufficient accuracy for clinical applications.

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We design a impulse radio ultrawideband radar monitoring system to track the chest wall movement of a human subject during respiration. Multiple sensors are placed at different locations to ensure that the backscattered signal could be detected by at least one sensor no matter which direction the human subject faces. We design a hidden Markov model to infer the subject facing direction and his or her chest movement. We compare the performance of our proposed scheme on 15 human volunteers with the medical gold standard using respiratory inductive plethysmography (RIP) belts, and show that on average, our estimation is over 81% correlated with the measurements of a RIP belt system. Furthermore, in order to automatically differentiate between periods of normal and abnormal breathing patterns, we develop a change point detection algorithm based on perfect simulation techniques to detect changes in the subject's breathing. The feasibility of our proposed system is verified by both the simulation and experiment results.

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This paper studies the possibility of distinguishing between benign and malignant masses by exploiting the morphology-dependent temporal and spectral characteristics of their microwave backscatter response in ultra-wideband breast cancer detection. The spiculated border profiles of 2-D breast masses are generated by modifying the baseline elliptical rings based upon the irregularity of their peripheries. Furthermore, the single- and multilayer lesion models are used to characterize a distinct mass region followed by a sharp transition to background, and a blurred mass border exhibiting a gradual transition to background, respectively. Subsequently, the complex natural resonances (CNRs) of the backscatter microwave signature can be derived from the late-time target response and reveal diagnostically useful information. The fractional sequence CLEAN algorithm is proposed to estimate the lesions' delay intervals and identify the late-time responses. Finally, it is shown through numerical examples that the locations of dominant CNRs are dependent on the lesion morphologies, where 2-D computational breast phantoms with single and multiple lesions are investigated. The analysis is of potential use for discrimination between benign and malignant lesions, where the former usually possesses a better-defined, more compact shape as opposed to the latter.

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It has been shown that remote monitoring of pulmonary activity can be achieved using ultra-wideband (UWB) systems, which shows promise in home healthcare, rescue, and security applications. In this paper, a geometry-based statistical channel model is developed for simulating the reception of UWB signals in the indoor propagation environment. This model enables replication of time-varying multipath profiles due to the displacement of a human chest. Subsequently, a UWB distributed cognitive radar system (UWB-DCRS) is developed for the robust detection of chest cavity motion and the accurate estimation of respiration rate. The analytical framework can serve as a basis in the planning and evaluation of future measurement programs.

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