Locally Minimum Storage Regenerating Codes in Distributed Cloud Storage Systems

In distributed cloud storage systems, inevitably there exist multiple node failures at the same time. The existing methods of regenerating codes, including minimum storage regenerating(MSR) codes and minimum bandwidth regenerating(MBR) codes, are mainly to repair one single or several failed nodes, unable to meet the repair need of distributed cloud storage systems. In this paper, we present locally minimum storage regenerating(LMSR) codes to recover multiple failed nodes at the same time. Specifically, the nodes in distributed cloud storage systems are divided into multiple local groups, and in each local group(4, 2) or(5, 3) MSR codes are constructed. Moreover, the grouping method of storage nodes and the repairing process of failed nodes in local groups are studied. Theoretical analysis shows that LMSR codes can achieve the same storage overhead as MSR codes. Furthermore, we verify by means of simulation that, compared with MSR codes, LMSR codes can reduce the repair bandwidth and disk I/O overhead effectively.

Integration Interval Determination and Decision Threshold Optimization for Improved TRPC-UWB Communication Systems

Integration interval and decision threshold issues were investigated for improved transmitted reference pulse cluster （iTRPC-） ultra-wideband （UWB） systems. Our analysis shows that the bit error rate （BER） performance of iTRPC-UWB systems can be significantly improved via integration interval determination （IID） and decision threshold optimization. For this purpose, two modifications can be made at the autocorrelation receiver as follows. Firstly, the liD processing is performed for autocorrelation operation to capture multi-path energy as much as possible. Secondly, adaptive decision threshold （ADT） instead of zero decision threshold （ZDT）, is used as estimated optimal decision threshold for symbol detection. Performance of iTRPCUWB systems using liD and ADT was evaluated in realistic IEEE 802.15.4a UWB channel models and the simulation results demonstrated our theoretical analysis.

Low-Density Parity-Check Codes for Noncoherent UWB Communication Systems

In order to guarantee reliable data transmission, powerful channel coding techniques are usually required in noncoherent ultra-wideband(UWB) communication systems. Accordingly, several forward error correction(FEC) codes, such as Reed-Solomon and convolutional codes have been used in noncoherent UWB systems to improve the bit error rate(BER) performance. In this paper, low-density parity-check(LDPC) codes are further studied as more powerful FEC candidates for noncoherent UWB systems. Two LDPC codes and the corresponding decoding procedures are presented for noncoherent UWB systems. Moreover, performance comparison between the LDPC codes and other FEC codes are provided for three major noncoherent UWB communication systems, namely, noncoherent pulse position modulation(NC-PPM), transmitted reference(TR) and transmitted reference pulse cluster(TRPC). Both theoretical analysis and simulation results show that the two investigated LDPC codes outperform other existing FEC codes with limited penalty in terms of complexity and therefore they are promising FEC candidates for noncoherent UWB systems with low-cost and low-power consumption.

Detection of Ventricular Fibrillation Using Random Forest Classifier

Early warning and detection of ventricular fibrillation is crucial to the successful treatment of this life-threatening condition. In this paper, a ventricular fibrillation classification algorithm using a machine learning method, random forest, is proposed. A total of 17 previously defined ECG feature metrics were extracted from fixed length segments of the echocardiogram (ECG). Three annotated public domain ECG databases (Creighton University Ventricular Tachycardia database, MIT-BIH Arrhythmia Database and MIT-BIH Malignant Ventricular Arrhythmia Database) were used for evaluation of the proposed method. Window sizes 3 s, 5 s and 8 s for overlapping and non-overlapping segmentation methodologies were tested. An accuracy (Acc) of 97.17%, sensitivity (Se) of 95.17% and specificity (Sp) of 97.32% were obtained with 8 s window size for overlapping segments. The results were benchmarked against recent reported results and were found to outper-form them with lower complexity.