For mission-critical URLLC applications, timely status updates are essential. This paper investigates the age of information (AoI) of the three HARQ schemes specified in 5G R16, targeting to provide guidelines for future grant-free access design in 5G-Advanced and beyond. Specifically, we analyze two packet management policies: First-come-first-serve (FCFS) and preemption policy where new packets always preempt the buffer. We also study the AoI in a latency-sensitive scenario where expired packets are discarded. We derive exact expressions of AoI and peak AoI for all schemes and their lower bounds, revealing that the number of the maximum consecutive transmissions is critical for information freshness. Simulation results validate the theoretical analysis and show that Proactive HARQ scheme outperforms K-repetition HARQ scheme unconditionally and Reactive HARQ scheme with moderate system load or above. And discarding expired packets enhances system robustness for overload systems but yields larger AoI.

The lightweight and energy-efficient Frame Slotted Aloha (FSA) protocol has become a promising MAC protocol in large-scale IoT systems. Existing work on minimizing the age of information (AoI) of FSA protocol cannot significantly benefit from frequent packet generations when the packet generation rate$\lambda$exceeds its throughput$e^{-1}$. To fill this gap, this paper proposes two age threshold-based algorithms to reduce the AoI of FSA systems for$\lambda > e^{-1}$, namely TF and TF+. Their core ideas are to only allow the nodes with age gain over the configured thresholds to send their packets so that the FSA systems are slimmed to a stable one with$\lambda < e^{-1}$and a polling system, respectively. Technically, we design the threshold configuration rules for the two algorithms and characterize the normalized average AoI. We also conduct simulation and the results show that TF and TF+ achieve lower AoI than the prior works.

Frame slotted Aloha (FSA) is the de facto MAC layer standard protocol in many ultra-low-power IoT applications, such as Radio Frequency Identification (RFID) and Machine to Machine (M2M) communications. As the age of information (AoI) is an emerging and critical metric for quantifying the freshness of the status update information collected in time-sensitive IoT applications, systematic analysis of AoI for FSA is called for. However, very limited work has been done on this topic despite its both theoretical and practical implications for the operation and optimization of FSA. To fill this void, this paper delivers a comprehensive analysis of AoI for four versions of FSA, namely synchronous and asynchronous FSA with and without retransmission. The core technique of our analysis is to model the AoI for FSA as Markov chains to derive statistics on the delay and inter-delivery time. Our central results consist of the lower bounds of AoI, the exact AoI expressions in the four FSA protocols and the optimum frame length for the AoI of FSA. Our analysis reveals the impact of the arrival rate and the protocol parameters on AoI, and also shows that the retransmission would improve AoI when the arrival rate is small.

As the symbol rate gradually increases, inter-symbol interference becomes more serious. High-performance and low-resource equalization technology has become one of the research hotspots in the signal processing area. In this paper, for channel with constant characteristics, a digital-analog hybrid equalizer of broadband signals based on equivalent time sampling is proposed. The equivalent time sampling technology can reconstruct signals at a sampling rate much lower than the Nyquist sampling rate, thus, it can greatly alleviate the pressure on the ADC and reduce the processor's resource consumption. In addition, the dual-mode blind equalization algorithm can achieve fast convergence and accurately compensate for errors caused by non-ideal channel. Moreover, the numerical results show that the performance of the dual-mode multi-modulus algorithm and decision-directed algorithm (MMA-DD) is better than the multi-modulus algorithm (MMA) which can further reduce the residual error to accurately track the channel characteristics.

Deriving accurate snow depth is of great importance since snow cover is an informative indicator of climate change. The objective of this study is to develop a snow-depth retrieval algorithm based on a deep learning approach by fusing passive microwave remote sensing brightness temperature, station observations and GNSS-R snow-depth product to improve the accuracy of snow-depth retrieval. The results show that DBN performs the best compared with three alternative algorithms.

Automatic seizure detection is of great significance in the monitoring and diagnosis of epilepsy. In this study, a novel method is proposed for automatic seizure detection in intracranial electroencephalogram (iEEG) recordings based on kernel collaborative representation (KCR). Firstly, the EEG recordings are divided into 4s epochs, and then wavelet decomposition with five scales is performed. After that, detail signals at scales 3, 4 and 5 are selected to be sparsely coded over the training sets using KCR. In KCR, l2-minimization replaces l1-minimization and the sparse coefficients are computed with regularized least square (RLS), and a kernel function is utilized to improve the separability between seizure and nonseizure signals. The reconstructed residuals of each EEG epoch associated with seizure and nonseizure training samples are compared and EEG epochs are categorized as the class that minimizes the reconstructed residual. At last, a multi-decision rule is applied to obtain the final detection decision. In total, 595 h of iEEG recordings from 21 patients with 87 seizures are employed to evaluate the system. The average sensitivity of 94.41%, specificity of 96.97%, and false detection rate of 0.26/h are achieved. The seizure detection system based on KCR yields both a high sensitivity and a low false detection rate for long-term EEG.

The automatic identification of epileptic EEG signals is significant in both relieving heavy workload of visual inspection of EEG recordings and treatment of epilepsy. This paper presents a novel method based on the theory of sparse representation to identify epileptic EEGs. At first, the raw EEG epochs are preprocessed via Gaussian low pass filtering and differential operation. Then, in the scheme of sparse representation based classification (SRC), a test EEG sample is sparsely represented on the training set by solving l1-minimization problem, and the represented residuals associated with ictal and interictal training samples are computed. The test EEG sample is categorized as the class that yields the minimum represented residual. So unlike the conventional EEG classification methods, the choice and calculation of EEG features are avoided in the proposed framework. Moreover, the kernel trick is employed to generate a kernel version of the SRC method for improving the separability between ictal and interictal classes. The satisfactory recognition accuracy of 98.63% for ictal and interictal EEG classification and for ictal and normal EEG classification has been achieved by the kernel SRC. In addition, the fast speed makes the kernel SRC suit for the real-time seizure monitoring application in the near future.

The feature analysis of epileptic EEG is very significant in diagnosis of epilepsy. This paper introduces two nonlinear features derived from fractal geometry for epileptic EEG analysis. The features of blanket dimension and fractal intercept are extracted to characterize behavior of EEG activities, and then their discriminatory power for ictal and interictal EEGs are compared by means of statistical methods. It is found that there is significant difference of the blanket dimension and fractal intercept between interictal and ictal EEGs, and the difference of the fractal intercept feature between interictal and ictal EEGs is more noticeable than the blanket dimension feature. Furthermore, these two fractal features at multi-scales are combined with support vector machine (SVM) to achieve accuracies of 97.58% for ictal and interictal EEG classification and 97.13% for normal, ictal and interictal EEG classification.

Distributed multimedia applications such as video on demand (VoD), require dynamic quality of service (QoS) guarantee from service servers for their continuous multimedia streams. In this paper, we build the service availability Markovian model for unified failure-recovery mechanism under variant load scenarios, by calculating local and global kernels of the Markovain model, we get the steady-state availability and unavailability probabilities. Numerical results show that there exist differences between the system availability and the request perceived availability under variant load state (the nature of this kind of system's scenario). This provides strategies for improving VoD system's availability

Introduces a new recursive algorithm for solving highly ill-conditioned linear algebraic equations without any cutoff error. It has the following properties: (1) all arithmetic operations are just related to integer additions, abstractions, multiplications, and divisions that can be precisely completed without any remainder; (2) the results of every recursion could be verified automatically by the algorithm itself, and (3) the total arithmetic operations are comparable with those of other direct methods. This algorithm is specially suitable for solving the highly ill-conditioned equations; it can also be used in digital signal processing and other related areas.>