This work studies the jitter buffer management algorithm for Voice over IP in WebRTC. In particular, it details the core concepts of WebRTC’s jitter buffer management. Furthermore, it investigates how jitter buffer management algorithm behaves under network conditions with packet bursts. It also proposes an approach, different from the default WebRTC algorithm, to avoid distortions that occur under such network conditions. Under packet bursts, when the packet buffer becomes full, the WebRTC jitter buffer algorithm may discard all the packets in the buffer to make room for incoming packets. The proposed approach offers a novel strategy to minimize the number of packets discarded in the presence of packet bursts. Therefore, voice quality as perceived by the user is improved. ITU-T Rec. P.863, which also confirms the improvement, is employed to objectively evaluate the listening quality.

DDoS attacks remain one of the top cyber threats targeting the financial, health care, retail, gaming, and political sectors, which affects Internet service disruption, data or monetary loss. Security experts have predicted that the development of 5G technology will increase the frequency and the vector of DDoS attacks. Moreover, enhanced DDoS attack technology utilises artificial intelligence [1], which will escalate the level of difficulty to identify malicious traffic correctly to mitigate the attack effectively. The Internet service provider (ISP) is the connector between the users and the Internet. Deploying DDoS mitigation systems within the ISP domain can offer an efficient solution. Therefore, we propose a dynamic learning system (DLS) for the ISP. The DLS is an unsupervised ensemble model using the Complete Autoencoder (CA) as base learners to classify network traffic. The utmost difference between the CA and the regular Autoencoder is that the CA exploits the imbalanced characteristic of the attack data to generate a binary classification via a class switch. When the predicted number of normal IP addresses is over 50% of the total IP addresses, the CA swaps the class of the IP addresses. The CA is directed by a reference object (RO), which is either a reference limit or the mean of a reference error function (RL1¯), to furnish the automation to the DLS. The DLS was trained with a TCP-ICMP flood attack and tested with a UDP-TCP and a UDP-TCP-ICMP flood attack data set. The average Recall, Precision and F1 Score are all above 0.97. Additionally, the DLS outperformed the K-means and the Self-Organising Map models on a UDP flood attack data set.

We present a fuzzy technique for approximate k-mer matching that combines the speed of hashing with the sensitivity of dynamic programming. Our approach exploits the collision detection mechanism used by hash maps, unifying the two phases of "seed and extend" into a single operation that executes in close to O(1) average time.

This paper describes the design, development and clinical trials completed on an ambulatory, wireless ECG / vital signs monitor, which was designed to work on standard wireless LAN networking systems (802.11b). It discusses the trials, results collected from two prototype sensor devices, the limitations of the system in place due to architecture and site location. Future works identified from the clinical trials completed will also be discussed.

This paper outlines a system for detection of cardiac arrhythmias within ECG signals, based on a Bayesian artificial neural network (ANN) classifier. The Bayesian (or probabilistic) ANN classifier is built by the use of a logistic regression model and the backpropagation algorithm based on a Bayesian framework. Its performance for this task is evaluated by comparison with other classifiers including Naive Bayes, decision trees, logistic regression, and RBF networks. A paired t-test is employed in comparing classifiers to select the optimum model. The system is evaluated using noisy ECG data, to simulate a real-world environment. It is hoped that the system can be further developed and fine-tuned for practical application.

This paper describes a number of enhancements for the standard CORBA Event Service. The basic service definition has been extended to support stream events, multimedia data flows, event fragmentation, quality of service definition, as well as multicast event delivery. The paper evaluates the service performance and describes experiences using the enhanced service in the development of a test application.