Abstract-In future wireless multimedia networks, there will be a mixture of different traffic classes which have their own maximum tolerable bit error rate (BER) requirements. In this paper, a novel medium access control (MAC) protocol called wireless multimedia access control protocol with BER scheduling (in short form, WISPER) for CDMA-based systems is proposed. WISPER utilizes the novel idea of scheduling the transmission of multimedia packets according to their BER requirements. The scheduler assigns priorities to the packets, and performs an iterative procedure to determine a good accommodation of the highest-priority packets in the slots of a frame so that packets with equal or similar BER requirements are transmitted in the same slots. The proposed WISPER protocol has been validated using a software emulator on the cellular environment. Performance evaluation results based on the implementation are also included in the paper.

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Background/AimsTelemedicine is a convenient and efficient tool for remote education in various fields. The telemedicine system can also be used to educate doctors and medical students. The aim of our study was to establish the effectiveness of the telemedical system for use in a live endoscopic multichannel demonstration conference and to test the effectiveness and usefulness of a multicenter-based live endoscopic demonstration through live, interactive, high resolution video transmission using advanced networks and the digital video transport system (DVTS).MethodsThis study is a prospective multicenter pilot study. A live demonstration of an endoscopic submucosal dissection (ESD) and an endoscopic retrograde cholangiopancreatography (ERCP) using advanced network technology was performed.ResultsThe DVTS successfully transmitted uncompressed, high-resolution, digital lectures with endoscopy video during a multichannel endoscopic live demonstration of ESD and ERCP over multiple advanced networks. The overall satisfaction rating when the endoscopic lecture demonstration was performed by combining DVTS was generally good.ConclusionsWe believe that a multicenter-based live endoscopic demonstration is a very effective conferencing method when using advanced networks and DVTS.

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Many machine learning algorithms and almost all deep learning architectures are incapable of processing plain texts in their raw form. This means that their input to the algorithms must be numerical in order to solve classification or regression problems. Hence, it is necessary to encode these categorical variables into numerical values using encoding techniques. Categorical features are common and often of high cardinality. One-hot encoding in such circumstances leads to very high dimensional vector representations, raising memory and computability concerns for machine learning models. This paper proposes a deep-learned embedding technique for categorical features encoding on categorical datasets. Our technique is a distributed representation for categorical features where each category is mapped to a distinct vector, and the properties of the vector are learned while training a neural network. First, we create a data vocabulary that includes only categorical data, and then we use word tokenization to make each categorical data a single word. After that, feature learning is introduced to map all of the categorical data from the vocabulary to word vectors. Three different datasets provided by the University of California Irvine (UCI) are used for training. The experimental results show that the proposed deeplearned embedding technique for categorical data provides a higher F1 score of 89% than 71% of onehot encoding, in the case of the Long short-term memory (LSTM) model. Moreover, the deep-learned embedding technique uses less memory and generates fewer features than one-hot encoding.

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