A high-performance brain switch based on code-modulated visual evoked potentials

Objective. Asynchronous brain-computer interfaces (BCIs) are more practical and natural compared to synchronous BCIs. A brain switch is a standard asynchronous BCI, which can automatically detect the specified change of the brain and discriminate between the control state and the idle state. The current brain switches still face challenges on relatively long reaction time (RT) and high false positive rate (FPR). Approach. In this paper, an online electroencephalography-based brain switch is designed to realize a fast reaction and keep long idle time (IDLE) without false positives (FPs) using code-modulated visual evoked potentials (c-VEPs). Two stimulation paradigms were designed and compared in the experiments: multi-code concatenate modulation (concatenation mode) and single-code periodic modulation (periodic mode). Using a task-related component analysis-based detection algorithm, EEG data can be decoded into a series of code indices. Brain states can be detected by a template matching approach with a sliding window on the output series. Main results. The online experiments achieved an average RT of 1.49 seconds when the average IDLE for each FP was 68.57 minutes (1.46e-2 FP/min) or an average RT of 1.67 seconds without FPs. Significance. This study provides a practical c-VEP based brain switch system with both fast reaction and low FPR during idle state, which can be used in various BCI applications.

Energy-saving Scheme of 5G Base Station Based on LSTM Neural Network

As China’s new infrastructure,5G has received national and social attention. 5G promotes economic to grow rapidly. But, the high energy consumption caused by the massive deployment of 5G base stations cannot be ignored. The total annual power consumption is expected to reach 243 billion degrees when the 5G base station is fully built. In the tidal scene, some 5G base station in an idle state still power fully, which causes great power waste. The historical volume of base station business data is used to train LSTM model, and predict the future base station business. When the business is lower than the threshold, the base station will be closed to avoid unnecessary power waste. And the LSTM model prediction results fits the original data ideally. By implementing the power saving strategy, the energy consumption of the base station is reduced by 18.97 %. A single station can save 1174 degrees of electricity yearly. It can be seen that the energy saving effect is remarkable.

Outage Analysis of Cognitive Communication System Using Opportunistic Relay Selection and Secondary Users as Relays

Efficient bandwidth utilization is significant in new communication systems where secondary users can be used besides of primary users considering interference issues and idle state of primary users. Using secondary users as relays to transmit their own signals in addition to the primary signals can be applied for more reliability of the system where opportunistic relay selection can significantly enhance the performance of the system. The best-condition secondary user is selected as the optimum relay for retransmission of primary/secondary signal. Outage probability is analyzed in this paper based on decode and forward techniques in secondary users while the closed-form statement for outage probability is provided and verified by numerical evaluations.

Enhancing the Distribution of Idle Cost for Scheduling Tasks without Setup Cost in Cloud Computing

Issues of task scheduling in the centre of cloud computing are becoming more important, and the cost is one of the most important parameters used for scheduling tasks. This study aims to investigate the problem of online task scheduling of the identified job of MapReduce on cloud computing infrastructure. It was proposed that the virtualized cloud computing setup comprised machines that host multiple identical virtual machines (VMs) that need to be activated earlier and run continuously, and booting a VM requires a constant setup time. A VM that remains running even though it is no longer used is considered an idle VM. Furthermore, this study aims to distribute the idle cost of the VMs rather than the cost of setting up them among tasks in a fair manner. This study also is an extension of previous studies which solved the problems that occurred when distributing the idle cost and setting up the cost of VMs among tasks. It classifies the tasks into three groups (long, mid, and short) and distributes the idle cost among the groups then among the tasks of the groups. The main contribution of this paper is the developing of a clairvoyant algorithm that addressed important factors such as the delay and the cost that occurred by waiting to setup VM (active VM). Also, when the VMs are run continually and some VMs become in idle state, the idle cost will be distributed among the current tasks in a fair manner. The results of this study, in comparison with previous studies, showed that the idle cost and the setup cost that was distributed among tasks were better than the idle cost and the setup cost distributed in those studies.

An Emergency Alert Broadcast Based on the Convergence of 5G and ATSC 3.0

We propose a novel emergency alert broadcast mechanism for mobile phone users, which is based on the convergence of 5G and ATSC 3.0. Cellular networks including 5G adopt a broadcast technique for emergency alert. This technique just delivers a text-based message. Moreover, the message only includes a limited number of characters. Therefore, cellular networks cannot afford to provide abundant information in emergency cases. Broadcast networks such as ATSC 3.0 also offer an emergency alert broadcast service. This service can deliver a multimedia-based message in emergency cases. Therefore, the ATSC 3.0 supports more abundant information in the cases of emergency alert broadcasts. Especially, the ATSC 3.0 employs wake-up functionality and location information, which enables the delivery of emergency alerts to idle-state receivers in emergency areas. However, it is unlikely that the wake-up functionality and the location information are directly applicable to mobile phone users due to some practical issues. In order to improve the emergency alert broadcast service in mobile environments, we converge the 5G and the ATSC 3.0 networks, which effectively exploits the advantages of the networks. For the convergence network, we suggest a modified table, which associates the 5G message with the ATSC 3.0 message in the cases of emergency alerts. We also present a novel scenario for delivery of the emergency alert messages. Simulation results show that the convergence significantly enhances the receiver performance for emergency alert broadcast.

Decoding of Walking Imagery and Idle State Using Sparse Representation Based on fNIRS

Objectives. Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is expected to provide an optional active rehabilitation training method for patients with walking dysfunction, which will affect their quality of life seriously. Sparse representation classification (SRC) oxyhemoglobin (HbO) concentration was used to decode walking imagery and idle state to construct fNIRS-BCI based on walking imagery. Methods. 15 subjects were recruited and fNIRS signals were collected during walking imagery and idle state. Firstly, band-pass filtering and baseline drift correction for HbO signal were carried out, and then the mean value, peak value, and root mean square (RMS) of HbO and their combinations were extracted as classification features; SRC was used to identify the extracted features and the result of SRC was compared with those of support vector machine (SVM), K-Nearest Neighbor (KNN), linear discriminant analysis (LDA), and logistic regression (LR). Results. The experimental results showed that the average classification accuracy for walking imagery and idle state by SRC using three features combination was 91.55 ± 3.30%, which was significantly higher than those of SVM, KNN, LDA, and LR (86.37 ± 4.42%, 85.65 ± 5.01%, 86.43 ± 4.41%, and 76.14 ± 5.32%, respectively), and the classification accuracy of other combined features was higher than that of single feature. Conclusions. The study showed that introducing SRC into fNIRS-BCI can effectively identify walking imagery and idle state. It also showed that different time windows for feature extraction have an impact on the classification results, and the time window of 2–8 s achieved a better classification accuracy (94.33 ± 2.60%) than other time windows. Significance. The study was expected to provide a new and optional active rehabilitation training method for patients with walking dysfunction. In addition, the experiment was also a rare study based on fNIRS-BCI using SRC to decode walking imagery and idle state.

Operational characteristics of direct sowing machine-tractor unit

A study on some operational characteristics of a direct sowing machine-tractor unit has been carried out in wheat sowing. It is performed by Horsch Avatar 6.16 SD direct seeder aggregated to John Deere 7250 R tractor. It was found that 91% of the area is sown at sowing rate of 195 kg seed/ha. The remaining 9% are sown either at lower or higher rate. Engine rotations of the seed unit have been established at work mode, i.e. when the seeder is sowing, these are 1594.81 min-1, in idle state the rotations are 912.08 min-1, and in transportation mode are 1860.36 min-1. The relative share of engine use has also been determined: when the seeder is sowing, it is 56.68%, when the seeder unit is in idle mode, it is 14.54% and when the unit is in transportation mode – 28.78%, respectively. The actual operation speed of the seeder unit when sowing wheat was 10.40 km/h and the real hourly productivity – 3.7 ha/h.

NCMP-05. DECODING THE IMMUNE SYSTEM RESPONSE TO LEPTOMENINGEAL METASTASIS

For decades, the central nervous system was considered to be an immune privileged organ with limited access to systemic immunity. However, the leptomeninges, the cerebrospinal fluid (CSF)-filled anatomical structure that protects the brain and spinal cord, represent a relatively immune-rich environment. Despite the presence of immune cells, complications in the CSF, such as infectious meningitis and a neurological development of cancer known as leptomeningeal metastasis, are difficult to treat and are frequently fatal. We show that immune cells entering the CSF are held in an ‘idle’ state that limits their cytotoxic arsenal and antigen presentation machinery. To understand this underappreciated neuroanatomic niche, we used unique mouse models and rare patient samples to characterize its cellular composition and critical signaling events in health and disease at a single-cell resolution. Revealing the mediators of CSF immune response will allow us to re-evaluate current therapeutic protocols and employ rational combinations with immunotherapies, therefore turning the patient’s own immune system into an active weapon against pathogens and cancer.