Drowsy driving is one of the greatest threats to road safety, which increases the importance of intelligent systems that can monitor driver drowsiness. Electroencephalogram (EEG)–based monitoring systems have gained attention because EEG is known to directly measure brain activities that reflect the mental state of the driver. However, calibration is necessary before using the system because EEG signals vary between and within subjects. Therefore, generalized EEG-based drowsiness estimation has become challenging. In this paper, we propose an EEG-based driver drowsiness classification framework without the need for calibration, which can be generalized to unseen subjects. We augment the features of unseen domains (i.e., subjects) with a Dirichlet mixup between prototypes of source domains to complement other domain knowledge. The parameter$\boldsymbol{\alpha}$vector of the Dirichlet distribution adjusts the intensity of the mixup, allowing for diverse enhancement. Furthermore, we utilize an auxiliary batch normalization module for augmented samples to avoid inaccurate estimation by the difference in distribution. The experiments were carried out using two EEG datasets, each measured using different drowsiness indicators, the Karolinska sleepiness scale, and reaction time. In leave-one-subject-out cross-validation, the proposed framework achieved outstanding performance in both datasets, anF1-score of 62.69% and 70.33% and an area under the receiver operating characteristic curve (AUROC) of 71.73% and 73.80%, respectively. The experimental results demonstrate the potential for practical applications of brain-computer interfaces without calibration.

Drowsy driving causes severe road traffic accidents and significantly threatens road driving. Recently, electroencephalogram (EEG)-based drowsiness state classification has gained attention in the field of brain-computer interface (BCI). Because of the inter-and intra-subject variability of EEG signals, EEG-based drowsiness state classification is still challenging in developing an estimator applicable to unseen subjects. Generally, calibration sessions are required to tune the model with subject-specific data. In this paper, we propose an EEG-based driver drowsiness state (i.e., alert and drowsy) classification framework that improves the generalization performance to unseen subjects. Style features of multi-domain instances are mixed to generate unseen domains, and the distance of labels within classes is minimized to learn robust representations. Experiments were conducted on EEG data acquired from a drowsy driving experiment in a simulated-driving environment. Our proposed framework achieved an accuracy of 77.26%, an F1-score of 0.6266, and a recall of 0.6813 across eleven subjects in leave-one-subject-out cross-validation. The experimental results showed an improvement in the generalization performance for novel target subjects in driver drowsiness state classification and demonstrated the potential for calibration-free BCI.

In this paper, we investigate the issue of per-station fairness in TCP over IEEE 802.11-compliant wireless local area networks (WLANs), especially in Wi-Fi hot spot. It is asserted that the hot spot suffers from the unfairness among stations in exploiting the wireless medium. The source of this unfairness is analyzed from two aspects, TCP-induced asymmetry and MAC-induced asymmetry; the former causes TCP congestion control with a cumulative acknowledgment mechanism to prefer the sending stations to receiving stations, while the later exacerbates the unfairness problem in the hot spots. We investigate the interaction between TCP congestion control and MAC contention control, and propose a cross-layer feedback approach to assure per-station fairness and to ensure high channel utilization. In this approach, we introduce the notion of channel access cost to quantify the system-wide traffic load and per-station channel usage. The access cost is estimated at the MAC in an access point and conveyed to the TCP sender. Then, the TCP sender adjusts its sending rate based on the access cost, so as to assure per-station fairness. The simulation results indicate that the proposed approach can provide both per-station fairness and high channel utilization, irrespective of network configurations.

As public deployment of wireless local area networks (WLANs) has increased and various applications with different service requirements have emerged, fairness and quality of service (QoS) are two imperative issues in allocating wireless channels. This study proposes a fair QoS agent (FQA) to simultaneously provide per-class QoS enhancement and per-station fair channel sharing in WLAN access networks. FQA implements two additional components above the 802.11 MAC: a dual service differentiator and a service level manager. The former is intended to improve QoS for different service classes by differentiating service with appropriate scheduling and queue management algorithms, while the latter is to assure fair channel sharing by estimating the fair share for each station and dynamically adjusting the service levels of packets. FQA assures (weighted) fairness among stations in terms of channel access time without decreasing channel utilization. Furthermore, it can provide quantitative service assurance in terms of queuing delay and packet loss rate. FQA neither resorts to any complex fair scheduling algorithm nor requires maintaining per-station queues. Since the FQA algorithm is an add-on scheme above the 802.11 MAC, it does not require any modification of the standard MAC protocol. Extensive ns-2 simulations confirm the effectiveness of the FQA algorithm with respect to the per class QoS enhancement and per-station fair channel sharing

This paper focuses on the unfairness problem between TCP uplink and downlink flows in the 802.11 Wi-Fi hot spots and shows that the service is prone to be unfair. The cause of unfairness is analyzed from two aspects: TCP-induced asymmetry and MAC-induced asymmetry. Due to the asymmetric behavior of TCP congestion control with a cumulative acknowledgment mechanism between uplink and downlink flows, the service is biased toward the uplink flow and the downlink flow tends to starve. The contention-based channel access mechanism of 802.11 MAC exacerbates this unfairness problem because it intends to provide fair access opportunity only to the sending stations. Next, the analysis of the interaction between congestion control of TCP and contention control of MAC reveals interesting and counter-intuitive results: (i) Even when a station has a sufficiently large amount of traffic to send, it does not always participate in the MAC-layer contention, its opportunity for MAC-layer contention is controlled by the TCP congestion control, (ii) The aggregate throughput remains almost constant with respect to the number of stations sending/receiving TCP traffic. (Hi) Both TCP-induced unfairness and MAC-induced unfairness can be resolved if packet loss due to buffer overflow in an access point does not occur.