Focal cortical dysplasia (FCD) is a common cause of pharmacologically-intractable epilepsy, however, the precise mechanisms underlying the epileptogenicity of FCD remains to be determined. ...Neuropeptide Y (NPY), an endogenous anticonvulsant in the central nervous system, plays an important role in the regulation of neuronal excitability. Increased expression of NPY and its receptors has been identified in the hippocampus of patients with mesial temporal lobe epilepsy, presumed to act as an endogenous anticonvulsant mechanism. Therefore, we investigated whether expression changes in NPY receptors occurs in patients with FCD. We specifically investigated the expression of seizure-related NPY receptor subtypes Y1, Y2, and Y5 in patients with FCD versus autopsy controls. We found that Y1R and Y2R were up-regulated at the mRNA and protein levels in the temporal and frontal lobes in FCD lesions. By contrast, there was no significant change in either receptor detected in parietal lesions. Notably, overexpression of Y5R was consistently observed in all FCD lesions. Our results demonstrate the altered expression of Y1R, Y2R and Y5R occurs in FCD lesions within the temporal, frontal and parietal lobe. Abnormal NPY receptor subtype expression may be associated with the onset and progression of epileptic activity and may act as a therapeutic candidate for the treatment of refractory epilepsy caused by FCD.
The rapidly growing exploitation and utilization of marine resources by humans has sparked considerable interest in underwater object detection tasks. Targets captured in underwater environments ...differ significantly from those captured in general images owing to various factors, such as water turbidity, complex background conditions, and lighting variations. These adverse factors pose a host of challenges, such as high intensity noise, texture distortion, uneven illumination, low contrast, and limited visibility in underwater images. To address the specific difficulties encountered in underwater environments, numerous underwater object detection methods have been developed in recent years in response to these challenges. Furthermore, there has been a significant effort in constructing diverse and comprehensive underwater datasets to facilitate the development and evaluation of these methods. This paper outlines 14 traditional methods used in underwater object detection based on three aspects that rely on handmade features. Thirty-four more advanced technologies based on deep learning were presented from eight aspects. Moreover, this paper conducts a comprehensive study of seven representative datasets used in underwater object detection missions. Subsequently, the challenges encountered in current underwater object detection tasks were analyzed from five directions. Based on the findings, potential research directions are expected to promote further progress in this field and beyond.
In maritime emergency response operations, autonomous underwater vehicles (AUVs) can perform underwater search and transmit emergency data in real-time. To collect the sensed data from AUVs across ...the water-air interface, the traditional method is to deploy surface relays. However, the deployment of surface relays has some disadvantages, such as poor and imbalanced communication coverage of real-time connections with AUVs. Since the deployment of static relays may limit the dynamic search area of AUVs, whereas the deployment of free surface drifting relays may disrupt the connections while the relays are drifting away with the ocean current. To address these challenges, in this paper, we propose a novel method by leveraging the flexibility of multiple unmanned aerial vehicles (UAVs) equipped with underwater hydrophones and design a dynamic deployment scheme based on a multi-agent deep deterministic policy gradient (MADDPG) approach. By this scheme, multiple UAVs are able to deploy in a cooperative manner to deal with the coverage imbalance issue and provide maximum coverage service. The numerical simulations demonstrate that the proposed scheme is effective in terms of communication coverage, fairness, and energy consumption.
Unmanned aerial vehicles (UAVs) have the advantage of high mobility under harsh environments, and they can change their flight altitude to collect information, which is an informative path planning ...(IPP) problem. In this paper, we study the multi-granularity collaborative search issue via a swarm of UAVs in marine environment. To capture different probability of objects of interest (OOI) occupancy and the motion of OOI, we first use digital pheromone and the binary classifier of UAVs for sensing OOI based on the depth perception model of cameras. Then, we use multi-agent deep deterministic policy gradient (MADDPG) approach to learn the collaborative policy of UAVs in order to implement cooperative multi-granularity search for sea surface targets.
Unmanned aerial vehicles (UAVs) are considered as promising devices for intelligent mission execution in the smart ocean due to their flexibility, mobility, and ability to deploy as base stations. In ...this paper, we aim to design a multi-agent deep reinforcement learning-based control solution that uses UAVs as mobile acoustic sinks to provide optimal communication coverage for autonomous underwater vehicles (AUVs) to monitor complex conditions in the ocean. In order to solve the problem of cross-boundary communication between UAVs and AUVs, we establish real-time communication with the AUVs by equipping the UAVs with hydrophones that transmit the data collected by the AUVs. The goal is to address the coverage imbalance issue caused by the movement of the swarm of AUVs. We model the problem as a partially observable markov decision process (POMDP), and then design a reasonable reward function based on local information. Based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, we propose a dynamic deployment algorithm to optimize the dynamic deployment process of the UAVs in order to achieve the maximum communication coverage.
In this paper, we present a practical wireless sensor network for environmental monitoring (OceanSense) deployed on the sea. The system is mainly composed of TelosB motes, which are deployed on the ...surface of the sea collecting environmental data, such as temperature, light and RSSI from the testbed. The motes communicate with a base station, which transmits collected data to a visualization system running on a database server. The data can be accessed using a browser-based Web application. The OceanSense has been running for more than half a year, providing environmental monitoring data for further study.
Unmanned aerial vehicles (UAVs) have mobility in harsh environments and the flexibility to modify their flight altitude to acquire information in an adaptive manner, so these vehicles can be ...leveraged to solve the informative path planning (IPP) problem. As most of the research on IPP currently are on static targets and terrain detection tasks, in this paper, we focus on the search for dynamic targets on the sea surface, and study the issue of collaborative search with multiple granularities using a swarm of UAVs in marine environments. To capture the objects of interest (OOI), we design a binary classifier for UAVs to sense OOI based on a depth perception model of cameras. Afterwards, we propose a collaborative search scheme using the multi-agent deep deterministic policy gradient (MADDPG) approach to enable a swarm of UAVs to simultaneously maintain formation coordination while adapting to digital pheromone strength. The experimental results show that the proposed scheme can effectively detect targets on the sea surface in a multi-granular collaborative manner.
Ocean data collection via unmanned aerial vehicles (UAVs) has received widespread attention due to its flexibility and low cost. To further improve the efficiency of large maritime data collection ...between the UAVs and the ocean surface buoys, optical wireless transmission is considered as a promising technique because of its low latency and high bandwidth. However, the waves and other disturbances in the complex oceanic environment result in drift and instability of the buoy, which deteriorate and even interrupt the line-of-sight (LOS) optical transmission. To tackle these challenges, in this paper, we propose a reliable data collection scheme based on a deep reinforcement learning (DRL) approach. We first model the optical channel and calculate the maximum transmission range that satisfies a pre-defined bit error rate (BER) to ensure the quality of service (QoS). Then, we formulate the data collection procedure as a Markov decision process (MDP) aiming at maximizing the received signal intensity and minimizing the energy consumption, in which the system uncertainty caused by the oceanic environmental disturbances is taken into account. Finally, we propose a beam pointing adjustment algorithm based on deep deterministic policy gradient (DDPG) approach to alleviate the performance deterioration and maintain a stable LOS communication. Through extensive simulations, the results demonstrate that the proposed scheme is effective and achieves reliable data collection via the optical links.
Optical wireless communication (OWC) is an emerging technology for direct communication through the water-air interface. However, due to the high directionality of optical beams and the harsh oceanic ...environment, it faces significant challenges to achieve the alignment and preserve the link availability, as the waves cause beam deflection and the mobility of the transceivers makes the link worse. To tackle these challenges and achieve reliable optical communication between autonomous underwater vehicles and unmanned aerial vehicles, we propose a deep reinforcement learning algorithm assisted by an extended Kalman filter to solve the alignment issue. To improve the reliability of communication, we present an algorithm to obtain the optimal beam divergence angle to maximize the link availability. The numerical simulations demonstrate that the proposed scheme achieves better performance in terms of energy consumption and alignment accuracy, and the link availability is increased by 25% compared to that without adjustment.
Encouraged by the success in both terrestrial wired and wireless networks, software-defined networking (SDN) is envisioned as a promising technique to tackle inherent problems in underwater wireless ...sensor networks (UWSNs), and propel the development of UWSNs further. As the offshore application deployments of UWSNs are both expensive and labor-intensive, it is urgent to develop efficient experimental testbeds or platforms in order to test or evaluate SDN-based algorithms. In this paper, we propose a novel experimental testbed for SDN-based UWSNs, in which the implementation of the control plane is with one-hop out-of-band RF channel, e.g., LoRa or LoRaWAN, whereas the traditional underwater acoustic channel serves as the data plane. Armed with the global view of the whole testbed, the two-channel SDN-based architecture not only improves the testbed efficiency, but also increases the testbed flexibility and adaptability. We provide a use case to illustrate how to use the testbed by comparing three different routing control models, such as SDN-based centralized control, SDN-based semi-centralized control and distributed control, and hope to inspire more research with this novel testbed.
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