Highly responsive tellurium-hyperdoped black silicon photodiode with single-crystalline and uniform surface microstructure.

Femtosecond laser hyperdoped silicon, also known as the black silicon (BS), has a large number of defects and damages, which results in unstable and undesirable optical and electronic properties in photonics platform and optoelectronic integrated circuits (OEICs). We propose a novel method that elevates the substrate temperature during the femtosecond laser irradiation and fabricates tellurium (Te) hyperdoped BS photodiodes with high responsivity and low dark current. At 700 K, uniform microstructures with single crystalline were formed in the hyperdoped layer. The velocity of cooling and resolidification is considered as an important role in the formation of a high-quality crystal after irradiation by the femtosecond laser. Because of the high crystallinity and the Te hyperdoping, a photodiode made from BS processed at 700 K has a maximum responsivity of 120.6 A/W at 1120 nm, which is far beyond the previously reported Te-doped silicon photodetectors. In particular, the responsivity of the BS photodiode at 1300 nm and 1550 nm is 43.9 mA/W and 56.8 mA/W with low noise, respectively, which is valuable for optical communication and interconnection. Our result proves that hyperdoping at a high substrate temperature has great potential for femtosecond-laser-induced semiconductor modification, especially for the fabrication of photodetectors in the silicon-based photonic integration circuits.

A 3D Coverage Algorithm Based on Complex Surfaces for UAVs in Wireless Multimedia Sensor Networks.

Following the development of wireless multimedia sensor networks (WMSN), the coverage of the sensors in the network constitutes one of the key technologies that have a significant influence on the monitoring ability, quality of service, and network lifetime. The application environment of WMSN is always a complex surface, such as a hilly surface, that would likely cause monitoring shadowing problems. In this study, a new coverage-enhancing algorithm is presented to achieve an optimal coverage ratio of WMSN based on three-dimensional (3D) complex surfaces. By aiming at the complex surface, the use of a 3D sensing model, including a sensor monitoring model and a surface map calculation algorithm, is proposed to calculate the WMSN coverage information in an accurate manner. The coverage base map allowed the efficient estimation of the degree of monitoring occlusion efficiently and improved the system's accuracy. To meet the requests of complex 3D surface monitoring tasks for multiple sensors, we propose a modified cuckoo search algorithm that considers the features of the WMSN coverage problem and combines the survival of the fittest, dynamic discovery probability, and the self-adaptation strategy of rotation. The evaluation outcomes demonstrate that the proposed algorithm can describe the 3D covering field but also improve both the coverage quality and efficiency of the WMSN on a complex surface.

Barrier Coverage for 3D Camera Sensor Networks.

Barrier coverage, an important research area with respect to camera sensor networks, consists of a number of camera sensors to detect intruders that pass through the barrier area. Existing works on barrier coverage such as local face-view barrier coverage and full-view barrier coverage typically assume that each intruder is considered as a point. However, the crucial feature (e.g., size) of the intruder should be taken into account in the real-world applications. In this paper, we propose a realistic resolution criterion based on a three-dimensional (3D) sensing model of a camera sensor for capturing the intruder's face. Based on the new resolution criterion, we study the barrier coverage of a feasible deployment strategy in camera sensor networks. Performance results demonstrate that our barrier coverage with more practical considerations is capable of providing a desirable surveillance level. Moreover, compared with local face-view barrier coverage and full-view barrier coverage, our barrier coverage is more reasonable and closer to reality. To the best of our knowledge, our work is the first to propose barrier coverage for 3D camera sensor networks.

Compound Event Barrier Coverage in Wireless Sensor Networks under Multi-Constraint Conditions.

It is important to monitor compound event by barrier coverage issues in wireless sensor networks (WSNs). Compound event barrier coverage (CEBC) is a novel coverage problem. Unlike traditional ones, the data of compound event barrier coverage comes from different types of sensors. It will be subject to multiple constraints under complex conditions in real-world applications. The main objective of this paper is to design an efficient algorithm for complex conditions that can combine the compound event confidence. Moreover, a multiplier method based on an active-set strategy (ASMP) is proposed to optimize the multiple constraints in compound event barrier coverage. The algorithm can calculate the coverage ratio efficiently and allocate the sensor resources reasonably in compound event barrier coverage. The proposed algorithm can simplify complex problems to reduce the computational load of the network and improve the network efficiency. The simulation results demonstrate that the proposed algorithm is more effective and efficient than existing methods, especially in the allocation of sensor resources.

The Indoor Localization and Tracking Estimation Method of Mobile Targets in Three-Dimensional Wireless Sensor Networks.

Indoor localization is a significant research area in wireless sensor networks (WSNs). Generally, the nodes of WSNs are deployed in the same plane, i.e., the floor, as the target to be positioned, which causes the sensing signal to be influenced or even blocked by unpredictable obstacles, like furniture. However, a 3D system, like Cricket, can reduce the negative impact of obstacles to the maximum extent and guarantee the sensing signal transmission by using the line of sight (LOS). However, most of the traditional localization methods are not available for the new deployment mode. In this paper, we propose the self-localization of beacons method based on the Cayley-Menger determinant, which can determine the positions of beacons stuck in the ceiling; and differential sensitivity analysis (DSA) is also applied to eliminate measurement errors in measurement data fusion. Then, the calibration of beacons scheme is proposed to further refine the locations of beacons by the mobile robot. According to the robot's motion model based on dead reckoning, which is the process of determining one's current position, we employ the H∞ filter and the strong tracking filter (STF) to calibrate the rough locations, respectively. Lastly, the optimal node selection scheme based on geometric dilution precision (GDOP) is presented here, which is able to pick the group of beacons with the minimum GDOP from all of the beacons. Then, we propose the GDOP-based weighting estimation method (GWEM) to associate redundant information with the position of the target. To verify the proposed methods in the paper, we design and conduct a simulation and an experiment in an indoor setting. Compared to EKF and the H∞ filter, the adopted STF method can more effectively calibrate the locations of beacons; GWEM can provide centimeter-level precision in 3D environments by using the combination of beacons that minimizes GDOP.

An Indoor Mobile Location Estimator in Mixed Line of Sight/Non-Line of Sight Environments Using Replacement Modified Hidden Markov Models and an Interacting Multiple Model.

Localization as a technique to solve the complex and challenging problems besetting line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions has recently attracted considerable attention in the wireless sensor network field. This paper proposes a strategy for eliminating NLOS localization errors during calculation of the location of mobile terminals (MTs) in unfamiliar indoor environments. In order to improve the hidden Markov model (HMM), we propose two modified algorithms, namely, modified HMM (M-HMM) and replacement modified HMM (RM-HMM). Further, a hybrid localization algorithm that combines HMM with an interacting multiple model (IMM) is proposed to represent the velocity of mobile nodes. This velocity model is divided into a high-speed and a low-speed model, which means the nodes move at different speeds following the same mobility pattern. Each moving node continually switches its state based on its probability. Consequently, to improve precision, each moving node uses the IMM model to integrate the results from the HMM and its modified forms. Simulation experiments conducted show that our proposed algorithms perform well in both distance estimation and coordinate calculation, with increasing accuracy of localization of the proposed algorithms in the order M-HMM, RM-HMM, and HMM + IMM. The simulations also show that the three algorithms are accurate, stable, and robust.

MICDnet: Multimodal information processing networks for Crohn's disease diagnosis.

Crohn's disease (CD) is a chronic inflammatory disease with increasing incidence worldwide and unclear etiology. Its clinical manifestations vary depending on location, extent, and severity of the lesions. In order to diagnose Crohn's disease, medical professionals need to comprehensively analyze patients' multimodal examination data, which includes medical imaging such as colonoscopy, pathological, and text information from clinical records. The processes of multimodal data analysis require collaboration among medical professionals from different departments, which wastes a lot of time and human resources. Therefore, a multimodal medical assisted diagnosis system for Crohn's disease is particularly significant. Existing network frameworks find it hard to effectively capture multimodal patient data for diagnosis, and multimodal data for Crohn's disease is currently lacking. In addition,a combination of data from patients with similar symptoms could serve as an effective reference for disease diagnosis. Thus, we propose a multimodal information diagnosis network (MICDnet) to learn CD feature representations by integrating colonoscopy, pathology images and clinical texts. Specifically, MICDnet first preprocesses each modality data, then uses encoders to extract image and text features separately. After that, multimodal feature fusion is performed. Finally, CD classification and diagnosis are conducted based on the fused features. Under the authorization, we build a dataset of 136 hospitalized inspectors, with colonoscopy images of seven areas, pathology images, and clinical record text for each individual. Training MICDnet on this dataset shows that multimodal diagnosis can improve the diagnostic accuracy of CD, and the diagnostic performance of MICDnet is superior to other models.

Research on a hybrid neural network task assignment algorithm for solving multi-constraint heterogeneous autonomous underwater robot swarms.

Studying the task assignment problem of multiple underwater robots has a broad effect on the field of underwater exploration and can be helpful in military, fishery, and energy. However, to the best of our knowledge, few studies have focused on multi-constrained underwater detection task assignment for heterogeneous autonomous underwater vehicle (AUV) clusters with autonomous decision-making capabilities, and the current popular heuristic methods have difficulty obtaining optimal cluster unit task assignment results. In this paper, a fast graph pointer network (FGPN) method, which is a hybrid of graph pointer network (GPN) and genetic algorithm, is proposed to solve the task assignment problem of detection/communication AUV clusters, and to improve the assignment efficiency on the basis of ensuring the accuracy of task assignment. A two-stage detection algorithm is used. First, the task nodes are clustered and pre-grouped according to the communication distance. Then, according to the clustering results, a neural network model based on graph pointer network is used to solve the local task assignment results. A large-scale cluster cooperative task assignment problem and a detection/communication cooperative work mode are proposed, which transform the cooperative cooperation problem of heterogeneous AUV clusters into a Multiple Traveling salesman problem (MTSP) for solving. We also conducted a large number of experiments to verify the effectiveness of the algorithm. The experimental results show that the solution efficiency of the method proposed in this paper is better than the traditional heuristic method on the scale of 300/500/750/1,000/1,500/2,000 task nodes, and the solution quality is similar to the result of the heuristic method. We hope that our ideas and methods for solving the large-scale cooperative task assignment problem can be used as a reference for large-scale task assignment problems and other related problems in other fields.

Vital information matching in vision-and-language navigation.

With the rapid development of artificial intelligence technology, many researchers have begun to focus on visual language navigation, which is one of the most important tasks in multi-modal machine learning. The focus of this multi-modal field is how to fuse multiple inputs, which is crucial for the integrated feedback of intrinsic information. However, the existing models are only implemented through simple data augmentation or expansion, and are obviously far from being able to tap the intrinsic relationship between modalities. In this paper, to overcome these challenges, a novel multi-modal matching feedback self-tuning model is proposed, which is a novel neural network called Vital Information Matching Feedback Self-tuning Network (VIM-Net). Our VIM-Net network is mainly composed of two matching feedback modules, a visual matching feedback module (V-mat) and a trajectory matching feedback module (T-mat). Specifically, V-mat matches the target information of visual recognition with the entity information extracted by the command; T-mat matches the serialized trajectory feature with the direction of movement of the command. Ablation experiments and comparative experiments are conducted on the proposed model using the Matterport3D simulator and the Room-to-Room (R2R) benchmark datasets, and the final navigation effect is shown in detail. The results prove that the model proposed in this paper is indeed effective on the task.

High-Performance Free-Standing Flexible Photodetectors Based on Sulfur-Hyperdoped Ultrathin Silicon.

Flexible photodetectors (PDs) prepared with silicon-based materials have received considerable attention for their use in a wide range of portable and wearable applications. In this study, we present the first free-standing flexible PD based on sulfur-hyperdoped ultrathin silicon, which was fabricated using a femtosecond laser in a SF6 atmosphere. It is found that the fabricated device exhibits excellent performance of broadband photoresponse from 400 to 1200 nm, with a peak responsivity of 63.79 A/W @ 870 nm at a low bias voltage of -2 V, corresponding to an external quantum efficiency reaching 9092%, which surpasses most values reported for silicon-based flexible PDs. In addition, the device shows a fast response speed (rise time τr = 68 µs) and stable detection performance with good mechanical flexibility. The high-performance PD described here suggests a promising way in flexible applications for sensors, imaging systems, and optical communication systems.