Search

This paper introduces an indoor-monitoring LiDAR sensor for patients with Alzheimer disease residing in long-term care facilities (LTCFs), and this sensor exploits an optoelectronic analog front-end (AFE) to detect light signals from targets by utilizing on-chip avalanche photodiodes (APDs) realized in a 180 nm CMOS process and a neural processing unit (NPU) used for motion detection and decisions, especially for incidents of falls occurring in LTCFs. The AFE consists of an on-chip CMOS P+/N-well APD, a linear-mode transimpedance amplifier, a post-amplifier, and a time-to-digital converter, whereas the NPU exploits network sparsity and approximate processing elements for low-power operation. This work provides a potential solution of low-cost, low-power, indoor-monitoring LiDAR sensors for patients with Alzheimer disease in LTCFs. [ABSTRACT FROM AUTHOR]

Long-term, automated fish detection provides invaluable data for deep-sea aquaculture, which is crucial for safe and efficient seawater aquafarming. In this paper, we used an infrared camera installed on a deep-sea truss-structure net cage to collect fish images, which were subsequently labeled to establish a fish dataset. Comparison experiments with our dataset based on Faster R-CNN as the basic objection detection framework were conducted to explore how different backbone networks and network improvement modules influenced fish detection performances. Furthermore, we also experimented with the effects of different learning rates, feature extraction layers, and data augmentation strategies. Our results showed that Faster R-CNN with the EfficientNetB0 backbone and FPN module was the most competitive fish detection network for our dataset, since it took a significantly shorter detection time while maintaining a high AP50 value of 0.85, compared to the best AP50 value of 0.86 being achieved by the combination of VGG16 with all improvement modules plus data augmentation. Overall, this work has verified the effectiveness of deep learning-based object detection methods and provided insights into subsequent network improvements. [ABSTRACT FROM AUTHOR]

The advent of simultaneous wireless information and power (SWIPT) has been regarded as a promising technique to provide power supplies for an energy sustainable Internet of Things (IoT), which is of paramount importance due to the proliferation of high data communication demands of low-power network devices. In such networks, a multi-antenna base station (BS) in each cell can be utilized to concurrently transmit messages and energies to its intended IoT user equipment (IoT-UE) with a single antenna under a common broadcast frequency band, resulting in a multi-cell multi-input single-output (MISO) interference channel (IC). In this work, we aim to find the trade-off between the spectrum efficiency (SE) and energy harvesting (EH) in SWIPT-enabled networks with MISO ICs. For this, we derive a multi-objective optimization (MOO) formulation to obtain the optimal beamforming pattern (BP) and power splitting ratio (PR), and we propose a fractional programming (FP) model to find the solution. To tackle the nonconvexity of FP, an evolutionary algorithm (EA)-aided quadratic transform technique is proposed, which recasts the nonconvex problem as a sequence of convex problems to be solved iteratively. To further reduce the communication overhead and computational complexity, a distributed multi-agent learning-based approach is proposed that requires only partial observations of the channel state information (CSI). In this approach, each BS is equipped with a double deep Q network (DDQN) to determine the BP and PR for its UE with lower computational complexity based on the observations through a limited information exchange process. Finally, with the simulation experiments, we verify the trade-off between SE and EH, and we demonstrate that, apart from the FP algorithm introduced to provide superior solutions, the proposed DDQN algorithm also shows its performance gain in terms of utility to be up to 1.23-, 1.87-, and 3.45-times larger than the Advantage Actor Critic (A2C), greedy, and random algorithms, respectively, in comparison in the simulated environment. [ABSTRACT FROM AUTHOR]

Future wireless networks promise immense increases on data rate and energy efficiency while overcoming the difficulties of charging the wireless stations or devices in the Internet of Things (IoT) with the capability of simultaneous wireless information and power transfer (SWIPT). For such networks, jointly optimizing beamforming, power control, and energy harvesting to enhance the communication performance from the base stations (BSs) (or access points (APs)) to the mobile nodes (MNs) served would be a real challenge. In this work, we formulate the joint optimization as a mixed integer nonlinear programming (MINLP) problem, which can be also realized as a complex multiple resource allocation (MRA) optimization problem subject to different allocation constraints. By means of deep reinforcement learning to estimate future rewards of actions based on the reported information from the users served by the networks, we introduce single-layer MRA algorithms based on deep Q-learning (DQN) and deep deterministic policy gradient (DDPG), respectively, as the basis for the downlink wireless transmissions. Moreover, by incorporating the capability of data-driven DQN technique and the strength of noncooperative game theory model, we propose a two-layer iterative approach to resolve the NP-hard MRA problem, which can further improve the communication performance in terms of data rate, energy harvesting, and power consumption. For the two-layer approach, we also introduce a pricing strategy for BSs or APs to determine their power costs on the basis of social utility maximization to control the transmit power. Finally, with the simulated environment based on realistic wireless networks, our numerical results show that the two-layer MRA algorithm proposed can achieve up to 2.3 times higher value than the single-layer counterparts which represent the data-driven deep reinforcement learning-based algorithms extended to resolve the problem, in terms of the utilities designed to reflect the trade-off among the performance metrics considered. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel multi-view image denoising algorithm based on convolutional neural network (MVCNN). Multi-view images are arranged into 3D focus image stacks (3DFIS) according to different disparities. The MVCNN is trained to process each 3DFIS and generate a denoised image stack that contains the recovered image information for regions of particular disparities. The denoised image stacks are then fused together to produce a denoised target view image using the estimated disparity map. Different from conventional multi-view denoising approaches that group similar patches first and then perform denoising on those patches, our CNN-based algorithm saves the effort of exhaustive patch searching and greatly reduces the computational time. In the proposed MVCNN, residual learning and batch normalization strategies are also used to enhance the denoising performance and accelerate the training process. Compared with the state-of-the-art single image and multi-view denoising algorithms, experiments show that the proposed CNN-based algorithm is a highly effective and efficient method in Gaussian denoising of multi-view images. [ABSTRACT FROM AUTHOR]

The behavior of fiber reinforced polymer (FRP) composites at high temperature is a critical issue that needs to be clearly understood for their structural uses in civil engineering. However, due to technical difficulties during testing at high temperature, limited experimental investigations have been conducted regarding the thermal behavior of basalt fiber reinforced polymer (BFRP) composites, especially for the in-plane shear modulus of BFRP laminates. To this end, both an analytical derivation and an experimental program were carried out in this work to study the in-plane shear modulus of BFRP laminates. After the analytical derivation, the in-plane shear modulus was investigated as a function of the elastic modulus in different directions (0°, 45° and 90° of the load-to-fiber angle) and Poisson's ratio in the fiber direction. To obtain the in-plane shear modulus, the four parameters were tested at different temperatures from 20 to 250 °C. A novel non-contacting digital image correlation (DIC) sensing system was adopted in the high-temperature tests to measure the local strain field on the FRP samples. Based on the test results, it was found that the elastic moduli in different directions were reduced to a very low level (less than 20%) from 20 to 250 °C. Furthermore, the in-plane shear modulus of BFRP at 250 °C was only 3% of that at 20 °C. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of environmental monitoring by designing a cooperative control scheme for mobile sensor networks. The proposed cooperative control scheme includes three main modules: a wireless communication module, a direction decision module, and a motion control module. In the wireless communication module, an event-based communication rule is proposed, which means that mobile sensor nodes do not send their positions, velocities, and the data of environmental attributes to the other sensor nodes in real-time for the coordination and control of mobile sensor networks. Due to using the event-based communication rule, the communication bandwidth can be saved. In the direction decision module, a radial basis function network is used to model the monitored environment and is updated in terms of the sampled environmental data and the environmental data from the other sensor nodes by the wireless communication module. The updated environment model is used to guide the mobile sensor network to move towards the region of interest in order to efficiently model the distribution map of environmental attributes, such as temperature, salinity, and pH values for the monitored environment. In the motion control module, a finite-time consensus control approach is proposed to enable the sensor nodes to quickly change their movement directions in light of the gradient information from the environment model. As a result of using the event-based communication rule in the wireless communication module, the proposed control approach can also lower the updating times of the control signal. In particular, the proposed cooperative control scheme is still efficient under the directed wireless communication situation. Finally, the effectiveness of the proposed cooperative control scheme is illustrated for the problem of environmental monitoring. [ABSTRACT FROM AUTHOR]

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%. [ABSTRACT FROM AUTHOR]

A novel wearable multi-sensor data glove system is developed to explore the relation between finger spasticity and voluntary movement in patients with stroke. Many stroke patients suffer from finger spasticity, which is detrimental to their manual dexterity. Diagnosing and assessing the degrees of spasticity require neurological testing performed by trained professionals to estimate finger spasticity scores via the modified Ashworth scale (MAS). The proposed system offers an objective, quantitative solution to assess the finger spasticity of patients with stroke and complements the manual neurological test. In this work, the hardware and software components of this system are described. By requiring patients to perform five designated tasks, biomechanical measurements including linear and angular speed, acceleration, and pressure at every finger joint and upper limb are recorded, making up more than 1000 features for each task. We conducted a preliminary clinical test with 14 subjects using this system. Statistical analysis is performed on the acquired measurements to identify a small subset of features that are most likely to discriminate a healthy patient from patients suffering from finger spasticity. This encouraging result validates the feasibility of this proposed system to quantitatively and objectively assess finger spasticity. [ABSTRACT FROM AUTHOR]

Magnetic flux leakage (MFL) testing is widely used in non-destructive testing of ferromagnetic components. In view of the serious attenuation of the leakage magnetic field (LMF) caused by the transmission of LMF in the lift-off layer between the measuring point and the workpiece, this paper introduces an MFL detection method based on the slotted ferromagnetic lift-off layer (SFLL). The conventional non-ferromagnetic lift-off layer is changed to a ferromagnetic lift-off layer with a rectangular slot. The magnetic sensor is fixed above the slot and scans the workpiece together with the lift-off layer. First, the detection mechanism of the new method was studied by an equivalent LMF coil model. The permeability perturbation effect and the magnetization enhancement effect were analyzed in the new method. Based on the detection mechanism, the lift-off tolerance of the new method was investigated. Then, the LMF enhancement and lift-off tolerance of the new method in the steel plate detection model were studied. Finally, experiments were conducted to compare the new method with the conventional method. The simulation and experimental results show that the slotted ferromagnetic lift-off layer enhances the amplitude of the MFL signal and is tolerant to the lift-off value. This method provides a new idea for optimizing the design of the MFL sensor and improving the sensitivity of MFL detection at a large lift-off value. [ABSTRACT FROM AUTHOR]

In a network architecture, an intrusion detection system (IDS) is one of the most commonly used approaches to secure the integrity and availability of critical assets in protected systems. Many existing network intrusion detection systems (NIDS) utilize stand-alone classifier models to classify network traffic as an attack or as normal. Due to the vast data volume, these stand-alone models struggle to reach higher intrusion detection rates with low false alarm rates(FAR). Additionally, irrelevant features in datasets can also increase the running time required to develop a model. However, data can be reduced effectively to an optimal feature set without information loss by employing a dimensionality reduction method, which a classification model then uses for accurate predictions of the various network intrusions. In this study, we propose a novel feature-driven intrusion detection system, namely χ 2 -BidLSTM, that integrates a χ 2 statistical model and bidirectional long short-term memory (BidLSTM). The NSL-KDD dataset is used to train and evaluate the proposed approach. In the first phase, the χ 2 -BidLSTM system uses a χ 2 model to rank all the features, then searches an optimal subset using a forward best search algorithm. In next phase, the optimal set is fed to the BidLSTM model for classification purposes. The experimental results indicate that our proposed χ 2 -BidLSTM approach achieves a detection accuracy of 95.62% and an F-score of 95.65%, with a low FAR of 2.11% on NSL-KDDTest+. Furthermore, our model obtains an accuracy of 89.55%, an F-score of 89.77%, and an FAR of 2.71% on NSL-KDDTest−21, indicating the superiority of the proposed approach over the standard LSTM method and other existing feature-selection-based NIDS methods. [ABSTRACT FROM AUTHOR]

An optimal camera placement problem is investigated. The objective is to maximize the area of the field of view (FoV) of a stitched video obtained by stitching video streams from an array of cameras. The positions and poses of these cameras are restricted to a given set of selections. The camera array is designed to be placed inside the abdomen to support minimally invasive laparoscopic surgery. Hence, a few non-traditional requirements/constraints are imposed: Adjacent views are required to overlap to support image registration for seamless video stitching. The resulting effective FoV should be a contiguous region without any holes and should be a convex polygon. With these requirements, traditional camera placement algorithms cannot be directly applied to solve this problem. In this work, we show the complexity of this problem grows exponentially as a function of the problem size, and then present a greedy polynomial time heuristic solution that approximates well to the globally optimal solution. We present a new approach to directly evaluate the combined coverage area (area of FoV) as the union of a set of quadrilaterals. We also propose a graph-based approach to ensure the stitching requirement (overlap between adjacent views) is satisfied. We present a method to find a convex polygon with maximum area from a given polygon. Several design examples show that the proposed algorithm can achieve larger FoV area while using much less computing time. [ABSTRACT FROM AUTHOR]