Search

By implementing new technologies, university technology transfer (UTT) is crucial for economic advancement. However, developing countries face numerous challenges regarding UTT. This study proposes a unique approach by combining Multi-Criteria Decision Making (MCDM) methods such as Delphi and Decision-Making Trial And Evaluation Laboratory (DEMATEL) with Fuzzy Theories, specifically Spherical Fuzzy Sets (SFSs) and T – Spherical Fuzzy Sets (T-SFSs), to examine the barriers in UTT. The distinctive aspect of this study lies in addressing the limitations of SFSs and upgrading them to T-SFSs by incorporating them with the DEMATEL approach. Initially, SFS Delphi is employed to identify and eliminate nonimportant barriers. Subsequently, SF DEMATEL is utilized to classify barriers into cause-effect groups and determine their interactive relationships. However, SFSs have revealed specific weaknesses during this step, prompting the adoption of T-SF DEMATEL as a replacement. The findings highlight the perfect compatibility of T-SFSs with the research model and identify several primary barriers that hinder UTT, including C14, inconsistent rules and regulations, C9, misalignment between research and commercialization objectives, C3- lack of recognition for university-industry linkages, and C5- lack of resources. This study not only improves the understanding of UTT for decision makers, entrepreneurs, and policymakers, facilitating the creation of solutions, but also serves as a reference for researchers in the application of T-SFSs in other research domains.

There has been an ongoing demand for monitoring landslides due to the heavy economic losses and casualties caused by such natural disasters. In this paper, we introduce a swift landslide detection system that can detect and segment landslides occurring on roads. To tackle the challenges of data collection, we propose an automatic annotation procedure to create a new landslide dataset consisting of 2963 images, termed the LandslidePTIT dataset. Additionally, we construct a novel deep-learning architecture that can perform both classification and segmentation tasks well from a few annotated images of landslides. Specifically, the model consists of four main modules that are delicately designed to solve the few-shot segmentation problem using landslide images, namely hypercorrection construction, attentive squeeze block, a cross-feature layer, and broadcast and squeeze layer. Experimental results exhibit the superiority of the proposed method in comparison with competitive baselines, in terms of both quantitative and qualitative manners.

Advanced Driver Assistance Systems (ADAS) are a collection of intelligent solutions integrated into next-generation vehicles to assist in safe driving. When building ADAS systems, the main goals are that they are stable, flexible, easy to maintain, and allow for error tracing. If a driving assistance algorithm is designed to be implemented on one machine or in one model, there is a potential disadvantage that if one component fails, then the entire system would stop. We work on modularizing the ADAS system to be flexible to accommodate any changes or improvements based on up-to-date requirements. Using advanced current edge (or network) devices, we propose a Detection-based Driving Assistance algorithm, which can collaborate or integrate with an existing system in a vehicle. The core of any process is to ensure that the system has a predictable level of functionality and that any misbehavior can be easily traced to the root cause. The proposed system shows fast, real-time performance on edge devices with limited computing power.

This study investigates the integration of an intelligent reflecting surface (IRS) into an unmanned aerial vehicle (UAV) platform to utilize the advantages of these leading technologies for sixth-generation communications, e.g., improved spectral and energy efficiency, extended network coverage, and flexible deployment. In particular, we investigate a downlink IRS–UAV system, wherein single-antenna ground users (UEs) are served by a multi-antenna base station (BS). To assist the communication between UEs and the BS, an IRS mounted on a UAV is deployed, in which the direct links are obstructed owing to the complex urban channel characteristics. The beamforming at the BS, phase shift at the IRS, and the 3D placement of the UAV are jointly optimized to maximize the sum rate. Because the optimization variables, particularly the beamforming and IRS phase shift, are highly coupled with each other, the optimization problem is naturally non-convex. To effectively solve the formulated problem, we propose an iterative algorithm that employs block coordinate descent and inner approximation methods. Numerical results demonstrate the effectiveness of our proposed approach for a UAV-mounted IRS system on the sum rate performance over the state-of-the-art technology using the terrestrial counterpart.

Content-defined chunking is a common method in many applications such as data deduplication and data synchronization. In recent years, new CDC algorithms using non-hashing methods have been developed, and positive results have been obtained. However, most of the algorithms are developed for single-thread computation on microprocessors. After analyzing some popular CDC algorithms, we observed that the algorithms using the basic sliding window protocol are more feasible to process in parallel. In this work, we proposed a new parallel chunking method that aims for hardware implementation. Additionally, we used the PCI algorithm, which does not include hash functions, to implement a multi-thread chunking system on FPGA devices. By exploiting the strength of the FPGAs, our proposed design achieves not only high computational speed but also great scalability.

This study investigates a combined system comprising non-orthogonal multiple access (NOMA) and beamforming in a downlink network. To fully exploit the advantages of NOMA, user (UE) pairing and beamforming design are jointly optimized via a generalized model for UE association, subject to energy efficiency maximization. Owing to the combination of binary variables and nonconvex constraints, the resulting optimization problem belongs to the class of mixed-integer nonconvex programming. An innovative algorithm, integrating the inner-approximation and Dinkelbach methods, is proposed herein to address a nonconvex fractional function. By introducing a pairing matrix and relaxing the binary variables into continuous ones, our approach is capable of reaching an optimal solution, where two arbitrary UEs are optimally paired regardless of geographical or spatial constraints. For practical scenarios, we further propose a robust design to manage the effect of channel estimation errors under settings involving channel uncertainty. Numerical results show that our proposed designs, even with the presence of the imperfect channel state information at the base station, significantly outperform the conventional beamforming and existing pairing schemes.

This paper addresses the local minimum phenomenon, routing path enlargement, and load imbalance problems of geographic routing in wireless sensor networks (WSNs) with holes. These issues may degrade the network lifetime of WSNs since they cause a long detour path and a traffic concentration around the hole boundary. Aiming to solve these problems, in this work, we propose a novel geographic routing protocol for WSNs, namely, Q-learning Inspired Hole bypassing (QIH), which is lightweight and efficient. QIH’s conceptual idea is to leverage Q-learning to estimate the distance from a node to the holes. QIH makes routing decisions following the nodes’ residual energy, their estimated distance to the holes, and their distance to the destination. We first confirm the effectiveness of QIH by theoretical analysis. Then, we conduct extensive simulations of QIH in comparison to state-of-the-art protocols. The simulation results show that QIH outperforms the other protocols in terms of network lifetime, packet latency, and energy consumption.

Relative sensor-target geometry is well known to significantly affect the performance of target localization using a sensor network. This article analyzes the optimal sensor-target geometries for the problem of target localization with Bayesian priors. We present a unified geometry optimization framework for different types of sensor networks including bearing-only sensor network, range-only sensor network, received signal strength-only sensor network and mixed network of these sensor types. For geometry optimization of Bayesian target localization with these sensor networks, we establish the equivalence between the A-optimality criterion (i.e., minimizing the estimation mean squared error) and the D-optimality criterion (i.e., minimizing the area of the estimation uncertainty ellipse). Under these optimality criteria, the geometry optimization problem is shown to be mathematically equivalent to the problem of minimizing the modulus of a vector sum. This thus makes the optimal geometry conditions algebraically simple and easy to be computed. We conclude the article with extensive simulation studies to verify the accuracy of the analytical findings.

In this paper, an adaptive neural command filtered backstepping scheme is proposed for the pneumatic active suspension with the vertical displacement constraint of sprung mass and actuator saturation. A quarter car model with a pneumatic spring is first fabricated on the basis of thermodynamic theory to describe the dynamic characteristics. To overcome the lumped unknown nonlinearities and enhance the requirement of modeling precision, the radial basis function neural networks (RBFNNs) are proposed to approximate unknown continuous functions caused by the uncertain body mass and other factors of pneumatic spring. To solve the explosion of complexity problem in the traditional backstepping designs, a proposed command filter control is applied by using the Levant differentiators which approach the derivative of the virtual control signals. Nussbaum gain technique is then incorporated into the controller to avoid the problem of the completely unknown control gain and control directions of a pneumatic actuator. In addition, the prescribed performance function (PPF) is suggested to guarantee that the tracking error of the sprung mass displacement does not violate the constraint boundaries. Based on the command filtered backstepping control with PPF, the Lyapunov theorem is then applied to indicate the system stability analysis. Finally, the comparative simulation examples for the pneumatic suspension are given to verify the effectiveness and reliability of the proposed control.

The concentration of fine particulate matter (PM2.5), which represents inhalable particles with diameters of 2.5 micrometers and smaller, is a vital air quality index. Such particles can penetrate deep into the human lungs and severely affect human health. This paper studies accurate PM2.5 prediction, which can potentially contribute to reducing or avoiding the negative consequences. Our approach’s novelty is to utilize the genetic algorithm (GA) and an encoder-decoder (E-D) model for PM2.5 prediction. The GA benefits feature selection and remove outliers to enhance the prediction accuracy. The encoder-decoder model with long short-term memory (LSTM), which relaxes the restrictions between the input and output of the model, can be used to effectively predict the PM2.5 concentration. We evaluate the proposed model on air quality datasets from Hanoi and Taiwan. The evaluation results show that our model achieves excellent performance. By merely using the E-D model, we can obtain more accurate (up to 53.7%) predictions than those of previous works. Moreover, the GA in our model has the advantage of obtaining the optimal feature combination for predicting the PM2.5 concentration. By combining the GA-based feature selection algorithm and the E-D model, our proposed approach further improves the accuracy by at least 13.7%.

Private integer comparison has been an essential computation function for many applications, including online auction, credential identification, data mining, and joint bidding. In the setting of two-party computation, two parties with private inputs ( $x$ and $y$ ) want to jointly compare them without revealing the value of those inputs to others (also known as the Millionaires’ problem) while the output should ensure correctness and preserve data privacy. The private inputs only can be revealed if they are equal, i.e., $x=y$ . Many related works have been proposed to solve the integer comparison problem in various settings, focusing on different properties such as round and computation complexity. Most solutions decompose integers into bitwise representation and then securely evaluate the function in a Boolean circuit on encrypted bits. However, this type of solution is costly (especially for large integers) as each bit requires encryption and decryption. In this paper, we transform the private integer comparison into a block comparison problem. In particular, we employ a block vectorization mechanism to encode the private inputs into blocks. We show the security of our two-party protocol in the semi-honest model. Also, we implement the protocol to demonstrate its efficiency using block vectorization mechanism and homomorphic encryption. The experimental result proves that our proposed solution achieves high efficiency, particularly for large integer comparisons.

Rectification is a core programming process that involves multiple views. In this study, we focus on uncalibrated cases that neglect intrinsic and extrinsic camera information. Existing uncalibrated rectification methods use feature-matching techniques to form relationships between different views and use those features to estimate optimized homography matrices. However, outliers are inevitable in feature matching. Using these outliers in a rectification process produces vertical disparity errors and unwanted geometric distortion. To tackle the problem, we propose a novel method that can learn from the rectification results, re-select the matching pairs, and find superior solutions. The proposed method introduces a novel workflow for uncalibrated rectification that incorporates three cores: field of view (FoV) neutralization, rectification, and feature matching re-selection (FMR). While the FoV neutralization module handles FoV differences among views, a combination of the rectification module and FMR module results in the optimal homography matrices. The rectification module takes neutral correspondences and estimates the optimized rectified matrices. Applying the results from the rectification module, the FMR module optimizes the correspondences and return them to the rectification module. For the rectification module, we apply adaptive geometric constraints and our updated optimization strategy to secure satisfied vertical disparity errors while maintaining low distortion levels. Multidisciplinary experiments are performed to analyze the capabilities of the proposed method. We combine existing datasets with extra samples, including various outdoor environments, to gauge the performance better. Compared with existing methods, the proposed method produces fewer rectification errors and keeps the rectified images under low distortion rates.

Remote screening physical frailty (PF) may assist in triaging patients with chronic obstructive pulmonary disease (COPD) who are in clinical priorities to visit a clinical center for preventive care. Conventional PF assessment tools have however limited feasibility for remote patient monitoring applications. To improve the safety of PF assessment, we previously developed and validated a quick and safe PF screening tool called Frailty Meter (FM). FM works by quantifying weakness, slowness, rigidity, and exhaustion during a 20-second repetitive elbow flexion/extension task using a wrist-worn sensor and generates a frailty index (FI) ranging from zero to one; higher values indicate progressively greater severity of frailty. However, the use of wrist-sensor limits its applications in telemedicine and remote patient monitoring. In this study, we developed a sensor-less FM based on deep learning-based image processing, which can be easily integrated into mobile health and enables remote assessment of physical frailty. The sensor-less FM extracts kinematic features of the forearm motion from the video of 20-second elbow flexion and extension recorded by a tablet camera, and then calculates frailty phenotypes and FI. To test the validity of sensor-less FM, 11 COPD patients admitted to a Telehealth pulmonary rehabilitation clinic and 10 healthy young volunteers (controls) were recruited. All participants completed the test indicating high feasibility. Strong correlations (0.72

Bushfire is a destructive force that would change the course of a country and even the Earth. They are causing casualties and affect the quality of life of millions of people. Governments are calling for remote sensing methods to monitor and detect active bushfires around-the-clock. To fulfill this call, we develop a remote sensing framework on top of imagery satellite streams to monitor and detect bushfire timely before beyond control. However, detecting bushfires from satellite images needs to take into account several aspects including spatial pattern of fire-positive pixels, temporal dependencies, spectral correlation between channels, and adversarial effects. In this article, we propose a multi-scale deep neural network model that combines both satellite images and weather data for detecting and locating bushfires at both image and pixel level. We illustrate that the weather information with careful spatio-temporal alignment can be utilised to augment imagery data. Experiments on real-world datasets show that the proposed model is better than the baselines with 93.4% accuracy and detects bushfires 1.2 times faster. It is also robust to the effects of cloud and night-time.

The performance of visual odometry is dependent upon the quality of features selected for computing the frame-to-frame transformation. In order to ensure the quality of selected features, conventional approaches consider the spatial distribution of the selected features, in addition to their counts and matching scores, in which a small number of features are selected randomly from each of the uniformly distributed buckets. In this paper, we show that features can be selected optimally, rather than randomly, using a well-defined mathematical formalism. The proposed method of optimal feature selection minimizes the degree of uncertainty in estimating the essential, fundamental, or homography matrix involved in visual odometry by maximizing the orthogonality index of individual equations and constraints associated with computation. We found that, at a constant noise level, the mean of the residual error and the variance of an estimated essential, fundamental, or homography matrix decrease monotonically with increasing orthogonality index. The simulation validates the increased accuracy of the feature selection based on the proposed orthogonality index compared with the conventional random selection. For instance, it enhances accuracy by as much as 35% when a small number of feature sets, say, 20 feature sets, are used. The experiments using the KITTI and Devon Island datasets further reinforce the performance enhancement of simulations by 9% and 20%, respectively.

This paper generalizes and proves the discrete and finite nature of the capacity-achieving signaling schemes for general classes of non-Gaussian point-to-point and multiple-access channels (MACs) under peak power constraints. Specifically, we first investigate the detailed characteristics of capacity-achieving inputs for a single-user channel that is impaired by two types of noise: a Gaussian mixture (GM) noise Z consisting of Gaussian elements with arbitrary means and the interference U with an arbitrary distribution. The only very mild condition imposed on U is that its second moment is finite. To this end, one of the important results is the establishment of the Kuhn-Tucker condition (KTC) on a capacity-achieving input and the proof of analyticity of the KTC using Fubini-Tonelli's and Morera's theorems. Using the Bolzano-Weierstrass's and Identity's theorems, we then show that a capacity-achieving input is continuous if and only if the KTC function is zero on the entire real line. However, by examining an upper bound on the tail of the output PDF, it is demonstrated that the KTC function must be bounded away from zero. As such, any capacity-achieving input must be discrete with a finite number of mass points. Finally, we exploit U having an arbitrary distribution to show that the optimal input distributions that achieve the sum-capacity of an M-user MAC under GM noise are discrete and finite. We also prove that there exist at least two distinct points that achieve the sum capacity on the rate region.

Infrastructures enabling experimental assessment of Internet of Things (IoT) solutions are scarce. Moreover, such infrastructures are typically bound to a specific application domain, thus, not facilitating the testing of solutions with a horizontal approach. This paper presents a platform that supports Experimentation as s Service (EaaS) over a federation of IoT testbeds. This platform brings two major advances. First, it leverages semantic web technologies to enable interoperability so that testbed agnostic access to the underlying facilities is allowed. Second, a set of tools ease both the experimentation workflow and the federation of other IoT deployments, independently of their domain of interest. Apart from the platform specification, this paper presents how this design has been actually instantiated into a cloud-based EaaS platform that has been used for supporting a wide variety of novel experiments targeting different research and innovation challenges. In this respect, this paper summarizes some of the experiences from these experiments and the key performance metrics that this instance of the platform has exhibited during the experimentation.

The research of social influence is an important topic in online social network analysis. Influence maximization is the problem of finding k nodes that maximize the influence spread in a specific social network. Robust influence maximization is a novel topic that focuses on the uncertainty factors among the influence propagation models and algorithms. It aims to find a seed set with a definite size that has robust performance with different influence functions under various uncertainty factors. In this paper, we propose a centrality-based edge activation probability evaluation method in the independent cascade model. We consider four different types of centrality measurement methods and add a modification coefficient to evaluate the edge probability. We also propose two algorithms, called NewDiscount and GreedyCIC, by incorporating the edge probability space into previous algorithms. With extensive experiments on various real online social network data sets, we find that our PageRank-based greedy algorithm has the best influence spreads and lowest running times, compared with other algorithms, on some large data sets. The experiment for evaluating the robustness performance shows that all algorithms have optimal robustness performance when the modification coefficient is set to 0.01 under the independent cascade model. This result suggests some further research directions under this model.