Omnidirectional depth estimation is an emerging research topic and has received significant attention in recent years. However, the existing methods were developed based on the theory of planar stereo matching; and introduce the nonlinear epipolar constraint and significant distortions of re-projections. In this paper, we propose a novel approach that use spherical CNNs and the epipolar constraint

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Crowdsourcing is simply the outsourcing of different tasks or work to a diverse group of individuals in an open call for the purpose of utilizing human intelligence. Crowdsourcing nowadays used to support and enhance software engineering in different aspects. In this proposed study, a systematic literature review was conducted for the last 10 years from 2010 to 2019. During the filtering process

Usage of agile methods for software development has increased in recent times. Rapid delivery of software products is ensured by these methods with less expense and high user gratification. Application of these methods on a large scale causes many De-Motivators or challenges as initially these methods were aimed at those developmental teams that worked on small scale. However, no effort has been made

Accurate eye blink artifact detection is essential for electroencephalogram (EEG) analysis and auxiliary analysis of nervous system diseases, especially in the presence of the frontal epileptiform discharges. In this paper, we develop a novel eye blink artifact detection algorithm based on optimally selected multi-dimensional EEG features. Specific efforts have been paid to filtering the frontal epileptiform

This study investigates how external vertical forces on the pelvis change the stability of stairmill climbing and other gait parameters such as kinematics and muscle activity. We use a Tethered Pelvic Assist Device (TPAD) to apply forces on the pelvis during continuous ascent on a stairmill. Ten young healthy subjects participated in three one-minute stair ascent with no force, a 10% body weight (BW)

Three-dimensional contrast source inversion-electrical properties tomography (3-D CSI-EPT) is an iterative reconstruction method that estimates the electrical properties of tissue from transmit field magnetic resonance data. However, in order to bring 3-D CSI-EPT into practice for complex tissue structures and to understand the origin and effect of errors, insight in the sensitivities of reconstruction

We present a low rank approximation approach for topology optimization of parametrized linear elastic structures. The parametrization is considered on loading and stiffness of the structure. The low rank approximation is achieved by identifying a parametric connection among coarse finite element models of the structure (associated with different design iterates) and is used to inform the high fidelity

We investigate the isogeometric analysis approach based on the extended Catmull–Clark subdivision for solving the PDEs on surfaces. As a compatible technique of NURBS, subdivision surfaces are capable of the refinability of B-spline techniques, and overcome the major difficulties of the interior parameterization encountered by the isogeometric analysis. The surface is accurately represented as the

Single flux quantum (SFQ) logic is a promising technology to replace complementary metal-oxide-semiconductor logic for future exa-scale supercomputing but requires the development of reliable EDA tools that are tailored to the unique characteristics of SFQ circuits, including the need for active splitters to support fanout and clocked logic gates. This article is the first work to present a physical

In this article, we present a low-energy inference method for convolutional neural networks in image classification applications. The lower energy consumption is achieved by using a highly pruned (lower-energy) network if the resulting network can provide a correct output. More specifically, the proposed inference method makes use of two pruned neural networks (NNs), namely mildly and aggressively

The study aimed to analyze the application of diffusion tensor imaging (DTI) in the surgery of benign and malignant intracranial tumors through improved fuzzy C-means (FCM). First, a method of combining the maximum and minimum distances was proposed to improve the FCM algorithm. Then, the optimized FCM was applied to the diffusion tensor imaging (DTI) diagnosis. The patients were rolled into the benign

In order to realize the automatic recognition of intracranial hematoma and accurate measurement of hematoma volume in patients with ICH (intracerebral hemorrhage), the FCM algorithm (fuzzy c-means algorithm) was improved in this study, and a new level set segmentation algorithm based on FCM was obtained, FCRLS (fuzzy c-means regularized level set). Then, 120 ICH patients were used as the research objects

Green development is the hot spot of cold chain logistics today. Therefore, this paper converts carbon emission into carbon emission cost, comprehensively considers cargo damage, refrigeration, carbon emission, time window, and other factors, and establishes the optimization model of location of low-carbon cold chain logistics in the Beijing-Tianjin-Hebei metropolitan area. Aiming at the problems of

The design of a distribution network under uncertainty is addressed by using the minmax regret paradigm. Economic or technical uncertain factors may have a strong influence on the behavior of a distribution system and should be taken into account in the design process. These factors can affect to the construction and operating costs, origin or destination of the supplied commodities, required demands

Due to the great potential to enable collaboration and improve consolidation, auctions have been identified as a possible effective option to improve the efficiency of instant delivery. Instant delivery markets are complex and dynamic systems influenced by highly random demand. Conventional bidding strategies require perfect market information and cannot be adjusted effectively according to the evolution

Classical supply chain networks primarily focus on economic optimization by determining the optimal supply chain configuration in a chaotic business environment. Owing to rapidly increasing greenhouse gas emissions, there has been immense pressure from society to design eco-efficient supply chain networks to reduce the carbon emissions generated from supply chain activities at a reasonable cost. This

Supply network (SN) robustness has become an important issue in SN management. In this paper, we refer to SN as robust, if it maintains its performance in the presence of uncertainty in SN parameters, namely uncertain changes in customer demand. A customer forecasts its demand in terms of requested quantity and time of delivery. This forecasted demand can be changed until a certain time. After that

Context Validating discrete-event computer simulations for a particular problem domain often involves the help of a domain expert. This means that a certain structural closeness between the simulator’s inner workings and the modeled system is needed in order to allow the expert to follow the implementation in analogy. Process-based simulation imposes an object-oriented view onto a modeled system which

Context: Mobile applications (known as “apps”) usage continues to rapidly increase, with many new apps being developed and deployed. However, developing a mobile app is challenging due to its dependencies on devices, technologies, platforms, and deadlines to reach the market. One potential approach is to use Model Driven Development (MDD) techniques that simplify the app development process, reduce

Traditional physical education methods are unable to meet this requirement due to the practical nature of sports skill teaching. As a result, as the times demanded, the flipped classroom based on neural network technology arose. It has the potential to not only promote the modernization of physical education but also to ensure that it has a positive educational impact. This is a mode of instruction

Digital Microfluidics is an emerging technology for automating laboratory procedures in biochemistry. With more and more complex biochemical protocols getting mapped to biochip devices and microfluidics receiving a wide adoption, it is becoming indispensable to develop automated tools and synthesis platforms that can enable a smooth transformation from complex cumbersome benchtop laboratory procedures

We study a security game over a network played between a defender and k attackers. Every attacker chooses, probabilistically, a node of the network to damage. The defender chooses, probabilistically as well, a connected induced subgraph of the network of \(\lambda \) nodes to scan and clean. Each attacker wishes to maximize the probability of escaping her cleaning by the defender. On the other hand

Soils are inherently anisotropic, which may significantly influence their mechanical behavior and the performance of related geostructures. In this study, the response of strip footing on anisotropic sand was investigated using a J2-type deformation model coupled with state-dependent dilatancy and fabric evolution. The capability of the model was validated by simulating several element tests and a

The mechanism of the effect of salt solution on the water retention capacity of compacted clay is studied from the microscopic perspective. Based on the fractal theory and the double-porosity characteristic of compacted clay, the fractal model and dual-porosity model for predicting the soil-water retention curve of compacted clay are established. And the gravimetric water content and matric suction

Triangle boundaries are crucial geometric objects in the discrete element method (DEM). In this study, an improvement for the uniform grid method is proposed and implemented on the GPU. The improved algorithm includes the spatial discretization of triangles in which triangles are classified by their size and motion. The large and relative static triangles are separated into grid cells such that it

The Automatic Scene Detection Problem (ASDP) is a combinatorial optimization problem that arises in the context of video processing and that has a central role in the management, storing and content retrieval of videos. The problem consists of partitioning the shots of a given video into scenes by optimizing a measure related to the similarity between the given shots. In this article, we build up upon

This paper addresses the customer order scheduling problem in parallel production environment commonly appearing in the pharmaceutical and paper industries. The problem aims to minimize the total completion time of the orders with their jobs processed on dedicated machines in parallel. To deal with the computational challenge of large-scale problems, we propose a learning-based two-stage optimization

Multi-objective (MO) optimization, i.e., the simultaneous optimization of multiple conflicting objectives, is gaining more and more attention in various research areas, such as evolutionary computation, machine learning (e.g., (hyper-)parameter optimization), or logistics (e.g., vehicle routing). Many works in this domain mention the structural problem property of multimodality as a challenge from

This paper deals with a multi-period extension of the p-center problem, in which arc traversal times vary over time, and facilities are mobile units that can be relocated multiple times during the planning horizon. The problem arises in several applications, such as emergency services, in which vehicles can be relocated in anticipation of traffic congestion. First, we analyze the problem structure

In this paper, we investigate vehicle routing problems with third-party transshipment facilities that arise in the context of city logistics. Contrary to classical vehicle routing problems, where each customer request is delivered directly to its destination, the problems considered in this paper feature the alternative possibility of delivering customer requests to third-party transshipment facilities

We study the Multiple Allocation Tree of Hubs Location Problem where a tree topology is required among the hubs and transportation cost of sending flows between OD pairs is minimized. Unlike most studies in the literature that assume a complete network with costs satisfying the triangle inequality to formulate the problem, we define the problem on non-complete networks and develop a modeling approach

Rail is fast becoming a key instrument in the transportation of passengers and cargo. Especially, high-speed trains are gaining more importance in recent decades to passenger transportation. However, some problem arises for passenger transportation due to passengers' improper scheduling or trains' dwell time. These problems negatively affect customer satisfaction and revenue management. Although there

In the wake of the COVID-19 pandemic, many firms lacked a strategy to cope with disruptions and maintain resiliency. In this study, we develop a measurement method to evaluate the impact of resilience strategies in a multi-stage supply chain (SC) in the presence of a pandemic. For the first time, we propose a method to deduce quantitative resilience assessment from simulation. We implement two resilience

Over the years, sustainable supplier selection (SSS) has become increasingly popular among scholars and practitioners as a viable means to actualize supply chain sustainability. Little, however, is known about the impact of the COVID-19 pandemic on sustainable supplier selection particularly in the manufacturing sector. In this paper, we present pandemic response strategies as a significant aspect

Line planning is a critical issue in railway planning as a connecting link between supply and demand. This paper proposes an approach to optimize line planning problem (LPP) for the high-speed railway (HSR) network to provide higher service level for time-varying demand. A bi-level programming model based on Stackelberg game theory is constructed, incorporating passenger flow assignment into line planning

A dual-channel supply chain is considered in which a manufacturer sells its product through a traditional retailer and an e-tailer, wherein the e-tailer may adopt two, i.e., the conventional batch ordering and the drop-shipping, order fulfillment policies. The problem is modeled as Stackelberg games in which the manufacturer is the leader and the retailer and the e-tailer are the followers competing

Cyber-Physical Systems (CPSs) are increasingly more complex and integrated into our everyday lives forming the basis of smart infrastructures, products, and services. Consequently, there is a greater need for their ability to perform their required functions under expected and unexpected adverse events. Moreover, the multitude of threats and their rapid evolution pushes the development of approaches

Increasingly, climate neutrality is becoming a consensus among many countries. While this can motivate toward higher proportions of renewable energy, it can also result in significant increases in the volatility of power systems. Concentrating solar power (CSP) combines the features of zero-emissions and dispatching capability, rendering it an ideal technology for sustainable power systems. In comparison

Haptic interaction is essential for the dynamic dexterity of animals, which seamlessly switch from an impedance to an admittance behaviour using the force feedback from their proprioception. However, this ability is extremely challenging to reproduce in robots, especially when dealing with complex interaction dynamics, distributed contacts, and contact switching. Current model-based controllers require

Sleep disorder diagnosis relies on the analysis of polysomnography (PSG) records. As a preliminary step of this examination, sleep stages are systematically determined. In practice, sleep stage classification relies on the visual inspection of 30-second epochs of polysomnography signals. Numerous automatic approaches have been developed to replace this tedious and expensive task. Although these methods

Electroencephalogram (EEG)-based neurofeedback has been widely studied for tinnitus therapy in recent years. Most existing research relies on experts’ cognitive prediction, and studies based on machine learning and deep learning are either data-hungry or not well generalizable to new subjects. In this paper, we propose a robust, data-efficient model for distinguishing tinnitus from the healthy state

Assessment of self-feeding kinematics is seldom performed in an ecological setting. In preparation for development of an instrumented spoon for measurement of self-feeding in children with cerebral palsy (CP), the current work aimed to evaluate upper extremity kinematics of self-feeding in young children with typical development (TD) and a small, age-matched group of children with CP in a familiar

User gait phase estimation plays a key role for the seamless control of the lower-limb robotic assistive devices (e.g., exoskeletons or prostheses) during ambulation. To achieve this, several studies have attempted to estimate the gait phase using a thigh or shank angle. However, their estimation resulted in some deviation from the actual walking and varied across the walking speeds. In this study

Independent mobility poses a great challenge to the visually impaired individuals. This paper proposes a novel system to understand dynamic crosswalk scenes, which detects the key objects, such as crosswalk, vehicle, and pedestrian, and identifies pedestrian traffic light status. The indication of where and when to cross the road is provided to the visually impaired based on the crosswalk scene understanding

This study aimed to develop a sensitive index from transcranial Doppler (TCD) signals for quantitatively evaluating the effects of long-term external counterpulsation (ECP) treatment on stroke rehabilitation. We recruited 27 patients with unilateral ischemic stroke and a good acoustic window within 7 days of stroke onset. 15 of them received 35 daily 1-hour ECP treatment (ECP group) and the others

Structured illumination microscopy (SIM) enhances spatial resolution by projecting sinusoidal patterns with various orientations and lateral phase shifts. Here, we report a framework, termed DNN-SIM, powered by a deep neural network that learns the physical relationship between images with different lateral phase shifts. This approach captures one image per sinusoidal pattern orientation and infers

Wrinkling instabilities occur when a stiff thin film bonded to an elastic substrate undergoes compression. Regardless of the nature of compression, this phenomenon has been extensively studied through local models based on classical continuum mechanics. However, the experimental behavior is not yet fully understood and the influence of nonlocal effects remains largely unexplored. The objective of this

The importance of swarm robotics systems in both academic research and real-world applications is steadily increasing. However, to reach widespread adoption, new models that ensure the secure cooperation of large groups of robots need to be developed. This work introduces a method to encapsulate cooperative robotic missions in an authenticated data structure known as a Merkle tree. With this method

Decision makers can often be confronted with the need to select a subset of objects from a set of candidate objects by just counting on preferences regarding the objects’ features. Here we formalise this problem as the dominant set selection problem. Solving this problem amounts to finding the preferences over all possible sets of objects. We accomplish so by: (i) grounding the preferences over features

It is generally observed that the fraction of live lines in shared last-level caches (SLLC) is very small for chip multiprocessors (CMPs). This can be tackled using promotion-based replacement policies like re-reference interval prediction (RRIP) instead of LRU, dead-block predictors, or reuse-based cache allocation schemes. In GPU systems, similar LLC issues are alleviated using various cache bypassing

In this paper, we consider robust output tracking for an Euler–Bernoulli beam equation under the guidance of the internal model principle, where the disturbances in all possible channels are considered. Three typical cases are investigated in terms of different regulated outputs. The first case is based on boundary displacement output, for which only asymptotic convergence can be achieved due to the

The berth allocation problem (BAP) has been extensively studied in the literature, which seeks to determine berths and times for upcoming ships. The previous studies do not distinguish berths and ships among different liner carriers. By taking into account the increasing use of dedicated berths in practice, this paper proposes a special BAP where berths and ships could be distinguished among different