Split Architecture Research Articles

Understanding Human Cognition Through Computational Modeling.

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans' belief on AI's operations and provide human-centered explanations to enhance human-AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems.

Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT

Network energy resources are limited in communication systems, which may cause energy shortages in mobile devices at the user end. Active Reconfigurable Intelligent Surfaces (A-RIS) not only have phase modulation properties but also enhance the signal strength; thus, they are expected to solve the energy shortage problem experience at the user end in 6G communications. In this paper, a resource allocation algorithm for maximizing the sum of harvested energy is proposed for an active RIS-assisted Simultaneous Wireless Information and Power Transfer (SWIPT) system to solve the problem of low performance of harvested energy for users due to multiplicative fading. First, in the active RIS-assisted SWIPT system using a power splitting architecture to achieve information and energy co-transmission, the joint resource allocation problem is constructed with the objective function of maximizing the sum of the collected energy of all users, under the constraints of signal-to-noise ratio, active RIS and base station transmit power, and power splitting factors. Second, the considered non-convex problem can be turned into a standard convex problem by using alternating optimization, semi-definite relaxation, successive convex approximation, penalty function, etc., and then an alternating iterative algorithm for harvesting energy is proposed. The proposed algorithm splits the problem into two sub-problems and then performs iterative optimization separately, and then the whole is alternately optimized to obtain the optimal solution. Simulation results show that the proposed algorithm improves the performance by 45.2% and 103.7% compared to the passive RIS algorithm and the traditional without-RIS algorithm, respectively, at the maximum permissible transmitting power of 45 dBm at the base station.

Unifying Splitting

AVATAR is an elegant and effective way to split clauses in a saturation prover using a SAT solver. But is it refutationally complete? And how does it relate to other splitting architectures? To answer these questions, we present a unifying framework that extends a saturation calculus (e.g., superposition) with splitting and that embeds the result in a prover guided by a SAT solver. The framework also allows us to study locking, a subsumption-like mechanism based on the current propositional model. Various architectures are instances of the framework, including AVATAR, labeled splitting, and SMT with quantifiers.

Vehicle Re-Identification Based on Quadratic Split Architecture and Auxiliary Information Embedding

Vehicle Re-Identification Based on Quadratic Split Architecture and Auxiliary Information Embedding

Non-orthogonal transmission for user and control plane split architecture in 5G systems and beyond: Performance analysis and design insights

In next generation networks, mobility management will be a critical issue due to dense base station (BS) deployment, for which user and control plane split architecture provides a promising solution. Jointly designing such architecture with nonorthogonal transmission brings in more flexibility to further improve system efficiency. This paper proposes a non-orthogonal transmission design for user and control plane split architecture. In this design, user equipments (UEs) will select the BS providing the strongest received signal to associate its data channel, but constantly connect its control channel to the nearest macro-cell BS (MBS). Upon non-orthogonal transmission, an MBS can multiplex data traffics and control signals on the same resource. Stochastic geometry based analysis is carried out to investigate outage probability, which extends its regular definition by jointly considering data and control channels, and then mobility-aware outage rate. Numerical results show that: 1) The proposed split architecture alleviates the increase in handover rate for ultra dense networking, compared with conventional architecture. 2) Non-orthogonal transmission outperforms traditional orthogonal transmission in the split architecture, because it is capable of accommodating more control channels. 3) By carefully adjusting power levels, minimum outage probabilities can be reached for macro-cell UEs in the proposed design.

Edge-SLAM: Edge-Assisted Visual Simultaneous Localization and Mapping

Localization in urban environments is becoming increasingly important and used in tools such as ARCore [18], ARKit [34] and others. One popular mechanism to achieve accurate indoor localization and a map of the space is using Visual Simultaneous Localization and Mapping (Visual-SLAM). However, Visual-SLAM is known to be resource-intensive in memory and processing time. Furthermore, some of the operations grow in complexity over time, making it challenging to run on mobile devices continuously. Edge computing provides additional compute and memory resources to mobile devices to allow offloading tasks without the large latencies seen when offloading to the cloud.In this article, we present Edge-SLAM, a system that uses edge computing resources to offload parts of Visual-SLAM. We use ORB-SLAM2 [50] as a prototypical Visual-SLAM system and modify it to a split architecture between the edge and the mobile device. We keep the tracking computation on the mobile device and move the rest of the computation, i.e., local mapping and loop closing, to the edge. We describe the design choices in this effort and implement them in our prototype. Our results show that our split architecture can allow the functioning of the Visual-SLAM system long-term with limited resources without affecting the accuracy of operation. It also keeps the computation and memory cost on the mobile device constant, which would allow for the deployment of other end applications that use Visual-SLAM. We perform a detailed performance and resources use (CPU, memory, network, and power) analysis to fully understand the effect of our proposed split architecture.

A Conserved Histidine Residue Drives Extein Dependence in an Enhanced Atypically Split Intein.

Split intein-mediated protein trans-splicing (PTS) is widely applied in chemical biology and biotechnology to carry out traceless and specific protein ligation. However, the external residues immediately flanking the intein (exteins) can reduce the splicing rate, thereby limiting certain applications of PTS. Splicing by a recently developed intein with atypical split architecture ("Cat") exhibits a stark dependence on the sequence of its N-terminal extein residues. Here, we further developed Cat using error-prone polymerase chain reaction (PCR) and a cell-based selection assay to produce Cat*, which exhibits greatly enhanced PTS activity in the presence of unfavorable N-extein residues. We then applied solution nuclear magnetic resonance spectroscopy and molecular dynamics simulations to explore how the dynamics of a conserved B-block histidine residue (His78) contribute to this extein dependence. The enhanced extein tolerance of Cat* reported here should expand the applicability of atypically split inteins, and the mechanism highlights common principles that contribute to extein dependence.

Graphical Modelling and Optimization of RAN function split deployed through UAVs

In this paper, we demonstrate the potentials of deploying radio access network function split architecture through aerial network and present a graph-based model to analyse and improve the performance. We consider radio units (RUs) are deployed on Unmanned aerial vehicles, while central unit (CU) is on the ground, then model the air-to-ground communication as a maximum flow problem on the connectivity graph. The air-to-ground connections include both network (RUs) to users and the RUs to the CU connections. Using this model, we build a framework that ensures maximizing throughput in the access network, while maintaining the essential latency on the fronthaul, i.e. connection between RUs and CU. We further develop a heuristic solution with low complexity and hence the ability to scale up to a large network. The results examine close-to-optimal solutions of the heuristic method, and the impact of flying networks’ deployment parameters, such as flying height, on the performance.

Decentralized Vehicular Mobility Management Study for 5G Identifier/Locator Split Networks

The identifier/locator split (ILS) architectures are highly promising to reduce the signaling latency of frequent handovers in fifth generation (5G) networks, while decentralized vehicular mobility management holds greater potential than the traditional centralized management to enhance the critical performance of highly dynamic and dense cell networks. By carefully exploiting ILS, dual connectivity, and multiaccess edge computing (MEC) concepts, this paper proposes a decentralized vehicular mobility management mechanism in the network with dense 5G Non-Standalone deployment. Under such a mechanism, we design an ILS-based local anchor handover management architecture to reduce signaling costs and handover latency. Specifically, we propose a quality of service- (QoS-) based handover decision algorithm using a long short-term memory- (LSTM-) based trajectory prediction method to obtain the cell sojourn time of connected vehicles (CVs) in predefined QoS coverage areas. Combining a built-in dynamic handover trigger condition, this algorithm can ensure a flexible load balance as well as low handover times. Extensive simulation results are presented to verify the effectiveness of the proposed mechanism in improving network performance.

Energy enhancement and efficient route selection mechanism using H-SWIPT for multi-hop IoT networks

Simultaneous wireless information and power transfer (SWIPT) is recently emerging as one of the vital solutions to prolong the lifetime of energy constrained wireless sensor nodes. However, current works on SWIPT considered only the immediate past-hop node's RF signal as a source of energy harvesting in multi-hop Internet of things (IoT) networks. In case of weak radio frequency (RF) signal, the amount of harvested energy does not support for continuous communication. Hence, in this paper a new energy harvesting mechanism is proposed which considers multiple sources (MS) such as (1) sink broadcasting energy, (2) co-channel interference, (3) neighbor nodes' RF signal, and (4) immediate past-hop node's RF signal for energy harvesting. Towards such prospect, a new SWIPT architecture is proposed called hybrid SWIPT (H-SWIPT) by integrating time switching (TS) and power splitting (PS) architectures. Furthermore, an efficient route selection mechanism is introduced to minimize the total energy consumption of the path based on an energy cost metric. To validate the proposed mechanism, simulation experiments are conducted and obtained the superiority of H-SWIPT compared with existing methods in terms of average harvested energy. Further, the effectiveness of proposed method performance is investigated through energy cost at different node density and barrier rates.

RGN-Net: A Global Contextual and Multiscale Information Association Network for Medical Image Segmentation

Segmentation of medical images is a necessity for the development of healthcare systems, particularly for illness diagnosis and treatment planning. Recently, convolutional neural networks (CNNs) have gained amazing success in automatically segmenting medical images to identify organs or lesions. However, the majority of these approaches are incapable of segmenting objects of varying sizes and training on tiny, skewed datasets, both of which are typical in biomedical applications. Existing solutions use multi-scale fusion strategies to handle the difficulties posed by varying sizes, but they often employ complicated models more suited to broad semantic segmentation computer vision issues. In this research, we present an end-to-end dual-branch split architecture RGN-Net that takes the benefits of the two networks into greater account. Our technique may successfully create long-term functional relationships and collect global context data. Experiments on Lung, MoNuSeg, and DRIVE reveal that our technique reaches state-of-the-art benchmarks in order to evaluate the performance of RGN-Net.

Impact of the hybrid electric architecture on the performance and emissions of a delivery truck with a dual-fuel RCCI engine

Reactivity controlled compression ignition combustion showed great advantages in terms of NOx and soot emissions reduction, leading to virtually zero emissions. However, the average brake thermal efficiency of this concept is like that found with conventional diesel operation. The powertrain electrification using electric motors and battery packages appears as a potential solution to reduce the CO2 emissions. For this reason, several solutions for the powertrain electrification can be found currently in the market as the parallel, series and power split powertrain architectures. The aim of this work is to evaluate the hybrid architecture impact on the fuel consumption and emissions of a delivery truck (Volvo-FL) intended for urban and urban–rural applications. The truck equipped with a reactivity-controlled compression ignition diesel-gasoline engine is evaluated and compared against the conventional diesel case. In addition, to evaluate the impact of new e-fuels on the well-to-wheel CO2 emissions, a synthetic gasoline coming from carbon capture and green electricity is evaluated. The results show that hybridization allows reducing the tank-to-wheel CO2 emissions above 15% with the parallel hybrid set-up. The series and power split architectures show CO2 benefits of 12% with respect to the baseline diesel non-hybrid case. Using synthetic gasoline as low reactivity fuel allows to achieve a 50% well-to-wheel CO2 reduction in the P2 and 70% well-to-wheel CO2 reduction for the series and power split cases due to the higher average gasoline fraction used in the driving cycle.

Performance Analysis of Identifier Locator Communication Cache Effects on ILNPv6 Stack

Identifier-locator network protocol (ILNP) is a host-based identifier/locator split architecture scheme (ILSA), which depends on address rewriting to support end-to-end mobility and multihoming. The address rewriting is performed by hosts using a network layer logical cache that stores state information related to the communicated hosts, which is called identifier-locator communication cache (ILCC). Since address rewriting is executed on a packet basis in ILNP, ILCC lookups are required at each packet reception and transmission. This leads to a strong correlation between the host’s network stack performance and ILCC performance. This paper presents a study of the effect of ILCC size on network stack performance. Within this paper, a direct comparison of the performance of two ILNP prototypes that differ by ILCC management mechanism is conducted. We present ILCC size measurements and study their effects on the host’s network stack performance. The results show that ILCC growth caused by correspondents increase has a significant effect on the latency of both network and transport layers. The obtained results show that controlling ILCC size through an effective policy strongly enhances ILNP network stack performance.

Predicting Stance Polarity and Intensity in Cyber Argumentation With Deep Bidirectional Transformers

In online deliberation, participants argue in support or opposition to one another's arguments and ideas to advocate their position. Often their stance expressed in their posts is implicit and must be derived from the post's text. Existing stance detection models predict the polarity of the user's stance from the text, but do not consider the stance's intensity. We introduce a new research problem, stance polarity, and intensity prediction in response relationships between posts. This problem seeks to predict both the stance polarity and intensity of a replying post toward its parent post in online deliberation. Using our cyber argumentation platform, we have collected an empirical data set with explicitly labeled stance polarity and intensity relationships. In this work, we create six models: five are adapted from top-performing stance detection models and another novel model that fine-tunes the deep bidirectional transformer model BERT. We train and test these six models on our empirical data set to compare their performance for stance polarity and intensity prediction and stance detection. Our results demonstrate that our method of encoding the stance polarity and intensity labels allows the models to predict stance polarity and intensity without compromising their accuracy for stance detection, making these models more versatile. Our results reveal that a novel split architecture for fine-tuning the BERT model outperforms the other models for stance polarity and intensity prediction by 5% accuracy. This work is the first to train models for predicting both the stance polarity and intensity in one combined task while maintaining good accuracy.

HTTP Adaptive Streaming on Improved LISP-MN Architecture

The increasing consumption of video content using mobile devices calls for a more efficient techniques of handling the enormous data sent and received across wireless Internet. The Loc/ID split architecture is a group of proposals geared towards enhancing the mobility of nodes on the Internet. Although Loc/ID protocols guarantee handover, they suffer excessive packet loss and long service disruption time during the process. In this paper, we posit that redirecting incoming packets to a nearby buffer node, while a handover is taking place, and forwarding the packets to the mobile device after it reconnects will reduce the negative impact of packet loss on user experience. Improvement in the performance of video services is one manifestation that could be observed. In line with this, we propose an improved architecture for one of the most popular Loc/ID protocols, Locator-Identifier Separation Protocol Mobile Node (LISP-MN) by adding a new node, termed loc-server, to LISP-MN components. Loc-server caches and forwards packets to a mobile device during its handover process. To evaluate the performance of the improved architecture, we implemented the system on a laboratory tested and developed a number of MPEG-DASH compliant players. The performance of the players streaming over the proposed architecture is then compared against vanilla LISP-MN architecture. Results of the evaluation show improved performance when running the players on top of the new architecture.

Edge-Based Intrusion Detection for IoT devices

As the Internet of Things (IoT) is estimated to grow to 25 billion by 2021, there is a need for an effective and efficient Intrusion Detection System (IDS) for IoT devices. Traditional network-based IDSs are unable to efficiently detect IoT malware and new evolving forms of attacks like file-less attacks. In this article, we present a system level Device-Edge split IDS for IoT devices. Our IDS profiles IoT devices according to their “behavior” using system-level information like running process parameters and their system calls in an autonomous, efficient, and scalable manner and then detects anomalous behavior indicative of intrusions. The modular design of our IDS along with a unique device-edge split architecture allows for effective attack detection with minimal overhead on the IoT devices. We have extensively evaluated our system using a dataset of 3,973 traditional IoT malware samples and 8 types of sophisticated file-less attacks recently observed against IoT devices in our testbed. We report the evaluation results in terms of detection efficiency and computational.

Development of a Novel IoT-Enabled Power- Monitoring Architecture With Real-Time Data Visualization for Use in Domestic and Industrial Scenarios

With the increased interest in smart cities and smart infrastructures, the need for energy conservation is increasing. Especially with the current electrical energy production mainly relying on nonrenewable resources, conservation of electrical energy is one of the challenging aspects across the globe. However, one can only perform energy conservation optimally by identifying consumption patterns at a granular level, which requires accurate and ubiquitous monitoring infrastructure. Because the electrical energy wastage can occur at any granularity (from a small house-hold appliance to grid-level wastage), the development of a low-cost, easy-to-install, and accurate power-monitoring infrastructure is need of the hour. Hence, in this article, we propose the developed designs for IoT-enabled power monitoring. First is the noninvasive power monitor with voltage connection. The second design introduces a novel split architecture with centralized voltage measurement, which removes the need for local voltage measurements. We have proposed the third and final version of the IoT-enabled power monitor to fulfill the need for three-phase power monitoring. Unlike first and second designs, this design can be used with noninvasive and invasive current sensors. The proposed architecture also supports essential features, such as secure data transfer. Developed devices transmit real-time data to the cloud server, which makes the data ubiquitously available anywhere and anytime. For analyzing the performance of the proposed architecture, the developed devices are deployed in real industrial scenarios. As an example use case, the electrical anomaly detection framework using the data collected is also explained, and the corresponding results are discussed.

Can Fine-Grained Functional Split Benefit to the Converged Optical-Wireless Access Networks in 5G and Beyond?

The centralized radio access network (C-RAN) is an effective architecture to promote CAPEX/OPEX reduction and cell cooperation derived from its centralized baseband processing. However, there is a contradiction between centralization gain and transport resource saving, which hinders the vision of a resource-efficient and cost-effective RAN deployment. Advanced RAN architectures with functional splits are then introduced to cope with this challenge. Distinguished with other studies, we are intended to investigate whether a fine-grained functional split architecture could benefit to the RAN evolution, and how it impacts on the converged optical-wireless access networks. To this end, we establish a quantitative model to analyze the performance of this architecture. With the fine-grained split, baseband unit (BBU) is divided into a set of fine-grained units (FU) to be placed in desired processing pools (PP) as a service chain. To analyze the placement performance, we propose a mixed-integer linear programming model (MILP) considering the PP selection, routing, wavelength and bandwidth assignment, as well as latency control to minimize the number of PPs, bandwidth, latency, and functions deployment cost. We compare its performance with other two coarse-grained split architectures, i.e., SBBU (adopt low-PHY split like BBU in 4G) and recently emerged DU-CU in both small-scale and large-scale network scenarios. Our analyses provide insights into the modeling and design of efficient converged optical-wireless access networks in 5G and beyond.

Resource allocation of simultaneous wireless information and power transmission of multi-beam solar power satellites in space–terrestrial integrated networks for 6G wireless systems

The technique of simultaneous wireless information and power transmission (SWIPT) has been applied to wireless sensor networks, which employ static or mobile base stations (BSs) such as drones and ships to charge passively powered devices. SWIPT can be strongly expanded by solar power satellites (SPSs), which collect solar energy and transmit it to the earth through microwaves to alleviate the power shortage problem. Furthermore, multi-beam SPSs can serve a broader range than terrestrial BSs for information transmission.In 6G networks, satellites are core devices in space-terrestrial integrated networks (STINs) supporting super Internet-of-Things. However, when discussing 6G wireless systems, previous works did not consider SWIPT applied in STINs through multi-beam SPSs. Therefore, this work proposes a novel resource allocation problem for SWIPT performed by multi-beam SPSs in the STIN while optimizing the following two objectives: minimizing deficit or excess of information transmission rate and maximizing power transmission based on two receiving architectures of terrestrial devices for information decoding and energy harvesting. Different from previous works, this problem considers not only assigning power to one of multiple satellite beams but also further allocating power in each beam into two parts for information and power transmission. This problem is NP-hard as it includes an NP-hard problem. Artificial intelligence (AI) algorithms can be used to optimize the network resource management. Hence, this problem with continuous decision variables is further solved by a classical and two recent AI algorithms specially designed for continuous variables, i.e., particle swarm optimization, improved harmony search algorithm, and monkey algorithm. Through simulation, the most appropriate AI algorithms to the concerned problem are analyzed, and the results show that for the two special designed receiving architectures of the terrestrial devices, the power splitting architecture generally outperforms the time switching architecture.

Energy-Efficient SWIPT-Empowered D2D Mode Selection

While mode selection has been envisioned as the most cost-effective way to address the interference issue in Device-to-Device (D2D) communications, existing works have been largely conducted without consideration of the energy depletion of devices. In this paper we investigate simultaneous wireless information and power transfer (SWIPT) empowered mode selection based on stochastic geometry. As a mean of solving it, system energy efficiency is formulated by determining the closed-form ergodic energy-harvested and ergodic capacity of D2D and cellular users in reuse, dedicated, and cellular communication modes with the time switching and power splitting architectures of SWIPT. We then leverage the derived results, along with the energy efficiency to design an energy-efficient mode selection mechanism. Our simulation results show that the developed mechanism is able to select the best mode for D2D communication with better energy efficiency, especially in an ultra-dense cellular network as compared with a state-of-the-art mode selection approach.