Adaptive Allocation Algorithm Research Articles

Energy-Efficient Federated Training on Mobile Device

On-device deep learning technology has attracted increasing interest recently. CPUs are the most common commercial hardware on devices and many training libraries have been developed and optimized for them. However, CPUs still suffer from poor training performance (i.e., training time) due to the specific asymmetric multiprocessor. Moreover, the energy constraint imposes restrictions on battery-powered devices. With federated training, we expect the local training to be completed rapidly therefore the global model converges fast. At the same time, energy consumption should be minimized to avoid compromising the user experience. To this end, we consider energy and training time and propose a novel framework with a machine learning-based adaptive configuration allocation strategy, which chooses optimal configuration combinations for efficient ondevice training. We carry out experiments on the popular library MNN and the experimental results show that the adaptive allocation algorithm reduces substantial energy consumption, compared to all batches with fixed configurations on off-the-shelf CPUs.

Nonlinear burn control in ITER using adaptive allocation of actuators with uncertain dynamics

ITER will be the first tokamak to sustain a fusion-producing, or burning, plasma. If the plasma temperature were to inadvertently rise in this burning regime, the positive correlation between temperature and the fusion reaction rate would establish a destabilizing positive feedback loop. Careful regulation of the plasma’s temperature and density, or burn control, is required to prevent these potentially reactor-damaging thermal excursions, neutralize disturbances and improve performance. In this work, a Lyapunov-based burn controller is designed using a full zero-dimensional nonlinear model. An adaptive estimator manages destabilizing uncertainties in the plasma confinement properties and the particle recycling conditions (caused by plasma–wall interactions). The controller regulates the plasma density with requests for deuterium and tritium particle injections. In ITER-like plasmas, the fusion-born alpha particles will primarily heat the plasma electrons, resulting in different electron and ion temperatures in the core. By considering separate response models for the electron and ion energies, the proposed controller can independently regulate the electron and ion temperatures by requesting that different amounts of auxiliary power be delivered to the electrons and ions. These two commands for a specific control effort (electron and ion heating) are sent to an actuator allocation module that optimally maps them to the heating actuators available to ITER: an electron cyclotron heating system (20 MW), an ion cyclotron heating system (20 MW), and two neutral beam injectors (16.5 MW each). Two different actuator allocators are presented in this work. The first actuator allocator finds the optimal mapping by solving a convex quadratic program that includes actuator saturation and rate limits. It is nonadaptive and assumes that the mapping between the commanded control efforts and the allocated actuators (i.e. the effector model) contains no uncertainties. The second actuator allocation module has an adaptive estimator to handle uncertainties in the effector model. This uncertainty includes actuator efficiencies, the fractions of neutral beam heating that are deposited into the plasma electrons and ions, and the tritium concentration of the fueling pellets. Furthermore, the adaptive allocator considers actuator dynamics (actuation lag) that contain uncertainty. This adaptive allocation algorithm is more computationally efficient than the aforementioned nonadaptive allocator because it is computed using dynamic update laws so that finding the solution to a static optimization problem is not required at every time step. A simulation study assesses the performance of the proposed adaptive burn controller augmented with each of the actuator allocation modules.

Spatial-Temporal Adaptive Optimal Allocation of Archival Tasks

Archival task allocation for Modern Canton Customs (1861-1949) is a heavy workload due to the massive quantities of data. An archival task allocation system is used to make the allocation process easier. However, traditional methods applied in the allocation system lead to lower efficiency and waste of human force because of the irrational hypotheses. This article presents two algorithms of allocating to spatial-temporal system users based on the heuristic rules, namely offline (ALGOOFF) and adaptive (ALGOAD) allocation algorithms, which significantly improve the performance of the archival task allocation system. With simulation data and authentic data, ALGOAD, by employing just 48 percent of the system users, can achieve the same accuracy rate as the commonly used circular policies. And the additional experiments with simulation data composed of the randomly selected system users verify the following conclusions: (1) the adaptive method is better than the offline task allocation method; (2) the adaptive algorithm can save more human force even when the skills of adaptive archive allocation system users and the difficulty of the archival translation tasks are varied; (3) the adaptive algorithm has continuability without affecting its performance; (4) the adaptive method saves resources even when the completion time the adaptive system users spend on archival translation tasks is different.

Energy-efficient adaptive GTS allocation algorithm for IEEE 802.15.4 MAC protocol

The standard IEEE 802.15.4 fails to perform optimally for railway monitoring applications due to the deficiency of guaranteed time slots (GTSs). In the present work, an adaptive GTS allocation algorithm (AGAA) is proposed which efficiently deals with a deficiency of GTSs and slot underutilization problem. The proposed AGAA algorithm saves unutilized bandwidth of GTS and the saved bandwidth is utilized by the needy nodes in contention access period (CAP) and contention-free period (CFP). An independent superframe order ( $$SO_{CFP}$$ ) is introduced for adaptive adjustment of slot size in the CFP. The proposed AGAA protocol also deals with the problem of GTS request-failure due to congestion during CAP. A separate contention period is used for GTS request in AGAA protocol. The comparison has been done between the proposed AGAA protocol and the standard IEEE 802.15.4 medium access control (MAC) protocol. Throughput and energy consumption are analyzed and results show that the slot optimization using $$SO_{CFP}$$ saves a fair amount of bandwidth which improves the throughput of the network $$\approx $$ 22%. Moreover, the separate contention period for the GTS request frame reduces the idle state energy consumption of those devices which generate time-constraint data frames. The proposed AGAA protocol consumes $$\approx $$ 18% less energy than the standard IEEE 802.15.4 MAC protocol. The analysis of results also reveals an important fact that the algorithm saves more energy for homogeneous traffic in comparison to heterogeneous traffic which can further be improved in future works.

Data Transmission and Management Based on Node Communication in Opportunistic Social Networks

Due to the rapid popularization of various short distance communication mobile devices, the use scenarios of opportunistic networks are increasing day by day. However, in opportunistic networks, because of the complexity of community structure, many methods lack of symmetry between application and theoretical research. Thus, the connection strength between nodes is different, and the degree of message diffusion is different. If the above factors cannot be accurately estimated and analyzed, and effective data forwarding and scheduling strategies cannot be formulated, the delivery ratio will be low, the delay will be relatively high, and the network overhead will be large. In light of improving symmetry problems in opportunistic networks, this paper establishes the message duplicate adaptive allocation and spray routing strategy (MDASRS) algorithm model, measures the connection strength between nodes through social pressure, and estimates the diffusion of current messages in the network through the probability of messages leaving the current node successfully, so as to develop the self-adaptive control replication transmission mode and achieve the effect of reducing the network burden and network overhead. This is done through experiments and comparison of social network algorithms, comparing the MDASRS with Epidemic, Spray and Wait, and EIMST algorithms. The experiment results showed that this method improves the cache utilization of nodes, reduces data transmission delay, and improves the network’s overall efficiency.

Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks

The wireless mesh network (WMN) has proven to be a great choice for network communication technology. WMNs are composed of access points (APs) that are installed and communicate with each other through multi-hop wireless networks. One or more of these APs acts as a gateway (GW) to the internet. Hosts of WMNs are stationary or mobile. According to the structure of WMNs, some network features may be affected, such as the overall performance, channel interference, and AP connectivity. In this paper, we propose a new adaptive channel allocation algorithm for a multi-radio multi-channel wireless mesh network. The algorithm is aimed to minimize the number of channel reassignments while maximizing the performance under practical constraints. The algorithm defines a decision function for the channel reassignments. The decision function aims to minimize the traffic around the GW. Whenever the traffic changes in the wireless mesh network, the decision function decides which channel radio reassignment should be done. We demonstrated the effectiveness of our algorithm through extensive simulations using Network Simulator 2 (NS-2).

Adaptive Pilot Allocation for Multi-cell Massive MIMO Systems

In the upcoming 5G system, the massive MIMO technology will perform an important role in significantly improving the system performance. To sufficiently exploit the potential of massive MIMO, the transceiver should perform appropriate accurate channel estimation, including carefully designed quantities of pilot resource and its allocation among different users. In this letter, a low-complexity adaptive pilot allocation algorithm using the second-order channel statistics of all users is proposed, which helps in finding a good balance between the allocated pilot length and pilot training interference. Numerical results indicate that the proposed algorithm can significantly improve the net spectrum efficiency in multi-cell massive MIMO systems.

HDA Video Transmission Scheme for DASH

In recent years, with the development of Internet communication technology, more and more people tend to get information through the network. And people prefer multimedia video streaming services. Traditional streaming media technology cannot meet people's needs because of its limitations. Recently, HTTP-based Dynamic Adaptive Streaming Over HTTP (DASH) has emerged as a new approach. In this paper, we consider two hybrid digital and analog video transmission schemes for DASH and propose three methods of enhancing the video quality of experience (QoE) of DASH users. First of all, considering the importance of data to video quality, we propose an energy-aware recombination method of reorganizing and packaging data and define the priority of the generated DASH layer in transmission. Second, we design an adaptive bitrate allocation algorithm to improve bandwidth utilization and video quality. Finally, we propose a retransmission mechanism to address the situation in which the digital stream at the receiving end cannot be decoded due to channel distance errors. Experiments show that our two schemes are superior to traditional DASH schemes.

Adaptive resources allocation algorithm based on modified PSO for cognitive radio system

Radio spectrum has become a rare resource due to the rapid development of wireless communication technique. Cognitive radio is one of important techniques to deal with this radio spectrum problem. But the resource allocation in cognitive radio also has its own issues, such as the flexibility of the allocation algorithm, the performance of resource allocation, and so on. In order to increase the flexibility of the allocation algorithm for cognitive radio, more and more researches are focusing on the evolutionary algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO). Evolutionary algorithm can greatly improve the flexibility of the allocation algorithm for cognitive radio system in different communication scenarios, but the performances are relatively lower than the original mathematical methods. So in this paper, we proposed an adaptive resource allocation algorithm based on modified PSO for cognitive radio system to solve these problems. Modified particle swarm optimization (Modified PSO) has both genetic algorithm (GA) and particle swarm optimization (PSO)'s updating processes which makes this modified PSO overcame PSO's own disadvantages and keep advantages. Simulation results showed our proposed algorithm has enough flexibility to meet cognitive radio systems' requirements, and also has a better performance than original PSO.

Адаптивный робастный алгоритм выделения треков низкоорбитальных космических объектов на одиночном снимке звѐздного неба

Адаптивный робастный алгоритм выделения треков низкоорбитальных космических объектов на одиночном снимке звѐздного неба

Single and Multi-Domain Adaptive Allocation Algorithms for VNF Forwarding Graph Embedding

Network function virtualization (NFV) will simplify deployment and management of network and telecommunication services. NFV provides flexibility by virtualizing the network functions and moving them to a virtualization platform. In order to achieve its full potential, NFV is being extended to mobile or wireless networks by considering virtualization of radio functions. A typical network service setup requires the allocation of a virtual network function-forwarding graph (VNF-FG). A VNF-FG is allocated considering the resource constraints of the lower infrastructure. This topic has been well-studied in existing literature, however, the effects of variations of networks over time have not been addressed yet. In this paper, we provide a model of the adaptive and dynamic VNF allocation problem considering also VNF migration. Then we formulate the optimization problem as an integer linear programming (ILP) and provide a heuristic algorithm for allocating multiple VNF-FGs. The idea is that VNF-FGs can be reallocated dynamically to obtain the optimal solution over time. First, a centralized optimization approach is proposed to cope with the ILP-resource allocation problem. Next, a decentralized optimization approach is proposed to deal with cooperative multi-operator scenarios. We adopt AD3, an alternating direction method of multipliers-based algorithm, to solve this problem in a distributed way. The results confirm that the proposed algorithms are able to optimize the network utilization, while limiting the number of reallocations of VNFs which could interrupt network services.

Amplitude‐adaptive spread‐spectrum data embedding

In this study, the authors consider additive spread-spectrum (SS) data embedding in transform-domain host data. Conventional additive SS embedding schemes use an equal-amplitude modulated carrier to deposit one information symbol across a group of host data coefficients which act as interference to SS signal of interest. If there is a flexibility of assigning different amplitudes across symbol bits, the probability of error can be further reduced by adaptively allocating amplitude to each symbol bit based on its own host/interference. In this study, they present a novel amplitude-adaptive SS embedding scheme. Particularly, symbol-by-symbol adaptive amplitude allocation algorithms are developed to compensate for the impact from the known interference. They aim at designing the SS embedding amplitude for each symbol adaptively in order to minimise the receiver bit-error-rate (BER) at any given distortion level. Then, optimised amplitude allocation for multi-carrier/multi-message embedding in the same host data is studied as well. Finally, they consider the problem of amplitude optimisation for an ideal scenario where no external noise is introduced during embedding and transmission. Extensive experimental results illustrate that the proposed amplitude-adaptive SS embedding scheme can provide order-of-magnitude performance improvement over several other state-of-the-art SS embedding schemes.

Improved strategies and adaptive capability allocation algorithm for query result caching

Query result caching has attracted overwhelming attention in recent years to enhance the performance of search engines. In this paper, we present approaches to improve cost-aware strategies for static and dynamic caching, and further propose an adaptive capability allocation algorithm for hybrid caching. More precisely, we introduce a query repeated distance factor to improve the classical static result caching. We describe the worth of caching based on the query popularity and design a novel mechanism to further improve the dynamic policies. By designing a Queue-Hash cache internal structure, our adaptive capability allocation algorithm is employed to adjust the cache capability to further improve the efficiency in a hybrid cache combining static and dynamic caching policies. The experimental results demonstrate that the improved approaches can increase the overall system performance, particularly in terms of the average processing time.

Adaptive subcarrier-distribution algorithm for routing and spectrum allocation in OFDM-based elastic optical networks

In this paper, we have proposed the adaptive subcarriers-distribution routing and spectrum allocation (ASD-RSA) algorithm, which is the first elastic optical network routing and spectrum allocation algorithm based on distributed subcarriers. It allocates lightpaths to request adaptively and proved to achieve much lower bandwidth blocking probability than traditional routing and spectrum allocation algorithms based on centralized subcarriers with integer linear programming and dynamic simulation methods. Additionally, the ASD-RSA algorithm performs the best with three alternate routing paths; this character will decrease the calculating amount of both alternate routing path searching and spectrum allocation immensely in large networks.

Antibacterial honey for the prevention of peritoneal-dialysis-related infections (HONEYPOT): a randomised trial

There is a paucity of evidence to guide the best strategy for prevention of peritoneal-dialysis-related infections. Antibacterial honey has shown promise as a novel, cheap, effective, topical prophylactic agent without inducing microbial resistance. We therefore assessed whether daily application of honey at the exit site would increase the time to peritoneal-dialysis-related infections compared with standard exit-site care plus intranasal mupirocin prophylaxis for nasal carriers of Staphylococcus aureus. In this open-label trial undertaken in 26 centres in Australia and New Zealand, participants undergoing peritoneal dialysis were randomly assigned in a 1:1 ratio with an adaptive allocation algorithm to daily topical exit-site application of antibacterial honey plus standard exit-site care or intranasal mupirocin prophylaxis (only in carriers of nasal S aureus) plus standard exit-site care (control group). The primary endpoint was time to first infection related to peritoneal dialysis (exit-site infection, tunnel infection, or peritonitis). The trial is registered with the Australian New Zealand Clinical Trials Registry, number 12607000537459. Of 371 participants, 186 were assigned to the honey group and 185 to the control group. The median peritoneal-dialysis-related infection-free survival times were not significantly different in the honey (16·0 months [IQR not estimable]) and control groups (17·7 months [not estimable]; unadjusted hazard ratio 1·12, 95% CI 0·83-1·51; p=0·47). In the subgroup analyses, honey increased the risks of both the primary endpoint (1·85, 1·05-3·24; p=0·03) and peritonitis (2·25, 1·16-4·36) in participants with diabetes. The incidences of serious adverse events (298 vs 327, respectively; p=0·1) and deaths (14 vs 18, respectively; p=0·9) were not significantly different in the honey and control groups. 11 (6%) participants in the honey group had local skin reactions. The findings of this trial show that honey cannot be recommended routinely for the prevention of peritoneal-dialysis-related infections. Baxter Healthcare, Queensland Government, Comvita, and Gambro.

Novel robust control framework for morphing aircraft

This paper develops a robust control methodology for a class of morphing aircraft, which is called innovative control effector (ICE) aircraft. For the ICE morphing aircraft, the distributed arrays of hundreds of shape-change devices are employed to stabilize and maneuver the air vehicle. Because the morphing aircraft have the inherent uncertainty and varying dynamics due to the alteration of their configuration, a desired control performance can not be satisfied with a fixed feedback controller. Therefore, a novel control framework including an adaptive flight control law and an adaptive allocation algorithm is proposed. Firstly, a state feedback adaptive control law is designed to guarantee closed-loop stability and state tracking in the presence of uncertain dynamics caused by the wing shape change due to different flight missions. In the control allocation, many distributed arrays are managed in an optimal way to improve the robustness of the system. The scheme is used to an uncertain morphing aircraft model, and the simulation results demonstrate their performance.

P02.39. Leveraging grant awards to enhance the research infrastructure at a CAM institution

Results Web applications included: the Centralized Participant Database System and Project/Users Permissions System to securely capture participant contact information and control personnel access to web modules; a real-time participant tracking report to monitor recruitment and participant flow; a role-specific Reminder System to track outstanding activities requiring personnel follow-up; and double key-entry verification on web data used for an adaptive treatment allocation algorithm. Templates of these tools can be incorporated into any new trial’s web application. Study protocol templates facilitate timely, accurate translation of research proposals into detailed protocols for IRB applications, DSMC review and training. A revised training plan included role-specific training logs for tracking training objective completion and a thorough certification process for key protocols to ensure accurate and consistent performance. Quality control templates support the informed consent process and fidelity of trial interventions and outcomes assessment. We implemented these enhanced resources in 5 clinical trials. To date, our team has completed 3378 phone screens, conducted 1439 first and 473 second in-person screening visits, enrolled 394 participants, and followed 315 through trial end. The study protocol templates have been used in two additional clinical trials currently undergoing IRB review.

AdNoC: Runtime Adaptive Network-on-Chip Architecture

Networsk-on-chip (NoCs) have emerged as a promising on-chip interconnect for future multi/many-core architectures as NoCs are able to scale communication links with the growing number of cores. State-of-the-art NoC designs rely mainly on a static network configuration using fixed routing algorithms and buffer placements. These approaches are not effective in dealing with hard-to-predict system behavior, for instance due to user behavior or varying workloads, since in order for static NoCs to cover these scenarios, they would have to be designed for worst case scenarios. In this paper, we address these problems with a runtime adaptive network-on-chip (AdNoC). Focusing on the architecture-level adaptation, we present an adaptive route allocation algorithm which provides a required level of QoS (guaranteed bandwidth) coupled with an adaptive buffer assignment scheme which reassigns buffer blocks on-demand. Furthermore, the adaptivity requires a comprehensive, hardly intrusive, runtime observability infrastructure, i.e., using monitoring components, in order to gather data on the system state. The area overhead introduced by the adaptive scheme can be traded off against the flexibility gained. Moreover, the area overhead is also reduced by resource multiplexing due to the on-demand buffer assignment at each output port (we achieved on an average 42% buffer saving in our experiments). We demonstrate the advantage by using various digital media applications and compare our approach to the state-of-the-art static NoC architectures e.g., Xpipe, QNoC, and AEthereal.

An Adaptive Allocation Algorithm Using Directional CSMA/CA over mmWave Wireless Personal Area Networks

Directional antennas have the considerable benefits of higher antenna gain, long transmission distance and spatial reuse compared to omni-antennas. To support a directional antenna, IEEE 802.15.3c specifies a high data transmission rate and short frequency range communication based on the characteristics of 60GHz band. However, the contention-based protocol of IEEE 802.15.3c may cause channel collisions and throughput degradation as the number of stations in the network increases. In order to resolve this problem and reduce channel access latency, we propose an adaptive allocation algorithm in which the contention window size for optimal transmission probability is derived after the directional information has been obtained by means of AP control procedures. Extensive simulations demonstrate that the proposed algorithm outperforms the existing channel access scheme in IEEE 802.15.3c wireless personal area networks under different situations, especially when the number of contending stations is large.

Adaptive Control Allocation in the Presence of Actuator Failures

This paper proposes a control allocation framework where a feedback adaptive signal is designed for a group of redundant actuators and then it is adaptively allocated among all group members. In the adaptive control allocation structure, cooperative actuators are grouped and treated as an equivalent control effector. A state feedback adaptive control signal is designed for the equivalent effector and adaptively allocated to the member actuators. Two adaptive control allocation algorithms, guaranteeing closed-loop stability and asymptotic state tracking when partial and total loss of control effectiveness occur, are developed. Proper grouping of the actuators reduces the controller complexity without reducing their efficacy. The implementation and effectiveness of the strategies proposed is demonstrated in detail using several examples.