Designing a fuzzy system for controlling the armament fire in dynamic siege

This paper presents a fuzzy system for controlling the fire command in a surface-to-surface engagement by assuming siege of friendly side by enemy forces. The system must be capable of interacting with a dynamic and uncertain battle world in a real-time manner. In siege situation, it is assumed that the enemy troops are advancing toward friendly side in different directions and the defence resources are limited and nonrenewable at a single platform. Thus, resource allocation problem in a real-time manner is an important and vital component of battle management until coming of auxiliary forces. This paper addresses the design of a fuzzy system as an efficient tool for real-time decision-making in order to optimize the defense resource allocation in engagement.     

Improving fairness in IEEE 802.11 networks using MAC layer opportunistic retransmission

This paper introduces DAFMAC (Decode And Forward MAC), a scalable opportunistic cooperative retransmission enhancement for the IEEE 802.11 MAC protocol which operates without the need for additional explicit control signalling. Distributed opportunistic retransmission algorithms rely on selecting a single suitable relay without direct arbitration between nodes. Simulations show that DAFMAC offers a significant improvement in fairness for both throughput and jitter, giving multiple parallel data flows a more equal opportunity to utilise the channel. DAFMAC cooperative retransmissions are shown to reduce node energy consumption for a given throughput. Further, the DAFMAC relay selection algorithm is shown to scale very well in terms of complexity and memory requirements in comparison to other cooperative retransmission schemes.     

Green synthesis of 5-arylidene-2,4-thiazolidinedione, 5-benzylidene rhodanine and dihydrothiophene derivatives catalyzed by hydrated ionic liquid tetrabutylammonium hydroxide in aqueous medium

An efficient synthesis of 5-arylidene-2,4-thiazolidinediones and 5-benzylidene rhodanines by the Knoevenagel condensation of 2,4-thiazolidinedione or rhodanine with aromatic aldehydes was studied. It proceeded smoothly in the presence of tetrabutylammonium hydroxide/H ₂O-EtOH to afford the corresponding products in high yields at 50°C. Also, a series of dihydrothiophene derivatives were synthesized via the four-component reaction of aldehyde, malonitrile, 2,4-thiazolidinedione, and piperidine in the presence of Bu₄NOH as a basic ionic liquid in aqueous medium. This new method offers several advantages, such as excellent yields, short reaction times, and simple procedure.     

Parallel processing of continuous queries over data streams

In this paper, we propose parallel processing of continuous queries over data streams to handle the bottleneck of single processor DSMSs. Queries are executed in parallel over the logical machines in a multiprocessing environment. Scheduling parallel execution of operators is performed via finding the shortest path in a weighted graph called Query Mega Graph (QMG), which is a logical view of K machines. By lapse of time, number of tuples waiting in queues of different operators may be very different. When a queue becomes full, re-scheduling is done by updating weight of edges of QMG. In the new computed path, machines with more workload will be used less. The proposed system is formally presented and its correctness is proved. It is also modeled in PetriNets and its performance is evaluated and compared with serial query processing as well as the Min-Latency scheduling algorithm. The presented system is shown to outperform them w.r.t. tuple latency (response time), memory usage, throughput and also tuple loss- critical parameters in any data stream management systems.     

Application-aware virtual paths insertion for NOCs

Network-on-chip (NoC) has rapidly become a promising alternative for complex system-on-chip architectures including recent multicore architectures. Additionally, optimizing NoC architectures with respect to different design objectives that are suitable for a particular application domain is crucial for achieving high-performance and energy-efficient customized solutions. Despite the fact that many researches have provided various solutions for different aspects of NoCs design, a comprehensive NoCs system solution has not emerged yet. This paper presents a novel methodology to provide a solution for complex on-chip communication problems to reduce power, latency and area overhead. Our proposed NoC communication architecture is based on setting up virtual source–destination paths between selected pairs of NoCs cores so that the packets belonging to distance nodes in the network can bypass intermediate routers while traveling through these virtual paths. In this scheme, the paths are constructed for an application based on its task-graph at the design time. After that, the run time scheduling mechanism is applied to improve the buffer management, virtual channel and switch allocation schemes and hence, the constructed paths are optimized dynamically. Moreover, in our design the router complexity and its overheads are reduced. Additionally, the suggested router has been implemented on Xilinx Virtex-5 FPGA family. The evaluation results captured by SPLASH-2 benchmark suite reveal that in comparison with the conventional NoC router, the proposed router takes 25% and 53% reduction in latency and energy, respectively besides 3.5% area overhead. Indeed, our experimental results demonstrate a significant reduction in the average packet latency and total power consumption with negligible area overhead.     

A fault-tolerant and congestion-aware architecture for wireless networks-on-chip

Wireless Networks - The combination of traditional wired links for regular transmissions and express wireless paths for long distance communications is a promising solution to prevent multi-hop...     

Robust event‐triggered output feedback learning algorithm for voltage source inverters with unknown load and parameter variations

We consider the output feedback event‐triggered control of an off‐grid voltage source inverter (VSI) with unknown inductance‐capacitance (L ? C) filter dynamics and connected load in the presence of an input disturbance acting at the inverter. Due to uncertain dynamics and unmodeled parameters in the L ? C filter connected to the VSI, we use an adaptive observer to reconstruct the system's states by measuring only the voltage at the output. The control mechanism is constructed based on an impulsive actor/critic framework that approximates the cost, the event‐triggered controller, and the worst case disturbance and generates the desired AC output with the least energy dissipation. We provide rigorous stability proofs and illustrate the applicability of our results through a simulation example.     

Standalone noise and anomaly detection in wireless sensor networks: A novel time-series and adaptive Bayesian-network-based approach

Wireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this research is to design a local time-series-based data noise and anomaly detection approach for WSN. The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data. An adaptive Bayesian network is used as the classification algorithm for prediction and identification of outliers in each sensor node locally. Results of our approach are compared with four well-known algorithms using benchmark real-life datasets, which demonstrate that LODA can achieve higher (up to 89%) accuracy in the prediction of outliers in real sensory data.