One of the obvious reasons for most disorders in network service provisioning is network path congestion. Congestion avoidance in today's networks is too costly and sometimes impossible. With the introduction of SDN, centralizing the equipment's control plane has become possible. This paper presents an enhanced method named ESV-DBRA to avoid congestion in multi-tenant SDN networks. At first, ESV-DBRA monitors the traffic load and delay of all network paths for each tenant individually. Then, by merging the parameters obtained from the monitoring, the Service Level Agreements (SLA), and a novel proposed cost function, it calculates the cost of the network paths per tenant. As a result, traffic for each tenant is routed through the path/paths at the lowest possible cost from the tenant's perspective. Next, the bandwidth quotas will be calculated and assigned to the tenants over their optimal routes. Afterward, whenever congestion is likely to occur in a path, ESV-DBRA automatically changes the route or bandwidth of the tenants' traffic related to this path to avoid congestion. Related algorithms are also proposed.Eventually, simulations show that the proposed method effectively increases bandwidth utilization by 10.76%.

This paper introduces a vehicle wheel slip tracking using an in-wheel BLDC (brushless direct current) motor. The control objective is to track a reference input wheel slip. The control architecture consists of a two-layer structure. In the upper level of the control system, the controller design is based on a sliding mode control strategy and generates the required torque for each wheel as the wheel slip tracks the reference value. In the lower level, the torque controller is a current controller with the duty cycle of the PWM (pulse width modulation) pulses to achieve the desired torque demanded by the upper level. In torque controller, a BLDC motor controller with a three-phase inverter is designed using Hall Effect sensor feedback and current sensors. The design is performed so that the wheel slip tracks any reference input wheel slip. Simulations are performed to demonstrate the effectiveness of the proposed two-layer controller.

One of the important features of an interconnection network is its ability to efficiently simulate programs or parallel algorithms written for other architectures. Such a simulation problem can be mathematically formulated as a graph embedding problem. In this paper we compute the lower bound for dilation and congestion of embedding onto wheel-like networks. Further, we compute the exact dilation of embedding wheellike networks into hypertrees, proving that the lower bound obtained is sharp. Again, we compute the exact congestion of embedding windmill graphs into circulant graphs, proving that the lower bound obtained is sharp. Further, we compute the exact wirelength of embedding wheels and fans into 1, 2-fault hamiltonian graphs. Using this we estimate the exact wirelength of embedding wheels and fans into circulant graphs, generalized Petersen graphs, augmented cubes, crossed cubes, Möbius cubes, twisted cubes, twisted n-cubes, locally twisted cubes, generalized twisted cubes, odd-dimensional cube connected cycle, hierarchical cubic networks, alternating group graphs, arrangement graphs, 3-regular planer hamiltonian graphs, star graphs, generalised matching networks, fully connected cubic networks, tori and 1-fault traceable graphs.

One of the most significant operational aspects of vehicles is their safety in critical braking maneuvers. Proper braking performance occurs when the stopping distance is minimized while the vehicle’s steerability is maintained. To meet these objectives, the tire slip ratio should be regulated at its optimum value. In this study, a control system is designed for simultaneously controlling the brake torque of the front and rear wheels using the nonlinear prediction-based control method. In the control design process, the pitch dynamics have been considered which plays an important role in braking maneuvers. Moreover, the instantaneous optimum wheel slip is determined via an artificial neural network (ANN) to improve the performance of the system and achieve the maximum possible brake deceleration. The performance of the designed control system is investigated through conducted simulations in the CarSim and Matlab/Simulink software environments. The obtained results confirm the enhancement in the braking performance along with a considerable reduction in the stopping distance.

One of the most significant operational aspects of vehicles is their safety in critical braking maneuvers. Proper braking performance occurs when the stopping distance is minimized while the vehicle’s steerability is maintained. To meet these objectives, the tire slip ratio should be regulated at its optimum value. In this study, a control system is designed for simultaneously controlling the brake torque of the front and rear wheels using the nonlinear prediction-based control method. In the control design process, the pitch dynamics have been considered which plays an important role in braking maneuvers. Moreover, the instantaneous optimum wheel slip is determined via an artificial neural network (ANN) to improve the performance of the system and achieve the maximum possible brake deceleration. The performance of the designed control system is investigated through conducted simulations in the CarSim and Matlab/Simulink software environments. The obtained results confirm the enhancement in the braking performance along with a considerable reduction in the stopping distance.