Nowadays, the technology of digital systems is moving towards increasing the number of processing elements on a chip, which requires scalable and efficient communication infrastructure to achieve higher performance. Network-on-chip (NoC) is a high-performance solution for dealing with many on-chip communication challenges, such as the wiring complexity and the integration of a large number of transistors on a chip. In NoC, communication protocols, routing algorithms and topologies play important roles in the overall system performance. In this paper, a multicast adaptive routing algorithm with fuzzy-based load control for mesh-based NoCs is proposed. This algorithm, due to the non-uniform production and distribution of unicast and multicast packets, prevents dead-locks and live-locks dynamically by the fuzzy control system. The proposed algorithm leads to efficient congestion management, latency reduction and improvement in network throughput and reliability, and even reduction in power consumption. The results of evaluations and comparison with recent multicast routing algorithms demonstrate that the proposed algorithm provides higher reliability and better performance for both 2D and 3D NoCs with mesh topology, than the other works.

The production of energy from renewable sources, such as the energy from electricity-water, which reduces the amount of CO2, is called clean energy. Today, technologies and methods that reduce energy consumption in computer networks and Co2 production in the environment are called green networks. Now, high consumption of energy in networks and increasing production of Co2 is an important global challenge. This research analyzes the three important principles of optimization, not draining resources and not wasting resources from green network standards. This research shows that high-performance optimal routing algorithms do not always guarantee green standards in networks, and the principles of optimality and greenness must be combined. Therefore, by combining the principles of being green and the principles of linear optimization, a new green optimal routing algorithm in computer networks is presented. Next, three types of USCP, OUMR and OMMR routing algorithms are analyzed according to green standards and it is proven that they are not green despite being optimal. Then, based on the strengths and weaknesses of these methods, a new green optimal routing algorithm is presented. The simulation results and comparisons show that the new method, while increasing the network efficiency, also improves the green standards of data transmission.

To control 3-DOF model of human arm movement in page and to reach robust control in external disturbance, unmodeled dynamics and uncertainties of model with time-varying properties, continues terminal sliding mode control as an adaptive-robust control was used. This controller have exponential convergence to zero tracing error, but chattering phenomenon in sliding control isn’t decrease desirable. In this paper, to decrease chattering, we coupled a recurrent neural network by a single hidden layer into the terminal sliding control (TSM). Moreover, because of systematic redundancy in the model of arm, don’t exist unique joint trajectories to considering as a default, so to reach online desired trajectories in reaching, online routing algorithm was used with Neuro-TSM control. For testing the robustness in control, we applied disturbance signals of torque. The results have shown,Neuro-TSM along with online routing algorithm, in addition to reducing chattering, could track joint trajectories and end effector path with very low errors.

Nowadays, machine vision systems are extensively used in agriculture. The application of this technology in the field can help preserve agricultural resources while reducing manual labor and production costs. In the field of agricultural automation, accurately detecting crop rows is recognized as a crucial and challenging issue for weed identification and the automatic guidance of machines. Therefore, it is necessary to explore practical solutions to optimize this process. Hence, the purpose of this study is the precise identification of basil cultivation rows to enable the automatic navigation of robots in the cultivation field.

In the first stage of this research, six images from each growth period of basil plants (third, fourth, and fifth week) were taken and weeds were removed from the area between the crop rows using three different methods of area opening, dimensional removal, and masking. In the next stage, six images of crop rows without weeds were examined by performing image processing operations and implementing several routing algorithms, namely, Hough transform, wavelet transform, Gabor filter, linear regression, and an additional algorithm proposed in this study. The output of each of these algorithms was compared with the ideal path identified by the user. For this purpose, after capturing an image, green areas were extracted from it by performing the segmentation process. By applying each of the routing algorithms to the image, plant cultivation lines were identified and their equations were determined. Finally, the performance of the designed robot was evaluated using the most appropriate routing algorithm.

Examining the performance of three different methods of weed removal in three periods of plant growth (third, fourth, and fifth week) showed that during this interval, the masking method had the lowest error rate compared to the ideal path and the shortest average operation time of 1.64 seconds, followed by the dimensional removal and the area opening methods. Comparing the routes detected by different routing algorithms with the ideal routes and according to the results of the t-test at 5% probability level, the order of the studied routing methods from the most superior is as follows: the proposed algorithm, Gabor filter, linear regression, Hough transform and wavelet transform algorithm. Overall, the proposed algorithm had the highest rate of adaptation to the ideal path (with an average error of 3.65 pixels) and the shortest operation time (4.79 seconds) and was selected as the most appropriate routing algorithm and the performance of the designed robot was evaluated using it.

A reliable crop row detection algorithm can reduce production costs and preserve the environment. In this study, the masking method was used for removing weeds from the images. The new proposed routing algorithm has superior performance when compared with common routing algorithms such as the Gabor filter, linear regression, Hough transform, and wavelet transform. Additionally, it was shown that the designed robot using the proposed algorithm (with an average error of 3.65 pixels) has the desired performance.

Quick development of technologies and emergence of Internet of Things technology that can link everything environment us is one of the most amazing developments environ the universe. Need to connect and manage fixed and mobile nodes in this type of networks in order to create a proper interaction among them, addition to solving many serious limitations, has caused researchers to address various challenges of this technology. One of the most important challenges is to provide an efficient routing protocol that is able to manage different aspects of network efficiency alongside manages mobility of nodes and dynamic topology of network. RPL (Routing Protocol for Low Power & Lossy Networks) is one of the famous routing protocols is proposed for internet of things networks. This protocol is designed specifically for Internet of Things networks with fixed nodes, so we developed a new routing algorithm called A-RPL to support mobility and solved some challenges caused by mobility. Our algorithm works better than some superior algorithms were proposed in recent years, because they still suffer from various disadvantages, especially for mobile networks

Recently، much attention has been paid to Vehicular Ad hoc Network (VANET). VANETs address direct communication between vehicle-to-vehicle and vehicles to roadside units (RSUs). They are similar to the Mobile and Ad hoc Networks (MANET) in their rapid and dynamic network topology changes due to the fast motion of nodes. High mobility of nodes and network resources limitations have made the routing، one of the most important challenges in VANET researches. Therefore، guaranteeing a stable and reliable routing algorithm over VANET is one of the main steps to realize an effective vehicular communications. In this paper، a two-step AODV-based routing protocol is proposed for VANET networks. At first، node-grouping is done using their mobility information such as speed and movement direction. If the first step cannot respond efficiently، the algorithm enters the second step which uses link expiration time (LET) information in the formation of the groups. The goal of the proposed protocol is increasing the stability of routing algorithm by selecting long-lived routes and decreasing link breakages. The comparison of proposed algorithm with AODV and DSR protocols is performed via the Network Simulator NS-2. It is shown that the proposed algorithm increases the delivery ratio and also decreases the routing control overhead.

Today, video conferencing is one of the most sought after features of the Internet. The purpose of routing algorithms in online video conferencing is to increase the number of requests accepted according to Quality of Service (QoS) satisfaction. Most research in this field focuses solely on bandwidth, and relatively few studies consider both bandwidth and delay constraints. In this paper, we propose a new routing algorithm in the context of MPLS networks with fuzzy rules approach that considers both bandwidth limitation and end-to-end delay (bandwidth-delay) for path searches. The fuzzy system is a fuzzy weighted prediction model for filtering high resource requests. A combination of traditional routing and MPLS improves the mobility mechanism, scalability and overall network performance. We name the proposed algorithm as Fuzzy Bandwidth and Delay Routing Algorithm (FBDRA). The FBDRA is attempting to delay requests with high bandwidth and maximum end-to-end delay. We will use Matlab R2017a to simulate many different scenarios and measure criteria such as the number of requests received, average route length, and load balancing. In addition, we compare the performance of FBDRA compared to some of the previous related work such as MHA, WSP, MIRA, BCRA, MIRAD, BGDG, BGLC, SAMCRA and MDMF. The simulation results show that the FBDRA provides the best performance for video conferencing.

In recent years, Vehicular Ad-hoc Networks (VANET) as an emerging technology have tried to reduce road damage and car accidents through intelligent traffic controlling. In these networks, the rapid movement of vehicles, topology dynamics, and the limitations of network resources engender critical challenges in the routing process. Therefore, providing a stable and reliable routing algorithm is a necessary requirement to maintain the Quality of Service (QoS) parameters of VANETs. In this paper, a new routing algorithm based on the clustering technique is proposed, which is called GCAR. In the proposed algorithm, the appropriate cluster heads are selected based on the genetic algorithm then two vehicles are selected between the neighboring clusters as the gateways and a vehicle chain is formed by these vehicles. Moreover, a combination of genetic algorithm and simulated annealing is applied to identify the suitable clusters. The conducted simulations in MATLAB tool indicate that, respectively, path discovery ratio, the number of clusters, throughput, and packet delivery ratio of the proposed algorithm have been improved by 18.4%, 2.55%, 3.45%, and 14.18% in comparison to the PassCAR approach. Furthermore, evaluation of the convergence, standard deviation, and standard error of the proposed algorithm prove its high convergence speed and stability.

Abstract-Nowadays, because of recent achievements in wireless technology and the need for pervasive usage of services, wireless networks usage have been soared widely. Also, Adhoc wireless networks are popular with researcher for not requiring central controller, adaptation to movements, and its high customisability. The important aspects of this networks are routing algorithm and establishing connection between source and destination. In this paper we propose a proactive routing algorithm for wireless adhoc networks based on swarm intelligence and reinforcement learning. The proposed algorithm, decreases the packet delays in network, lowers the costs of receiving packets, and improves performance of network as a whole. Functionality of this algorithm has been tested and with ns simulator and results have been compared with, DSR, and AODV algorithms

Since providing service quality for critical and real time systems is essential, online test and fault tolerance are considered to be of high significance in designing chips for these systems. The dominant routing algorithm of Network on Chip (NoC) in critical and real time systems is deterministic routing algorithm. Due to the effects of crosstalk, NoC communication infrastructure is highly prone to fault and failure. Thus, effective and efficient testing mechanisms should be designed for detecting faults in the form of both online testing and fault tolerance so that fault tolerance time is minimized. In this paper, a method was proposed for online testing of interconnections among non-adaptive routers with deterministic routing algorithms. Indeed, by embedding a test pattern generator and four comparators in each router and producing interconnection redundancy among routers, the need for stopping usual system operations while testing is obviated. In the proposed architecture, interconnections were tested with chip frequency. As faults and failures are detected, the opportunity for tolerating them is created simultaneously. The results of conduct simulations in this study revealed that the proposed router was able to significantly reduce test execution time with minimum area overhead and power consumption.