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Translating written or spoken words from one language to another while preserving their original intent, meaning, and tone is referred to as language translation and it is both an art and a science. Communication across linguistic borders is made possible by language translation, which is essential for tying people, businesses, and cultures together. Translation issues include mistranslated words and the challenge of translating lengthy sentences. We researched sentence-level machine translation techniques based on deep learning and developed an electronic dictionary for Chinese and English. In this study, we proposed a novel battle royale dynamic recurrent neural network (BR-DRNN) for translating Chinese to English. In this research, we gathered information from the WiLI- dataset. The suggested approach and translation model have been used to train the data. The proposed method is compared to the other existing algorithms. The result shows the BR-DRNN has better performance in terms of accuracy, BLEU score, precision, recall, and F1-score than other algorithms. This study highlights artificial intelligence’s (AI) potential to transform language communication by providing a scalable and effective solution. The translation system powered by a neural network improves communication in a variety of circumstances.

Considering the time lag produced by the transmission in chaos-communication, we present self-time-delay synchronization (STDS) of complex chaotic systems. STDS implies that the synchronization between the time-delay system (the receiver) and the original system (the transmitter) while maintaining the structure and parameters of systems unchanged, thus these various problems produced by time-delay in practice are avoided. It is more suitable to simulate real communication situation. Aimed to time-delay coupled complex chaotic systems, the control law is derived by active control technique. Based on STDS, a novel communication scheme is further designed according to chaotic masking. In simulation, we take time-delay coupled complex Lorenz system transmitting actual speech signal (analog signal) and binary signal as examples. The speech signal contains two components, which are transmitted by the real part and imaginary part of one complex state variable. Two sequences of binary bits are converted into analog signals by 2^M-ary and zero-order holder, then added into the real part and imaginary part of one complex state variable. Therefore, the STDS controller is realized by one critical state variable. It is simple in principle and easy to implement in engineering. Moreover, the communication system is robust to noise. It is possible to adopt cheap circuits with time-delay, which is economical and practical for communication.

This paper explores the law of emotion transmission on the social Internet. By applying higher-order network theory, ER random networks are reconstructed and higher-order network structures with multiple interaction points are generated. The I-state of the SI model is extended to set up the propagation rules of emotions based on higher-order networks. Concepts such as sentiment recession rate are introduced to make sentiment propagate through different simplexes, so as to obtain the propagation rules of higher-order networks. Qualitative analysis of emotional changes during the propagation process verifies the phase transition phenomenon of emotions in the network. By comparing simulated results with actual data, we demonstrate the accuracy of our higher-order network-based emotion propagation model in reflecting real-world trends and outcomes. The study also explores the effects of factors such as the initial proportion of extreme emotions on emotion propagation. This study provides valuable insights into understanding the operation of complex systems and offers important implications for government predictions of opinion development.

Information must take up space, must weigh, and its flux must be limited. Quantum limits on communication and information storage leading to these conclusions are described here. Quantum channel capacity theory is reviewed for both steady state and burst communication. An analytic approximation is given for the maximum signal information possible with occupation number signal states as a function of mean signal energy. A theorem guaranteeing that these states are optimal for communication is proved. A heuristic "proof" of the linear bound on communication is given, followed by rigorous proofs for signals with specified mean energy, and for signals with given energy budget. And systems of many parallel quantum channels are shown to obey the linear bound for a natural channel architecture. The time-energy uncertainty principle is reformulated in information language by means of the linear bound. The quantum bound on information storage capacity of quantum mechanical and quantum field devices is reviewed. A simplified version of the analytic proof for the bound is given for the latter case. Solitons as information caches are discussed, as is information storage in one-dimensional systems. The influence of signal self-gravitation on communication is considered. Finally, it is shown that acceleration of a receiver acts to block information transfer.

Coscheduling of communication and computation is considered one of the crucial points to obtain good performance out of fast communication systems. Various techniques have been examined in literature ranging from strict "gang scheduling" of all processes possibly involved in message exchange to "implicit coscheduling" in which the communication support system may act on the scheduling of sending and receiving processes trying to improve performance without explicit coordination by special purpose message exchanges. Based on the experience in implementing the GAMMA communication system, we are convinced that some form of coscheduling is needed in order to obtain best performance in communication. However we believe that most of the approaches described in literature so far are too simplistic to be really effective. In this paper we point out and classify some of the major problems a system that attempts to coschedule communication and computation should address. We hope to clarify the goals of a coscheduler by taking some of the crucial characteristics of the communication into account. We also hope to be able to devise some more integrated and coherent strategies of coordination between process scheduling and choice of communication modes.

A heuristic has shown that the results achieved by earlier algorithms for gossiping on cube-connected-cycles (CCCs) in the unit-cost telephone model were not optimal, but no general pattern was revealed. In this paper new gossiping algorithms are presented that apply to CCCs of all sizes. Most importantly, we show that for gossiping on the "k-dimensional" network, CCC_k, with k even, gossiping can be performed in 5/2 · k - 2 communication rounds, which exactly matches the lower bound, thus completely solving the gossiping problem for these networks. For odd k we improve upon the best previous algorithm without matching the lower bound.

We address the problem of performing a pipelined broadcast on a mesh architecture. Meshes require a different approach than other topologies, and their very nature puts a tighter bound on the performance that one can hope to achieve. By using the appropriate techniques, however, one can obtain excellent performance for sufficiently long messages. The resulting algorithm will work on meshes of any dimension with any number of nodes. Our model assumes that the mesh is a torus and/or that it has bidirectional links and uses wormhole routing. Performance data from the Cray T3D are included.

We examine multiprocessor runtime support for fine-grained, irregular directed acyclic graphs (DAGs) such as those that arise from sparse-matrix triangular solves. We conduct our experiments on the CM-5, whose lower latencies and active-message support allow us to achieve unprecedented speedups for a general multiprocessor. Where as previous implementations have maximum speedups of less than 4 on even simple banded matrices, we are able to obtain scalable performance on extremely small and irregular problems. On a matrix with only 5300 rows, we are able to achieve scalable performance with a speedup of 34 for 128 processors, resulting in an absolute performance of over 33 million double-precision floating point operations per second.

We achieve these speedups with non-matrix-specific methods which are applicable to any DAG. We compare a range of run-time preprocessed and dynamic approaches on matrices from the Harwell-Boeing benchmark set. Although precomputed data distributions and execution schedules produce the best performance, we find that it is challenging to keep their cost low enough to make them worthwhile on small, fine-grained problems.

Additionally, we find that a policy of frequent network polling can reduce communication overhead by a factor of three over the standard CM-5 policies. We present a detailed study of runtime overheads and demonstrate that send and receive processor overhead still dominate these applications on the CM-5. We conclude that these applications would highly benefit from architectural support for low-overhead communication.

Data-parallel languages such as High Performance Fortran, Vienna Fortran and Fortran D include directives such as alignment and distribution that describe how data and computation are mapped onto the processors in a distributed-memory multiprocessor. A compiler for HPF that generates code for each processor has to compute the sequence of local memory addresses accessed by each processor and the sequence of send and receives for a given processor to access non-local data. In this paper, we present a novel approach for the generation of communication sets that exploits a pttern of send-receive index pairs. In addition, we present an algorithm for code generation. Experimental results demonstrate the viability of this technique.

Due to the complexity of programming scalable multiprocessors with physically distributed memories, it is onerous to manually generate parallel code for these machines. As a consequense, there has been much research on the development of compiler techniques to simplify programming, to increase reliability, and to reduce development costs. For code generation, a compiler applies a number of transformations in areas such as data privatization, data copying and replication, synchronization, and data and work distribution. In this paper, we discuss our recent work on the development and implementation of a few compiler techniques for some of these transformations. We use Polaris, a parallelizing Fortran restructurer developed at Illinois, as the infrastructure to implement our algorithms. The paper includes experimental results obtained by applying our techniques to several benchmark codes.

This paper describes the implementation of a parallel image processing algorithm, the aim of which is to give good contrast enhancement in real time, especially on the boundaries of an object of interest defined by a grey homogeneity (for example, an object of medical interest having a functional or morphologic homogeneity, like a bone or tumor). The implementation of a neural network algorithm which does this contrast enhancement has been done on a SIMD massively parallel machine (a MasPar of 8192 processors) and the communication between its processors has been optimized.

In the neighbourhood gossiping problem, each node of a network starts with a unique message and must learn the messages of all of its neighbours. In this paper, we prove upper and lower bounds for neighbourhood gossiping in hypercubes under the single-port half-duplex and single-port full-duplex communication models.

Beams of neutrinos have been proposed as a vehicle for communications under unusual circumstances, such as direct point-to-point global communication, communication with submarines, secure communications and interstellar communication. We report on the performance of a low-rate communications link established using the NuMI beam line and the MINERvA detector at Fermilab. The link achieved a decoded data rate of 0.1 bits/sec with a bit error rate of 1% over a distance of 1.035 km, including 240 m of earth.

The efficiency of the fractional state that is generated between a single atom and field by using anti-Jaynes–Cummings model (AJCM) is discussed. The fractional degree and the interaction parameters may be used as controllers to increase the efficiency of the fractional state in the context of quantum communication. It has been shown that, the quantum correlation, capacity, and the ability of the fractional state, increase suddenly/gradually at small/large fractional degree, respectively. At small fractional orders, the constant behavior for all these phenomena is displayed at a short interaction time. The fractional quantum state that is generated by the anti-Jaynes–Cummings model is more efficient than that generated by the well-known Jaynes–Cummings model (JCM), where it can be used to teleport an unknown two-qubit state with larger fidelity.

The Time-Multiplexed FPGA (TMFPGA) architecture can improve dramatically logic utilization by time-sharing logic but it needs a large amount of registers among sub-circuits for partitioning the given sequential circuits. In this paper, we propose an improved TMFPGA architecture to simplify the precedence constraints so that the number of the registers among sub-circuits can be reduced for sequential circuits partitioning. To demonstrate the practicability of the architecture, we also present a greedy algorithm to minimize the maximum number of the registers. Experimental results demonstrate the effectives of the algorithm.

Due to their structure and complexity, chaotic systems have been introduced in several domains such as electronic circuits, commerce domain, encryption and network security. In this paper, we propose a novel multidimensional chaotic system with multiple parameters and nonlinear terms. Then, a two-phase algorithm is presented for investigating the chaotic behavior using bifurcation and Lyapunov exponent (LE) theories. Finally, we illustrate the performances of our proposal by constructing three (03) chaotic maps (3-D, 4-D and 5-D) and implementing the 3-D map on Field-Programmable-Gate-Array (FPGA) boards to generate random keys for securing a client–server communication purpose. Based on the achieved results, the proposed scheme is considered an ideal candidate for numerous resource-constrained devices and internet of the things (IoT) applications.

New advancements in deep learning issues, motivated by real-world use cases, frequently contribute to this growth. Still, it’s not easy to recognize the speaker’s emotions from what they want to say. The proposed technique combines a deep learning-based brain-inspired prediction-making artificial neural network (ANN) through social ski-driver (SSD) optimization techniques. When assessing speaker emotion recognition (SER), the recognition results are compared with the existing convolutional neural network (CNN) and long short-term memory (LSTM)-based emotion recognition methods. The proposed method for classification based on ANN decreases the computational costs. The SER algorithm allows for a more in-depth classification of different emotions because of its relationship to ANN and LSTM. The SER model is based on ANN and the recognition impact of the feature reduction. The SER in this proposed research work is based on the ANN emotion classification system. Speaker recognition accuracy values of 96.46%, recall values of 95.39%, precision values of 95.21%, and F-Score values of 96.10% are obtained in this proposed result, which is higher than the existing result. The average accuracy results by using the proposed ANN classification technique are 4.38% and 2.89%, better than the existing CNN and LSTM techniques, respectively. The average precision results by using the proposed ANN classification technique are 4.67% and 2.49%, better than the existing CNN and LSTM techniques, respectively. The average recall results by using the proposed ANN classification technique are 2.90% and 1.42%, better than the existing CNN and LSTM techniques, respectively. The average precision results using the proposed ANN classification technique are 3.80% and 3.10%, better than the existing CNN and LSTM techniques, respectively.

Internet of vehicles (IoV) has become an important research topic due to its direct effect on intelligent transportation systems (ITS) development. There are many challenges in the IoV environment, such as communication, big data and best route assigning. In this paper, an effective IoV architecture is proposed. This architecture has four main objectives. The first objective is to utilize a powerful communication scheme in which three tiers of coverage tools — Internet, satellite, high-altitude platform (HAP) — are utilized. Therefore, the vehicles maintain a continuous connection to the IoV environment everywhere. The second objective is to apply filtering and prioritization mechanisms to reduce the detrimental effects of IoV big data. The third objective is to assign the best route for a vehicle after determining its real-time priority. The fourth objective is to analyze the IoV data. The proposed architecture performance is measured using a simulation environment that is created by the NS-3 package. The simulation results proved that the proposed IoV architecture has a positive impact on the IoV environment according to the performance metrics: energy, success rate of route assignment, filtering effect, data loss, delay, usage of coverage tools and throughput.

In this Letter, a new demodulation approach to communication using chaotic signals is presented. Information signal is modulated by applying it as an input to a chaotic system. At the receiver, a synchronous subsystem is constructed for estimates on the states of the chaotic system. The key innovation in our communication system is a new robust filter that recovers the information signal from the scalar transmitted signal.

This paper is meant to serve as a tutorial describing the link between symbolic dynamics as a description of a chaotic attractor, and how to use control of chaos to manipulate the corresponding symbolic dynamics to transmit an information bearing signal. We use the Lorenz attractor, in the form of the discrete successive maxima map of the z-variable time-series, as our main example. For the first time, here, we use this oscillator as a chaotic signal carrier. We review the many previously developed issues necessary to create a working control of symbol dynamics system. These include a brief review of the theory of symbol dynamics, and how they arise from the flow of a differential equation. We also discuss the role of the (symbol dynamics) generating partition, the difficulty of finding such partitions, which is an open problem for most dynamical systems, and a newly developed algorithm to find the generating partition which relies just on knowing a large set of periodic orbits. We also discuss the importance of using a generating partition in terms of considering the possibility of using some other arbitrary partition, with discussion of consequences both generally to characterizing the system, and also specifically to communicating on chaotic signal carriers. Also, of practical importance, we review the necessary feedback-control issues to force the flow of a chaotic differential equation to carry a desired message.