Adaptive Hybrid Forward Error Correction Coding Scheme for Video Transmission

This paper proposes an adaptive hybrid forward error correction(AH-FEC)coding scheme for coping with dynamic packet loss events in video and audio transmission.Specifically,the proposed scheme consists of a hybrid Reed-Solomon and low-density parity-check(RS-LDPC)coding system,combined with a Kalman filter-based adaptive algorithm.The hybrid RS-LDPC coding accommodates a wide range of code length requirements,employing RS coding for short codes and LDPC coding for medium-long codes.We delimit the short and medium-length codes by coding performance so that both codes remain in the optimal region.Additionally,a Kalman filter-based adaptive algorithm has been developed to handle dynamic alterations in a packet loss rate.The Kalman filter estimates packet loss rate utilizing observation data and system models,and then we establish the redundancy decision module through receiver feedback.As a result,the lost packets can be perfectly recovered by the receiver based on the redundant packets.Experimental results show that the proposed method enhances the decoding performance significantly under the same redundancy and channel packet loss.

Neurogenesis dynamics in the olfactory bulb:deciphering circuitry organization, function, and adaptive plasticity

Adult neurogenesis persists after birth in the subventricular zone, with new neurons migrating to the granule cell layer and glomerular layers of the olfactory bulb, where they integrate into existing circuitry as inhibitory interneurons. The generation of these new neurons in the olfactory bulb supports both structural and functional plasticity, aiding in circuit remodeling triggered by memory and learning processes. However, the presence of these neurons, coupled with the cellular diversity within the olfactory bulb, presents an ongoing challenge in understanding its network organization and function. Moreover,the continuous integration of new neurons in the olfactory bulb plays a pivotal role in regulating olfactory information processing. This adaptive process responds to changes in epithelial composition and contributes to the formation of olfactory memories by modulating cellular connectivity within the olfactory bulb and interacting intricately with higher-order brain regions. The role of adult neurogenesis in olfactory bulb functions remains a topic of debate. Nevertheless, the functionality of the olfactory bulb is intricately linked to the organization of granule cells around mitral and tufted cells. This organizational pattern significantly impacts output, network behavior, and synaptic plasticity, which are crucial for olfactory perception and memory. Additionally, this organization is further shaped by axon terminals originating from cortical and subcortical regions. Despite the crucial role of olfactory bulb in brain functions and behaviors related to olfaction, these complex and highly interconnected processes have not been comprehensively studied as a whole. Therefore, this manuscript aims to discuss our current understanding and explore how neural plasticity and olfactory neurogenesis contribute to enhancing the adaptability of the olfactory system. These mechanisms are thought to support olfactory learning and memory, potentially through increased complexity and restructuring of neural network structures, as well as the addition of new granule granule cells that aid in olfactory adaptation. Additionally, the manuscript underscores the importance of employing precise methodologies to elucidate the specific roles of adult neurogenesis amidst conflicting data and varying experimental paradigms. Understanding these processes is essential for gaining insights into the complexities of olfactory function and behavior.

Adaptive Clutch Engaging Process Control for Automatic Mechanical Transmission

Based on detail analysis of clutch engaging process control targets and adaptive demands, a control strategy which is based on speed signal, different from that of based on main clutch displacement signal, is put forward. It considers both jerk and slipping work which are the most commonly used quality evaluating indexes of vehicle starting phase. The adaptive control system and its reference model are discussed profoundly. Taking the adaptability to different starting gears and different road conditions as examples, some proving field test records are shown to illustrate the main clutch adaptive control strategy at starting phase. Proving field test gives acceptable results.

Adaptive lag synchronization of uncertain dynamical systems with time delays via simple transmission lag feedback

In this paper we present an adaptive scheme to achieve lag synchronization for uncertain dynamical systems with time delays and unknown parameters. In contrast to the nonlinear feedback scheme reported in the previous literature, the proposed controller is a linear one which only involves simple feedback information from the drive system with signal popagation lags. Besides, the unknown parameters can also be identified via the proposed updating laws in spite of the existence of model delays and transmission lags, as long as the linear independence condition between the related function elements is satisfied. Two examples, i.e., the Mackey-Glass model with single delay and the Lorenz system with multiple delays, are employed to show the effectiveness of this approach. Some robustness issues are also discussed, which shows that the proposed scheme is quite robust in switching and noisy environment.

A Joint Rate Control and AMC Algorithm for Adaptive Transmission Systems

The traditional communication system is effectively designed for the worst-case channel state and it can not use the spectral efficiently over the time-varying multipath channel. In order to improve the spectral efficiency and ensure robust and spectrally-efficient transmission over the time-varying multipath channel,a joint rate control and adaptive modulation and coding ( AMC) algorithm for adaptive transmission systems is proposed in this paper. Firstly,the proposed algorithm can formulate a modulation and coding scheme ( MCS) switching table according to the offline simulation results and the target bit error rate ( BER) . Then,the optimal MCS is selected in MCS switching table according to the channel state information ( CSI) and then passes to the transmitter and receiver to implement. So the adaptive system which always uses the optimal MCS to transmit signals uses the spectral efficiently. The simulation results validate the proposed algorithm and show that under the premise of meeting the target BER,the adaptive system performing the proposed algorithm has a higher average spectral efficiency ( ASE) than that of the non-adaptive system.

An Adaptive Transmission Scheme for Deep Space Communication

Deep space communication is quite different from conventional ground communication due to its time-varying,complexity and large signal delay,which consequently affects communication quality and system efficiency.Adjusting the transmission parameters when the channel environment changes during the communication can guarantee the performance index of the system,and therefore improve communication efficiency. An adaptive transmission scheme of transceiver based on Consultative Committee for Space Data Systems(CCSDS)protocols is proposed in this paper. According to the variation of the deep space channel,the symbol rate of transmission data is adjusted dynamically by estimating the signal-to-noise ratio(SNR) of the receiver in real time and adjusting the channel environment. This scheme can improve the channel utilization and system throughput under the premise of limiting the system bit error rate. Furthermore,this scheme is successfully implemented in Xilinx Virtex-5 FPGA board.

A novel OFDM transmission scheme with length-adaptive Cyclic Prefix

Conventional OFDM transmission system uses a fixed-length Cyclic Prefix to counteract Inter-Symbol Inter- ferences (ISI) caused by channel delay spreading under wireless mobile environment. This may cause considerable per- formance deterioration when the CP length is less than the channel RMS delay spread, or may decrease the system power and spectrum efficiency when it is much larger. A novel Orthogonal Frequency Division Multiplexing (OFDM) transmission scheme is proposed in this paper to adapt the CP length to the variation of channel delay spread. AOFDM-VCPL utilizes the preamble or pilot sub-carriers of each OFDM packet to estimate the channel RMS delay spread; and then uses a criterion to calculate the CP length , which finally affects the OFDM transmitter. As illustrated in the simulation section, by deploying this scheme in a typical wireless environment, the system can transmit at data rate 11.5 Mb/s higher than conventional non-adaptive system while gaining a 0.65 dB power saving at the same BER performance.

Self-Adaptive Matching Method of Signal and Energy Transmission

Aimed at the rapid message setting of the passive systems, the efficiency of energy transmission and the characteristics of signal transmission in the message setting are analyzed, which is based on the methods of the circuit analysis and the electromagnetic induction technology. A self-adaptive impedance matching method of signal and energy transmission, which can set the message rapidly and effectively, is put forward. The electromagnetic induct setting system will reach the highest energy transmission efficiency when the equivalent resistance of the second loop is equal to the resistance of the first loop. The greater the ratio of the equivalent resistances is, the higher efficient the signal transmission has. Moreover, the validity of the method is verified by circuit design and tests.

Adaptive Data Transmission Method According to Wireless State in Long Range Wide Area Networks

The Internet of Things(IoT)has enabled various intelligent services,and IoT service range has been steadily extended through long range wide area communication technologies,which enable very long distance wireless data transmission.End-nodes are connected to a gateway with a single hop.They consume very low-power,using very low data rate to deliver data.Since long transmission time is consequently needed for each data packet transmission in long range wide area networks,data transmission should be efficiently performed.Therefore,this paper proposes a multicast uplink data transmission mechanism particularly for bad network conditions.Transmission delay will be increased if only retransmissions are used under bad network conditions.However,employing multicast techniques in bad network conditions can significantly increase packet delivery rate.Thus,retransmission can be reduced and hence transmission efficiency increased.Therefore,the proposed method adopts multicast uplink after network condition prediction.To predict network conditions,the proposed method uses a deep neural network algorithm.The proposed method performance was verified by comparison with uplink unicast transmission only,confirming significantly improved performance.

Adaptive Relay Selection Scheme for Minimization of the Transmission Time

As the installation of small cells increases,the use of relay also increases.The relay operates as a base station as well as just an amplifier.As the roles and types of relays become more diverse,appropriate relay selection technology is an effective way to improve communication performance.Many researches for relay selection have been studied to secure the reliability of relay communication.In this paper,the relay selection scheme is proposed for a cooperative system using decode-and-forward(DF)relaying scheme in the mobile communication system.To maintain the transmission rate,the proposed scheme classifies a candidate group considering the outage probability of multiple relays.For the applicable candidate group,the proposed scheme selects the relay considering the amount of data allocated to each user.Therefore,the proposed scheme defines the unit transmission time through each user’s data and relay capacity.Finally,the proposed scheme selects a relay that minimizes the total transmission time through the relay transmission time that calculates the unit transmission time for all users.With this adaptive relay selection scheme,an optimal relay can be assigned for each user.For the same transmission rate and the amount of data,the proposed scheme improves the performance of transmission time and reliability.Simulation results show that the proposed scheme reduces the total transmission time for the same amount of data and signal to noise ratio(SNR).

Adaptive Application Offloading Decision and Transmission Scheduling for Mobile Cloud Computing

Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. And the performance analysis is also given. Finally, simulation results show that,compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.

A Reliable Adaptive High-Definition Transmission System for Distance Learning

There is an increasing demand for supporting high-quality real-time audiovisual services for the next generation wired and wireless networks. However, due to variety of bandwidths of different networks, it is a great challenge for deployment. In this paper, a novel high-definition (HD) video transmission system was proposed which depends upon reliable compound multicast protocols and QoS control over the various kinds of networks. This system detects client's network condition and assigns it to a proper proxy. Each proxy is capable of detecting network parameters and adaptively tuning such transport parameters as bit rate, video resolution, frame rate and QoS mechanisms to this condition. It also provides FEC error recovery under consideration of characteristics of MPEG4 video codec. Our simulation demonstrates that different network clients such as ADSL, CERNET, and CERNET2 can receive more video reliability with less delay.

Adaptive optimization on ultrasonic transmission tomography-based temperature image for biomedical treatment

Hyperthermia has proven to be beneficial to treating superficial malignancies, particularly chest wall recurrences of breast cancer. During hyperthermia, monitoring the time–temperature profiles in the target and surrounding areas is of great significance for the effect of therapy. An ultrasound-based temperature imaging method has advantages over other approaches. When the temperature around the tumor is calculated by using the propagation speed of ultrasound, there always exist overshoot artifacts along the boundary between different tissues. In this paper, we present a new method combined with empirical mode decomposition（EDM）, similarity constraint, and continuity constraint to optimize the temperature images. Simulation and phantom experiment results compared with those from our previously proposed method prove that the EMD-based method can build a better temperature field image, which can adaptively yield better temperature images with less computation for assistant medical treatment control.

Permanent Fault Identification Method for Single-Phasea Adaptive Reclosure of UHVAC Transmission Line

In order to avoid the UHVAC (Ultra High Voltage Alternating Current) transmission line with shunt reactor fault voltage smaller problems, through the analysis of single-phase permanent fault when tripping phase terminal voltage characteristics, this paper presents a fault phase voltage signal of the two order derivative and the original signal ratio of a new method for steady-state frequency discrimination single-phase permanent fault. The principle of this method is simple, and it can avoid the problem that the fault voltage caused by the installation of shunt reactor is small. The adaptability and correctness of the proposed method are verified by a large number of simulations.

Multichannel Adaptive Polarization Mode Dispersion Compensation with One Compensator in Dense Wavelength Division Multiplexing Transmission Systems

A simple two-section polarization mode dispersion(PMD) compensator is proposed for multichannel PMD compensation, which can compensate two or even more channels simultaneously. Because of the statistical characteristics and the frequency-dependence of PMD, for current single mode fiber with moderate PMD, the probability that all channels are severely degraded at the same time is extremely small, which makes it possible to compensate a dense wavelength division multiplexing(DWDM) transmission system with moderate PMD using this compensator. It is shown that the outage probability of a 40×43 Gb/s DWDM transmission system using this compensator is decreased significantly from 3.6×10-3 to 3.6×10-5.

Adaptive modulation MIMO system based on minimizing transmission power

Adaptive modulation (AM) is an effective technique to approach the theoretical bound of multi-input and multi-output (MIMO) channel. In most previous studies, the AM parameters were obtained by maximizing the transmission rate for a given total transmit power. In this paper, a novel AM-MIMO algorithm is presented, which is based on minimizing total transmit power when the link’s QoS requirements are given. By taking the QoS requirements into account directly, the proposed algorithm not only makes the system more flexible, but also makes the cross layer design of wireless network easier. At last, the numerical results of the proposed scheme are presented.

Novel Adaptive Neural Controller Design Based on HVDC Transmission System to Damp Low Frequency Oscillations and Sub Synchronous Resonance

This paper presents the effect of the high voltage direct current (HVDC) transmission system based on voltage source converter (VSC) on the sub synchronous resonance (SSR) and low frequency oscillations (LFO) in power system. Also, a novel adaptive neural controller based on neural identifier is proposed for the HVDC which is capable of damping out LFO and sub synchronous oscillations (SSO). For comparison purposes, results of system based damping neural controller are compared with a lead-lag controller based on quantum particle swarm optimization (QPSO). It is shown that implementing adaptive damping controller not only improves the stability of power system but also can overcome drawbacks of conventional compensators with fixed parameters. In order to determine the most effective input of HVDC system to apply supplementary controller signal, analysis based on singular value decomposition is performed. To evaluate the performance of the proposed controller, transient simulations of detailed nonlinear system are considered.

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.