DSP implementation

With deregulation of the power industry, many tie lines between control areas are driven to operate near their maximum capacity, especially those serving heavy load centers. Wide area control systems (WACSs) using wide-area or global signals can provide remote auxiliary control signals to local controllers such as automatic voltage regulators, power system stabilizers, etc to damp out inter-area oscillations. This paper presents the design and the DSP implementation of a nonlinear optimal wide area controller based on adaptive critic designs and neural networks for a power system on the real-time digital simulator (RTDS®). The performance of the WACS as a power system stability agent is studied using the Kundur's two area power system example. The WACS provides better damping of power system oscillations under small and large disturbances even with the inclusion of local power system stabilizers.

The fifth generation (5G) of mobile telecommunication standards is intended to offer better performance and efficiency. One of the most significant difficulties in delivering safe data transfer through the transmission channel in the emerging 5G technology is channel-coding security. This research primarily focused on offering information transmission that is secure in the place of novel assaults such as side-channel attacks. In this research, we present a low-density parity check (LDPC) encoder that is constructed using the multiplicative masking method to overcome side-channel attacks, one of the most significant security concerns for the upcoming 5G system. As a result, it offers greater security, reduced complexity, and higher performance. Power, area, and delay can all be calculated using LDPC codes. Comparing multiplicative masking implemented LDPC encoders to ordinary channel coding techniques in terms of security seen that multiplicative masking implemented LDPC encoders offer more security. The program Xilinx ISE 14.7 can synthesize the analysis. The advantage of multiplicative masking is that it offers a promising level of security through the principle of randomization, which is the foundation of the procedure. According to the analysis, the secured transmission of the data by the proposed multiplicative masking implemented LDPC encoder is increased.

Low-density parity check (LDPC) codes, are a family of error-correcting codes, their performances close to the Shannon limit make them very attractive solutions for digital communication systems. There are several algorithms for decoding LDPC codes that show great diversity in terms of performance related to error correction. Also, very recently, many research papers involved the genetic algorithm (GA) in coding theory, in particular, in the decoding linear block codes case, which has heavily contributed to reducing the bit error rate (BER). In this paper, an efficient method based on the GA is proposed and it is used to improve the power of correction in terms of BER and the frame error rate (FER) of LDPC codes. Subsequently, the proposed algorithm can independently decide the most suitable moment to stop the decoding process, moreover, it does not require channel information (CSI) making it adaptable for all types of channels with different noise or intensity. The simulations show that the proposed algorithm is more efficient in terms of BER compared to other LDPC code decoders.

Recently, nonlinear amplifiers based on the supercritical Andronov-Hopf bifurcation have become a focus of attention, especially in the modeling of the mammalian hearing organ. In general, to gain deeper insights in the inputoutput behavior, the analysis of bifurcation based amplifiers requires a flexible framework to exchange equations and adjust certain parameters. A DSP implementation is presented which is capable to analyze various amplifier systems. Amplifiers based on the Andronov-Hopf and Neimark-Sacker bifurcations are implemented and compared exemplarily. It is shown that the Neimark-Sacker system remarkably outperforms the Andronov-Hopf amplifier regarding the CPU usage. Nevertheless, both show a similar input-output behavior over a wide parameter range. Combined with an USBbased control interface connected to a PC, the digital framework provides a powerful instrument to analyze bifurcation based amplifiers.

Optimal energy harvesting is a key point in any photovoltaic system where economic and efficiency aspects are strongly interrelated. In this paper a novel artificial bee colony optimization-based MPPT is proposed. The proposed Bee’s algorithm allows the tracking of the maximal available power from a PV array under uniform and nonuniform illuminating conditions. A co-simulation methodology, combining Matlab/Simulink and Cadence/Pspice, has been used to verify the effectiveness of Bee’s algorithm to track the MPP of serially connected PV modules subject to various shading patterns. In addition, a performance comparison with Particle Swarm Optimization (PSO) based MPPT algorithm is also presented. The experimental resuls have shown the validity of the developed heuristic algorithm and its good tracking capabilities under shading conditions.

Optimal energy harvesting is a key point in any photovoltaic system where economic and efficiency aspects are strongly interrelated. In this paper a novel artificial bee colony optimization-based MPPT is proposed. The proposed Bee's algorithm allows the tracking of the maximal available power from a PV array under uniform and nonuniform illuminating conditions. A co-simulation methodology, combining Matlab/Simulink TM and Cadence/Pspice TM , has been used to verify the effectiveness of Bee's algorithm to track the MPP of serially connected PV modules subject to various shading patterns. In addition, a performance comparison with Particle Swarm Optimization (PSO) based MPPT algorithm is also presented. The experimental resuls have shown the validity of the developed heuristic algorithm and its good tracking capabilities under shading conditions.

Optimal energy harvesting is a key point in any photovoltaic system where economic and efficiency aspects are strongly interrelated. In this paper a novel artificial bee colony optimization-based MPPT is proposed. The proposed Bee's algorithm allows the tracking of the maximal available power from a PV array under uniform and nonuniform illuminating conditions. A co-simulation methodology, combining Matlab/Simulink TM and Cadence/Pspice TM , has been used to verify the effectiveness of Bee's algorithm to track the MPP of serially connected PV modules subject to various shading patterns. In addition, a performance comparison with Particle Swarm Optimization (PSO) based MPPT algorithm is also presented. The experimental resuls have shown the validity of the developed heuristic algorithm and its good tracking capabilities under shading conditions.

In recent days, extensive digital communication process has been performed. Due to this phenomenon, a proper maintenance of authentication, communication without any overhead such as signal attenuation code rate fluctuations during digital communication process can be minimized and optimized by adopting parallel encoder and decoder operations. To overcome the above-mentioned drawbacks by using proposed reconfigurable code rate cooperative (RCRC) and low-density parity check (LDPC) method. The proposed RCRC-LDPC is capable to operate over gigabits/sec data and it effectively performs linear encoding, dual diagonal form, widens the range of code rate and optimal degree distribution of LDPC mother code. The proposed method optimize the transmission rate and it is capable to operate on 0.98 code rate. It is the highest upper bounded code rate as compared to the existing methods. The proposed method optimizes the transmission rate and is capable to operate on a 0.98 code rate. It is the highest upper bounded code rate as compared to the existing methods. the proposed method's implementation has been carried out using MATLAB and as per the simulation result, the proposed method is capable of reaching a throughput efficiency greater than 8.2 (1.9) gigabits per second with a clock frequency of 160 MHz.

The Euclidean geometry (EG) based low-density parity check (LDPC) codes are enciphered and deciphered in various modes. These algorithms have the back-and-forth between decoding delay, and power usage, device unpredictability resources, and error rate efficacy are all available with these methods. As a result, the goal of this paper is to develop a comprehensive method to describe both soft and burst error bits for optimal data transfer. As a result, for EG-LDPC codes, a hybrid soft bit flipping (HSBF) decoder is suggested, which decreases decoding complications while improving message data transfer. A simulation model is formed using Xilinx synthesis report to study decoding latency, hardware usage, and power usage. A HSBF decoder is used in this paper, which accepts a 64-bit coding sequence and assigns 64 Adjustable nodes to it. It checks all customizable cluster connections and quantifies adjustable node values and actions. As a consequence of the data collected, our simulation model demonstrates that the HSBF technique outperforms soft bit flipping (SBF) algorithms. As a result, the techniques are ideal for usage in intermediate applications and as well as in cyber security processing technologies, medical applications.

This paper introduces a hybrid maximum power point tracking (MPPT) technique for photovoltaic (PV) arrays working under partial shading conditions. This new algorithm can combine a traditional MPPT algorithm, such as perturb and observe, or incremental conductance, with the artificial neural network (ANN) technique. The proposed hybrid MPPT algorithm is based on the ANN and used to predict the global MPP region by estimating its voltage boundaries. Consequently, the conventional MPPT algorithm searches for the MPP in the predicted region. The proposed technique is modeled and simulated using MATLAB/Simulink. The results show the effectiveness of the proposed hybrid MPPT technique to track the global MPP accurately with a rapid response comparing to the ANN; this increases the output power level of the PV array under various shading patterns.

Recently, short block codes are in great demand due to the emergent applications requiring the transmission of a short data unit and can guarantee speedy communication, with a minimum of latency and complexity which are among the technical challenges in today’s wireless services and systems. In the context of channel coding using low density parity check (LDPC) codes, the shorter length LDPC block codes are more likely to have short cycles with lengths of 4 and 6. The effect of the cycle with the minimum size is that this one prevents the propagation of the information in the Tanner graph during the iterative process. Therefore, the message decoded by short block code is assumed to be of poor quality due to short cycles. In this work, we present a study of the evolution of the messages on check nodes during the iterative decoding process when using the LDPC decoding algorithm normalized min sum (NMS), to see the destructive effect of short cycles and justify the effectiveness of the...

This paper presents a new high-throughput, lowcomplexity Bit Flipping (BF) decoder for Low-Density Parity-Check (LDPC) codes on the Binary Symmetric Channel (BSC), called Probabilistic Parallel Bit Flipping (PPBF). The advantage of PPBF comes from the fact that, no global operation is required during the decoding process and from that, all of the computations could be parallelized and localized at the computing units. Also in PPBF, the probabilistic feature in flipping the Variable Node (VN) is incorporated for all satisfaction level of its CN neighbors. This type of probabilistic incorporation makes PPBF more dynamic to correct some error patterns which are unsolvable by other BF decoders. PPBF offers a faster decoding process with an equivalent error correction performance to the Probabilistic Gradient Descent Bit Flipping (PGDBF) decoder, which is better than all so-far introduced BF decoders in BSC channel. A hardware implementation architecture of PPBF is also presented in this paper with detailed circuits for the probabilistic signal generator and processing units. The implementation of PPBF on FPGA confirms that, the PPBF complexity is much lower than that of the PGDBF and even lower than the one of the deterministic Gradient Descent Bit Flipping (GDBF) decoder. The good decoding performance along with the high throughput and low complexity lead PPBF decoder to become a brilliant candidate for the next generation of communication and storage standards.

Low-Density-Parity-Check (LDPC) codes have gained popularity in communication systems and standards due to their capacity-approaching error-correction performance. In this paper, we first expose the tradeoff between decoding performance and hardware performance across three LDPC hard-decision decoding algorithms: Gallager B (GaB), Gradient Descent Bit Flipping (GDBF), and Probabilistic Gradient Descent Bit Flipping (PGDBF). We show that GaB architecture delivers the best throughput while using fewest Field Programmable Gate Array (FPGA) resources, however performs the worst in terms of decoding performance. We then modify the GaB architecture, introduce a new Probabilistic stimulation function (PGaB), and achieve dramatic decoding performance improvement over the GaB, exceeding the performance of GDBF, without sacrificing its superior maximum operating frequency.

Optimal energy harvesting is a key point in any photovoltaic system where economic and efficiency aspects are strongly interrelated. In this paper a novel artificial bee colony optimization-based MPPT is proposed. The proposed Bee's algorithm allows the tracking of the maximal available power from a PV array under uniform and nonuniform illuminating conditions. A co-simulation methodology, combining Matlab/Simulink TM and Cadence/Pspice TM , has been used to verify the effectiveness of Bee's algorithm to track the MPP of serially connected PV modules subject to various shading patterns. In addition, a performance comparison with Particle Swarm Optimization (PSO) based MPPT algorithm is also presented. The experimental resuls have shown the validity of the developed heuristic algorithm and its good tracking capabilities under shading conditions.

Optimal energy harvesting is a key point in any photovoltaic system where economic and efficiency aspects are strongly interrelated. In this paper a novel artificial bee colony optimization-based MPPT is proposed. The proposed Bee's algorithm allows the tracking of the maximal available power from a PV array under uniform and nonuniform illuminating conditions. A co-simulation methodology, combining Matlab/Simulink TM and Cadence/Pspice TM , has been used to verify the effectiveness of Bee's algorithm to track the MPP of serially connected PV modules subject to various shading patterns. In addition, a performance comparison with Particle Swarm Optimization (PSO) based MPPT algorithm is also presented. The experimental resuls have shown the validity of the developed heuristic algorithm and its good tracking capabilities under shading conditions.

For low density parity check (LDPC) decoding, hard-decision algorithms are sometimes more suitable than the soft-decision ones. Particularly in the high throughput and high speed applications. However, there exists a considerable gap in performances between these two classes of algorithms in favor of soft-decision algorithms.  In order to reduce this gap, in this work we introduce two new improved versions of the hard-decision algorithms, the adaptative gradient descent bit-flipping (AGDBF) and adaptative reliability ratio weighted GDBF (ARRWGDBF).  An adaptative weighting and correction factor is introduced in each case to improve the performances of the two algorithms allowing an important gain of bit error rate. As a second contribution of this work a real time implementation of the proposed solutions on a digital signal processors (DSP) is performed in order to optimize and improve the performance of these new approchs. The results of numerical simulations and DSP implementation...

Flash memories are gaining prominence for utilizing in large scale data centers (DCs) due to their high memory density, low power consumption and heat dissipation, and high access speed characteristics. The rate of degradation for a flash memory is largely affected by the amount and frequency of the erase/write operations, which is a challenge in the DC context that serves dynamically changing workloads. Adaptive Error Correction Code (AECC) schemes have been introduced for changing the error correction algorithm based on the reliability state of the flash. In this study we show that hard decision (bit-flipping) and soft decision decoding (Belief Propagation) class of algorithms for Low Density Parity Check (LDPC) decoders complement each other for utilizing in the flash based DCs in order to meet the dynamically changing reliability level. We propose a new family of ECC to improve the reliability of flash memory. Our Monte-Carlo simulations and Field Programmable Gate Array (FPGA) ...

This paper offers a review of recent developments in non-deterministic error correction decoding methods, which can be described in two broad classes. The first class uses stochastic computation to emulate the arithmetic operations of conventional decoding algorithms. The second class achieves noise enhancement by randomly perturbing the calculations of a standard decoder. Stochastic decoders inherit analysis techniques from the conventional algorithms they emulate, but the noiseenhanced algorithms are newer, more difficult to explain, and not yet fully understood. We describe a Markov chain analysis technique to both explain and optimize noise enhancement in these algorithms. Circuit implementation is also discussed, including both conventional hardware architectures and circuits based on memristor threshold logic, where memristor non-determinism can be exploited for noise enhancement.

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink TM and Cadence/Pspice TM , is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.

For low density parity check (LDPC) decoding, hard-decision algorithms are sometimes more suitable than the soft-decision ones. Particularly in the high throughput and high-speed applications. However, there exists a considerable gap in performances between these two classes of algorithms in favor of softdecision algorithms. In order to reduce this gap, in this work we introduce two new improved versions of the hard-decision algorithms, the adaptative gradient descent bit-flipping (AGDBF) and adaptative reliability ratio weighted GDBF (ARRWGDBF). An adaptative weighting and correction factor is introduced in each case to improve the performances of the two algorithms allowing an important gain of bit error rate. As a second contribution of this work a real time implementation of the proposed solutions on a digital signal processor (DSP) is performed in order to optimize and improve the performance of these new approchs. The results of numerical simulations and DSP implementation reveal a faster convergence with a low processing time and a reduction in consumed memory resources when compared to soft-decision algorithms. For the irregular LDPC code, our approachs achieves gains of 0.25 and 0.15 dB respectively for the AGDBF and ARRWGDBF algorithms.

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink TM and Cadence/Pspice TM , is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.