Constraint Length Research Articles

Experimental and numerical studies of the multi-tube assembled buckling-restrained braces

In this paper, the multi-tube assembled bolt-connected buckling-restrained brace (MT-BRB) with four internally restrained rectangular steel tubes and one externally restrained square steel tube is experimentally analyzed. This study conducted cyclic loading tests on five specimens with different parameters to investigate the effects of different loading directions of the core plate, the presence or absence of unbonded materials, the gap-to-width ratio and the width-to-thickness ratio on the mechanical properties, seismic performance and failure mode of MT-BRB. The experimental results indicated that the MT-BRB specimens made of unbonded materials had stable cyclic performance and energy dissipation capacity. Subsequently, the same numerical model as the experimental specimen was established, and the hysteresis and stress curves extracted from the finite element model were compared with the experimental results. The simulation had ideal agreement with the experiment result, demonstrating that the model was effective for performance predictions. Based on this, the working mechanism of MT-BRB was further explored through finite element analysis. Finally, parameter analysis was conducted on the constraint ratio and constraint length ratio to further investigate the energy dissipation performance of MT-BRB. This study is beneficial for promoting the application of MT-BRB in energy dissipation and vibration reduction engineering.

KNN-Based ML Model for the Symbol Prediction in TCM Trellis Coded Modulation TCM Decoder

Machine Learning is a booming technology today. In a machine learning set of training, data is to be provided to the model for training and that model predicts the output. Machine Learning models are trained using a computer program known as ML algorithms.The new machine learning-based Transition Metric Unit (TMU) of 4D- 8PSK Trellis coded Modulation TCM Decoder is presented in this work. The classic Viterbi decoder's branch metric unit, or TMU, takes on a complex structure. Trellis coded Modulation (TCM) is a combination of 8 PSK modulations and Error Correcting Code (ECC). TMU is one of the complex units of the TCM decoder, which is essentially a Viterbi decoder. Similar to how the first Branch metric is determined in the straightforward Viterbi decoder, the TCM decoder performs this BM computation via the TMU unit. The TMU becomes challenging and uses more dynamic power as a result of the enormous constraint length and the vast number of encoder states.In the proposed algorithm innovative KNN (K nearest neighbours) based ML model is developed. It is a supervised learning model in which input and output both are provided to the model, training data also called the labels, when a new set of data will come the model will give output based on its previous set experience and data.Here we are using this ML model for the symbol prediction at the receiver end of the TCM decoder based on the previous learning. Using the proposed innovation, the paper perceives the optimization of the TCM Decoder which will further reduce the H/W requirements and low latency which results in less power consumption.

Design of Ternary Convolutional Code using Reconfigurable Architecture

The need for trustworthy and efficient digital data transmission and storage systems has increased in recent years. The demand to handle and store digital information has grown in the business sector, the military, and the government as a direct result of the widespread availability of high-speed, large-scale data networks. This requirement must be met in order for the design of these systems to keep up with the rapid pace set by communication and computer technologies. When trying to overcome the variable degradation in real time, one of the most important factors to consider is the dependability of the broadband communication channel. Therefore, the use of convolutional codes and other channel-coding strategies is an essential component of any broadband communication system. DSL, WLAN, and 3G standards all require different configurations of convolutional coding, each of which must achieve a specific level of coding performance despite operating at a unique data rate (constraint length and code rate) Therefore, from the perspective of channel-coding techniques, hardware implementations for the development of an encoder are essential. This encoder should be able to support multiple networks using a reconfigurability approach and should be able to function across a variety of standards. Additionally, flexibility and hardware performance should both be prioritised. This calls for forward error control coding with reconfigurable logic, which provides high-speed, low-power dynamically dedicated hardware architectures under a number of speed/power performance constraints at different time intervals that can function within a variety of channel conditions. The ternary computation system has a number of benefits, the most notable of which are the availability of a high data rate, improved spectral efficiency, enhanced coverage, and lower latency. As a result, there is a growing demand for ternary-based systems that use convolutional encoders as a result of developments in technology

Implementation of Novel Block and Convolutional Encoding Circuit Using FS-GDI

A novel implementation of Block and Convolutional encoding circuits approach using Gate Input Diffusion-Full Swing (FS-GDI) and Complementary Metal Oxide Semiconductor (CMOS) logic styles has been presented. Performance analysis of the proposed encoding circuits has been performed using Cadence Virtuoso (CV) S/W package. The performance metrics of FS-GDI-based encoding circuits have been compared with traditional CMOS-based designs. The proposed encoding circuits involve Block Hamming (15, 11), Convolutional (2, 1, 3), and Recursive Systematic Convolutional (2, 1, 2) encoding circuits; these presented circuits are simulated and tested using a word length of 11 bits. The simulation experiments revealed that the proposed implemented circuits achieve delay time improving by 27.91% and 33.27% for Hamming (15, 11) and Convolutional (2, 1, 3) codes, respectively. The simple proposed RSC encoder performs better than the Convolutional (2, 1, 3) due to shorter constraint length and less H/W complexity. On the other hand, the Transistor Count-Hardware (TC-H/W) is enhanced by 50% for the Block and Convolutional encoding based on employing area-efficient FS-GDI-XOR gate. From the analysis of the results, the proposed designs achieve efficient power and delay optimization of Hamming and Convolutional encoding utilizing the FS-GDI logic style. Also, it is clear that the characteristics of the proposed encoding circuits are vital and effective in the digital signal processing circuits and encoding/decoding schemes implementation in low-power wireless networks.

Wrap-Around Based Frequency-Varying Viterbi Detection for Windowed OFDM Systems in Doubly Selective Fading Channels

In doubly selective fading channels, the conventional orthogonal frequency division multiplexing (OFDM) system suffers high inter-carrier interferences (ICIs). To deal with the ICIs, combining the time-domain windowing and Viterbi detection, we propose a wrap-around based frequency-varying Viterbi detection scheme for windowed OFDM systems. In a doubly selective fading channel, we theoretically derive the input-output relationship of windowed OFDM systems. It is observed that by selecting a proper window function, the equivalent channel matrix is sparse. Therefore, the severe ICIs can be significantly mitigated by our proposed wrap-around based frequency-varying Viterbi detection scheme. It is shown by simulation results that our proposed scheme achieves the performance very close to that of the maximum likelihood detector, even when the normalized Doppler frequency is as high as 0.667. Furthermore, the computational complexity of our proposed detection method is linear to the number of subcarriers, given the maximum iteration times and the constraint length.

Breaking the Computational Bottleneck: Probabilistic Optimization of High-Memory Spatially-Coupled Codes

Spatially-coupled (SC) codes, known for their threshold saturation phenomenon and low-latency windowed decoding algorithms, are ideal for streaming applications and data storage systems. SC codes are constructed by partitioning an underlying block code, followed by rearranging and concatenating the partitioned components in a convolutional manner. The number of partitioned components determines the memory of SC codes. In this paper, we investigate the relation between the performance of SC codes and the density distribution of partitioning matrices. While adopting higher memories results in improved SC code performance, obtaining finite-length, high-performance SC codes with high memory is known to be computationally challenging. We break this computational bottleneck by developing a novel probabilistic framework that obtains (locally) optimal density distributions via gradient descent. Starting from random partitioning matrices abiding by the obtained distribution, we perform low-complexity optimization algorithms that minimize the number of detrimental objects to construct high-memory, high-performance quasi-cyclic SC codes. We apply our framework to various objects of interest, from the simplest short cycles, to more sophisticated objects such as concatenated cycles aiming at finer-grained optimization. Simulation results show that codes obtained through our proposed method notably outperform state-of-the-art SC codes with the same constraint length and optimized SC codes with uniform partitioning. The performance gain is shown to be universal over a variety of channels, from canonical channels such as additive white Gaussian noise and binary symmetric channels, to practical channels underlying flash memory and magnetic recording systems.

Dual Networks Based 3D Multi-Person Pose Estimation From Monocular Video.

Monocular 3D human pose estimation has made progress in recent years. Most of the methods focus on single persons, which estimate the poses in the person-centric coordinates, i.e., the coordinates based on the center of the target person. Hence, these methods are inapplicable for multi-person 3D pose estimation, where the absolute coordinates (e.g., the camera coordinates) are required. Moreover, multi-person pose estimation is more challenging than single pose estimation, due to inter-person occlusion and close human interactions. Existing top-down multi-person methods rely on human detection (i.e., top-down approach), and thus suffer from the detection errors and cannot produce reliable pose estimation in multi-person scenes. Meanwhile, existing bottom-up methods that do not use human detection are not affected by detection errors, but since they process all persons in a scene at once, they are prone to errors, particularly for persons in small scales. To address all these challenges, we propose the integration of top-down and bottom-up approaches to exploit their strengths. Our top-down network estimates human joints from all persons instead of one in an image patch, making it robust to possible erroneous bounding boxes. Our bottom-up network incorporates human-detection based normalized heatmaps, allowing the network to be more robust in handling scale variations. Finally, the estimated 3D poses from the top-down and bottom-up networks are fed into our integration network for final 3D poses. To address the common gaps between training and testing data, we do optimization during the test time, by refining the estimated 3D human poses using high-order temporal constraint, re-projection loss, and bone length regularizations. We also introduce a two-person pose discriminator that enforces natural two-person interactions. Finally, we apply a semi-supervised method to overcome the 3D ground-truth data scarcity. Our evaluations demonstrate the effectiveness of the proposed method and its individual components. Our code and pretrained models are available publicly: https://github.com/3dpose/3D-Multi-Person-Pose.

Linear polymer chain diffusion in semi-flexible polymer network: A dissipative particle dynamics study

Linear polymer chains transport in the crowded biological environment is profoundly important to biomedical engineering and nanotechnology. Cytoskeleton, which can be modeled as a semi-flexible polymer network, acts as a barrier when linear polymers diffuse inside the cell. The diffusion of linear polymers with length N in this polymer network is investigated by the dissipative particle dynamics (DPD) in the present study. Rouse theory is applied to analyze the conformational dynamics of the linear polymers based on the numerical results. It is found that the geometric constraint length Na is a crucial parameter to describe the role of the network of the polymer diffusion. Analyses on Rouse modes show that, in a short wavelength regime, the relaxation time obtained in numerical simulation follows the prediction of the Rouse theory. With the increasing wavelength, the linear polymer exhibits a transition from reptation behavior to the spatially homogeneous behavior at critical length scale Na, which is illustrated by different scaling laws dependent on wavelength. Based on the analyses on the Rouse modes and mean square displacements of the linear polymer, we present a non-dimensional conformational dynamics function dependent on time, with which a scaling law is proposed to predict the long time diffusivity of the linear polymer in a semi-flexible polymer network with different mesh sizes. It is shown that the prediction is well consistent with our DPD simulation results.

A novel method for identifying recursive systematic convolutional encoders based on the cuckoo search algorithm

The existing methods for identifying recursive systematic convolutional encoders with high robustness require to test all the candidate generator matrixes in the search space exhaustively. With the increase of the codeword length and constraint length, the search space expands exponentially, and thus it limits the application of these methods in practice. To overcome the limitation, a novel identification method, which gets rid of exhaustive test, is proposed based on the cuckoo search algorithm by using soft-decision data. Firstly, by using soft-decision data, the probability that a parity check equation holds is derived. Thus, solving the parity check equations is converted to maximize the joint probability that parity check equations hold. Secondly, based on the standard cuckoo search algorithm, the established cost function is optimized. According to the final solution of the optimization problem, the generator matrix of recursive systematic convolutional code is estimated. Compared with the existing methods, our proposed method does not need to search for the generator matrix exhaustively and has high robustness. Additionally, it does not require the prior knowledge of the constraint length and is applicable in any modulation type.

Sliding Window Decoding for QC-SC-LDPC Codes Under the Constraint of Implementation Complexity

Sliding window decoding has been employed as the decoding scheme of quasi-cyclic spatially-coupled low-density parity-check (QC-SC-LDPC) codes, with the decoding latency and complexity independent of the coupling length but proportional to the window size. The window size was empirically chosen at least three times the constraint length in previous works, which results in the preference of QC-SC-LDPC codes with small constraint length, to reduce the decoding latency and complexity in practical systems. However, the code design freedom and the decoder throughput are limited for these QC-SC-LDPC codes. In this paper, the optimal window size under the constraint of practical decoding complexity is analyzed employing multi-edge-type density evolution (MET-DE). It can be verified from the MET-DE analytical result that the optimal window size could achieve less than twice the constraint length for several edge spreading patterns, which largely loose this restriction in practical code design. Furthermore, it could be observed that besides the constraint length, the specific structure of the edge spreading pattern could affect the optimal window size as well, and thus the MET-DE analytical method for the optimal window size could effectively guide the design of QC-SC-LDPC codes in practical systems. Then an improved sliding window decoding scheme is proposed, including the optimal choice of window size and a parity-check based early termination scheme. Finally, the performance and robustness of the proposed sliding window decoding scheme for QC-SC-LDPC codes are demonstrated via simulation, with the decoding complexity at most twice as that of the conventional LDPC coded schemes.

Design of ACS Architecture Using FinFET and CNTFET Devices for Low-Power Viterbi Decoder Using Asynchronous Techniques for Digital Communication Systems

Viterbi algorithm is the most popular algorithm used to decode the convolution code, but its computational complexity increases exponentially with the increasing constraint length due to a large number of Trellis transitions. However, high constraint length is necessary to improve the accuracy of the decoding process for the high rate convolution code. In particular, the Add-Compare-Select (ACS) module of the Viterbi Decoder will have large numbers of trellis states and trellis transitions with increased constraint lengths, which give rise to high hardware complexity and large power consumption. As the performance of the Viterbi decoder mainly depends on its efficient implementation of the ACS module, in the literature, several methods are presented for the implementation of ACS for the Viterbi decoder. The methods based on Precharge Half Buffer (PCHB) and Weak Conditioned Half Buffer, Shannon’s decomposition circuits, body-biased pseudo-NMOS logic and Quasi Delay Insensitive (QDI) timing model performance is analyzed. The methods are implemented using CMOS technology. In this paper, FinFET and CNTFET-based ACS implementation is performed. From the analysis, it has been found that the Carbon Nanotube-based implementation is better in performance when compared to the CMOS and FinFET technology. The proposed QDI model and retiming circuits for ACS block operate above 1[Formula: see text]GHz with high driving current and low power.

Improved Block Oriented Unit Memory Convolutional Codes

This paper is concerned with a special class of unit memory convolutional codes (UMCCs), called block oriented UMCCs (BOUMCCs). Distinguished from conventional UMCCs, which usually have small constraint lengths, the BOUMCCs have relatively large constraint lengths. We conduct the performance analysis by assuming a first-order Markov model, which indicates that the performance of the BOUMCCs depends critically on both the error propagation and the sub-frame error rate of the first layer. The error propagation can be alleviated by the use of partial superposition, which is specified by a superposition matrix with a fraction of columns being nulled. Given a superposition fraction, we propose a tree growing and pruning algorithm (TGPA) with a tunable sliding window, which provides a convenient way to trade off the decoding delay and the performance. We also present a structured construction and show by simulation that there is no performance degradation compared with random construction. Numerical results also show that, by taking the TBCCs as basic codes, the performance of BOUMCCs with TGPA is comparable to that of other short codes but with a more flexible construction or a lower complexity.

Implementation of Convolutional Codes with Viterbi Decoding in Satellite Communication Link using Matlab Computational Software

This paper describes the design of convolutional encoders encoding broadband data with coding rates (r) 3/4 and 5/6 at the transmitter, and the Viterbi decoders for the decoding of the encoded data with same coding rates at the receiver. This design uses BPSK (Binary Phase Shift Keying) modulating scheme in an AWGN (Additive White Gaussian Noise) channel in analysing the BER (Bit-error rate) performance of next generation broadband wireless access systems. The constraint length of the convolutional encoder is K=7, with number of states n=2k-1, and the design was implemented using Matlab Computational Software. The higher coding rates of the convolutional encoders were achieved using puncturing codes and de-puncturing codes for the corresponding Viterbi decoders. In satellite network design, it is vital to compliment the system design with the BER requirement of the network. Plots of BER against SNR ( ) were made for the various coding rates and for data rates of 50000 bits and 12000 bits. The results were analysed based on comparison of coding gain obtained for the different coding rates, theoretical BER and calculated BER. In this paper, Matlab simulation of the convolutional encoder and Viterbi decoder shows improved coding gain for higher data rates and data sequences.

List Decoding of Arıkan's PAC Codes.

Polar coding gives rise to the first explicit family of codes that provably achieve capacity with efficient encoding and decoding for a wide range of channels. However, its performance at short blocklengths under standard successive cancellation decoding is far from optimal. A well-known way to improve the performance of polar codes at short blocklengths is CRC precoding followed by successive-cancellation list decoding. This approach, along with various refinements thereof, has largely remained the state of the art in polar coding since it was introduced in 2011. Recently, Arıkan presented a new polar coding scheme, which he called polarization-adjusted convolutional (PAC) codes. At short blocklengths, such codes offer a dramatic improvement in performance as compared to CRC-aided list decoding of conventional polar codes. PAC codes are based primarily upon the following main ideas: replacing CRC codes with convolutional precoding (under appropriate rate profiling) and replacing list decoding by sequential decoding. One of our primary goals in this paper is to answer the following question: is sequential decoding essential for the superior performance of PAC codes? We show that similar performance can be achieved using list decoding when the list size L is moderately large (say, ). List decoding has distinct advantages over sequential decoding in certain scenarios, such as low-SNR regimes or situations where the worst-case complexity/latency is the primary constraint. Another objective is to provide some insights into the remarkable performance of PAC codes. We first observe that both sequential decoding and list decoding of PAC codes closely match ML decoding thereof. We then estimate the number of low weight codewords in PAC codes, and use these estimates to approximate the union bound on their performance. These results indicate that PAC codes are superior to both polar codes and Reed–Muller codes. We also consider random time-varying convolutional precoding for PAC codes, and observe that this scheme achieves the same superior performance with constraint length as low as .

Construction of Time Invariant Spatially Coupled LDPC Codes Free of Small Trapping Sets

In this paper, we propose a design technique for the construction of variable-regular time-invariant spatially-coupled low-density parity-check (SC-LDPC) codes with small constraint length and low error floor. The proposed technique reduces the error floor by imposing simple constraints on the short cycles in the code's Tanner graph, which in turn, result in the elimination of the most dominant trapping sets of the code. In some cases, we also derive lower bounds on the syndrome former memory for satisfying such constraints. The designed codes are superior to the state-of-the-art in terms of error floor performance and/or decoding complexity and latency.

Wave Pipelined VLSI Architecture for a Viterbi Decoder Using Self Reset Logic with 0.65nm Technology

In 3G mobile terminals the Viterbi Decoder consumes approximately one third of the power consumption of a base band mobile transceiver. Viterbi decoders employed in digital wireless communications are complex and dissipate large power. A low power Viterbi decoder is designed in circuit level using self reset logic and wave pipelining technique is implemented for high speed operation. The Viterbi decoder consists of four units like branch metric unit, add compare and select unit and the survivor path memory unit. All these units are designed using the self reset logic and wave pipelining, and simulated with its layout using MICROWIND TOOL in the 0.65nm technology, 1.8V Vdd and at a frequency of 10GHz. The simulation result shows that the power consumption is reduced by 70.55% and the speed of the circuit is increased by 45.83% compared to the Single Rail Domino Logic for constraint length of K =3 to 7.

Hot tearing behavior of NZ30K Mg alloy under progressive solidification

Progressive solidification is usually considered an effective strategy to reduce the hot tearing susceptibility of a cast component. In this study, special constrained plate castings with progressive changes in cross-section were designed, which enabled progressive solidification. The hot tearing behavior of a newly developed NZ30K Mg alloy (Mg-3.0Nd-0.2Zn-Zr, wt.%) was studied under progressive solidification using various mold temperature distributions and constraint lengths. Of these, a homogeneous mold temperature distribution is found to be the best option to avoid hot tearing, followed by a local low mold temperature distribution (with a chiller), then a gradient mold temperature distribution. Unexpectedly, compared with the homogeneous mold temperature distribution, adding a chiller does not provide any further reduction in the hot tearing susceptibility of the NZ30K Mg alloy. A high mold temperature and a short constraint length increase the hot tearing resistance of cast Mg alloys. Progressive solidification is not a sufficient and necessary condition to avoid the formation of hot tearing. The two key factors that determine the occurrence of hot tearing under progressive solidification are the maximum cooling rate and the constraint length. Decreasing these values can reduce the incidence of hot tearing.

Information rate analysis of ASK-based molecular communication systems with feedback

In this paper we develop lower and upper bounds on the capacity of an amplitude shift keying (ASK)-based molecular communication (MC) system with feedback. Analyzing the effect of feedback on the performance of MC is motivated by the growing use of feedback in controlling drug delivery. Based on causal knowledge of the number of transmitted and received molecules, the input probability of symbols is adapted so as to maximize directed information in the molecular communication channel. We considered one dimensional channel with drift velocity caused by blood flow. In our system, molecules propagate in a fluid with a drift velocity; the receiver absorbs the molecule unless it is saturated (saturation models a limit on ligand binding). The input is limited by a toxicity constraint of injected molecules. We also study the effects of feedback on the achievable information rates in terms of the time delay in receiving the feedback. We show that, especially for higher values of toxicity constraint and sequence length, feedback, in terms of causal knowledge of the number of delivered molecules, improves performance of ASK-based molecular communication.

Analytical investigation of rectangular steel pipe shear connector

Rectangular steel pipe (RSP) which is a new kind of connector in a composite slab of steel–concrete has been proposed. Research on the shear properties of RSPs is limited, but such data are needed if RSPs are to be used as shear connectors in composite slabs. Here, a nonlinear finite-element (FE) model that accounts for the nonlinear material properties and the RSP–concrete interface is proposed. A comparison between the finite element method (FEM) results and the static push-out test data showed good agreement in terms of shear stiffness, shear capacity, and failure mode. This verified FE model was then used in a parametric study to investigate the effects of constraint in push-out test, concrete stiffness, concrete compressive strength, RSP dimensions, RSP thickness, and RSP welding length on RSP shear capacity. The analytical results showed that constraint, concrete strength, RSP dimensions, RSP thickness, and welding length all affected RSP shear capacity, whereas concrete stiffness showed light effects. The shear capacity obtained using the FE model was compared with the calculated result using the Japan Society of Civil Engineers (JSCE) equations for the shear capacity of L-shaped channels.

Error Correction LDPC Encoder with Bit-Flipping Decoder

The communication method conveys information from the transmitter to the receiver over an intermediate medium. The competence of received information is determined by medium and clatter. There is a great need for robust error correction techniques with ever accumulative usage of wireless expedients such as portable electronic items and broadband modems. Wireless communication schemes depend on advanced error rectification techniques for their suitable functioning. Hence transferring and getting information with less or without error, while utilizing the accessible bandwidth is a foremost proposal, project necessities for present digital wireless communication systems comprising of high throughput, low power consumption, and less area. In this article, the functional analysis is performed for error control coding technique i.e., Low-Density-Parity-Check (LDPC) coding in terms of device utilization and power. The functional verification and the simulation are performed using Verilog HDL. Error correction techniques such as Linear block codes, RS codes, Convolutional codes are employed to implement forward error correction but as the constraint length increases the design of decoders becomes more complex. LDPC is a type of block code that is capable of correcting random errors. They are popular because of their eminent detection and correction capabilities. LDPC encoding technique provides high throughput and aims to improve BER when compared to conventional techniques. The decoding of LDPC codeword employs the Message Passing Algorithm, where each bit in a codeword is allocated to special nodes. The performance of these codes is hence better and easily attains Shannon’s limit than the previously used codes. Synthesis for the technique is performed and utilization reports are derived from the synthesis in Vivado.