Area and Energy Efficient QCA Based Compact Serial Concatenated Convolutional Code Encoder

Quantum-dot Cellular Automata (QCA) is a transistor-less technology known for its low power consumption and higher clock rate. Serial Concatenated Convolutional Coding (SCCC) encoder is a class of forward error correction. This paper picturizes the implementation of the outer encoder as a (7, 4, 1) Bose Chaudhary Hocquenghem encoder that serves the purpose of burst error correction, a pseudo-random inter-leaver used for permuting of systematic code words and finally the inner encoder which is used for the correction of random errors in QCA. Two different architectures of the SCCC encoder have been proposed and discussed in this study. In the proposed two architectures, the first based on external clock signals whereas the second based on internal clock generation. The sub-blocks outer encoder, pseudo-random inter-leaver and inner encoder of the SCCC encoder are optimized, implemented and simulated using QCADesigner and then integrated to design a compact SCCC encoder. The energy dissipation is computed using QCADesigner-E. The proposed SCCC encoder reduced the total area by 46% and energy dissipation by 50% when compared to the reference SCCC encoder. The proposed encoders are more efficient in terms of cell count, energy dissipation and area occupancy respectively.

On the State Approach Representations of Convolutional Codes over Rings of Modular Integers

In this study, we prove the existence of minimal first-order representations for convolutional codes with the predictable degree property over principal ideal artinian rings. Further, we prove that any such first-order representation leads to an input/state/output representation of the code provided the base ring is local. When the base ring is a finite field, we recover the classical construction, studied in depth by J. Rosenthal and E. V. York. This allows us to construct observable convolutional codes over such rings in the same way as is carried out in classical convolutional coding theory. Furthermore, we prove the minimality of the obtained representations. This completes the study of the existence of input/state/output representations of convolutional codes over rings of modular integers.

Bluetooth Low Energy Error Correction Based on Convolutional Coding

Contemporarily, the Internet of Things (IoT) is recently a newly emerging technology for connecting small devices into a platform; the IoT has been an increasingly demanded front-edge technology in terms of connecting different devices using information transmission and storage technology. To adapt to the small capacities of device batteries, Bluetooth Low Energy is adopted as the protocol of communication. However, the existing standards do not have a suitable and specific error correction method. As there is no ideal information transmission channel, there must be errors that occurred during message transmission. The performance and capacity of error correction become decisive factors in evaluating how efficient the IoT communication system performs. This article uses convolutional coding—a better-performing coding scheme than block coding—to correct errors in information transmission and reception on Internet of Things devices. It is better competent to control and correct bit errors in information transmission. To achieve this goal, convolutional coding algorithms devised by Dr Justin Coon at the University of Oxford have been referred to. By simulation using MATLAB, it has been found that the error rate is enhanced significantly for high Signal-to-Noise Ratio (SNR) in convolutional codes compared to uncoded messages.

Magnetic DNA random access memory with nanopore readouts and exponentially-scaled combinatorial addressing

The storage of data in DNA typically involves encoding and synthesizing data into short oligonucleotides, followed by reading with a sequencing instrument. Major challenges include the molecular consumption of synthesized DNA, issues with basecalling errors, and limitations with scaling up read access operations for individual data elements. Addressing these challenges, we describe a DNA storage system called MDRAM (Magnetic DNA-based Random Access Memory) that enables repetitive and efficient readouts of targeted files with nanopore-based sequencing. Through conjugation of synthesized DNA to magnetic beads, we enabled repeated readouts of data while preserving the original DNA analyte and maintaining data readout quality. MDRAM also utilizes an efficient convolutional coding scheme that leverages soft information in raw nanopore sequencing signals to achieve information reading costs comparable to Illumina sequencing despite substantially higher error rates. Finally, we demonstrate a proof-of-concept DNA-based proto-filesystem that enables an exponentially-scalable data address space using only small numbers of targeting primers for assembly and readout.