Hardware implementation of non-binary turbo code for DVB/RCS

Double binary convolutional turbo codes, using Circular Recursive Systematic Convolutional (CRSC) codes as component codes, have been shown to outperform binary turbo codes. These codes are adopted in the Digital Video Broadcasting--Return Channel via Satellite (DVB-RCS) standard. The outstanding coding performance of these codes intrigues the investigation of hardware implementation issues. In this thesis, first a simplified Max_Log_MAP algorithm is derived for the Non-binary convolutional turbo code, and then different aspects of the implementation issues of the decoder with VLSI are explored. In addition, a complete decoder VLSI design of non-binary convolutional turbo code for DVB/RCS will be presented. After discussing several quantization and normalization schemes, a new optimal renormalization approach will be proposed. With this new approach, the decoder can be speeded up considerably. In order to save area, a practical simplification method of branch metric calculation is introduced, which makes the whole design much more efficient. From an architectural point of view, an optimal full pipelined structure is designed with the forward path metric and backward path metric recursive circuits being optimized for speed and other functions including concise interleaver generation, data input, branch metric calculation being optimized for area. In the last part of this thesis, another pipelined area saving method is proposed. The design is modeled in Very high speed integrated circuit Hardware Description Language (VHDL) and synthesized on a single chip FPGA (Xilinx Virtex-E). According to the RTL level and gate level simulation results and the in-chip test result, the decoder can work up to 7 Mbits/s data rate at 6 iterations with VirtexE FPGA

Engineering the energy gap near the valence band edge in Mn-incorporated Cu3Ga5Te9 for an enhanced thermoelectric performance

Cu3Ga5Te9-based compounds Cu3-xGa5MnxTe9 (x=0-0.2) with Mn substitution for Cu have been synthesized. The engineered energy gap (∆EA) between impurity and valence band is reduced from 44.4 meV at x=0 to 25.7 meV at x=0.1, which is directly responsible for the reduction of potential barrier for thermal excitation of carriers and enhancement in carrier concentration. However, the Seebeck coefficient shows an increasing tendency with the increasing of determined Hall carrier concentration (n). This anomalous behavior suggests that the Pisarenko plots under assumed effective masses do not fit the current relationship between the Seebeck coefficient and carrier density. With the combination of enhanced electrical conductivities and reduced thermal conductivities at high temperatures, the maximum thermoelectric (TE) figure of merit (ZT) of 0.81 has been achieved at 804 K with x=0.1, which is about 1.65 and 2.9 times the value of current and reported intrinsic Cu3Ga5Te9. The remarkable improvement in TE performance proves that we have succeeded in engineering the energy gap near the valence band edge upon Mn incorporation in Cu3Ga5Te9

Engineering band structure via the site preference of Pb2+ in the In+ site for enhanced thermoelectric performance of In6Se7

Although binary In-Se based alloys as thermoelectric (TE) candidates are of interests in recent years, little attention has been paid into In6Se7 based compounds. With substituting Pb in In6Se7, the preference of Pb2+ in the In+ site has been observed, allowing the Fermi level (Fr) shift towards the conduction band and the localized state conduction becomes dominated. Consequently, the Hall carrier concentration (nH) has been enhanced significantly with the highest nH value being about 2~3 orders of magnitude higher than that of Pb-free sample. Meanwhile, the lattice thermal conductivity (κL) tends to be reduced as nH value increases, owing to an increased phonon scattering on carriers. As a result, a significantly enhanced TE performance has been achieved with the highest TE figure of merit (ZT) of 0.4 at ~850 K. This ZT value is 27 times that of intrinsic In6Se7 (ZT=0.015 at 640 K), which proves a successful band structure engineering through site preference of Pb in In6Se7

The role of excess Sn in Cu4Sn7S16 for modification of the band structure and a reduction in lattice thermal conductivity

In this work, we have investigated the band structures of ternary Cu4Sn7+xS16 (x = 0–1.0) compounds with an excess of Sn, and examined their thermoelectric (TE) properties. First principles calculations reveal that the excess Sn, which exists as Sn2+ and is preferentially located at the intrinsic Cu vacancies, unpins the Fermi level (Fr) and allows Fr to enter the conduction band (CB) at x = 0.5. Accordingly, the Hall carrier concentration (nH) is enhanced by about two orders of magnitude when the x value increases from x = 0 to x = 0.5. Meanwhile, the lattice thermal conductivity (κL) is reduced significantly to 0.39 W K−1 m−1 at 893 K, which is in reasonably good agreement with the estimation using the Callaway model. As a consequence, the dimensionless TE figure of merit (ZT) of the compound Cu4Sn7+xS16 with x = 0.5 reaches 0.41 at 863 K. This value is double that of the stoichiometric Cu4Sn7S16, proving that excess Sn in Cu4Sn7S16 is beneficial for improving the TE performance

Significantly Enhanced Thermoelectric Performance of γ-In2Se3 through Lithiation via Chemical Diffusion

γ-In2Se3 is selected as a thermoelectric candidate because it has a unique crystal structure and thermal stability at relatively high temperatures. In this work we have prepared lithiated γ-In2Se3 through chemical diffusion and investigated its band structures and thermoelectric performance. After lithiation of γ-In2Se3 in lithium acetate (CH3COOLi) solution at 50oC, we have observed a high Hall carrier concentration (nH) up to ≤1.71×1018 cm-3 at room temperature (RT), which is about ∼4 orders of magnitude compared to that of pristine γ-In2Se3. The enhancement in nH is directly responsible for the remarkable improvement in electrical conductivity, and can be elucidated as the Fermi level (Fr) unpinning and moving towards the conduction band (CB) through the dominant interstitial occupation of Li+ in the γ-In2Se3 lattice. Combined with the minimum lattice thermal conductivity (κL=0.30-0.34 WK-1m-1) at ~923 K, the highest ZT value of 0.62-0.67 is attained, which is about 9-10 times that of pristine γ-In2Se3, proving that the lithiation in γ-In2Se3 is an effective approach on the improvement of the thermoelectric performance

Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network

Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perform poorly in real-world super-resolution tasks, where the paired high-resolution and low-resolution images are unavailable and the low-resolution images are degraded by complicated and unknown kernels. To break these limitations, we propose the Unsupervised Bi-directional Cycle Domain Transfer Learning-based Generative Adversarial Network (UBCDTL-GAN), which consists of an Unsupervised Bi-directional Cycle Domain Transfer Network (UBCDTN) and the Semantic Encoder guided Super Resolution Network (SESRN). First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain. Second, the SESRN has the ability to super-resolve the approximated real-like LR image to a photo-realistic HR image. Extensive experiments on unpaired real-world image benchmark datasets demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods.Comment: 12 pages, 5 figures,3 tables. This work is submitted to IEEE Transactions on Systems, Man, and Cybernetics: Systems (2022). It's under review by IEEE Transactions on Systems, Man, and Cybernetics: Systems for no

Enhancing thermoelectric performance of Cu3SnS4-based solid solutions through coordination of the Seebeck coefficient and carrier concentration

Improving the thermoelectric (TE) performance of Cu3SnS4 is challenging because it exhibits a metallic behavior, therefore, a strategy should be envisaged to coordinate the carrier concentration (nH) and Seebeck coefficient (α). The coordination in this work has been realized through the Fermi level (Ef) unpinning and shifting towards the conduction band (CB) via addition of excess Sn in Cu3SnS4. As a result, the solid solution Cu3Sn1+xS4 (x = 0.2) has a moderate α (178.0 μV K−1) at 790 K and a high nH (1.54 × 1021 cm−3) value. Along with the lowest lattice thermal conductivity κL (0.39 W K−1 m−1) caused by the increased phonon scattering by carriers, the highest ZT value of 0.75 is attained at ∼790 K. This value is 2.8 times that of the stoichiometric Cu3SnS4, and stands among the highest for ternary Cu–Sn–S sulfide thermoelectrics at the corresponding temperatures. More importantly, this approach used in the case of ternary Cu3SnS4 provides a guidance or reference to improve the TE performance of other materials

Extended Target Recognition in Cognitive Radar Networks

We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches