Design methodology followed for the hardware. by R. Sakthivel (2589547)

“…<div><p>Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. …”

Block diagram for 10-bit input. by R. Sakthivel (2589547)

“…<div><p>Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. …”

Comparative graph depicting worst case delay. by R. Sakthivel (2589547)

“…<div><p>Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. …”

Comparative results with state of art design. by R. Sakthivel (2589547)

“…<div><p>Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. …”

Comparative graph depicting area used. by R. Sakthivel (2589547)

“…<div><p>Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. …”

Complete layout of the proposed architecture. by R. Sakthivel (2589547)

“…<div><p>Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. …”

Comparative graph depicting bit usage. by R. Sakthivel (2589547)

“…<div><p>Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. …”

The MAPPO adopts a mode of centralized training with decentralized execution, where multiple agents interact independently with the environment. by Hongjie Zhang (136127)

Parameter estimates of mixed generalized Gaussian distribution for modelling the increments of electroencephalogram data by Zoe Salinger (19090613)

“…MixGGD was fitted to each channel of every child's recording separately, hence for each channel a total of 12 parameter estimates were obtained.  </p><p>The data is presented in a matrix form (dimension 79*228) in a .csv format and consists of 79 rows where the first row is a header row which contains the names of the variables and the subsequent 78 rows represent parameter estimates of one instance (i.e. one child, without identifiers that could be related back to a specific child). …”

Integrated Error Correction to Enhance Efficiency of Digital Data Storage Based on DNA Nanostructures by Cuiping Mao (1474171)

“…However, errors during DNA reading pose challenges to data integrity. Conventional error-correcting codes add redundancy during encoding to ensure data integrity, thereby reducing storage density and increasing costs. …”

Data Availability for Barrier Island Response to Energetic Storms: a Global View by Valeria Fanti (14857549)

“…</p><p dir="ltr">- Mean overtopping event duration (s): calculated by first identifying the start and end times of each overtopping event (using a peak detection algorithm on the run-up data). The duration of each event was computed as the difference between the start and end times, and the average of these durations was then taken to give the mean overtopping event duration in seconds.…”

Supporting data for "Entanglement Microscopy: Tomography and Entanglement Measures via Quantum Monte Carlo" by Menghan Song (13008894)

“…Raw data for Ising reduced density matrices generated by the quantum Monte Carlo algorithm at 1D and 2D is provided here. …”

Heavy-load transfer steel platform. by Meijun Shang (22806461)

“…The load-bearing mechanism of the proposed steel platform was analyzed theoretically, and finite element analysis (FEA) was employed to evaluate the stresses and deflections of key members. …”

Traditional scaffolding reinforcement system. by Meijun Shang (22806461)

“…The load-bearing mechanism of the proposed steel platform was analyzed theoretically, and finite element analysis (FEA) was employed to evaluate the stresses and deflections of key members. …”

Iteration curve of the optimization process. by Meijun Shang (22806461)

“…The load-bearing mechanism of the proposed steel platform was analyzed theoretically, and finite element analysis (FEA) was employed to evaluate the stresses and deflections of key members. …”

Variability measurement and stability analysis. by Danqiangyu Zhou (22146196)

“…The improved decision tree algorithm exhibits consistent performance across varying data volumes, achieving an accuracy exceeding 93% when handling 15,000 to 50,000 sentences, surpassing comparable state-of-the-art algorithms. …”

Model pseudocode. by Danqiangyu Zhou (22146196)

“…The improved decision tree algorithm exhibits consistent performance across varying data volumes, achieving an accuracy exceeding 93% when handling 15,000 to 50,000 sentences, surpassing comparable state-of-the-art algorithms. …”

Data Sheet 1_Unsupervised method for representation transfer from one brain to another.docx by Daiki Nakamura (20349885)

“…We defined the requirements to enable such brain representation transfer and developed an algorithm that distills the assumption of common similarity structure across the brain datasets into a rotational and reflectional transformation across low-dimensional hyperspheres using encoders for non-linear dimensional reduction. …”

Biomass data and prediction model of rice heading stage in Haitang district, Sanya City, Hainan Province. by Wanyi He (14164810)

“…After rigorous model screening and validation, the Back Propagation(BP) model is finally identified as the best algorithm model, whose validation set R² value is 0.689, root mean square error (RMSE) is 3.956Mg/ha, and relative Root mean square error (RRMSE) is 22.050%. …”

LSTM model’s equations. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”