THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Windmills, cars, and dental restoration demand polymer materials and composites that are easy to process, assemble, and recycle while exhibiting outstanding mechanical, thermal, and chemical resistance. Vitrimers, which are polymer networks able to shuffle chemical bonds through exchange reactions, could address these demands if they were prepared from existing plastics and processed with fast production rates and current equipment. We report the metathesis of dioxaborolanes, which is rapid and thermally robust, and use it to prepare vitrimers from polymers as different as poly(methyl methacrylate), polystyrene, and high-density polyethylene that, although permanently cross-linked, can be processed multiple times by means of extrusion or injection molding. They show superior chemical resistance and dimensional stability and can be efficiently assembled. The strategy is applicable to polymers with backbones made of carbon-carbon single bonds.

High-performance vitrimers from commodity thermoplastics through dioxaborolane metathesis

Dynamic covalent polymer networks have long been recognized. With the initial focus on the unintended impact of dynamic covalent linkages on the viscoelasticity of commercial rubbers, efforts in modern times have transitioned into designing dynamic covalent polymer networks with unique adaptive properties. Whereas self-healing and thermoset reprocessing have been the primary motivations for studying dynamic covalent polymer networks, the recent discovery of the vitrimeric rheological behavior and solid-state plasticity for this type of material have opened up new opportunities in material innovations. This, coupled with the revelation of the dynamic characteristics of commercially relevant polymer building blocks such as esters and urethanes, suggests a promising future for this class of materials.

Dynamic Covalent Polymer Networks: from Old Chemistry to Modern Day Innovations.

Over the past decades, interest in shape memory polymers (SMPs) has persisted, and immense efforts have been dedicated to developing SMPs and their multifunctional composites. As a class of stimuli-responsive polymers, SMPs can return to their initial shape from a programmed temporary shape under external stimuli, such as light, heat, magnetism, and electricity. The introduction of functional materials and nanostructures results in shape memory polymer composites (SMPCs) with large recoverable deformation, enhanced mechanical properties, and controllable remote actuation. Because of these unique features, SMPCs have a broad application prospect in many fields covering aerospace engineering, biomedical devices, flexible electronics, soft robotics, shape memory arrays, and 4D printing. Herein, a comprehensive analysis of the shape recovery mechanisms, multifunctionality, applications, and recent advances in SMPs and SMPCs is presented. Specifically, the combination of functional, reversible, multiple, and controllable shape recovery processes is discussed. Further, established products from such materials are highlighted. Finally, potential directions for the future advancement of SMPs are proposed.

A Review of Shape Memory Polymers and Composites: Mechanisms, Materials, and Applications

Dynamic covalent polymer networks (DCPN) have historically attracted attention for their unique roles in chemical recycling and self-healing, which are both relevant for sustainable societal development. Efforts in these directions have intensified in the past decade with notable progress in newly discovered dynamic covalent chemistry, fundamental material concepts, and extension toward emerging applications including energy and electronic devices. Beyond that, the values of DCPN in discovering/designing functional properties not offered by classical thermoplastic and thermoset polymers have recently gained traction. In particular, the dynamic bond exchangeability of DCPN has shown unparalleled design versatility in various areas including shape-shifting materials/devices, artificial muscles, and microfabrication. Going beyond this basic bond exchangeability, various molecular mechanisms to manipulate network topologies (topological transformation) have led to opportunities to program polymers, with notable concepts such as living networks and topological isomerization. In this review, we provide an overview of the above progress with particular focuses on molecular design strategies for the exploitation of functional material properties. Based on this, we point out the remaining issues and offer perspectives on how this class of materials can shape the future in ways that are complementary with classical thermoplastic and thermoset polymers.

Dynamic Covalent Polymer Networks: A Molecular Platform for Designing Functions beyond Chemical Recycling and Self-Healing.

Generalized multiple maximum scatter difference feature extraction using QR decomposition

A new parameter estimation of LFM signal is presented First it is assumed that delay multiplication and the Fourier transform can be employed to get a rough estimation of the frequency rate of the LFM signal, for determining a approximate range of the LFM signal's rotation angle in the fractional Fourier domain, and then the exact frequency rate and initial frequency are obtained through the optimum seeking method and fractional Fourier transform Simulation experiments show that this method greatly reduces the computational complexity on the premise of accuracy

Parameters estimation of the LFM signal based on the optimum seeking method and fractional Fourier transform

Aiming at the problems of poor signal detection effect caused by many interference factors in large-scale MIMO technology scene, this paper proposes a 5G massive MIMO signal detection algorithm based on deep learning. Firstly, the MIMO system model based on neural network is constructed, and Deep Neural Network (DNN) detection is introduced into the receiver of the traditional MIMO system to obtain the information bits or codewords and channel state information transmitted by transmitters. Then, the end-to-end training method is adopted to make neural network learn the mapping relationship of information bits or codewords transmitted by system transceivers. Furthermore, DNN detector is improved based on Simplified Message Passing Detection (sMPD) algorithm, and the correction factor is updated continuously to optimize network parameters to realize the accurate detection and decoding of the MIMO system. Finally, the proposed algorithm is experimentally analyzed based on the TensorFlow deep learning framework. Experimental results show that when signal-to-noise ratio is 10 dB, the bit error rate and mean square error are lower than 0.005 and 0.1, respectively.

/pdf/5g-massive-mimo-signal-detection-algorithm-based-on-deep-2etsl83h.pdf

5G Massive MIMO Signal Detection Algorithm Based on Deep Learning

In this paper, we introduce a hierarchical resampling (HR) algorithm and architecture for distributed particle filters (PFs). While maintaining the same accuracy as centralized resampling in statistics, the proposed HR algorithm decomposes the resampling step into two hierarchies including intermediate resampling (IR) and unitary resampling (UR), which suits PFs for distributed hardware implementation. Also presented includes a residual cumulative resampling (RCR) method that pipelines and accelerates the UR step. The corresponding architecture, when compared with traditional distributed architectures, eliminates the particle redistribution step, and has such advantages as short execution time and high memory efficiency. The prototype containing 8 PEs has been developed in Xilinx Virtex IV FPGA (XC4VFX100-12FF1152) for the bearings-only tracking (BOT) problem, and the result shows that the input observations can be processed at 37.21 KHz with 8 K particles and a clock speed of 80 MHz.

Hierarchical Resampling Algorithm and Architecture for Distributed Particle Filters

Multiple Maximum scatter difference (MMSD) discriminant criterion is an effective feature extraction method that computes the discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. However, singular value decomposition (SVD) of two times is involved in MMSD, making this method impractical for high dimensional data. In this paper, we propose a novel method for feature extraction and classification based on MMSD criterion, called generalized MMSD (GMMSD), which employs QR decomposition rather than SVD. Unlike MMSD, GMMSD does not require the computation of the whole scatter matrix. Instead, it computes the discriminant vectors from both the range of whitenizated input data matrix and the null space of the within-class scatter matrix. We evaluate the effectiveness of the GMMSD method in terms of classification accuracy in the reduced dimensional space. Our experiments on two facial expression databases demonstrate that the GMMSD method provides favorable performance in terms of both recognition accuracy and computational efficiency.

Ning Zheng

Papers

Generalized multiple maximum scatter difference feature extraction using QR decomposition

Parameters estimation of the LFM signal based on the optimum seeking method and fractional Fourier transform

5G Massive MIMO Signal Detection Algorithm Based on Deep Learning

Hierarchical Resampling Algorithm and Architecture for Distributed Particle Filters

Generalized MMSD feature extraction using QR decomposition