Rapid detection of multi-QR codes based on multistage stepwise discrimination and a compressed mobilenet.

Poor real-time performance in multi-QR codes detection has been a bottleneck in QR code decoding based Internet-of-Things (IoT) systems. To tackle this issue, we propose in this paper a rapid detection approach, which consists of Multistage Stepwise Discrimination (MSD) and a Compressed MobileNet. Inspired by the object category determination analysis, the preprocessed QR codes are extracted accurately on a small scale using the MSD. Guided by the small scale of the image and the end-to-end detection model, we obtain a lightweight Compressed MobileNet in a deep weight compression manner to realize rapid inference of multi-QR codes. The Average Detection Precision (ADP), Multiple Box Rate (MBR) and running time are used for quantitative evaluation of the efficacy and efficiency. Compared with a few state-of-the-art methods, our approach has higher detection performance in rapid and accurate extraction of all the QR codes. The approach is conducive to embedded implementation in edge devices along with a bit of overhead computation to further benefit a wide range of real-time IoT applications

An Alternative Approach to Nucleic Acid Memory

DNA is a compelling alternative to non-volatile information storage technologies due to its information density, stability, and energy efficiency. Previous studies have used artificially synthesized DNA to store data and automated next-generation sequencing to read it back. Here, we report digital Nucleic Acid Memory (dNAM) for applications that require a limited amount of data to have high information density, redundancy, and copy number. In dNAM, data is encoded by selecting combinations of single-stranded DNA with (1) or without (0) docking-site domains. When self-assembled with scaffold DNA, staple strands form DNA origami breadboards. Information encoded into the breadboards is read by monitoring the binding of fluorescent imager probes using DNA-PAINT super-resolution microscopy. To enhance data retention, a multi-layer error correction scheme that combines fountain and bi-level parity codes is used. As a prototype, fifteen origami encoded with ‘Data is in our DNA!\n’ are analyzed. Each origami encodes unique data-droplet, index, orientation, and error-correction information. The error-correction algorithms fully recover the message when individual docking sites, or entire origami, are missing. Unlike other approaches to DNA-based data storage, reading dNAM does not require sequencing. As such, it offers an additional path to explore the advantages and disadvantages of DNA as an emerging memory material

Neuromorphic Learning Systems for Supervised and Unsupervised Applications

The advancements in high performance computing (HPC) have enabled the large-scale implementation of neuromorphic learning models and pushed the research on computational intelligence into a new era. Those bio-inspired models are constructed on top of unified building blocks, i.e. neurons, and have revealed potentials for learning of complex information. Two major challenges remain in neuromorphic computing. Firstly, sophisticated structuring methods are needed to determine the connectivity of the neurons in order to model various problems accurately. Secondly, the models need to adapt to non-traditional architectures for improved computation speed and energy efficiency. In this thesis, we address these two problems and apply our techniques to different cognitive applications. This thesis first presents the self-structured confabulation network for anomaly detection. Among the machine learning applications, unsupervised detection of the anomalous streams is especially challenging because it requires both detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research need. We present AnRAD (Anomaly Recognition And Detection), a bio-inspired detection framework that performs probabilistic inferences. We leverage the mutual information between the features and develop a self-structuring procedure that learns a succinct confabulation network from the unlabeled data. This network is capable of fast incremental learning, which continuously refines the knowledge base from the data streams. Compared to several existing anomaly detection methods, the proposed approach provides competitive detection accuracy as well as the insight to reason the decision making. Furthermore, we exploit the massive parallel structure of the AnRAD framework. Our implementation of the recall algorithms on the graphic processing unit (GPU) and the Xeon Phi co-processor both obtain substantial speedups over the sequential implementation on general-purpose microprocessor (GPP). The implementation enables real-time service to concurrent data streams with diversified contexts, and can be applied to large problems with multiple local patterns. Experimental results demonstrate high computing performance and memory efficiency. For vehicle abnormal behavior detection, the framework is able to monitor up to 16000 vehicles and their interactions in real-time with a single commodity co-processor, and uses less than 0.2ms for each testing subject. While adapting our streaming anomaly detection model to mobile devices or unmanned systems, the key challenge is to deliver required performance under the stringent power constraint. To address the paradox between performance and power consumption, brain-inspired hardware, such as the IBM Neurosynaptic System, has been developed to enable low power implementation of neural models. As a follow-up to the AnRAD framework, we proposed to port the detection network to the TrueNorth architecture. Implementing inference based anomaly detection on a neurosynaptic processor is not straightforward due to hardware limitations. A design flow and the supporting component library are developed to flexibly map the learned detection networks to the neurosynaptic cores. Instead of the popular rate code, burst code is adopted in the design, which represents numerical value using the phase of a burst of spike trains. This does not only reduce the hardware complexity, but also increases the result\u27s accuracy. A Corelet library, NeoInfer-TN, is implemented for basic operations in burst code and two-phase pipelines are constructed based on the library components. The design can be configured for different tradeoffs between detection accuracy, hardware resource consumptions, throughput and energy. We evaluate the system using network intrusion detection data streams. The results show higher detection rate than some conventional approaches and real-time performance, with only 50mW power consumption. Overall, it achieves 10^8 operations per Joule. In addition to the modeling and implementation of unsupervised anomaly detection, we also investigate a supervised learning model based on neural networks and deep fragment embedding and apply it to text-image retrieval. The study aims at bridging the gap between image and natural language. It continues to improve the bidirectional retrieval performance across the modalities. Unlike existing works that target at single sentence densely describing the image objects, we elevate the topic to associating deep image representations with noisy texts that are only loosely correlated. Based on text-image fragment embedding, our model employs a sequential configuration, connects two embedding stages together. The first stage learns the relevancy of the text fragments, and the second stage uses the filtered output from the first one to improve the matching results. The model also integrates multiple convolutional neural networks (CNN) to construct the image fragments, in which rich context information such as human faces can be extracted to increase the alignment accuracy. The proposed method is evaluated with both synthetic dataset and real-world dataset collected from picture news website. The results show up to 50% ranking performance improvement over the comparison models

VRCodes : embedding unobtrusive data for new devices in visible light

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 97-101).This thesis envisions a public space populated with active visible surfaces which appear different to a camera than to the human eye. Thus, they can act as general digital interfaces that transmit machine-compatible data as well as provide relative orientation without being obtrusive. We introduce a personal transceiver peripheral, and demonstrate this visual environment enables human participants to hear sound only from the location they are looking in, authenticate with proximal surfaces, and gather otherwise imperceptible data from an object in sight. We present a design methodology that assumes the availability of many independent and controllable light transmitters where each individual transmitter produces light at different color wavelengths. Today, controllable light transmitters take the form of digital billboards, signage and overhead lighting built for human use; light-capturing receivers take the form of mobile cameras and personal video camcorders. Following the software-defined approach, we leverage screens and cameras as parameterized hardware peripherals thus allowing flexibility and development of the proposed framework on general-purpose computers in a manner that is unobtrusive to humans. We develop VRCodes which display spatio-temporally modulated metamers on active screens thus conveying digital and positional information to a rolling-shutter camera; and physically-modified optical setups which encode data in a point-spread function thus exploiting the camera's wide-aperture. These techniques exploit how the camera sees something different from the human. We quantify the full potential of the system by characterizing basic bounds of a parameterized transceiver hardware along with the medium in which it operates. Evaluating performance highlights the underutilized temporal, spatial and frequency dimensions available to the interaction designer concerned with human perception. Results suggest that the one-way point-to-point transmission is good enough for extending the techniques toward a two-way bidrectional model with realizable hardware devices. The new visual environment contains a second data layer for machines that is synthetic and quantifiable; human interactions serve as the context.by Grace Woo.Ph.D

Deep R Programming

Deep R Programming is a comprehensive course on one of the most popular languages in data science (statistical computing, graphics, machine learning, data wrangling and analytics). It introduces the base language in-depth and is aimed at ambitious students, practitioners, and researchers who would like to become independent users of this powerful environment. This textbook is a non-profit project. Its online and PDF versions are freely available at . This early draft is distributed in the hope that it will be useful.Comment: Draft: v0.2.1 (2023-04-27

Self-organising an indoor location system using a paintable amorphous computer

This thesis investigates new methods for self-organising a precisely defined pattern of intertwined number sequences which may be used in the rapid deployment of a passive indoor positioning system's infrastructure.A future hypothetical scenario is used where computing particles are suspended in paint and covered over a ceiling. A spatial pattern is then formed over the covered ceiling. Any small portion of the spatial pattern may be decoded, by a simple camera equipped device, to provide a unique location to support location-aware pervasive computing applications.Such a pattern is established from the interactions of many thousands of locally connected computing particles that are disseminated randomly and densely over a surface, such as a ceiling. Each particle has initially no knowledge of its location or network topology and shares no synchronous clock or memory with any other particle.The challenge addressed within this thesis is how such a network of computing particles that begin in such an initial state of disarray and ignorance can, without outside intervention or expensive equipment, collaborate to create a relative coordinate system. It shows how the coordinate system can be created to be coherent, even in the face of obstacles, and closely represent the actual shape of the networked surface itself. The precision errors incurred during the propagation of the coordinate system are identified and the distributed algorithms used to avoid this error are explained and demonstrated through simulation.A new perimeter detection algorithm is proposed that discovers network edges and other obstacles without the use of any existing location knowledge. A new distributed localisation algorithm is demonstrated to propagate a relative coordinate system throughout the network and remain free of the error introduced by the network perimeter that is normally seen in non-convex networks. This localisation algorithm operates without prior configuration or calibration, allowing the coordinate system to be deployed without expert manual intervention or on networks that are otherwise inaccessible.The painted ceiling's spatial pattern, when based on the proposed localisation algorithm, is discussed in the context of an indoor positioning system

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

Ageing with Smartphones in Urban China: From the cultural to the digital revolution in Shanghai

If we want to understand contemporary China, the key is through understanding the older generation. This is the generation in China whose life courses almost perfectly synchronised with the emergence and growth of the ‘New China’ under the rule of the Communist Party (1949). People in their 70s and 80s have double the life expectancy of their parents’ generation. The current oldest generation in Shanghai was born in a time when the average household could not afford electric lights, but today they can turn their lights off via their smartphone apps. Based on 16-month ethnographic fieldwork in Shanghai, Ageing with Smartphones in Urban China tackles the intersection between the ‘two revolutions’ experienced by the older generation in Shanghai: the contemporary smartphone-based digital revolution and the earlier communist revolutions. We find that we can only explain the smartphone revolution if we first appreciate the long-term consequences of these people’s experiences during the communist revolutions. The context of this book is a wide range of dramatic social transformations in China, from the Cultural Revolution to the individualism and Confucianism in Digital China. Supported by detailed ethnographic material, the observations and analyses provide a panoramic view of the social landscape of contemporary China, including topics such as the digital and everyday life, ageing and healthcare, intergenerational relations and family development, community building and grassroots organizations, collective memories and political attitudes among ordinary Chinese people

Second year technical report on-board processing for future satellite communications systems

Advanced baseband and microwave switching techniques for large domestic communications satellites operating in the 30/20 GHz frequency bands are discussed. The nominal baseband processor throughput is one million packets per second (1.6 Gb/s) from one thousand T1 carrier rate customer premises terminals. A frequency reuse factor of sixteen is assumed by using 16 spot antenna beams with the same 100 MHz bandwidth per beam and a modulation with a one b/s per Hz bandwidth efficiency. Eight of the beams are fixed on major metropolitan areas and eight are scanning beams which periodically cover the remainder of the U.S. under dynamic control. User signals are regenerated (demodulated/remodulated) and message packages are reformatted on board. Frequency division multiple access and time division multiplex are employed on the uplinks and downlinks, respectively, for terminals within the coverage area and dwell interval of a scanning beam. Link establishment and packet routing protocols are defined. Also described is a detailed design of a separate 100 x 100 microwave switch capable of handling nonregenerated signals occupying the remaining 2.4 GHz bandwidth with 60 dB of isolation, at an estimated weight and power consumption of approximately 400 kg and 100 W, respectively