Massive machine type communication (mMTC) has attracted new coding schemes optimized for reliable short message transmission. In this paper, a novel deep learning-based near-orthogonal superposition (NOS) coding scheme is proposed to transmit short messages in multiple-input multiple-output (MIMO) channels for mMTC applications. In the proposed MIMO-NOS scheme, a neural network-based encoder is optimized via end-to-end learning with a corresponding neural network-based detector/decoder in a superposition-based auto-encoder framework including a MIMO channel. The proposed MIMO-NOS encoder spreads the information bits to multiple near-orthogonal high dimensional vectors to be combined (superimposed) into a single vector and reshaped for the space-time transmission. For the receiver, we propose a novel looped$K$-best tree-search algorithm with cyclic redundancy check (CRC) assistance to enhance the error correcting ability in the block-fading MIMO channel. For a comprehensive understanding of the proposed MIMO-NOS scheme, we further quantify the gain from individual components/modules in the framework, and analyze the decoding complexity measured by the floating point operations (FLOPs). Simulation results show the proposed MIMO-NOS scheme outperforms maximum likelihood (ML) MIMO detection combined with a polar code with CRC-assisted list decoding by 1 – 2 dB in various MIMO systems for short (32 – 64 bit) message transmission.

Although feedback cannot increase the channel capacity of memoryless channels, it can enhance the error rate performance and/or shorten the codeword length for the target performance. This work is based on an early work by Viterbi in 1965 that utilizes instantaneous feedback for reliable uncoded communications. We build on this work by incorporating convolutional codes as a new variable-symbol-length digital communication scheme using instantaneous feedback. In the proposed system, called Opportunistic Symbol Length Adaptation (OSLA), the symbol length opportunistically adapts to the noise realization observed within a sub-symbol interval to minimize the packet/codeword error rate. It is shown that the proposed OSLA scheme combined with tail-biting convolutional codes or turbo codes outperforms state-of-the-art non-feedback codes as well as a deep learning-based feedback scheme with up to 1.5 dB gain in noiseless and noisy feedback channels.

In this paper, we introduce Hyper-Dimensional Modulation (HDM) for massive machine-type communications (mMTC). HDM enables robust communication of short packets by spreading information bits across many elements in a hyper-dimensional vector and superimposing a set of such non-orthogonal vectors. The proposed CRC-aided K-best decoding algorithm for HDM can achieve a very low packet error rate (PER) in additive white Gaussian noise (AWGN) channels for short packets. Furthermore, extended decoding algorithms are proposed to combat overwhelming interference in an mMTC network. Comprehensive simulation and real-world experiment results show that HDM outperforms sparse superposition codes in AWGN channels and state-of-the-art short codes such as polar and tail-biting convolutional codes in interference-heavy channels for short packet transmissions.

Massive machine type communication (mMTC) has attracted new coding schemes optimized for reliable short message transmission. In this paper, a novel deep learning based near-orthogonal superposition (NOS) coding scheme is proposed for reliable transmission of short messages in the additive white Gaussian noise (AWGN) channel for mMTC applications. Similar to recent hyper-dimensional modulation (HDM), the NOS encoder spreads the information bits to multiple near-orthogonal high dimensional vectors to be combined (superimposed) into a single vector for transmission. The NOS decoder first estimates the information vectors and then performs a cyclic redundancy check (CRC)-assisted K-best tree-search algorithm to further reduce the packet error rate. The proposed NOS encoder and decoder are deep neural networks (DNNs) jointly trained as an auto-encoder and decoder pair to learn a new NOS coding scheme with near-orthogonal codewords. Simulation results show the proposed deep learning-based NOS scheme outperforms HDM and Polar code with CRC-aided list decoding for short (32-bit) message transmission.

Considerable sequence data are produced in genome annotation projects that relate to molecular levels, structural similarities, and molecular and biological functions. In structural genomics, the most essential task involves resolving protein structures efficiently with hardware or software, understanding these structures, and assigning their biological functions. Understanding the characteristics and functions of proteins enables the exploration of the molecular mechanisms of life. In this paper, we examine the problems of protein classification. Because they perform similar biological functions, proteins in the same family usually share similar structural characteristics. We employed this premise in designing a classification algorithm. In this algorithm, auxiliary graphs are used to represent proteins, with every amino acid in a protein to a vertex in a graph. Moreover, the links between amino acids correspond to the edges between the vertices. The proposed algorithm classifies proteins according to the similarities in their graphical structures. The proposed algorithm is efficient and accurate in distinguishing proteins from different families and outperformed related algorithms experimentally.

It is well known that although feedback cannot increase the channel capacity of memoryless channels, it can enhance reliability or shorten codeword length. This work is based on an early result by Viterbi in 1965 that utilizes instantaneous feedback for reliable communications. We build on this work by incorporating (tail-biting) convolutional codes and designing a system where the decoder interacts with the transmitter by sending feedback during the decoding process. The proposed system is called Opportunistic Symbol Length Adaptation (OSLA), in which the symbol length opportunistically adapts to noise realization of each symbol to ensure that the target reliability is achieved. It is shown that, combined with tail-biting convolutional codes, the proposed scheme outperforms state-of-the-art non-feedback codes, as well as a recently proposed deep learning-based feedback scheme with up to 1.5 dB gain in noise-less and noisy feedback channels.

Each fall, millions of monarch butterflies across the northern US and Canada migrate up to 4,000 km to overwinter in the exact same cluster of mountain peaks in central Mexico. To track monarchs precisely and study their navigation, a monarch tracker must obtain daily localization of the butterfly as it progresses on its 3-month journey. And, the tracker must perform this task while having a weight in the tens of milligram (mg) and measuring a few millimeters (mm) in size to avoid interfering with monarch's flight. This paper proposes mSAIL, 8 × 8 × 2.6 mm and 62 mg embedded system for monarch migration tracking, constructed using 8 prior custom-designed ICs providing solar energy harvesting, an ultra-low power processor, light/temperature sensors, power management, and a wireless transceiver, all integrated and 3D stacked on a micro PCB with an 8 × 8 mm printed antenna. The proposed system is designed to record and compress light and temperature data during the migration path while harvesting solar energy for energy autonomy, and wirelessly transmit the data at the overwintering site in Mexico, from which the daily location of the butterfly can be estimated using a deep learning-based localization algorithm. A 2-day trial experiment of mSAIL attached on a live butterfly in an outdoor botanical garden demonstrates the feasibility of individual butterfly localization and tracking.

Massive Machine Type Communication (mMTC) enables novel applications but its dense deployment and short packet properties lead to new challenges for physical layer design. This paper investigates hyper-dimensional modulation (HDM), a recently proposed novel non-orthogonal modulation, for short packet communications with superior interference tolerance in mMTC. We propose a new tree-based K-best decoding algorithm for HDM to improve the packet error rate performance in both additive white Gaussian noise (AWGN) and interference-limited scenarios. Simulation results show that the proposed algorithm can achieve 0.5 - 4 dB gain in AWGN and interference-limited channels compared to the Polar code with CRC (cyclic redundancy check)-aided list decoding.

In this paper, we proposed an implementation of fall detection system on Raspberry Pi with the Intel̅ Neural Compute Stick 2. Firstly, we used skeleton extraction algorithm to obtain the important skeleton information. Secondly, we proposed a robustness neural network using pruning method to reduce the parameter and calculation and combined with the neural compute stick to execute the module. The final result will transfer to the Raspberry Pi to display on the monitor. As a result, it can be implemented on the smaller embedded system.

Ultra Narrowband (UNB) has recently received great attention for its potential to realize ultra- reliable, massive scale Low Power Wide Area Networks (LPWAN). Elaborate frequency planning and multiple access schemes have been regarded as an essential part of LPWAN because random frequency- and time-domain ALOHA accesses lead to significant network performance degradation due to inevitable packet collisions. In this paper, we propose a novel network scheme based on non-orthogonal modulation and multiple access (NOMMA) that is tolerant to packet collisions. The proposed scheme uses hyper-dimensional modulation (HDM) to outperform conventional orthogonal modulation and multiple access schemes with and without prior knowledge of interference in highly congested network scenarios. Simulation results show that HDM based NOMMA can achieve 70% higher network throughput than a conventional orthogonal modulation and multiple access scheme.