Leveraging gesture-based controls within a remote interface for automated material handling operations presents a promising avenue to optimize experience in outdoor environments. We present a gesture-based interactive system designed to facilitate collaboration between a human operator and a semi-autonomous forklift in material handling processes. Our interface supports commands that include starting the forklift, defining the loading/unloading area and the amount of pallets that need to be transported. We tested and compared the proposed solution to a touch-based User Interface in a field study to explore its effectiveness and identify potential challenges for implementation and adoption in industrial real-world settings.

As IoT systems have increased the number of deployed embedded devices drastically and most of these devices are used in safety or security critical environments, the education of embedded software engineers is more important than ever. A critical part of their education is the development of their intuition for secure and safe software. In this paper11This research was funded by the city of Vienna (MA-23 call 21, project no. 9). we present an evaluation system used to generate fast and accurate feedback for student submission in, but not limited to, embedded software development courses. The system can be used as a first feedback loop to outline to the students where problems exist in their code and give them the opportunity to analyze and correct their errors. These extra steps ensure that the students can and will be notified early about their mistakes and can search for correct solutions, supporting the student's learning process. We present the implementation of the system and analyze its deployment in a microcontroller software development lecture. This analysis was done by means of surveys of the students and lecturers as well as a statistical analysis of the student submissions. The results show that the students made use of this extra features and even would prefer to have this feedback in other software development lectures as well.

Polar codes are designed for parallel binary-input additive white Gaussian noise (BiAWGN) channels with an average power constraint. The two main design choices are: the mapping between codeword bits and channels of different quality, and the power allocation under the average power constraint. Information theory suggests to allocate power such that the sum of mutual information (MI) terms is maximized. However, a power allocation specific to polar codes shows significant gains.

This work shows how non-binary low-density parity-check codes over GF(2^p) can be combined with probabilistic amplitude shaping (PAS) (Böcherer, et al., 2015), which combines forward-error correction with non-uniform signaling for power-efficient communication. Ultra-sparse low-density parity-check codes over GF(64) and GF(256) gain 0.6 dB in power efficiency over state-of-the-art binary LDPC codes at a spectral efficiency of 1.5 bits per channel use and a blocklength of 576 bits. The simulation results are compared to finite length coding bounds and complemented by density evolution analysis.

A protograph-based low-density parity-check (LDPC) code design technique for bandwidth-efficient coded modulation with probabilistic shaping is presented. The approach jointly optimizes the LDPC code node degrees and the mapping of the coded bits to the bit-interleaved coded modulation (BICM) bit-channels. For BICM with uniform inputs and for BICM with probabilistic shaping, binary-input symmetric-output surrogate channels for the code design are used. The constructed codes for uniform inputs perform as good as the multi-edge type codes of Zhang and Kschischang (2013). For 8-ASK and 64-ASK with probabilistic shaping, codes of rates 2/3 and 5/6 with blocklength 64800 are designed, which operate within 0.63 and 0.69 dB of 1/2 log2(1 + SNR) for a target frame error rate of 10-3at spectral 1 efficiencies of 1.38 and 4.25 bits/channel use, respectively.

We study the problem of compression for the purpose of similarity identification, where similarity is measured by the mean square Euclidean distance between vectors. While the asymptotical fundamental limits of the problem - the minimal compression rate and the error exponent - were found in a previous work, in this paper we focus on the nonasymptotic domain and on practical, implementable schemes. We first present a finite blocklength achievability bound based on shape-gain quantization: The gain (amplitude) of the vector is compressed via scalar quantization and the shape (the projection on the unit sphere) is quantized using a spherical code. The results are numerically evaluated and they converge to the asymptotic values as predicted by the error exponent. We then give a nonasymptotic lower bound on the performance of any compression scheme, and compare to the upper (achievability) bound. For a practical implementation of such a scheme, we use wrapped spherical codes, studied by Hamkins and Zeger, and use the Leech lattice as an example for an underlying lattice. As a side result, we obtain a bound on the covering angle of any wrapped spherical code, as a function of the covering radius of the underlying lattice.

A protograph-based low-density parity-check (LDPC) code design technique for bandwidth-efficient coded modulation is presented. The approach jointly optimizes the LDPC code node degrees and the mapping of the coded bits to the bit-interleaved coded modulation (BICM) bit-channels. For BICM with uniform input and for BICM with probabilistic shaping, binary-input symmetric-output surrogate channels are constructed and used for code design. The constructed codes perform as good as multi-edge type codes of Zhang and Kschischang (2013). For 64-ASK with probabilistic shaping, a blocklength 64800 code is constructed that operates within 0.69 dB of 1 over 2 log2(1 + SNR) at a spectral efficiency of 4.25 bits/channel use and a frame error rate of 10-3.

A new coded modulation scheme is proposed. At the transmitter, the concatenation of a distribution matcher and a systematic binary encoder performs probabilistic signal shaping and channel coding. At the receiver, the output of a bitwise demapper is fed to a binary decoder. No iterative demapping is performed. Rate adaption is achieved by adjusting the input distribution and the transmission power. The scheme is applied to bipolar amplitudeshift keying (ASK) constellations with equidistant signal points and it is directly applicable to two-dimensional quadrature amplitude modulation (QAM). The scheme is implemented by using the DVB-S2 low-density parity-check (LDPC) codes. At a frame error rate of 10-3, the new scheme operates within less than 1.1 dB of the AWGN capacity 1/2 log2(1 + SNR) at any spectral efficiency between 1 and 5 bits/s/Hz by using only 5 modes, i.e., 4-ASK with code rate 2/3, 8-ASK with 3/4, 16-ASK and 32-ASK with 5/6, and 64-ASK with 9/10.

We study the problem of compression of a Gaussian vector for the purpose of similarity identification, where similarity is defined by the mean square Euclidean distance between vectors. While the asymptotical fundamental limits of the problem - the minimal compression rate and the error exponent - were found in a previous work, in this paper we focus on the nonasymptotic domain. We first present a finite blocklength achievability bound based on shape-gain quantization: The gain (amplitude) of the vector is compressed via scalar quantization, and the shape (the projection on the unit sphere) is quantized using a spherical code. The results are numerically evaluated, and they converge to the asymptotic values as predicted by the error exponent. For a practical implementation of such a scheme, we use wrapped spherical codes, studied by Hamkins and Zeger, and use the Leech lattice as an example for an underlying lattice. As a side result, we obtain a bound on the covering angle of any wrapped spherical code, as a function of the covering radius of the underlying lattice.

In this paper, we analyze the fundamental trade-off between information transfer and power gain by means of an information-theoretic framework in communications circuits. This analysis is of interest as many of today's applications require that maximum information and maximum signal power are extracted (or transferred) through the circuit at the same time for further processing so that a compromise concerning the signal spectral shape as well as the matching network has to be found. To this end, the optimization framework is applied to a two-port circuit, which is used as an abstraction for a broadband amplifier. Thereby, we characterize the involved Pareto bound by considering different optimization problems. The first one aims at optimizing the input power spectral density (PSD) as well as the source and load admittances, whereas the second approach assumes the PSD to be fixed and uniformly distributed within a fixed bandwidth and optimizes the source and load admittances only. Moreover, we will show that additional matching networks may help to improve the trade-off.