21,064 research outputs found
New Constructions of Zero-Correlation Zone Sequences
In this paper, we propose three classes of systematic approaches for
constructing zero correlation zone (ZCZ) sequence families. In most cases,
these approaches are capable of generating sequence families that achieve the
upper bounds on the family size () and the ZCZ width () for a given
sequence period ().
Our approaches can produce various binary and polyphase ZCZ families with
desired parameters and alphabet size. They also provide additional
tradeoffs amongst the above four system parameters and are less constrained by
the alphabet size. Furthermore, the constructed families have nested-like
property that can be either decomposed or combined to constitute smaller or
larger ZCZ sequence sets. We make detailed comparisons with related works and
present some extended properties. For each approach, we provide examples to
numerically illustrate the proposed construction procedure.Comment: 37 pages, submitted to IEEE Transactions on Information Theor
Human gesture classification by brute-force machine learning for exergaming in physiotherapy
In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods
Sparse Complementary Pairs with Additional Aperiodic ZCZ Property
This paper presents a novel class of complex-valued sparse complementary
pairs (SCPs), each consisting of a number of zero values and with additional
zero-correlation zone (ZCZ) property for the aperiodic autocorrelations and
crosscorrelations of the two constituent sequences. Direct constructions of
SCPs and their mutually-orthogonal mates based on restricted generalized
Boolean functions are proposed. It is shown that such SCPs exist with arbitrary
lengths and controllable sparsity levels, making them a disruptive sequence
candidate for modern low-complexity, low-latency, and low-storage signal
processing applications
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