262 research outputs found
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Parallel convolutional coder
A parallel convolutional coder (104) comprising: a plurality of serial convolutional coders (108) each having a register
with a plurality of memory cells and a plurality of serial coder outputs,- input means (120) from which data can be transferred
in parallel into the registers,- and a parallel coder output (124) comprising a plurality of output memory cells each of which is connected
to one of the serial coder outputs so that data can be transferred in parallel from all of the serial coders to the parallel coder
output
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Facts, scope, style: a guide to writing papers for IEEE transactions on consumer electronics
In a previous article, I wrote a brief piece on how to enhance papers that have been published at one of the IEEE Consumer Electronics (CE) Society conferences to create papers that can be considered for publishing in IEEE Transactions on Consumer Electronics (T-CE) [1]. Basically, it included some hints and tips to enhance a conference paper into what is required for a full archival journal paper and not fall foul of self-plagiarism. This article focuses on writing original papers specifically for T-CE.
After three years as the journal’s editor-in-chief (EiC), a previous eight years on the editorial board, and having reviewed some 4,000 T-CE papers, I decided to write this article to archive and detail for prospective authors what I have learned over this time. Of course, there are numerous articles on writing good papers—some are really useful [2], but they do not address the specific issues of writing for a journal whose topic (scope) is not widely understood or, indeed, is often misunderstood
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Learning models for semantic classification of insufficient plantar pressure images
Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and
effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set
learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose
an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are
introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset
of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by
using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)-
based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally,
the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained
CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition
methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H)
and time (training and evaluation). The proposed method for the plantar pressure classification task shows high
performance in most indices when comparing with other methods. The transfer learning-based method can be
applied to other insufficient data-sets of sensor imaging fields
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Identification of suitable biomarkers for stress and emotion detection for future personal affective wearable sensors
Skin conductivity (i.e., sweat) forms the basis of many physiology-based emotion and stress detection systems. However, such systems typically do not detect the biomarkers present in sweat, and thus do not take advantage of the biological information in the sweat. Likewise, such systems do not detect the volatile organic components (VOC’s) created under stressful conditions. This work presents a review into the current status of human emotional stress biomarkers and proposes the major potential biomarkers for future wearable sensors in affective systems. Emotional stress has been classified as a major contributor in several social problems, related to crime, health, the economy, and indeed quality of life. While blood cortisol tests, electroencephalography and physiological parameter methods are the gold standards for measuring stress; however, they are typically invasive or inconvenient and not suitable for wearable real-time stress monitoring. Alternatively, cortisol in biofluids and VOCs emitted from the skin appear to be practical and useful markers for sensors to detect emotional stress events. This work has identified antistress hormones and cortisol metabolites as the primary stress biomarkers that can be used in future sensors for wearable affective systems
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Privacy and security of consumer IoT devices for the pervasive monitoring of vulnerable people
The Internet of Things (IoT) promises highly innovative solutions to a wide range of activities. However, simply being a technology company does not exempt an IoT company from needing to comply with the legislation applicable to their operating region that safeguards personal information. This will result in security and privacy requirements for healthcare solutions. There are several mature frameworks that address these issues, but they have been developed within the context of organised hospitals and care providers, where there is the expertise, processing power, communications and electrical power to support highly robust security. However, for IoT solutions aimed at vulnerable people, either at home or within their local environment, there are significant additional constraints that must be overcome. These include technical (low processing capability, power constrained, intermittent communications) organisational (how to enrol and revoke users and devices, distribution of cryptographic keys) and user constraints (how does a patient with physical and/or mental challenges configure and update their devices).
This paper considers at the legal frameworks and the security and privacy requirements for healthcare solutions. An overview of some of the primary frameworks is then provided followed by an assessment of how this is constrained within an IoT system
Low-power Wearable Healthcare Sensors
Advances in technology have produced a range of on-body sensors and smartwatches that can be used to monitor a wearer’s health with the objective to keep the user healthy. However, the real potential of such devices not only lies in monitoring but also in interactive communication with expert-system-based cloud services to offer personalized and real-time healthcare advice that will enable the user to manage their health and, over time, to reduce expensive hospital admissions. To meet this goal, the research challenges for the next generation of wearable healthcare devices include the need to offer a wide range of sensing, computing, communication, and human–computer interaction methods, all within a tiny device with limited resources and electrical power. This Special Issue presents a collection of six papers on a wide range of research developments that highlight the specific challenges in creating the next generation of low-power wearable healthcare sensors
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Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs
Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method
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Fixed point dual carrier modulation performance for wireless USB
Dual Carrier Modulation (DCM) is currently used
as the higher data rate modulation scheme for Multiband
Orthogonal Frequency Division Multiplexing (MB-OFDM) in the
ECMA-368 defined Ultra-Wideband (UWB) radio platform.
ECMA-368 has been chosen as the physical radio platform for
many systems including Wireless USB (W-USB), Bluetooth 3.0
and Wireless HDMI; hence ECMA-368 is an important issue to
consumer electronics and the user’s experience of these products.
In this paper, Log Likelihood Ratio (LLR) demapping method
is used for the DCM demaper implemented in fixed point model.
Channel State Information (CSI) aided scheme coupled with the
band hopping information is used as the further technique to
improve the DCM demapping performance. The receiver
performance for the fixed point DCM is simulated in realistic
multi-path environments
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MIMO channel capacity and configuration selection for switched parasitic antennas
MIMO systems offer a significant enhancement of data rate and channel capacity compared to traditional systems. But correlation degrades the system performance and puts a practical limit on the number of antennas that can be squeezed into portable wireless devices. Switched Parasitic Antennas (SPAs) is a possible solution especially where it is difficult to obtain enough signal decorrelation with conventional means. The covariance matrix represents the correlation present in the propagation channel and has significant impact on the MIMO channel capacity. The results of this work demonstrate a significant improvement in the MIMO channel capacity by using SPA with the knowledge of the covariance matrix for all pattern configurations. By employing the ‘Water-Pouring Algorithm’ (WPA) to modify the covariance matrix, the channel capacity is significantly improved as compared to traditional systems which just spread power equally among all the transmit antennas. A Condition Number (CN) is also proposed as a selection metric, to select the optimal pattern configuration for SPAs. CN is a channel quality indicator which represents the Eigen Value Spread (EVS) of the covariance matrix
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