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Journal articles on the topic 'Sensor decoding'

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Author: Grafiati

Published: 10 March 2023

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1

Flamary, R., N. Jrad, R. Phlypo, M. Congedo, and A. Rakotomamonjy. "Mixed-Norm Regularization for Brain Decoding." Computational and Mathematical Methods in Medicine 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/317056.

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This work investigates the use of mixed-norm regularization for sensor selection in event-related potential (ERP) based brain-computer interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multitask learning situation where several similar classification tasks related to different subjects are learned simultaneously. In this case, multitask learning helps in leveraging data scarcity issue yielding to more robust classifiers. For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities. The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection. The multitask approaches are evaluated when a small number of learning examples are available yielding to significant performance improvements especially for subjects performing poorly.

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2

Snippe, Herman P. "Parameter Extraction from Population Codes: A Critical Assessment." Neural Computation 8, no. 3 (April 1996): 511–29. http://dx.doi.org/10.1162/neco.1996.8.3.511.

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In perceptual systems, a stimulus parameter can be extracted by determining the center-of-gravity of the response profile of a population of neural sensors. Likewise at the motor end of a neural system, center-of-gravity decoding, also known as vector decoding, generates a movement direction from the neural activation profile. We evaluate these schemes from a statistical perspective, by comparing their statistical variance with the minimum variance possible for an unbiased parameter extraction from the noisy neuronal ensemble activation profile. Center-of-gravity decoding can be statistically optimal. This is the case for regular arrays of sensors with gaussian tuning profiles that have an output described by Poisson statistics, and for arrays of sensors with a sinusoidal tuning profile for the (angular) parameter estimated. However, there are also many cases in which center-of-gravity decoding is highly inefficient. This includes the important case where sensor positions are very irregular. Finally, we study the robustness of center-of-gravity decoding against response nonlinearities at different stages of an information processing hierarchy. We conclude that, in neural systems, instead of representing a parameter explicitly, it is safer to leave the parameter coded implicitly in a neuronal ensemble activation profile.

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Dash, Debadatta, Alan Wisler, Paul Ferrari, Elizabeth Moody Davenport, Joseph Maldjian, and Jun Wang. "MEG Sensor Selection for Neural Speech Decoding." IEEE Access 8 (2020): 182320–37. http://dx.doi.org/10.1109/access.2020.3028831.

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Okunsky, M. V., and N. V. Nesterova. "Velodyne LIDAR method for sensor data decoding." IOP Conference Series: Materials Science and Engineering 516 (April 26, 2019): 012018. http://dx.doi.org/10.1088/1757-899x/516/1/012018.

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Aineto, Diego, Sergio Jimenez, and Eva Onaindia. "Observation Decoding with Sensor Models: Recognition Tasks via Classical Planning." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 11–19. http://dx.doi.org/10.1609/icaps.v30i1.6640.

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Observation decoding aims at discovering the underlying state trajectory of an acting agent from a sequence of observations. This task is at the core of various recognition activities that exploit planning as resolution method but there is a general lack of formal approaches that reason about the partial information received by the observer or leverage the distribution of the observations emitted by the sensors. In this paper, we formalize the observation decoding task exploiting a probabilistic sensor model to build more accurate hypothesis about the behaviour of the acting agent. Our proposal extends the expressiveness of former recognition approaches by accepting observation sequences where one observation of the sequence can represent the reading of more than one variable, thus enabling observations over actions and partially observable states simultaneously. We formulate the probability distribution of the observations perceived when the agent performs an action or visits a state as a classical cost planning task that is solved with an optimal planner. The experiments will show that exploiting a sensor model increases the accuracy of predicting the agent behaviour in four different contexts.

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Parfenov, V. I., and V. D. Le. "DISTRIBUTED DETECTION BASED ON USING SOFT DECISION DECODING IN A FUSION CENTER." Telecommunications, no. 1 (2022): 2–9. http://dx.doi.org/10.31044/1684-2588-2022-0-1-2-9.

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In this work, distributed detection by data from many sensors in the wireless sensor system is considered. A decision-making algorithm based on using soft-decision decoding in a fusion center is synthesized. Its gain in efficiency compared to efficiency of the algorithm based on hard-decision scheme is shown. It is noted that this algorithm is a generalization of earlier developed algorithms.

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Ganin, Dmitriy V., Mokhammed A. Y. Damdam, and Aleksandr L. Savkin. "PERMUTATION DECODING IN LOW-POWER WIRELESS SENSOR NETWORKS." Автоматизация процессов управления 2, no. 68 (2022): 37–44. http://dx.doi.org/10.35752/1991-2927-2022-2-68-37-44.

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Wireless sensor networks are currently considered for many subject areas as effective communication systems, including, for example, industrial and agricultural facilities and enterprises, monitoring of sensors of transport security systems, medicine applications, environmental and weather monitoring, etc. In such systems, the correction code (CC) is a classic approach used to increase the reliability of the data transmission channel while simultaneously reducing the required transmitted signal power, called the energy gain code. At the same time, the power of the transmitter is reduced due to additional power consumption in the receiver decoder. Stronger codes provide better performance with lower power requirements, but have more complex decoders with higher power consumption than relatively simple error control systems. If the additional power consumption at the output of the decoder exceeds the savings in transmitted power due to the use of CC, then such a code will not be energy efficient compared to a system with a non-redundant code. Various codes can be used to protect data, while the best codes for which the critical distance turns out to be large according to a number of studies are codes with a low parity check density. The paper proposes an alternative variant based on the synthesis of a permutation decoding system using the method of localization of a limited number of permutations and the organization of additional parity checks exclusively for the verification bits of the CC using the Bayesian approach to iterative transformations of integer soft solutions of symbols.

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Yufei, Wu, Wang Dandan, and Zhu Yanwei. "Research on the Advantages of Digital Sensor Equipment in Language Audio-Visual and Oral Teaching." Journal of Sensors 2021 (November 30, 2021): 1–13. http://dx.doi.org/10.1155/2021/3006397.

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Digital sensors use biotechnology and information processing technology to strengthen the processing of relevant visual and auditory information, which is helpful to ensure that the receiver can obtain more accurate information, so as to improve the learning effect and reduce the impact on the environment. This paper designs an experiment to explore the role of digital sensors in language audio-visual teaching, which provides a reference for the application of digital sensors in the future. The impulse response function in sensor technology is introduced. The speech time domain envelope and time-varying mouth area of the sensor device are calculated. The auditory attention transfer detection based on line of sight rotation estimation is carried out through the auditory attention decoding fusion technology and the sensor auditory attention conversion detection method. At the same time, the characteristic of sensor heog signal is analyzed. The results show that the algorithm proposed in this paper has good results.

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Cordeiro, Paulo J., and Pedro Assunção. "Distributed Coding/Decoding Complexity in Video Sensor Networks." Sensors 12, no. 3 (February 29, 2012): 2693–709. http://dx.doi.org/10.3390/s120302693.

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Dwivedi, Anany, Helen Groll, and Philipp Beckerle. "A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding." Sensors 22, no. 17 (August 23, 2022): 6319. http://dx.doi.org/10.3390/s22176319.

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Humans learn about the environment by interacting with it. With an increasing use of computer and virtual applications as well as robotic and prosthetic devices, there is a need for intuitive interfaces that allow the user to have an embodied interaction with the devices they are controlling. Muscle–machine interfaces can provide an intuitive solution by decoding human intentions utilizing myoelectric activations. There are several different methods that can be utilized to develop MuMIs, such as electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy. In this paper, we analyze the advantages and disadvantages of different myography methods by reviewing myography fusion methods. In a systematic review following the PRISMA guidelines, we identify and analyze studies that employ the fusion of different sensors and myography techniques, while also considering interface wearability. We also explore the properties of different fusion techniques in decoding user intentions. The fusion of electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy as well as other sensing such as inertial measurement units and optical sensing methods has been of continuous interest over the last decade with the main focus decoding the user intention for the upper limb. From the systematic review, it can be concluded that the fusion of two or more myography methods leads to a better performance for the decoding of a user’s intention. Furthermore, promising sensor fusion techniques for different applications were also identified based on the existing literature.

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Ismail, Mohamed, Imran Ahmed, and Justin Coon. "Low Power Decoding of LDPC Codes." ISRN Sensor Networks 2013 (January 17, 2013): 1–12. http://dx.doi.org/10.1155/2013/650740.

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Wireless sensor networks are used in many diverse application scenarios that require the network designer to trade off different factors. Two such factors of importance in many wireless sensor networks are communication reliability and battery life. This paper describes an efficient, low complexity, high throughput channel decoder suited to decoding low-density parity-check (LDPC) codes. LDPC codes have demonstrated excellent error-correcting ability such that a number of recent wireless standards have opted for their inclusion. Hardware realisation of practical LDPC decoders is a challenging area especially when power efficient solutions are needed. Implementation details are given for an LDPC decoding algorithm, termed adaptive threshold bit flipping (ATBF), designed for low complexity and low power operation. The ATBF decoder was implemented in 90 nm CMOS at 0.9 V using a standard cell design flow and was shown to operate at 250 MHz achieving a throughput of 252 Gb/s/iteration. The decoder area was 0.72 mm2 with a power consumption of 33.14 mW and a very small energy/decoded bit figure of 1.3 pJ.

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Albert, Keith J., Daljeet S. Gill, Tim C. Pearce, and David R. Walt. "Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays." Analytical and Bioanalytical Chemistry 373, no. 8 (August 2002): 792–802. http://dx.doi.org/10.1007/s00216-002-1406-8.

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Chen, June, and Ali Abedi. "Distributed Turbo Coding and Decoding for Wireless Sensor Networks." IEEE Communications Letters 15, no. 2 (February 2011): 166–68. http://dx.doi.org/10.1109/lcomm.2011.010311.102118.

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Song, Hongzhan, Shangsheng Wen, Chen Yang, Danlan Yuan, and Weipeng Guan. "Universal and Effective Decoding Scheme for Visible Light Positioning Based on Optical Camera Communication." Electronics 10, no. 16 (August 10, 2021): 1925. http://dx.doi.org/10.3390/electronics10161925.

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As a promising approach to implement indoor positioning, visible light positioning (VLP) based on optical camera communication (OCC) image sensor has attracted substantial attention. However, the decoding schemes of existing VLP systems still face many challenges. First, the transmission channel between transmitters and receivers can be easily affected by environmental changes, resulting in poor thresholding performance. Second, the inherently unsynchronized air transmission channel issue remains a big obstacle for decoding data. The above two problems limit the application of VLP systems, where various mobile devices are used as receivers and the properties of transmission channel are constantly changing with the movement of receivers. In this paper, a universal and effective decoding scheme named pixel-to-bit calculation (PBC) decoding algorithm for VLP systems is proposed and experimentally demonstrated. It includes a Staged Threshold Scheme which provides excellent thresholding performance for different transmission channel conditions, as well as a Synchronous Decoding Operation to automatically synchronize the clock between transmitters and receivers. A decoding rate of 95.62% at the height of 2.73 m is realized in a practical Robotic-based VLP system embedded with our proposed PBC decoding scheme. In addition, experimental results show that the average decoding rate of the proposed PBC decoding scheme reaches 99.9% when applying different transmitters and receivers.

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S. Eissa, Nour El Din, and Gamal I. Selim. "Cooperative Intrusion Detection Technique in Wireless Sensor Networks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 3 (March 30, 2014): 4256–64. http://dx.doi.org/10.24297/ijct.v13i3.2756.

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Wireless Sensor Networks (WSN) are becoming more and more popular everyday due to their increasing ability to monitor certain phenomenon over wide regions such as air pollution, natural disaster, industrial monitoring and underwater applications [1]. The simple security capabilities of the nodes in the sensors network makes it an easy target for an intruder to take over some of the node(s) in the sensor network and to start altering the data received or sent by these nodes before forwarding it to the other nodes in effort to prevent the destination from properly decoding or reading the received data. These attacked nodes lead to tremendous amount of unusable data travelling across the network. The objective of this paper aims to detect these malicious nodes and cast them outside the network using a cooperative local voting approach with a dynamic center and study its effect on the amount of aggregated data though the network and the time required to deliver the sensors data.

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Gligoric, Nenad, Srdjan Krco, Liisa Hakola, Kaisa Vehmas, Suparna De, Klaus Moessner, Kristoffer Jansson, Ingmar Polenz, and Rob van Kranenburg. "SmartTags: IoT Product Passport for Circular Economy Based on Printed Sensors and Unique Item-Level Identifiers." Sensors 19, no. 3 (January 30, 2019): 586. http://dx.doi.org/10.3390/s19030586.

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In this paper, we present a method that facilitates Internet of Things (IoT) for building a product passport and data exchange enabling the next stage of the circular economy. SmartTags based on printed sensors (i.e., using functional ink) and a modified GS1 barcode standard enable unique identification of objects on a per item-level (including Fast-Moving Consumer Goods—FMCG), collecting, sensing, and reading of parameters from environment as well as tracking a products’ lifecycle. The developed ontology is the first effort to define a semantic model for dynamic sensors, including datamatrix and QR codes. The evaluation of decoding and readability of identifiers (QR codes) showed good performance for detection of sensor state printed over and outside the QR code data matrix, i.e., the recognition ability with image vision algorithm was possible. The evaluation of the decoding performance of the QR code data matrix printed with sensors was also efficient, i.e., the QR code ability to be decoded with the reader after reversible and irreversible process of ink (dis)appearing was preserved, with slight drop in performance if ink density is low.

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Boateng Nti, Richard, and Ji-Hoon Yun. "Multi-Filter Decoding in WiFi Backscatter Communication." Sensors 21, no. 4 (February 20, 2021): 1481. http://dx.doi.org/10.3390/s21041481.

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WiFi backscatter communication has emerged as a promising enabler of ultralow-power connectivity for Internet of things, wireless sensor network and smart energy. In this paper, we propose a multi-filter design for effective decoding of WiFi backscattered signals. Backscattered signals are relatively weak compared to carrier WiFi signals and therefore require algorithms that filter out original WiFi signals without affecting the backscattered signals. Two multi-filter designs for WiFi backscatter decoding are presented: the summation and delimiter approaches. Both implementations employ the use of additional filters with different window sizes to efficiently cut off undesired noise/interference, thus enhancing frame detection and decoding performance, and can be coupled with a wide range of decoding algorithms. The designs are particularly productive in the frequency-shift WiFi backscatter communication. We demonstrate via prototyping and testbed experiments that the proposed design enhances the performance of various decoding algorithms in real environments.

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Zhang, Xi, Qiangqiang Xie, Siwei Xie, Xin Yu, Jianfeng Xu, and Qiyu Peng. "A Novel Portable Gamma Radiation Sensor Based on a Monolithic Lutetium-Yttrium Oxyorthosilicate Ring." Sensors 21, no. 10 (May 12, 2021): 3376. http://dx.doi.org/10.3390/s21103376.

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Portable radiation detectors are widely used in environmental radiation detection and medical imaging due to their portability feature, high detection efficiency, and large field of view. Lutetium-yttrium oxyorthosilicate (LYSO) is a widely used scintillator in gamma radiation detection. However, the structure and the arrangement of scintillators limit the sensitivity and detection accuracy of these radiation detectors. In this study, a novel portable sensor based on a monolithic LYSO ring was developed for the detection of environmental radiation through simulation, followed by construction and assessments. Monte Carlo simulations were utilized to prove the detection of gamma rays at 511 keV by the developed sensor. The simulations data, including energy resolutions, decoding errors, and sensitivity, showed good potential for the detection of gamma rays by the as-obtained sensor. The experimental results using the VA method revealed decoding errors in the energy window width of 50 keV less than 2°. The average error was estimated at 0.67°, a sufficient value for the detection of gamma radiation. In sum, the proposed radiation sensor appears promising for the construction of high-performance radiation detectors and systems.

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Tran, Thanh-Nam, Thanh-Long Nguyen, Vinh Truong Hoang, and Miroslav Voznak. "Sensor Clustering Using a K-Means Algorithm in Combination with Optimized Unmanned Aerial Vehicle Trajectory in Wireless Sensor Networks." Sensors 23, no. 4 (February 20, 2023): 2345. http://dx.doi.org/10.3390/s23042345.

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We examine a general wireless sensor network (WSN) model which incorporates a large number of sensors distributed over a large and complex geographical area. The study proposes solutions for a flexible deployment, low cost and high reliability in a wireless sensor network. To achieve these aims, we propose the application of an unmanned aerial vehicle (UAV) as a flying relay to receive and forward signals that employ nonorthogonal multiple access (NOMA) for a high spectral sharing efficiency. To obtain an optimal number of subclusters and optimal UAV positioning, we apply a sensor clustering method based on K-means unsupervised machine learning in combination with the gap statistic method. The study proposes an algorithm to optimize the trajectory of the UAV, i.e., the centroid-to-next-nearest-centroid (CNNC) path. Because a subcluster containing multiple sensors produces cochannel interference which affects the signal decoding performance at the UAV, we propose a diagonal matrix as a phase-shift framework at the UAV to separate and decode the messages received from the sensors. The study examines the outage probability performance of an individual WSN and provides results based on Monte Carlo simulations and analyses. The investigated results verified the benefits of the K-means algorithm in deploying the WSN.

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Xie, Liqiang, Tianxian Wang, Ping Wang, and Jianchun Xing. "On-Chip Correlator for Passive Wireless SAW Multisensor Systems." Journal of Sensors 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/2583063.

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For decoding the asynchronous superposition of response signals from different sensors, it is a challenge to achieve correlation in a code division multiplexing (CDM) based passive wireless surface acoustic wave (SAW) multisensor system. Therefore, an on-chip correlator scheme is developed in this paper. In contrast to conventional CDM-based systems, this novel scheme enables the correlations to be operated at the SAW sensors, instead of the reader. Thus, the response signals arriving at the reader are the result of cross-correlation on the chips. It is then easy for the reader to distinguish the sensor that is matched with the interrogating signal. The operation principle, signal analysis, and simulation of the novel scheme are described in the paper. The simulation results show the response signals from the correlations of the sensors. A clear spike pulse is presented in the response signals, when a sensor code is matched with the interrogating code. Simulations verify the feasibility of the on-chip correlator concept.

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Aldag, Nina, Andreas Büchner, Thomas Lenarz, and Waldo Nogueira. "Towards decoding selective attention through cochlear implant electrodes as sensors in subjects with contralateral acoustic hearing." Journal of Neural Engineering 19, no. 1 (February 1, 2022): 016023. http://dx.doi.org/10.1088/1741-2552/ac4de6.

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Abstract Objectives. Focusing attention on one speaker in a situation with multiple background speakers or noise is referred to as auditory selective attention. Decoding selective attention is an interesting line of research with respect to future brain-guided hearing aids or cochlear implants (CIs) that are designed to adaptively adjust sound processing through cortical feedback loops. This study investigates the feasibility of using the electrodes and backward telemetry of a CI to record electroencephalography (EEG). Approach. The study population included six normal-hearing (NH) listeners and five CI users with contralateral acoustic hearing. Cortical auditory evoked potentials (CAEP) and selective attention were recorded using a state-of-the-art high-density scalp EEG and, in the case of CI users, also using two CI electrodes as sensors in combination with the backward telemetry system of these devices, denoted as implant-based EEG (iEEG). Main results. In the selective attention paradigm with multi-channel scalp EEG the mean decoding accuracy across subjects was 94.8% and 94.6% for NH listeners and CI users, respectively. With single-channel scalp EEG the accuracy dropped but was above chance level in 8–9 out of 11 subjects, depending on the electrode montage. With the single-channel iEEG, the selective attention decoding accuracy could only be analyzed in two out of five CI users due to a loss of data in the other three subjects. In these two CI users, the selective attention decoding accuracy was above chance level. Significance. This study shows that single-channel EEG is suitable for auditory selective attention decoding, even though it reduces the decoding quality compared to a multi-channel approach. CI-based iEEG can be used for the purpose of recording CAEPs and decoding selective attention. However, the study also points out the need for further technical development for the CI backward telemetry regarding long-term recordings and the optimal sensor positions.

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Cheng, Hsu Chih, and Rong Shun Syu. "Optical Code Division Access Application for Fiber Loop Vibration Sensor." Applied Mechanics and Materials 65 (June 2011): 324–27. http://dx.doi.org/10.4028/www.scientific.net/amm.65.324.

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We proposed a fiber loop sensor systems by using optical code division multiple access technology. The encoding/decoding devices use the multiple fiber Bragg grating chains to provide the orthogonal properties in the wavelength domain. Also, the fiber loop sensor used to detect the vibration signal of object under test. The experimental results show that the proposed optical code division access multiple system method is effective.

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Manivasagam, M. A., and T. V. Ananthan. "Signal Strength based Self Reconfiguration to Ensure Reliability in Wireless Sensor Networks." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (May 1, 2018): 725. http://dx.doi.org/10.11591/ijeecs.v10.i2.pp725-732.

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<span lang="EN-US">Providing reliability in Wireless sensor networks is considered to be a challenging task, due to the limited capabilities in terms of energy, power and memory. The applications of these systems run in sensors with low level programming abstractions, limited capabilities and routing protocols. In this paper, we propose a strategy to adjust radios in the sensor network depending on the signal strength of the neighboring nodes to ensure reliability using self reconfiguration (S2R2). Redundancy-based reliability is achieved by performing encoding/decoding either at the source and the destination node or each pair of communicating sensor nodes from the source to the destination. Along with the reliability, the link and the stability of the link are checked. The stability of the route makes the route a valid one to send data. Simulation analysis shows that the proposed mechanism performs better in terms of stability and reliability compared to the existing mechanism</span>

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Chou, Chih Cheng, Chia Jui Chiang, Yu Hsuan Su, and Yong Yuan Ku. "Interpretation of the Oscillating Signals of the Smart NO_x Sensors Used in Urea Selective Catalyst Reduction Systems via Spectral Analysis." Applied Mechanics and Materials 479-480 (December 2013): 719–23. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.719.

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The oscillating signals of NOx sensor observed in urea selective catalytic reduction (SCR) tests are explained via spectral analysis. The NOx sensor built by NGK / Continental requires decoding following the SAE J1939 protocol and thus higher harmonic induced by the discretization is observed in the spectral analysis. The reciprocating motion of the urea pump induces fluctuation of the ammonia dosage, which combined with the nonlinear characteristic from ammonia dosage to NOx downstream SCR result in oscillating NOx sensor readings with different amplitude. The cross-sensitivity of the NOx sensor to ammonia concentration for higher-than-stoichiometric ammonia dosage also results in oscillating NOx sensor signal at lower frequencies.

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Zhang, Hang, Fanglin Niu, Ling Yu, and Si Zhang. "LT Codes with Double Encoding Matrix Reorder Physical Layer Secure Transmission." Journal of Sensors 2022 (January 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/6106786.

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In traditional wireless sensor networks, information transmission usually uses data encryption methods to prevent information from being stolen illegally. However, once the encryption methods are leaked, eavesdropping nodes can easily obtain information. LT codes are rateless codes; if it is attacked by random channel noise, the decoding process will change and the decoding overhead will also randomly change. When it is used for physical layer communication of wireless sensor networks, it ensures that the destination node recovers all the information without adding the key, while the eavesdropping node can only obtain part of the information to achieve wireless information security transmission. To reduce the intercept efficiency of eavesdropping nodes, a physical layer security (PLS) method of LT codes with double encoding matrix reorder (DEMR-LT codes) is proposed. This method performs two consecutive LT code concatenated encoding on the source symbol, and part of the encoding matrix is reordered according to the degree value of each column from large to small, which reduces the probability of eavesdropping nodes recovering the source information. Experimental results show that compared with other LT code PLS schemes, DEMR-LT codes only increase the decoding overhead by a small amount. However, it can effectively reduce the intercept efficiency of eavesdropping nodes and improve information transmission security.

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Abrardo, Andrea, Gianluigi Ferrari, Marco Martalò, and Fabio Perna. "Feedback Power Control Strategies inWireless Sensor Networks with Joint Channel Decoding." Sensors 9, no. 11 (November 3, 2009): 8776–809. http://dx.doi.org/10.3390/s91108776.

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Tang, Zhengqiang, Shintaro Arai, Tomohiro Yendo, Di He, and Takaya Yamazato. "Sequential maximum likelihood decoding incorporating reliability determination for image sensor communication." IEICE Communications Express 9, no. 8 (August 1, 2020): 365–70. http://dx.doi.org/10.1587/comex.2020xbl0058.

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Ramaiyan, K. P., C. R. Kreller, E. L. Brosha, R. Mukundan, U. Javed, and A. V. Morozov. "Quantitative Decoding of Complex Gas Mixtures Using Mixed-Potential Sensor Arrays." ECS Transactions 75, no. 16 (September 23, 2016): 107–11. http://dx.doi.org/10.1149/07516.0107ecst.

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Alevizos, Panos N., Aggelos Bletsas, and George N. Karystinos. "Noncoherent Short Packet Detection and Decoding for Scatter Radio Sensor Networking." IEEE Transactions on Communications 65, no. 5 (May 2017): 2128–40. http://dx.doi.org/10.1109/tcomm.2017.2665494.

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Jianwei Ma and M. Y. Hussaini. "Extensions of Compressed Imaging: Flying Sensor, Coded Mask, and Fast Decoding." IEEE Transactions on Instrumentation and Measurement 60, no. 9 (September 2011): 3128–39. http://dx.doi.org/10.1109/tim.2011.2122530.

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Fang, Weidong, Wuxiong Zhang, Lianhai Shan, Biruk Assefa, and Wei Chen. "LDPC Code’s Decoding Algorithms for Wireless Sensor Network: a Brief Review." Journal of New Media 1, no. 1 (2019): 45–50. http://dx.doi.org/10.32604/jnm.2019.05786.

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Xu, Qinbao, Rizwan Akhtar, Xing Zhang, and Changda Wang. "Cluster-Based Arithmetic Coding for Data Provenance Compression in Wireless Sensor Networks." Wireless Communications and Mobile Computing 2018 (June 27, 2018): 1–15. http://dx.doi.org/10.1155/2018/9576978.

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In wireless sensor networks (WSNs), data provenance records the data source and the forwarding and the aggregating information of a packet on its way to the base station (BS). To conserve the energy and wireless communication bandwidth, the provenances are compressed at each node along the packet path. To perform the provenances compression in resource-tightened WSNs, we present a cluster-based arithmetic coding method which not only has a higher compression rate but also can encode and decode the provenance in an incremental manner; i.e., the provenance can be zoomed in and out like Google Maps. Such a decoding method raises the efficiencies of the provenance decoding and the data trust assessment. Furthermore, the relationship between the clustering size and the provenance size is formally analyzed, and then the optimal clustering size is derived as a mathematical function of the WSN’s size. Both the simulation and the test-bed experimental results show that our scheme outperforms the known arithmetic coding based provenance compression schemes with respect to the average provenance size, the energy consumption, and the communication bandwidth consumption.

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Moreira, Luís, Joana Figueiredo, João Cerqueira, and Cristina P. Santos. "A Review on Locomotion Mode Recognition and Prediction When Using Active Orthoses and Exoskeletons." Sensors 22, no. 19 (September 20, 2022): 7109. http://dx.doi.org/10.3390/s22197109.

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Understanding how to seamlessly adapt the assistance of lower-limb wearable assistive devices (active orthosis (AOs) and exoskeletons) to human locomotion modes (LMs) is challenging. Several algorithms and sensors have been explored to recognize and predict the users’ LMs. Nevertheless, it is not yet clear which are the most used and effective sensor and classifier configurations in AOs/exoskeletons and how these devices’ control is adapted according to the decoded LMs. To explore these aspects, we performed a systematic review by electronic search in Scopus and Web of Science databases, including published studies from 1 January 2010 to 31 August 2022. Sixteen studies were included and scored with 84.7 ± 8.7% quality. Decoding focused on level-ground walking along with ascent/descent stairs tasks performed by healthy subjects. Time-domain raw data from inertial measurement unit sensors were the most used data. Different classifiers were employed considering the LMs to decode (accuracy above 90% for all tasks). Five studies have adapted the assistance of AOs/exoskeletons attending to the decoded LM, in which only one study predicted the new LM before its occurrence. Future research is encouraged to develop decoding tools considering data from people with lower-limb impairments walking at self-selected speeds while performing daily LMs with AOs/exoskeletons.

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Tsui, Lok-kun, Angelica Benavidez, Ponnusamy Palanisamy, Lindsey Evans, and Fernando Garzon. "Automatic signal decoding and sensor stability of a 3-electrode mixed-potential sensor for NOx/NH3 quantification." Electrochimica Acta 283 (September 2018): 141–48. http://dx.doi.org/10.1016/j.electacta.2018.06.133.

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Peng, Xiao Feng, Yi Yang, Chuan Yang, and Kai Li Wang. "Research on Data Transmission Technology Based on Raptor Code in Wireless Sensor Networks." Applied Mechanics and Materials 442 (October 2013): 538–43. http://dx.doi.org/10.4028/www.scientific.net/amm.442.538.

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Wireless sensor network is a data centric distributed self-organizing network. The transmission of data is the most important task of it. It is a topic worthy of study that how to ensure the reliability of data transmission in any environment .Fountain codes algorithm is simple, and has no ratio. It is very suitable for coding scheme for Wireless Sensor Networks. This paper mainly studies the Fountain codes of Raptor codes in wireless sensor network data transmission technology, and gives the concrete scheme and implementation ways. The application of this technology can improve the reliability of data transmission and transmission efficiency. It overcome the problems of very large packet error codec technology traditional rate and the decoding success rate.

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Tsitron, Julia, Addison D. Ault, James R. Broach, and Alexandre V. Morozov. "Decoding Complex Chemical Mixtures with a Physical Model of a Sensor Array." PLoS Computational Biology 7, no. 10 (October 20, 2011): e1002224. http://dx.doi.org/10.1371/journal.pcbi.1002224.

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Barros, J., and M. Tuchler. "Scalable decoding on factor trees: a practical solution for wireless sensor networks." IEEE Transactions on Communications 54, no. 2 (February 2006): 284–94. http://dx.doi.org/10.1109/tcomm.2005.861651.

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Liu, Yang. "Decoding mobile-phone image sensor rolling shutter effect for visible light communications." Optical Engineering 55, no. 1 (January 26, 2016): 016103. http://dx.doi.org/10.1117/1.oe.55.1.016103.

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Rahman, Md Habibur, Mohammad Abrar Shakil Sejan, Jong-Jin Kim, and Wan-Young Chung. "Reduced Tilting Effect of Smartphone CMOS Image Sensor in Visible Light Indoor Positioning." Electronics 9, no. 10 (October 3, 2020): 1635. http://dx.doi.org/10.3390/electronics9101635.

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Visible light positioning (VLP) using complementary metal–oxide–semiconductor (CMOS) image sensors is a cost-effective solution to the increasing demand for an indoor positioning system. However, in most of the existing VLP systems with an image sensor, researchers assume that the receiving image sensor is positioned parallel to the indoor floor without any tilting and, thus, have only focused on the high-precision positioning algorithm and ignored the proper light-emitting diode (LED)-ID recognition. To address these limitations, we present, herein, a smartphone CMOS image sensor and visible light-based indoor localization system for a receiver device in a tilted position, and we have applied a machine learning approach for optimized LED-ID detection. For detection of the LED-ID, we generated different features for different LED-IDs and utilize a machine learning method to identify each ID as opposed to using the conventional coding and decoding method. An image processing method was used for the image features extraction and selection. We utilized the rolling shutter mechanism of the smartphone CMOS image sensor in our indoor positioning system. Additionally, to improve the LED-ID detection and positioning accuracy with the tilting of the receiver, we utilized the embedded fusion sensors of the smartphone (e.g., accelerometer, gyroscope, and magnetometer, which can be used to extract the yaw, pitch, and roll angles). The experimental results for the proposed positioning system show that it can provide 2.49, 4.63, 8.46, and 12.20 cm accuracy with angles of 0, 5, 10, and 15°, respectively, within a 2 m × 2 m × 2 m positioning area.

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Nyein, Hnin Yin Yin, Mallika Bariya, Liisa Kivimäki, Sanna Uusitalo, Tiffany Sun Liaw, Elina Jansson, Christine Heera Ahn, et al. "Regional and correlative sweat analysis using high-throughput microfluidic sensing patches toward decoding sweat." Science Advances 5, no. 8 (August 2019): eaaw9906. http://dx.doi.org/10.1126/sciadv.aaw9906.

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Recent technological advancements in wearable sensors have made it easier to detect sweat components, but our limited understanding of sweat restricts its application. A critical bottleneck for temporal and regional sweat analysis is achieving uniform, high-throughput fabrication of sweat sensor components, including microfluidic chip and sensing electrodes. To overcome this challenge, we introduce microfluidic sensing patches mass fabricated via roll-to-roll (R2R) processes. The patch allows sweat capture within a spiral microfluidic for real-time measurement of sweat parameters including [Na+], [K+], [glucose], and sweat rate in exercise and chemically induced sweat. The patch is demonstrated for investigating regional sweat composition, predicting whole-body fluid/electrolyte loss during exercise, uncovering relationships between sweat metrics, and tracking glucose dynamics to explore sweat-to-blood correlations in healthy and diabetic individuals. By enabling a comprehensive sweat analysis, the presented device is a crucial tool for advancing sweat testing beyond the research stage for point-of-care medical and athletic applications.

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Ramya, K. P., R. Chithra Devi, M. K. Revathi, and P. Annapandi. "Sensor Data Hiding Based on Image Watermarking Using Interpolation Technique over Inter-Packet Delays." Applied Mechanics and Materials 573 (June 2014): 543–48. http://dx.doi.org/10.4028/www.scientific.net/amm.573.543.

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Large number of application areas, like location-based services, transaction logs, sensor networks are qualified by uninterrupted data stream from many. Sensor data handling of continuous data needs to cover various issues, admitting the storage efficiency, processing throughput, bandwidth conception and secure transmission. This paper addresses the challenges by providing secure and efficient transmission of sensor data by embedding it over the inter-packet delays (IPDs). The embedding of sensor data within a host medium makes this technique reminiscent of watermarking. Interpolation technique is used to hide the sensor data into an image which is send to another node. By enforcing linear enlargement to interpolation-errors, a extremely effective reversible watermarking scheme is achieved, which can ensure high image quality without sacrificing embedding capacity. Time-Based flow watermarking technique is proposed, that avoids data degradation due to traditional watermarking. Sensor data is extracted effectively based on the inter-packet delays that minimizes the probability of decoding error. The outcome of the observation depicts that this system is scalable and highly resilient in sensor data.

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Kim, Jaehong, Won-Yong Shin, Xin Kang, Han Lim Lee, and Jingon Joung. "Revisiting Information Detection and Energy Harvesting: A Power Splitting-Based Approach." Entropy 22, no. 12 (November 26, 2020): 1341. http://dx.doi.org/10.3390/e22121341.

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Wireless sensors are becoming essential in machine-type communications and Internet of Things. As the key performance metrics, the spectral efficiency as well as the energy efficiency have been considered while determining the effectiveness of sensor networks. In this paper, we present several power-splitting solutions to maximize the average harvested energy under a rate constraint when both the information and power are transmitted through the same wireless channel to a sensor (i.e., a receiver). More specifically, we first designed the optimal dynamic power-splitting policy, which decides the optimal fractional power of the received signal used for energy harvesting at the receiver. As effective solutions, we proposed two types of single-threshold-based power-splitting policies, namely, Policies I and II, which decide to switch between energy harvesting and information decoding by comparing the received signal power with some given thresholds. Additionally, we performed asymptotic analysis for a large number of packets along with practical statistics-based policies. Consequently, we demonstrated the effectiveness of the proposed power-splitting solutions in terms of the rate–energy trade-off.

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Li, Jia Li, Jian Ping Luo, Yu Hao Wang, and Cheng Zhi Long. "Research on the Strategy of Nodes Cooperative Transmission in Wireless Sensor Networks." Applied Mechanics and Materials 446-447 (November 2013): 1612–16. http://dx.doi.org/10.4028/www.scientific.net/amm.446-447.1612.

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The correlation techniques of nodes cooperative transmission strategy in wireless sensor networks with virtual MIMO are presented. A communication model between clusters is proposed. From it, we can see the correlation techniques include coding, forwarding, combining and decoding. The coding is generally divided into three categories: Layered Space-Time Coding, Space-Time Trellis Coding and Space-Time Block Coding. Forwarding can be broken down into Amplifying Forwarding, Decode Forwarding, and Selecting Dynamic Relay. Select combining, equal gain combining and maximum ratio combining are the common combine methods. The decode method is maximum likelihood algorithm.

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Kagami, Shingo, Masatsugu Shinmeimae, Takashi Komuro, Yoshihiro Watanabe, and Masatoshi Ishikawa. "A Pixel-Parallel Algorithm for Detecting and Tracking Fast-Moving Modulated Light Signals." Journal of Robotics and Mechatronics 17, no. 4 (August 20, 2005): 387–94. http://dx.doi.org/10.20965/jrm.2005.p0387.

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We developed a pixel-parallel image processing algorithm for detecting and tracking temporally modulated light signals for use as optical ID tags. The proposed algorithm requires no wavelength filters and obtains position information on signals at a high frame rate while decoding transmitted data. We implemented the proposed algorithm in a digital vision chip, a pixel-parallel processor embedded in a CMOS image sensor, and proved it to be operationally feasible.

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Arai, Shintaro, Haruna Matsushita, Yuki Ohira, Tomohiro Yendo, Di He, and Takaya Yamazato. "Maximum likelihood decoding based on pseudo-captured image templates for image sensor communication." Nonlinear Theory and Its Applications, IEICE 10, no. 2 (2019): 173–89. http://dx.doi.org/10.1587/nolta.10.173.

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Ebrahim, Mansoor, and Wai Chong Chia. "Multiview Image Block Compressive Sensing with Joint Multiphase Decoding for Visual Sensor Network." ACM Transactions on Multimedia Computing, Communications, and Applications 12, no. 2 (March 3, 2016): 1–23. http://dx.doi.org/10.1145/2818712.

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Davoli, F., M. Marchese, and M. Mongelli. "Non-linear coding and decoding strategies exploiting spatial correlation in wireless sensor networks." IET Communications 6, no. 14 (2012): 2198. http://dx.doi.org/10.1049/iet-com.2011.0799.

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Zhan, Chao, Sha Ou, Chao Zou, Min Zhao, and Chuan-De Wu. "A Luminescent Mixed-Lanthanide-Organic Framework Sensor for Decoding Different Volatile Organic Molecules." Analytical Chemistry 86, no. 13 (June 11, 2014): 6648–53. http://dx.doi.org/10.1021/ac5013442.

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Yahampath, Pradeepa. "Joint source-decoding in large scale sensor networks using Markov random field models." Signal Processing 90, no. 12 (December 2010): 3134–46. http://dx.doi.org/10.1016/j.sigpro.2010.05.019.

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Hsieh, Hsin-Yi, Yu-Hsuan Peng, Sheng-Fu Lin, Li-Ching Chen, Teng-Chien Yu, Chung-Fan Chiou, and Johnsee Lee. "Triple-Junction Optoelectronic Sensor with Nanophotonic Layer Integration for Single-Molecule Level Decoding." ACS Nano 13, no. 4 (March 11, 2019): 4486–95. http://dx.doi.org/10.1021/acsnano.9b00019.

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