Discrete Channel Research Articles

An improved discrete Fourier transformation channel estimation algorithm with low complexity for orthogonal frequency division multiplexing‐based power line carrier communication systems

AbstractOrthogonal frequency division multiplexing (OFDM) technology has been increasingly applied to power line carrier communication (PLC). Discrete Fourier Transformation (DFT)‐based channel estimation algorithm is suitable for OFDM‐based PLC due to its low complexity. In view of the problem that the traditional DFT channel estimation does not consider the influence of noise inside cyclic prefix (CP), an improved DFT channel estimation based on signal‐to‐noise ratio (SNR) estimation is proposed. First, least‐square (LS) algorithm is performed and the frequency domain channel estimation is converted to the time domain through inverse DFT, and the average SNR of the system is estimated according to the pilot sequence. Second, the substitute SNR for each sample point inside CP is defined and used to filter impulse noise inside CP. Third, the average SNR is converted into the threshold of the useful signal energy inside CP, and the widespread background noise inside CP is filtered. The simulation results show that the proposed algorithm can obtain more accurate channel estimation than other DFT channel estimation algorithms because it can effectively filter out noise inside CP. In addition, compared with other similar algorithms, the proposed algorithm dose not result in a significant increase in complexity.

2D imaging spin-filter for NanoESCA based on Au/Ir(001) or Fe(001)-p(1×1)O

A two-dimensional imaging spin-filter for photo-emission electron microscopy is described. The spin-filter is capable of imaging the electron spin polarization of either real space or momentum space electron distributions. As a scattering target either Au/Ir(001) comes into use, where spin sensitivity results from using spin–orbit scattering or Fe(001)-p(1×1)O that exploits exchange interaction. Both scattering targets were characterized with respect to their working points and Sherman function in a separate setup. Spin-polarization images of secondary electrons from the magnetic domains of a poly-crystalline iron sample are shown using both scattering targets. Images with a spin-filter using Au/Ir(001) show more than 104 discrete detection channels which increases the effective two-dimensional figure-of-merit (FoM) of this spin-filter by four orders of magnitude compared to single-channel spin detectors. Using the exchange scattering target two spin-components have been imaged for the first time. A method to detect all three spin-components is also outlined.

A Pulsed Lidar System With Ultimate Quantum Range Accuracy

A light detection and ranging (lidar) system using a multi-output quantum pulse gate (mQPG) for quantum uncertainty limited range resolution is presented. Due to the inherent phase-sensitivity of the process, the proposed mQPG enhanced lidar system is able to beat the theoretical Rayleigh range resolution limit and the single detection Craméer-Rao bound (CRB) of classic pulsed lidar. To achieve this behavior, gating pulses interact with the backscattered lidar pulse while propagating through the optically non linear waveguide within the mQPG. The intensity of the discrete spectral channels of the mQPG contains finally the target distance informations. The mQPG was fabricated in a periodically poled (PP) titanium-indiffused lithium niobate waveguide and the entire system was built up in a lab environment. Measurement up to 1500 μm were performed around an arbitrary set reference point, and show excellent agreement with the theory up to 600 μm for a pulse length of 1.56ps, and up to 1100 μm for a pulse length of 2.12ps.

SIMULATION AND ANALYSIS OF ENCODERS OF INTERFERENCE-RESISTANT CASCADE CODES FOR DYNAMIC SYSTEMS

For various channels of information transmission that can be used in local control systems of a wide range of processes, affordable, scalable, and inexpensive transmission methods are needed that allow the necessary information to be transmitted reliably without errors. Errors that occur in discrete, continuous communication channels are the main obstacle to the reliability of transmitted data. The main causes of errors are signal attenuation, noise and various interferences. As a result of the analysis of error statistics in discrete data transmission channels, it was concluded that single and double errors occur in the absolute majority of cases. It is proposed to combat errors by using tamper-resistant coding. Among the interference-resistant codes, redundant block-separated systematic codes are highlighted, among which is the Hamming code. The basis of the interference-resistant coding model is proposed to be the use of a systematic Hamming code followed by cascade coding by adding a parity check bit. The used model made it possible to deal with all the most likely cases of errors. Since this coding model must be flexibly used in different systems, a necessary criterion of the model is its universality. Therefore, the model was tested on a sample with code combinations of different numbers of bits. The purpose of this article is to analyze the dependence of the coding procedure execution time for encoders of different levels: a separate first-stage encoder, as well as a first-stage encoder with the addition of a second-stage encoder. Experiments were conducted with a large sample, which were subsequently analyzed and interpreted. Since the model proved to be flexible, simple, stable and effective as a result of experiments, and the process of combating interference by using cascade codes has proven itself well in world practice, it is recommended for use in various information management systems. The implementation of the coder for interference-resistant data transmission is given.

Identification Over Compound Multiple-Input Multiple-Output Broadcast Channels

The identification capacity region of the compound broadcast channel is determined under an average error criterion, where the sender has no channel state information. We give single-letter identification capacity formulas for discrete channels and multiple-input multiple-output Gaussian channels under an average input constraint. The capacity theorems apply to general discrete memoryless broadcast channels. This is in contrast to the transmission setting, where the capacity is only known for special cases, notably the degraded broadcast channel and the multiple-input multiple-output broadcast channel with private messages. Furthermore, the identification capacity region of the compound multiple-input multiple-output broadcast channel can be larger than the transmission capacity region. This is a departure from the single-user behavior of identification, since the identification capacity of a single-user channel equals the transmission capacity.

Experimental Study of Filtration Performance of Disc Filter with Discrete Channel Structure

The large head loss of the disc filter has always been one of its main limiting factors, which may affect its working efficiency and service life. Disc flow channel structure is an important factor affecting the performance of the disc filter. In this context, to reduce the head loss of the disc filter while ensuring the filtration effect, a new type of disc filter with a discrete channel structure was designed. Its performance in terms of head loss, sediment retention, uniformity of sediment distribution, and particle-size distribution in the runner was compared with that of a traditional linear channel disc filter. The results showed that the head loss of the new disc filter under the condition of clear water was 33.5%–50% lower than that of the traditional disc filter. Moreover, in sandy water, the head loss was 18.85%–47.07% lower than that of the traditional disc filter. Compared with the traditional disc filter, the average sediment retention and sediment uniformity ημ of the disc filter with discrete channels were increased by 6.32% and 1.5–2.5 times, respectively, when the flow rate was 1.5 m3/h. Considering the head loss, sediment retention, uniformity of sediment distribution, and other indicators, the hydraulic performance of the disc filter with a discrete channel is better than that of a traditional disc filter, and the filtration performance is slightly improved. The research results can provide a reference for the design and structural optimization of disc filter.

Simultaneous three-wave and six-wave mixing of microwave and optical fields in an atomic medium.

We experimentally investigate near-infrared optical field generation through simultaneous three-wave mixing (TWM) and six-wave mixing (SWM) processes in room-temperature 85Rb atoms. The nonlinear processes are induced using three hyperfine levels in the D1 manifold, which cyclically interact with pump optical fields and an idler microwave field. The simultaneous appearance of TWM and SWM signals in discrete frequency channels is made possible by breaking the three-photon resonance condition. This gives rise to coherent population oscillations (CPO), which are observed experimentally. We explain through our theoretical model the role of CPO in the generation of the SWM signal and its enhancement due to parametric coupling with the input seed field in contrast to the TWM signal. Our experiment proves that a single tone microwave can be converted to multiple optical frequency channels. The simultaneous existence of TWM and SWM processes can potentially enable various types of amplification to be achieved with a single neutral atom transducer platform.

Convolutional Neural Network-Driven Improvements in Global Cloud Detection for Landsat 8 and Transfer Learning on Sentinel-2 Imagery

A stable and reliable cloud detection algorithm is an important step of optical satellite data preprocessing. Existing threshold methods are mostly based on classifying spectral features of isolated individual pixels and do not contain or incorporate the spatial information. This often leads to misclassifications of bright surfaces, such as human-made structures or snow/ice. Multi-temporal methods can alleviate this problem, but cloud-free images of the scene are difficult to obtain. To deal with this issue, we extended four deep-learning Convolutional Neural Network (CNN) models to improve the global cloud detection accuracy for Landsat imagery. The inputs are simplified as all discrete spectral channels from visible to short wave infrared wavelengths through radiometric calibration, and the United States Geological Survey (USGS) global Landsat 8 Biome cloud-cover assessment dataset is randomly divided for model training and validation independently. Experiments demonstrate that the cloud mask of the extended U-net model (i.e., UNmask) yields the best performance among all the models in estimating the cloud amounts (cloud amount difference, CAD = −0.35%) and capturing the cloud distributions (overall accuracy = 94.9%) for Landsat 8 imagery compared with the real validation masks; in particular, it runs fast and only takes about 41 ± 5.5 s for each scene. Our model can also actually detect broken and thin clouds over both dark and bright surfaces (e.g., urban and barren). Last, the UNmask model trained for Landsat 8 imagery is successfully applied in cloud detections for the Sentinel-2 imagery (overall accuracy = 90.1%) via transfer learning. These prove the great potential of our model in future applications such as remote sensing satellite data preprocessing.

Multiple interfaces

AbstractIn India and its diaspora in the UK, online activities of various sorts—tweeting, blogging, messaging, trolling, and tagging—have become central to tensions surrounding religion's presence in public life and the stakes of belonging to the nation. Three clusters of social media practices undergird these digital mediations: piety, surveillance, and fun. Such practices reveal how internet‐enabled mediations reenergize religion as a political category of difference under majoritarian right‐wing regimes and the transnational context of Islamophobia, while also offering distinct possibilities for imagining politics through the pleasures, visibilities, and reflections induced by digital circulations. Rather than approaching the internet as an abstract technological context or discrete channels for communication, this analysis theoretically positions it as an arena of “multiple interfaces.” It signals contiguities and collisions that digital practice has opened up among the very real communities and structures of authority, under conditions shaped by longer colonial histories.

Neutronics and thermal-hydraulics coupling analysis for a small lead-based fast reactor based on the discrete ordinate nodal and parallel channel method

The full-core neutronics/thermal-hydraulics (NE/TH) coupling analysis for a small lead-based fast reactor (SLFR) was presented. The NE-to-TH coupling occurs via power distribution, whereas the TH-to-NE coupling occurs via the material cross section, which is related to the temperature distribution. In the NE module, a discrete ordinate (SN) nodal solver is implemented based on the hexagonal prism mesh. In the TH module, a parallel channel model is implemented with each hexagonal fuel assembly as a channel. The mass flow of each channel is searched according to the same pressure drop, the temperature and density of the coolant within each channel is calculated with a one-dimensional axial heat convection model, and the temperature distribution along the fuel pin is calculated with a one-dimensional radial heat conduction model. In order to update the cross section, a temperature-dependent homogenized microscopic cross-section library is prepared in advance, and based on the temperature distribution obtained from the TH calculation, the cross-section data required by the NE calculation are obtained by interpolation. The results of the NE/TH coupling analysis for SLFR indicate that the TH parameters (the maximum temperature of the fuel, cladding, and coolant and the maximum velocity of the coolant) are all within the design limits, and the TH feedback provides negative reactivity feedback (about −200 pcm) and has a fairly small effect on the power distribution (<1.0%).

Tunable Dual- and Multi-Channel Filter Based on Cantor Photonic Crystals Embedded With Graphene

We theoretically investigate the tunable dual- and multi-channel filter in one-dimensional aperiodic photonic crystals (PCs) embedded with graphene. The dielectrics slabs arrayed alternately submit to the Cantor sequence rule and graphene sheets are local at the interfaces of dielectrics layers. Cantor PCs are fractal structures and support a series of discrete resonant channels in transmission spectra, viz. fractal states. Consequently, dual- and multi-filtering channels could be achieved in the compound systems. The central wavelength of each channel and its value of transmission could be modulating by the chemical potential of grapheme and the incident angle as well. It shows that the dual-channel amplitude of transmission can be varied by over 100% and the multiple channels could be switched on and off by the chemical potential of graphene. This study will be very helpful in designing tunable optical filters.

Multispectral imaging via feature selection: A frugal innovation approach for pathogen identification

In order to develop an affordable clinical diagnostic instrument for use in more decentralized settings, we have assessed the feasibility to move from hyperspectral to multispectral imaging via parsimonious feature selection. The targeted application is the label-free identification at the species-level of uropathogens from images of bacterial colonies on their growth support. We show that the number of predictors (i.e., discrete spectral channels), can be dramatically reduced from 240 to less than 10 channels with limited performance loss. The impact of bandwidth is also investigated to consider the high degree of redundancy of raster images obtained by diffuse reflectance and propose a suitable design for a simple filter wheel based solution. Targeting the 8 most prevalent bacterial species responsible for > 80% of urinary tract infections, up to 94% of correct identification rate was reached using only 4 spectral windows.

Rate and Detection-Error Exponent Tradeoff for Joint Communication and Sensing of Fixed Channel States

We study the information-theoretic limits of joint communication and sensing when the sensing task is modeled as the estimation of a discrete channel state fixed during the transmission of an entire codeword. This setting captures scenarios in which the time scale over which sensing happens is significantly slower than the time scale over which symbol transmission occurs. The tradeoff between communication and sensing then takes the form of a tradeoff region between the rate of reliable communication and the state detection-error exponent. We investigate such tradeoffs for both mono-static and bi-static scenarios, in which the sensing task is performed at the transmitter or receiver, respectively. In the mono-static case, we develop an exact characterization of the tradeoff in open-loop, when the sensing is not used to assist the communication. We also show the strict improvement brought by a closed-loop operation, in which the sensing informs the communication. In the bi-static case, we develop an achievable tradeoff region that highlights the fundamentally different nature of the bi-static scenario. Specifically, the rate of communication plays a key role in the characterization of the tradeoff and we show how joint strategies, which simultaneously estimate message and state, outperform successive strategies, which only estimate the state after decoding the transmitted message.

DEVELOPMENT OF A MODEL OF INTERFERENCE-RESISTANT DATA TRANSMISSION FOR INFORMATION TECHNOLOGY OF CONTROL OPTIMIZATION OF DYNAMIC SYSTEMS

For data transmission channels used in local control systems for various processes, simple and inexpensive methods are needed that will allow the necessary information to be transmitted without errors. Errors that occur in continuous communication channels are one of the main obstacles to the reliable transmission of information. As a result of the analysis of error statistics in discrete channels, it was concluded that single and double errors occur most often. The purpose of this article is to develop a model of interference-resistant data transmission for information technology optimization of control of dynamic systems. The analyzed causes of errors are signal attenuation, noise and various interferences. The resulting transmitted signal is determined by the combination of the useful signal and the interference signal. When considering discrete signals, several causes of errors are highlighted – edge distortions, pulse splitting, and others. Considered methods of combating obstacles: means of operational and preventive nature; means of increasing interference resistance during the transmission of single elements; use of feedback; adding redundancy to the code; tamper-proof coding. It is proposed to combat errors using tamper-resistant coding. Among the interference-resistant codes, redundant block separable systematic codes are selected, which are divided into cyclic codes and Hamming code. The basis of the interference-resistant coding model is the use of a systematic code - a cyclic code or a Hamming code followed by cascade coding. Since the coding model must be adaptively used in different systems, a necessary criterion of the model is its universality. The model should work in different systems without changing the encoding and decoding algorithms. Among the many types of interference-resistant codes, cascade codes are chosen for use. Widespread cascade codes are universal, easily scalable, work stably, have proven themselves well in world practice, therefore they are recommended for use in various information management systems. The implementation of the interference-resistant data transmission model is presented.

“A Light in Sound, a Sound-Like Power in Light”: Poetry, Synaesthesia & Multimedia

Synaesthesia is a neurological phenomenon of cross-modal perception in which stimulation of one sensory pathway results in automatic, simultaneous, involuntary experiences in a second sensory pathway, such as the perceiving of sound as a colour. Creatively employed as a literary trope, synaesthesia involves the blending of different sense modalities, often in the form of figurative language. Exploring the interrelationship of synaesthetic aesthetics and experimentation from a historical perspective, this essay traces the transgressive artistic impulse to blur sensorial boundaries through technical innovation from the eighteenth-century audio-visual experiments of Louis Bertrand Castel’s ocular harpsichord (clavecin pour les yeux) to twenty-first century LED colour organs, speech coding, and Augmented/Virtual Reality interfaces used in multisensory art exhibitions. By outlining a few of the major historical developments of synaesthetic theory and poetics as well as some of the diverse immersive experiences made possible through original combinations of discrete sensory channels via transmedial artistic experimentation, the present paper aims to demonstrate how synaesthesia – located at the interface of literature and neuroscience, sensation and ideation, mind and body – might also fruitfully be explored in relation to lyric poetry at the interface of multisensoriality and multimedia.

Learning to control a BMI-driven wheelchair for people with severe tetraplegia.

Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the hypothesis that BMI skill acquisition by end-users is fundamental to control a non-invasive brain-actuated intelligent wheelchair in real-world settings. We demonstrate that three tetraplegic spinal-cord injury users could be trained to operate a non-invasive, self-paced thought-controlled wheelchair and execute complex navigation tasks. However, only the two users exhibiting increasing decoding performance and feature discriminancy, significant neuroplasticity changes and improved BMI command latency, achieved high navigation performance. In addition, we show that dexterous, continuous control of robots is possible through low-degree of freedom, discrete and uncertain control channels like a motor imagery BMI, by blending human and artificial intelligence through shared-control methodologies. We posit that subject learning and shared-control are the key components paving the way for translational non-invasive BMI.

Neural joint source-channel coding via Bernoulli latent straight-through estimator

Under infinite block length, from Shannon's separation theorem, it is well-known that by independent design of source coding and channel coding, the optimal throughput — the channel capacity — can be reached with careful design of the corresponding functionalities. However, when restricted to finite block length, the separation theorem does not necessarily hold, and hence joint source-channel coding (JSCC) theorem has been raised as an alternative strategy for achieving the capacity of the channel. However, as JSCC formulates highly non-convex problem which cannot be directly solved analytically, recently, deep learning has been proposed as a key enabler for JSCC. While most work on deep-learning-based JSCC focused on transmission of continuous signals through wireless channels, we focus in this paper the case of information-theoretic channel, namely binary symmetric channel, which can give useful insights on information-theoretic perspective on JSCC. Unlike recent work on deep-learning-based, or neural, JSCC for discrete channels that considered score function estimator to train the JSCC, in this paper, we improve the performance of neural JSCC by estimating the gradient more precisely as compared to the previous approach. Key idea is to consider soft codeword during training to enable path-wise gradient estimator which is proven to have lower variance than the score-function estimator. Experimental results on MNIST and CIFAR-10 datasets show that the proposed neural JSCC outperforms the previous work on JSCC for discrete channels, validating the effectiveness of the proposed gradient computation technique.

Recognizing genetically related depositional packages using 3D photogrammetric outcrop models in a fluvially dominated, tidally influenced meander-belt succession

Fluvio-tidal estuary deposits are characterized by abundant channel-associated dunes, compound dunes and point bar deposits. The delineation of boundaries between different point bars to identify genetically unrelated packages is difficult to assess, particularly where similarly dipping bars are superimposed on each other. This study uses an outcrop dataset of the McMurray Formation along the Christina River in NE Alberta, Canada, focuses on bedding relationships and the nature of sedimentary contacts pertaining to the two dominant channel-associated facies: 1) dune-scale, cross-bedded sandstone (CB) representing lower parts of estuary point-bar deposit or thalweg-associated dunes; and 2) inclined heterolithic stratified sandstone and mudstone (IHS) bedsets typically representing the middle and upper part of point bar deposits. This study lays a foundation for documenting and differentiating discrete channel deposits using sedimentological observations at the outcrop, photogrammetry, and surface-orientation analyses of the resulting 3D model. The results reveal the following findings: 1) the IHS units may be underlain by erosional surfaces, which may not be genetically associated with the underlying cross-bedded sandstone intervals; 2) the forward-accreted tidal compound dunes and laterally accreted fluvio-tidal point bars share similar facies characteristics, but are discernible using bedding orientation analysis; 3) these tidal compound dunes may be under-recognized in many tidally influenced estuary deposits; and 4) an inner estuary point-bar setting may be characterized by stacked small-scale point bars, or stacked lateral accretion sets of a single point bar. The result of this study provides a detailed view of facies architecture in a tide-influenced fluvial setting, and feasible solutions to the challenge of assigning genetic units in such environment.

Algorithm theoretical basis for ozone and sulfur dioxide retrievals from DSCOVR EPIC

Abstract. On board the Deep Space Climate Observatory (DSCOVR), the first Earth-observing satellite at the L1 point (the first Lagrangian point in the Earth–Sun system), the Earth Polychromatic Imaging Camera (EPIC) continuously observes the entire sunlit face of the Earth. EPIC measures the solar backscattered and reflected radiances in 10 discrete spectral channels, four of which are in the ultraviolet (UV) range. These UV bands are selected primarily for total ozone (O3) and aerosol retrievals based on heritage algorithms developed for the series of Total Ozone Mapping Spectrometers (TOMS). These UV measurements also provide sensitive detection of sulfur dioxide (SO2) and volcanic ash, both of which may be episodically injected into the atmosphere during explosive volcanic eruptions. This paper presents the theoretical basis and mathematical procedures for the direct vertical column fitting (DVCF) algorithm used for retrieving total vertical columns of O3 and SO2 from DSCOVR EPIC. This paper describes algorithm advances, including an improved O3 profile representation that enables profile adjustments from multiple spectral measurements and the spatial optimal estimation (SOE) scheme that reduces O3 artifacts resulting from EPIC's band-to-band misregistrations. Furthermore, this paper discusses detailed error analyses and presents intercomparisons with correlative data to validate O3 and SO2 retrievals from EPIC.

Study of Channel Capacity Optimization of Discrete Channel Based on Normalized Tangent Function

Channel capacity is the rate limit of information transmission; many researchers strive to find the maximum channel transmission rate while balancing the performance of the whole communication system and making it close to the channel capacity. We know in the discrete channel that, when the BER of the system is small enough, the transmission rate of the channel will be extremely close to the channel capacity. According to Shannon theory, the common way to increase the channel capacity is to increase the system bandwidth and the input signal power. However, the bandwidth resources are extremely limited, so it is impractical to expand the system bandwidth, and at the same time, increasing the input signal power is impractical as well, because it will reduce the economy of the communication system. In this paper, we proposed a normalized tangent function (NorTF) which can optimize the discrete channel probability distribution, reduce the BER of the channel, and improve the channel capacity of the discrete channel, which need not expand bandwidth and increase the signal input power of the system. Through theoretical analysis and experimental verification of this method, we found that the channel capacity of the improved system was indeed much higher than that of the preimproved system, so the method proposed in this paper has some theoretical guiding significance and is feasible.