Hard Decision Research Articles

Joint Latent Space and Label Inference Estimation with Adaptive Fused Data and Label Graphs

Recently, structured computing has become an interesting topic in the world of artificial intelligence, especially in the field of machine learning, as most researchers focus on the development of graph-based semi-supervised learning models. In this article, we present a new framework for graph-based semi-supervised learning. We present a powerful method for simultaneous label inference and linear transform estimation. The targeted linear transformation is used to obtain a discriminant subspace. To improve semi-supervised learning, our framework focuses on exploiting the data structure and soft labels of the available unlabeled samples. In the iterative optimization scheme used, the prior estimation of the label increases the supervision information indirectly through an introduced informative matrix called the label graph, thus avoiding the use of hard confidence-based decisions as used in self-supervised methods. In addition, the estimation of labels and projected data is made more robust by using smoothing concepts based on hybrid graphs. For each type of smoothing, the hybrid graph is an adaptive fusion of the two graphs encoding the similarity of the data and the similarity of the labels. The proposed method leads to an improved discriminative linear transformation. Several experimental results on real image datasets confirm the effectiveness of the proposed method. They also show superior performance compared to semi-supervised methods that use integration and label inference simultaneously.

Adaptive parallel decision deep neural network for high-speed equalization.

The equalization plays a pivotal role in modern high-speed optical wire-line transmission. Taking advantage of the digital signal processing architecture, the deep neural network (DNN) is introduced to realize the feedback-free signaling, which has no processing speed ceiling due to the timing constraint on the feedback path. To save the hardware resource of a DNN equalizer, a parallel decision DNN is proposed in this paper. By replacing the soft-max decision layer with hard decision layer, multi-symbol can be processed within one neural network. The neuron increment during parallelization is only linear with the layer count, rather than the neuron count in the case of duplication. The simulation results show that the optimized new architecture has competitive performance with the traditional 2-tap decision feedback equalizer architecture with 15-tap feed forward equalizer at a 28GBd, or even 56GBd, four-level pulse amplitude modulation signal with 30dB loss. And the training convergency of the proposed equalizer is much faster than its traditional counterpart. An adaptive mechanism of the network parameter based on forward error correction is also studied.

An efficient multi-agent approach to order picking and robot scheduling in a robotic mobile fulfillment system

This paper presents a Multi-Agent System (MAS) for optimizing the scheduling and routing of mobile robots and human pickers in a Robotic Mobile Fulfillment System (RMFS). The RMFS is a system designed for e-commerce warehousing where autonomous mobile robots are used to fetch inventory pods, also referred to as racks, from the storage area and transport them to the appropriate picking station where human pickers pick the required number of goods. The system requires the solution of several hard decision problems like: the order-to-picking station assignment and sequencing, the pod selection, and the multi-robot task allocation. The proposed solution approach employs decentralized scheduling mechanisms, i.e. auctions and dispatching rules, to communicate and distribute picking and retrieval tasks among the agents. Various dispatching rules are identified and analyzed over a wide set of problem instances of the RMFS with varying numbers of mobile robots, picking stations, and order sizes. The proposed MAS framework shows promising results and requires only a fraction of the computation time compared to a centralized scheduling algorithm. The MAS also includes both unidirectional and bidirectional lanes. Although additional complexity in collision avoidance is introduced when using bidirectional lanes, it allows for better system performance.

Identifying key-psychological factors influencing the acceptance of yet emerging technologies–A multi-method-approach to inform climate policy

The best combination of possible climate policy options (mitigation, adaptation and different climate engineering technologies) to tackle climate change is unknown. Climate policy is facing a hard decision in answering the question whether climate engineering technologies should be researched, limitedly deployed or even deployed at global scale. Such technologies bear large epistemic and ethical uncertainties and their use as well as non-use might have severe consequences. To deal with such uncertainties, the (ethical) assessment of climate engineering technologies should include the perspectives of various stakeholders including laypersons to inform climate policy. To facilitate (ethical) technology assessment, we propose a novel 2-step methodology to collect and analyze data on ethical concerns and the acceptability of climate engineering technologies. Thereby we focus on Stratospheric Aerosol Injection (SAI) as an use case. We propose an innovative combination of newly developed methods consisting of two data collection tools (Cognitive-Affective Mapping and large-scale survey) and two types of data analyses (using graph theory and factor analysis). Applying this multi-method approach we were able to identify (1) central ethical and governance related concerns regarding SAI (by Cognitive-Affective Maps) and (2) to estimate the relative importance of core constructs (positive and negative affect, risk and benefit perception, trust) on the acceptability of SAI (by large-scale survey).

Performance evaluation of hard‐decision and soft‐data aided cooperative spectrum sensing over Nakagami‐m fading channel

AbstractThe secondary users can opportunistically utilize the spectrum holes left by the licensed users (also called primary users [PUs]) with the help of cognitive radio (CR) technology. The cooperative spectrum sensing (CSS) is crucial for the implementation of CR algorithms in the next‐generation wireless networks, for the specific identification of spectral gaps of PUs under any fading/shadowing channel. Accurate spectrum sensing reveals that the CUs only provide data when the PUs report being idle. Yet, there is always a possibility that CU transmission will cause interference with PU communications, leading to inefficient spectrum use. So, this work explores the performance of a proposed CSS by considering such factors with the key objective of enhancing spectrum utilisation efficiency. This paper evaluates the role of the proposed CSS with a variety of hard decision fusion rules (HDFR) schemes as well as soft data fusion schemes (SDFS) methods. Toward this end, for the proposed system, closed‐form expressions for the evaluation of various performance metrics such as the false alarm, miss‐detection probabilities, total error probability (TEP), and throughput/energy efficiency are derived by taking into consideration the Nakagami‐m fading environment using different hard and soft algorithms and are validated by means of numerical simulations. Furthermore, by taking into account various network specifications, such as channel error probability (q), fading severity parameter (m), number of samples (M), number of CR nodes (N), average sensing (S) channel signal‐to‐noise ratio, and sensing threshold (λ), a performance evaluation between the SDFS and HDFR schemes has been carried out. Furthermore, using HDFR/SDFS schemes, analytical models are formulated for determining an optimal number of CR nodes () and optimal threshold () values for achieving the lowest TEP and highest energy efficiency.

Deep Learning-Based Soft Iterative-Detection of Channel-Coded Compressed Sensing-Aided Multi-Dimensional Index Modulation

The concept of Index Modulation (IM) has been actively researched as a benefit of its flexible trade-off between performance, achievable rate, energy efficiency, hardware cost and complexity. In order to fully exploit its the degrees of freedom, the concept of Multi-dimensional IM (MIM) has been developed in literature, where Compressed Sensing (CS) is often utilized to exploit the sparsity of the multi-dimensional transmitted signals. However, this flexibility and performance gains are attained at the cost of a substantially increased detection complexity. In this paper, we propose Deep Learning (DL) based detection for CS-aided MIM (CS-MIM), where both Hard-Decision (HD) as well as Soft-Decision (SD) detection combined with iterative decoding are conceived. More explicitly, firstly, we propose learning aided hard and soft detection for CS-MIM. Secondly, two novel neural network aided methods are proposed for Iterative Soft Detection (ISD), where iterations are carried out between the CS-MIM detector and a channel decoder. In contrast to the conventional detection of CS-MIM system, which critically relies on the knowledge of Channel State Information (CSI) at the receiver, the proposed learning-aided methods are capable of eliminating the overhead and complexity of Channel Estimation (CE), which results in an improved transmission rate. Explicitly, we develop an advanced DL architecture for blind-detection-aided MIM for the first time in the open literature, where the HD and SD CS algorithms are implemented by learning without the need for CE. Our simulation results demonstrate that the proposed learning techniques conceived for SD CS-MIM combined with iterative detection are capable of achieving near-capacity performance at a reduced complexity compared to the conventional model-based SD relying on CSI acquisition.

200 Gb/s Optical-Amplifier-Free IM/DD Transmissions Using a Directly Modulated O-Band DFB+R Laser Targeting LR Applications

We experimentally demonstrate an O-band single-lane 200 Gb/s intensity modulation direct detection (IM/DD) transmission system using a low-chirp, broadband, and high-power directly modulated laser (DML). The employed laser is an isolator-free packaged module with over 65-GHz modulation bandwidth enabled by a distributed feedback plus passive waveguide reflection (DFB+R) design. We transmit high baud rate signals over 20-km standard single-mode fiber (SSMF) without using any optical amplifiers and demodulate them with reasonably low-complexity digital equalizers. We generate and detect up to 170 Gbaud non-return-to-zero on-off-keying (NRZ-OOK), 112 Gbaud 4-level pulse amplitude modulation (PAM4), and 100 Gbaud PAM6 in the optical back-to-back configuration. After transmission over the 20-km optical-amplifier-free SSMF link, up to 150 Gbaud NRZ-OOK, 106 Gbaud PAM4, and 80 Gbaud PAM6 signals are successfully received and demodulated, achieving bit error rate (BER) performance below the 6.25%-overhead hard-decision (HD) forward-error-correction code (FEC) limit. The demonstrated results show the possibility of meeting the strict requirements towards the development of 200Gb/s/lane IM/DD technologies, targeting 800Gb/s and 1.6Tb/s LR applications.

Impact of episodes of lucidity on people living with dementia and their caregivers: A qualitative study

AbstractBackgroundEpisodes of lucidity (EL) are generally characterized by a spontaneous and unexpected return of abilities previously believed to be lost in individuals with Alzheimer’s disease and Alzheimer’s disease related dementias (AD/ADRD). Among the myriad implications of lucidity are the impact these episodes have on the person living with ADRD and their family caregivers. Family caregivers provide the majority of support for persons with AD/ADRD and frequently experience complex grief associated with identity and relationship changes over the course of the disease. Consequentially, a transient return of abilities and connection in the person with AD/ADRD may have a substantial impact on their conceptualization of consciousness in the care recipient living with AD/ADRD, approach to care, and emotional wellbeing. It is also not understood how persons living with AD/ADRD may respond to EL.MethodsThis study utilized a descriptive qualitative method employing semi‐structured interviews with family caregivers (N = 22) of persons living with AD/ADRD. Interviews elicited information about participant’s experiences with EL, interpretation of these events, and appraisal of the impact of the event on themselves and the person experiencing it. Interview data were analyzed using a modified, two‐phase thematic analysis approach.ResultsEL were commonly conceptualized by caregivers as illustrative of the ongoing presence of self and consciousness in the person living with AD/ADRD that they posit is at other times concealed due to cognitive impairment. Caregivers overwhelmingly reported that these episodes were valued moments that offered meaningful connection to their loved ones, also at times facilitating acts of saying goodbye or making hard decisions. Caregivers perceived EL as having variable impact on the person living with AD/ADRD. In some situations, EL were perceived as providing the person living with AD/ADRD greater capacity to engage. Sometimes, EL were described as being accompanied by insight regarding deficits and perceived as upsetting to the person living with AD/ADRD.ConclusionWe found that EL can have both positive and negative impacts on caregivers and people living with AD/ADRD. Future research is needed to address ethical considerations surrounding EL and to support caregivers in how to anticipate and respond to these events.

Hit, Miss, or Error? Predicting Errors in Design Decision Making for Radically Innovative Ideas using Individual Attributes

Abstract Researchers and practitioners alike agree that for companies to survive and thrive they must develop and support radical innovation. However, these ideas are complex and risky, and not all succeed. Because of this, decision makers are often left to make hard decisions in terms of which ideas can move on and which are abandoned. The goal of this article was to provide evidence on the impact of individuals’ preferences for creativity on the effectiveness of their decision making for radical ideas using principles from signal detection theory (SDT). To do this, we used data from a previous study of 2252 idea evaluations by engineering students and classified these decisions based on SDT to see if we could predict the likelihood of occurrence of hit (correct identification), miss (type 1 error), false alarm (type II error), and correct rejection. The results showed that lower levels of risk tolerance resulted in an increased likelihood that a hit occurred. On the other hand, higher levels of motivation resulted in an increased likelihood of a type I error occurring, or that an individual would more likely neglect a good idea that had a high chance of future success. Finally, increased risk tolerance resulted in an increased likelihood that type II error occurred, or that an individual would expend resources on an idea with limited likelihood of success. The results serve as empirical evidence on decision making in radically innovative tasks and provide a methodology for studying decision making in innovative design.

Efficacy of Decentralized CSS Clustering Model Over TWDP Fading Scenario

Cognitive Radio technology, which lowers spectrum scarcity, is a rapidly growing wireless communication technology. CR technology detects spectrum holes or unlicensed spectrums which primary users are not using and assigns it to secondary users. The dependability of the spectrum-sensing approach is significantly impacted from two of the most critical aspects, namely fading channels and neighboring wireless users. Users of non-cooperative spectrum sensing devices face numerous difficulties, including multipath fading, masked terminals, and shadowing. This problem can be solved using a cooperative- spectrum-sensing technique. For the user, CSS enables them to detect the spectrum by using a common receiver. It has also been divided into distributed CSS and centralized CSS. This article compares both ideas by using a set of rules to find out whether a licensed user exists or not. This thought was previously used to the conventional fading channels, such as the Rician, Rayleigh and the nakagami-m models. This work focused on D-CSS using clustering approach over TWDP fading channel using two-phase hard decision algorithms with the help of OR rule as well as AND rule. The evaluation of the proposed approaches clearly depicted that the sack of achieve a detection-probability of greater than 0.8; the values SNR varies between -14 dB to -8 dB. For all two-phase hard decision algorithms using proposed approach and CSS techniques, the detection probability is essentially identical while the value of signal to noise ratio is between -12 dB to -8dB. Throughout this work, we assess performance of cluster-based cooperative spectrum-sensing over TWDP channel with the previous findings of AWGN, Rayleigh, and wei-bull fading channels. The obtained simulation results show that OR-AND decision scheme enhanced the performance of the detector for the considered range of signal to noise ratios.

Laser Sharing Uplink Polarization Division Multiplexing FBMC Passive Optical Network

In this paper, we proposed and experimentally demonstrated a laser sharing filter bank multicarrier passive optical network (FBMC-PON) uplink architecture, to provide high spectrum efficiency frequency division multiple access (FDMA) to the optical network terminals (ONTs) with aggregated uplink data rate of up to 100 Gb/s/λ for each optical network unit (ONU), in a fiber to the frontage (FTTF) scenario utilizing existing copper infrastructure. Intensity modulation (IM) and heterodyne detection are implemented, combined with polarization division multiplexing (PDM) to double the system capacity. The uplink performance of FBMC-PON is emulated with two asynchronous ONT bands in transmission over 20 km single mode fiber (SMF). FBMC with prototype filters (PFs) PHYDYAS, extended Gaussian filter (EGF) and isotropic orthogonal transform algorithm (IOTA) is discussed in comparison with orthogonal frequency division multiplexing (OFDM) in terms of frequency guard band and multiple access interference (MAI). The laser from an external cavity laser (ECL) is split and shared among ONTs. The experimental results show that FBMC can achieve significant improved receiver sensitivity compared to OFDM in the asynchronous uplink. FBMC with PHYDYAS can avoid MAI with frequency guard interval of one time the subcarrier spacing. With optical receiving power (ROP) of −23 dBm, to support 2, 4 and 8 ONTs, the required optical power of the narrow linewidth laser is respectively 10, 16 and 22 dBm with bit error rate (BER) at the hard decision forward error correction (HD-FEC) threshold.

Parity versus Ignorance

Abstract Why are hard decisions hard? According to the incomparabilists, hard choices are hard because the options cannot be compared. Proponents of parity hold that hard choices are hard because the options can be compared but only in terms of a fourth value relation—parity—in addition to the three standard relations: better, worse, and equally good. Others claim that hard choices are hard because it is vague (or indeterminate) how the options relate in terms of the three standard relations. Lastly, there is the epistemicist. For the epistemicist, hard choices are hard because one is irresolvably ignorant about how the options compare. In the debate about hard choices, epistemicism is often mentioned but rarely defended. The present paper is a contribution to closing this gap.

New low-density-parity-check decoding approach based on the hard and soft decisions algorithms

<span lang="EN-US">It is proved that hard decision algorithms are more appropriate than a soft decision for low-density parity-check (LDPC) decoding since they are less complex at the decoding level. On the other hand, it is notable that the soft decision algorithm outperforms the hard decision one in terms of the bit error rate (BER) gap. In order to minimize the BER and the gap between these two families of LDPC codes, a new LDPC decoding algorithm is suggested in this paper, which is based on both the normalized min-sum (NMS) and modified-weighted bit-flipping (MWBF). The proposed algorithm is named normalized min sum- modified weighted bit flipping (NMSMWBF). The MWBF is executed after the NMS algorithm. The simulations show that our algorithm outperforms the NMS in terms of BER at 10-8 over the additive white gaussian noise (AWGN) channel by 0.25 dB. Furthermore, the proposed NMSMWBF and the NMS are both at the same level of decoding difficulty.</span>

Extremely Weak Signal Detection Algorithm of Multi-Pixel Photon Detector

For the detection of extremely weak signals in underwater long distance wireless optical communication, the Geiger mode of the multi pixel photon detector has serious pulse superposition effect and Poisson noise, which leads to counting difficulties and affects the measurement accuracy. This paper proposes a deconvolution joint generalized Anscombe transform (DUGAT) detection algorithm. First, the deconvolution filtering technology is used to process the weak signal to reduce the superposition effect and converge the pulse width. Secondly, the noise signal of Poisson+Gaussian (P+G) distribution is converted into Gaussian variable by using generalized Anscombe transform (GAT), which is equivalent to additive Gaussian noise signal. Finally, the symbols are counted by hard decision algorithm. The experimental results show that the sensitivity of this algorithm is improved by 8.57dB and 9.95dB respectively compared with the traditional AR and ML detection algorithms.

A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes

The conventional soft decision decoding (SDD) methods require various hard decision decoders (HDDs) based on different codes or re-manipulate the generator matrix by the complicated Gaussian elimination technique according to the bit reliability. This paper presents a general multi-class neural network (NN)-based decoder for the short linear block codes, where no HDD and Gaussian elimination are required once the NN is constructed. This network architecture performs multi-classification to select the messages with high occurrence probabilities and chooses the best codeword on a maximum likelihood basis. Simulation results show that the developed approach outperforms the existing deep neural network (DNN)-based decoders in terms of decoding time and bit error rate (BER). The error-correcting performance is also superior to the conventional Chase-II algorithm and is close to the ordered statistics decoding (OSD) in most cases. For Bose–Chaudhuri–Hocquenghem (BCH) codes, the SNR is improved by 1dB to 4dB as the BER is 10−4. For the (23, 12) quadratic residue (QR) code, the SNR is improved by 2dB when the BER is 10−3. The developed NN-based decoder is quite general and applicable to various short linear block codes with good BER performance.

Improving the Resistance of AO-OFDM Signal to Fiber Four-Wave Mixing Effect Based on Insertion Guard Interval

In this paper, a method to suppress the impact of the nonlinear effects on an all optical orthogonal frequency division multiplexing (AO-OFDM) system is proposed. By inserting a guard interval (GI), the duty cycle of the optical signal in each symbol period of each subcarrier is decreased, thus generating a ‘zeroed’ temporal. By giving different time delays to the sub-carriers, these ‘zeroed’ temporals and optical signals of the adjacent sub-carriers are interleaved, which reduces the coincidence of the optical signals between the sub-carriers and eliminates the products of the four-wave mixing (FWM) effect to suppress the influence of the nonlinear effect on the system. The simulation results show that for an AO-OFDM system with 32 subcarriers, inserting GI and introducing different delays for each subcarrier can improve the transmission distance by 30 km or enhance the spectral efficiency by 16.7%, considering the 7% hard decision forward error correction (HD-FEC) threshold. Moreover, even when the number of subcarriers is up to 256 and the power of each subcarrier is as high as 0 dBm, our proposed signal optimization scheme can still guarantee that the BER can satisfy the 7% HD-FEC threshold.

Frequency offset modeling in presence of ASE noise and corresponding low-complexity solution for discrete spectrum modulated nonlinear frequency division multiplexing system

For discrete spectrum modulated nonlinear frequency division multiplexing (DS-NFDM) transmission, the combined effect of laser frequency offset (FO) and amplifier spontaneous emission (ASE) noise would cause a perturbative shift of eigenvalues and corresponding spectral distortion. Moreover, the classical 4th-power feedforward frequency offset estimation (FOE) scheme is not suitable for DS-NFDM system with low baud rate. In this paper, we firstly deduce an FO impairment model with ASE noise in nonlinear Fourier domain (NFD). Meanwhile, we have proposed a training sequence-assisted FOE (TS-FOE) scheme, which effectively reduces the impact of ASE noise. The 2 GBaud DS-NFDM 16QAM simulation results illustrate that this scheme achieves an FOE error within 1 MHz using a training sequence (TS) with length of 64. Besides, its FOE range achieves [−Rs/2, +Rs/2], where Rs denotes the baud rate of signal. Furthermore, the effectiveness of TS-FOE scheme has been verified by 2 GBaud NFDM QPSK experimental system. The results demonstrate that when optical signal-to-noise ratio (OSNR) is set 13 dB, the TS-FOE scheme presents stable FO compensation performance for different FOs within 1 GHz, and the corresponding bit error rates (BERs) are all below the 7% hard decision forward error correction (HD-FEC) threshold. Meanwhile, its complexity is on the order of OL and related with the length L of TS, which is as one thousandth as that of angle search FOE scheme.

Echo State Network Based Nonlinear Equalization for 4.6 km 135 GHz D-Band Wireless Transmission

Reservoir computing (RC) is a novel computational framework derived from recurrent neural networks (RNN). It can reduce the training complexity of RNN and is suitable for time-series learning tasks. The echo state network (ESN) is one of the most popular forms of RC. In this paper, an ESN-based equalizer is applied to perform signal equalization in a wireless D-band communication system to compensate for the nonlinear distortion. Based on the photonics-based technology and multiple amplifiers, a long-range wireless transmission system is successfully established at D-band. We experimentally demonstrated that our proposed wireless transmission link can realize up to 4.6-km wireless delivery of over 8 Gbit/s quadrature phase shift keying (QPSK) millimeter-wave signal at 135 GHz with a bit-error-rate (BER) less than the hard decision forward error correction (HD-FEC) threshold of 3.8 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> . Compared with the traditional CMA equalizer and the Volterra equalizer, the experimental results show that the ESN-based equalizer can achieve a better balance between the BER performance and the computational complexity.

8192QAM Signal Transmission by an IM/DD System at W-Band Using Delta-Sigma Modulation

We present a low-cost intensity-modulated direct detection (IM/DD) system for the transmission of high-order QAM signals at millimeter-wave (MMW) band at 87 GHz. The system uses photon-assisted MMW generation techniques to transmit modulated signals and receive signals through low-cost envelope detection. Discrete multi-tone (DMT) signals up to 8192QAM are modulated into a one-bit quantized on/off keying (OOK) signal with delta-sigma modulation (DSM) to avoid envelope detector saturation effects and reduce nonlinear noise interference. Additionally, multi-level digital-to-analog converters (DACs) are not required. The 87 GHz W-band signal can transmit data at a baud rate of 12 G over a wireless propagation range of 1.2 m. The experimental results show that the bit error ratio (BER) of the 1024QAM signal can reach the hard decision forward error correction (HD-FEC) threshold, and the BER of the 8192QAM signal can reach the soft decision forward error correction (SD-FEC) threshold. To our knowledge, this is the first time that the DSM modulation format is used in the IM/DD W-band wireless transmission system.

High-security 3D CAP modulation scheme based on a pyramid constellation design for 7-core fiber.

This paper proposes a 3-dimensional (3D) carrier-less amplitude and phase modulation (CAP) based on a pyramid constellation design encryption scheme for 7-core fiber in passive optical network (PON). The chaos sequences generated by the 4D hyperchaotic system are applied to produce the masking factor, and the pyramid Rubik's cube rotation rules are used to flip and rotate the constellation points. To verify the performance of the proposed 3D CAP-PON system, 25.5Gb/s encrypted Pyraminx-3D-CAP-16 signal transmission over 2 km 7-core fiber is experimentally demonstrated. Compared with the traditional 3D-CAP-16 signal, the proposed Pyraminx-3D-CAP-16 signal achieves a sensitivity gain of 0.5 dB under the limit of hard decision forward error correction (HD-FEC) 3.8 × 10-3. Compared with the Pyraminx-3D-CAP-16 signal, the encrypted Pyraminx-3D-CAP-16 signal has little difference from that before encryption, which has a great application prospect in the physical layer security enhancement of the 3D-CAP-PON system.