This study presents a practically deployable anti-spoofing system for fingerprint biometrics based on electric finger channel response (FCR) to detect falsification attempts using artificial fake fingerprints. Meanwhile, electric signals passed through the human body are particularly vulnerable to distortion from electromagnetic interference (EMI) induced into the body channel by the body antenna effects. The proposed EMI-robust fake fingerprint detection (FFD) employed a neural fully-connected filter trained on the proposed detection features extracted from the amplitude variation and time dispersion characteristics of FCR. A valid FCR dataset for feature analysis was acquired using custom-developed devices in a designated experimental setup involving 20 subjects. Following compatibility verification of the FCR-based FFD with a capacitive-sensing fingerprint scanner in the implemented prototype, the performance evaluation under onsite EMI conditions—primarily modeled as wide-band pulsed-radiated emission and narrow-band sinusoidal wave interferences—showed that the proposed FFD achieved false rejection and acceptance rates of 0.9$\%$and 0.5$\%$, respectively.

This study presents a highly reliable approach to prevent fingerprint spoofing attacks based on electric body pulse responses (BPRs) in personal Internet of Things (IoT) gadgets. Real fingerprint pulse response (RFPR) and fake fingerprint pulse response (FFPR) data were collected from ten subjects for four weeks. The FFPR was obtained by wearing a fake fingerprint made of artificial substances, such as conductive silicone, over the finger. We analyzed different patterns of FFPR compared to RFPR using an electric circuit model of the proposed fingerprint anti-spoofing system based on BPRs. Simple features comprising ten, five, or three datapoints were selected by the minimum redundancy maximum relevance (MRMR) algorithm and led to reduction in processing complexity. We also validated its robustness to sampling offset errors caused by practical sampling operations in devices based on the evaluation of classification accuracy using machine learning algorithms, such as${k}$-nearest neighbor (KNN) and support vector machine (SVM). Finally, the effectiveness of the selected feature was evaluated using unsupervised anomaly detection algorithms, such as principal component analysis (PCA), one-class SVM (OC-SVM), and variational autoencoder (VAE), in a practical scenario with sampling offset errors in the training and test data. The VAE outperformed PCA and OC-SVM by achieving a detection accuracy of 99.76% using raw data under 100 datapoints and 97.60% with reduced features having only five datapoints, regardless of sampling offset errors. Therefore, the proposed anomaly detection system based on EPRs can provide promising fingerprint spoof detection in IoT devices with limited computing resources.

Spectral efficiency of turbo code can be improved by use of coded modulation methods such as bit‐interleaved coded modulation (BICM) with iterative decoding (BICM‐ID). On the other hand, the performance of the turbo‐BICM can be further improved by iteratively exchanging information between a decoder and a demodulator within a receiver and this scheme is known as the turbo‐BICM‐ID. Unfortunately, the required complexity of the turbo‐BICM‐ID is much higher than that of the turbo bit‐interleaved coded modulation with iterative decoding turbo‐BICM‐ID. In this study, the authors propose a complexity reduced turbo‐BICM‐ID by optimising the mapping of coded bits to modulation symbols and the amount of information fed back from the decoder to the demodulator.

In narrowband communication systems operating in VHF and UHF bands, the performance degradation due to a frequency offset is inevitable. In this paper, we propose a frequency offset reduction scheme using the dual-band FSK modulation for narrowband communications. The proposed scheme not only offer a relatively reliable performance under a severe frequency offsets, but also can obtain a frequency diversity gain in Rayleigh fading channel.

The decision-directed blind equalization algorithm is often used due to its simplicity and good convergence property when the eye pattern is open. However, in a channel where the eye pattern is closed, the decision-directed algorithm is not guaranteed to converge. Hence, a modified decision-directed algorithm using a hyperbolic tangent function for zero-memory nonlinear function has been proposed and applied to avoid this problem by Filho et al. But application of this algorithm includes the calculation of hyperbolic tangent function and its derivative or a look-up table which may need a large amount of memory due to channel variations. To reduce the computational and/or hardware complexity of Filho's algorithm, in this paper, an improved method for the decision-directed algorithm is proposed. It is shown that the proposed scheme, when it is combined with decision-directed algorithm, reduces the computational complexity drastically while it retains the convergence and steady-state performance of the Filho's algorithm.

The performance of turbo code is optimized when a slightly under estimated channel signal-to-noise ratio (SNR) is used within an iterative maximum a posteriori probability (MAP) turbo decoder. We investigate the behavior of iterative MAP turbo decoder under the channel SNR estimation offset and propose a new scheme for setting the channel reliability value for iterative MAP turbo decoders which is not a function of the channel SNR. Simulation results show that the new scheme provides better error performance compared to the traditional method in addition to not requiring an SNR estimator.

A 180/spl deg/ carrier phase ambiguity is inevitable in digital communication systems employing binary phase-shift keying modulation and non-data-aided carrier phase recovery. Here, we propose a simple modification to standard regular RA codes by exploiting differential encoding inherent to inner encoder of RA codes resulting in a 180/spl deg/ rotationally invariant RA code.

The sensitivity of the iterative decoder for repeat-accumulate (RA) codes to carrier phase and channel signal-to-noise ratio estimation errors is investigated, and efficient algorithms to estimate and correct these errors are developed. The behavior of RA codes with imperfect channel estimation is different from that of turbo codes, and correction algorithms specific to RA codes must be formulated. The proposed algorithms use the soft information generated within the iterative decoder, and thus, are not only hardware-efficient, but also offer excellent performance.

It is well known that turbo codes provide highly unequal error protection within a transmitted frame. Previous attempts to exploit this fact focused mainly on adding additional redundancy to provide extra protection for the error-prone bit positions. Here, instead, we use the error-detection capability of the cyclic redundancy check (CRC), which is almost always employed in practical systems. Once a frame is declared uncorrectable by the CRC, a process termed the error-prone bit processing procedure is activated in an attempt to correct the probable error patterns which are a priori identified as being error-prone.