In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.

The increasing integration of the Internet of Things (IoT) into daily life has led to significant changes in our social interactions. The advent of innovative IoT solutions, combined with the enhanced capabilities and expanded reach of 5G wireless networks, is altering the way humans interact with machines. Notably, the advancement of edge computing, underpinned by 5G networks within IoT frameworks, has markedly extended human sensory perception and interaction. A key biometric within these IoT applications is electroencephalography (EEG), recognized for its sensitivity, cost-effectiveness, and distinctiveness. Traditionally linked to brain–computer interface (BCI) applications, EEG is now finding applications in a wider array of fields, from neuroscience research to the emerging area of neuromarketing. The primary aim of this article is to offer a comprehensive review of the current challenges and future directions in EEG data acquisition, processing, and classification, with a particular focus on the increasing reliance on data-driven methods in the realm of 5G wireless network-supported EEG-enabled IoT solutions. Additionally, the article presents a case study on EEG-based emotion recognition, exemplifying EEG’s role as a biometric tool in the IoT domain, propelled by 5G technology.

A novel iterative algorithm for electronic dispersion compensation (EDC) at the transmitter is described and demonstrated for intensity modulation and direct detection (IM/DD) systems. The proposed algorithm is based on the Gerchberg-Saxton (GS) algorithm, where the link dispersion performs time-to-frequency mapping, while the unconstrained phase at the direct detection receiver acts as a degree of freedom. Simulation results for a transmitter employing a single drive Mach-Zehnder modulator (MZM) operating at 28 Gb/s non-return-to-zero on-off keying (NRZ-OOK), show pre-compensation of upto 100 km of single mode fiber (SMF), with 1 dBQ margin above the forward error correction (FEC) bit error rate (BER) limit of $3.8 \times 10^{-3}$ . It is shown that with 5 iterations of the proposed EDC algorithm in digital-signal processing (DSP) coupled with an 8-bit 56 GSa/s digital-to-analog converter (DAC), the transmission reach is extended by a factor of 5 in unamplified SMF links. Simulations for a 56 Gb/s four-level pulse-amplitude-modulation (PAM-4) show the compensation of upto 10 km with 5 iterations of the EDC algorithm at 1 dBQ above the FEC limit.

To combat chromatic dispersion (CD) in intensity modulation and direct detection (IM/DD) systems, three chirp-free demonstrations are experimentally performed with an iterative pre-electronic dispersion compensation (pre-EDC) algorithm at the transmitter end, for 28 GBaud non-return-to-zero on-off keying (NRZ-OOK), 56 GBaud NRZ-OOK and 28 GBaud four-level pulse-amplitude-modulation (PAM-4) signals, over distances of 100 km, 50 km and 40 km of single mode fiber (SMF), respectively. The iterative pre-EDC algorithm is based on the Gerchberg-Saxton (GS) algorithm, which treats the unconstrained phase at the direct detection receiver as a degree of freedom. At the receiver side, only a linear fractionally-spaced (T/2) post-feed-forward equalizer (post-FFE) is employed to combat the residual inter-symbol interference (ISI). Experimental results show that the aforementioned three demonstrations can approach the forward error correction (FEC) bit error rate (BER) threshold of 3.8 × 10⁻³ with (15 pre-EDC iterations and 5-tap post-FFE), (30 pre-EDC iterations and 15-tap post-FFE), and (10 pre-EDC iterations and 25-tap post-FFE), respectively. The results indicate the applicability of the pre-EDC algorithm in high-capacity IM/DD systems for transmission distances below 100 km of SMF.

Short-reach transmission systems traditionally utilize intensity modulation (IM) at the transmitter and direct detection (DD) at the receiver due to their cost-effectiveness, small footprint, and low power consumption. However, with the exponential increase in bandwidth demand, coherent optical communication systems have become necessary for long-haul distances, requiring application-specific integrated circuits (ASIC) and advanced digital signal processing (DSP) algorithms coupled with high-speed digital-to-analog and analog-to-digital converters to achieve Tbit/s speeds. As coherent technology matures, it will eventually become feasible for short-reach transmission. In this context, self-coherent systems have emerged as an intermediary solution, offering advantages over traditional IM/DD systems. While comprehensive review studies exist on self-coherent transceivers, they do not cover recent advances in phase retrieval methods for short-reach optical communications. This review article highlights recent developments in cost-effective self-coherent detection for short-reach systems through comparing the benefits of single sideband (SSB) transmission and Kramers-Kronig detection to carrier-assisted phase retrieval, the Gerchberg-Saxton (GS) algorithm, and the transport of intensity equation (TIE) method.