The concept of reconfiguring wireless propagation environments using intelligent reflecting surfaces (IRS)s has recently emerged, where an IRS comprises of a large number of passive reflecting elements that can smartly reflect the impinging electromagnetic waves for performance enhancement. Previous works have shown promising gains assuming the availability of perfect channel state information (CSI) at the base station (BS) and the IRS, which is impractical due to the passive nature of the reflecting elements. This paper makes one of the preliminary contributions of studying an IRS-assisted multiuser multiple-input single-output (MISO) communication system under imperfect CSI. Different from the few recent works that develop least-squares (LS) estimates of the IRS-assisted channel vectors, we exploit the prior knowledge of the large-scale fading statistics at the BS to derive the Bayesian minimum mean squared error (MMSE) channel estimates under a protocol in which the IRS applies a set of optimal phase shifts vectors over multiple channel estimation sub-phases. The resulting mean squared error (MSE) is both analytically and numerically shown to be lower than that achieved by the LS estimates. Joint designs for the precoding and power allocation at the BS and reflect beamforming at the IRS are proposed to maximize the minimum user signal-to-interference-plus-noise ratio (SINR) subject to a transmit power constraint. Performance evaluation results illustrate the efficiency of the proposed system and study its susceptibility to channel estimation errors.

Despite the growing interest in the interplay of machine learning and optimization, existing contributions remain scattered across the research board, and a comprehensive overview on such reciprocity still lacks at this stage. In this context, this paper visits one particular direction of interplay between learningdriven solutions and optimization, and further explicates the subject matter with a clear background and summarized theory. For instance, machine learning and its offsprings are trending because of their enhanced capabilities in automating analytical modeling. In this realm, learning-based techniques (supervised, unsupervised, and reinforcement) have grown to complement many of the optimization problems in testing and training. This paper overviews how machine learning-based techniques, namely deep neural networks, echo-state networks, reinforcement learning, and federated learning, can be used to solve complex and analytically intractable optimization problems, for which specific cases are examined in this paper. The paper particularly overviews when learning-based algorithms are useful at solving particular optimizing problems, especially those of random, dynamic, and mathematically complex nature. The paper then illustrates such applications by presenting particular use-cases in communications and signal processing including wireless scheduling, wireless offloading and resource management, power control, aerial base station placement, virtual reality, and vehicular networks. Lastly, the paper sheds light on some future research directions, where the dynamicity and randomness of the underlying optimization problems make deep learning-driven techniques a necessity, namely in sensing at the terahertz (THz) bands, cellular vehicleto-everything, 6G communication networks, underwater optical networks, distributed optimization, and applications of emerging learning-based techniques.

Intelligent reflecting surfaces (IRSs)-assisted wireless communication promises improved system performance, while posing new challenges in channel estimation (CE) due to the passive nature of the reflecting elements. Although a few CE protocols for IRS-assisted multiple-input single-output (MISO) systems have appeared, they either require long channel training times or are developed under channel sparsity assumptions. Moreover, existing works focus on a single IRS, whereas in practice multiple such surfaces should be installed to truly benefit from the concept of reconfiguring propagation environments. In light of these challenges, this paper tackles the CE problem for the distributed IRSs-assisted multi-user MISO system. An optimal CE protocol requiring relatively low training overhead is developed using Bayesian techniques under the practical assumption that the BS-IRSs channels are dominated by the line-of-sight (LoS) components. An optimal solution for the phase shifts vectors required at all IRSs during CE is determined and the minimum mean square error (MMSE) estimates of the BSusers direct channels and the IRSs-users channels are derived. Simulation results corroborate the normalized MSE (NMSE) analysis and establish the advantage of the proposed protocol as compared to benchmark scheme in terms of training overhead.

Coral reef degradation is a rising problem, driven by marine heatwaves, the spread of coral diseases, and human impact by overfishing and pollution. Our capacity to restore coral reefs lags behind in terms of scale, effectiveness, and cost-efficiency. While common restoration efforts rely on the formation of carbonate skeletons on structural frames for supported coral growth, this technique is a rate-limiting step in the growth of scleractinian corals. Reverse engineering and additive manufacturing technologies offer an innovative shift in approach from the use of concrete blocks and metal frames to sophisticated efforts that use scanned geometries of harvested corals to fabricate artificial coral skeletons for installation in coral gardens and reefs. Herein, we present an eco-friendly and sustainable approach for coral fabrication by merging three-dimensional (3D) scanning, 3D printing, and molding techniques. Our method, 3D CoraPrint, exploits the 3D printing technology to fabricate artificial natural-based coral skeletons, expediting the growth rate of live coral fragments and quickening the reef transplantation process while minimizing nursery costs. It allows for flexibility, customization, and fast return time with an enhanced level of accuracy, thus establishing an environmentally friendly, scalable model for coral fabrication to boost restorative efforts around the globe.

The technology of 3D bioprinting has gained significant interest in biomedical engineering, regenerative medicine, and the pharmaceutical industry. Providing a new scope in tissue and organ printing, 3D bioprinters are becoming commercialized for biological processes. However, the current technology is costly, ranging from USD$9,000-$30,000 and is limited to customized extrusion methods. Multiple microfluidic pump systems for bioink extrusion are commercially available at USD$30,000. Additionally, the use of Cartesian systems for 3D printing restricts the user to three axes of movement and makes multi-material modeling a challenge. Consequently, it was proposed to design a cost effective robotic 3D bioprinting system, compatible with peptide bioinks which were developed at KAUST Laboratory for Nanomedicine. The components of the system included a programmable robotic arm, an extruder for bioprinting, and multiple microfluidic pumps. The extruder was designed using a coaxial nozzle made of three inlets and one outlet. The programmable microfluidic pumps transported the peptide bioink, phosphate buffer saline (PBS) and human skin fibroblast cells (in cell culture media solution) through the nozzle to extrude a peptide nanogel thread. Model cell structures were printed and monitored for a period of two weeks and subsequently found to be alive and healthy. The system was kept well under a budget of USD$3,500. Future modifications of the current system will include adding a custom bioprinting arm to allow multi-material printing which can fully integrate and synchronize between the pumps and the robotic arm. This system will allow the production of a more advanced robotic arm-based 3D bioprinting system in the future.