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Current strategies of regenerative medicine are focused on the restoration of pathologically altered tissue architectures by transplantation of cells in combination with supportive scaffolds and biomolecules. In recent years, considerable interest has been given to biologically active scaffolds which are based on similar analogs of the extracellular matrix that have induced synthesis of tissues and organs. To restore function or regenerate tissue, a scaffold is necessary that will act as a temporary matrix for cell proliferation and extracellular matrix deposition, with subsequent ingrowth until the tissues are totally restored or regenerated. Scaffolds have been used for tissue engineering such as bone, cartilage, ligament, skin, vascular tissues, neural tissues, and skeletal muscle and as vehicle for the controlled delivery of drugs, proteins, and DNA. Various technologies come together to construct porous scaffolds to regenerate the tissues/organs and also for controlled and targeted release of bioactive agents in tissue engineering applications. In this paper, an overview of the different types of scaffolds with their material properties is discussed. The fabrication technologies for tissue engineering scaffolds, including the basic and conventional techniques to the more recent ones, are tabulated.

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Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.

A compact source is important for various applications utilizing terahertz (THz) waves. In this paper, the recent progress in resonant-tunneling diode (RTD) THz oscillators, which are compact semiconductor THz sources, is reviewed, including principles and characteristics of oscillation, studies addressing high-frequency and high output power, a structure which can easily be fabricated, frequency tuning, spectral narrowing, different polarizations, and select applications. At present, fundamental oscillation up to 1.98 THz and output power of 0.7 mW at 1 THz by a large-scale array have been reported. For high-frequency and high output power, structures integrated with cylindrical and rectangular cavities have been proposed. Using oscillators integrated with varactor diodes and their arrays, wide electrical tuning of 400–900 GHz has been demonstrated. For spectral narrowing, a line width as narrow as 1 Hz has been obtained, through use of a phase-locked loop system with a frequency-tunable oscillator. Basic research for various applications—including imaging, spectroscopy, high-capacity wireless communication, and radar systems—of RTD oscillators has been carried out. Some recent results relating to these applications are discussed.

Robot navigation is an indispensable component of any mobile service robot. Many path planning algorithms generate a path which has many sharp or angular turns. Such paths are not fit for mobile robot as it has to slow down at these sharp turns. These robots could be carrying delicate, dangerous, or precious items and executing these sharp turns may not be feasible kinematically. On the contrary, smooth trajectories are often desired for robot motion and must be generated while considering the static and dynamic obstacles and other constraints like feasible curvature, robot and lane dimensions, and speed. The aim of this paper is to succinctly summarize and review the path smoothing techniques in robot navigation and discuss the challenges and future trends. Both autonomous mobile robots and autonomous vehicles (outdoor robots or self-driving cars) are discussed. The state-of-the-art algorithms are broadly classified into different categories and each approach is introduced briefly with necessary background, merits, and drawbacks. Finally, the paper discusses the current and future challenges in optimal trajectory generation and smoothing research.

Human activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and smart homes. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscopes are widely used inertial sensors that can identify different physical conditions of human. In recent research, many works have been done regarding human activity recognition. Sensor data of smartphone produces high dimensional feature vectors for identifying human activities. However, all the vectors are not contributing equally for identification process. Including all feature vectors create a phenomenon known as ‘curse of dimensionality’. This research has proposed a hybrid method feature selection process, which includes a filter and wrapper method. The process uses a sequential floating forward search (SFFS) to extract desired features for better activity recognition. Features are then fed to a multiclass support vector machine (SVM) to create nonlinear classifiers by adopting the kernel trick for training and testing purpose. We validated our model with a benchmark dataset. Our proposed system works efficiently with limited hardware resource and provides satisfactory activity identification.

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