The L-Band Digital Aeronautical Communications System (LDACS) 1 is an alternative to the current Very High Frequency (VHF) technology which meets the needs of upcoming demands. LDACS1, an Orthogonal Frequency Division Multiplexing (OFDM), operates in the L-Band between 960 and 1167 MHz frequencies installed in the middle of two Distance Measuring Equipment (DME) channels with 500 kHz spectral gap. In this paper?LDACS1 OFDM and LDACS1 2 ? 2 Multiple Input Multiple Output (MIMO) are designed and simulated without any interference. The DME interference is introduced only to the LDACS1 OFDM, and significant degradation in the performance is observed. The pulse blanking technique helps in lowering the interference in the LDACS1 OFDM system, resulting in a performance close to the performance in the interference-free case. The Bit Error Rate (BER) performances for the conditions with and without interference and pulse blanking are compared. An improvement in the BER performance is seen with respect to design of LDACS1 2 ? 2 MIMO system.

Internet of Things (IoT) is an emerging and evergreen technology that has created worldwide networked machines and also devices that can help in exchanging communication. As the real-time applications have been increasing day-by-day, the need for smart connections has also increased. Challenging smart connectivity, IoT-based smart alarm clocks have been designed in this paper. For decades, alarm clocks have been in use, but as the technology progressed, mobile phones came into existence, and people find it easier to set alarms on mobiles. Among electronics hobbyists, alarm projects have always been in great demand. IoT alarm clock is built using Node MicroController Unit (NodeMCU), where alarm time can be set using a webpage without Internet connection.

Monitoring the activities of examinees during examination is very challenging. The paper recognizes and classifies activities of examinees as suspicious or normal during examination using machine learning techniques. The processing and analysis of image data follows a typical sequence of distinct steps referred to as the vision pipeline. Data was acquired with a surveillance camera and frames extracted from the videos. Preprocessing activities include selecting the required frames from frame sequences, and cropping and segmenting foreground/background object. Video conversion to frame was accomplished with MATLAB scripts, while segmentation of image frames was achieved with GrabCut algorithm. Shape/pose features were extracted from objects using Histogram of Oriented Gradient (HOG) and Regionprop algorithms, and represented in feature vectors that were fed into Support Vector Machine (SVM) classifier. Holdout validation technique was used for the classifier training and tested from the given datasets. 70% of the dataset was used for training, while 30% was used for testing. The model gave an accuracy of 98.1% and 100%, respectively, for each examination scenario. The model accuracy was visualized in confusion matrix and the Receiver Operating Characteristic (ROC). MATLAB software was used as the simulation environment. The model demonstrated excellent performance, indicating that the system can adequately complement the efforts of invigilators in examination invigilation.