We present a 4-port Multiple-Input-Multiple-Output (MIMO) antenna array operating in the mm-wave band for 5G applications. An identical two-element array excited by the feed network based on a T-junction power combiner/divider is introduced in the reported paper. The array elements are rectangular-shaped slotted patch antennas, while the ground plane is made defected with rectangular, circular, and a zigzag-shaped slotted structure to enhance the radiation characteristics of the antenna. To validate the performance, the MIMO structure is fabricated and measured. The simulated and measured results are in good coherence. The proposed structure can operate in a 25.5–29.6 GHz frequency band supporting the impending mm-wave 5G applications. Moreover, the peak gain attained for the operating frequency band is 8.3 dBi. Additionally, to obtain high isolation between antenna elements, the polarization diversity is employed between the adjacent radiators, resulting in a low Envelope Correlation Coefficient (ECC). Other MIMO performance metrics such as the Channel Capacity Loss (CCL), Mean Effective Gain (MEG), and Diversity gain (DG) of the proposed structure are analyzed, and the results indicate the suitability of the design as a potential contender for imminent mm-wave 5G MIMO applications.

Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. This paper presents an advanced signal processing method using the deep neural network (DNN) for emotion recognition based on EEG signals. The spectral and temporal components of the raw EEG signal are first retained in the 2D Spectrogram before the extraction of features. The pre-trained AlexNet model is used to extract the raw features from the 2D Spectrogram for each channel. To reduce the feature dimensionality, spatial, and temporal based, bag of deep features (BoDF) model is proposed. A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. Lastly, the emotion of each subject is represented using the histogram of the vocabulary set collected from the raw-feature of a single channel. Features extracted from the proposed BoDF model have considerably smaller dimensions. The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. For optimal classification performance, we use a support vector machine (SVM) and k-nearest neighbor (k-NN) to classify the extracted features for the different emotional states of the two data sets. The BoDF model achieves 93.8% accuracy in the SEED data set and 77.4% accuracy in the DEAP data set, which is more accurate compared to other state-of-the-art methods of human emotion recognition.

In this paper, a compact and fully passive bit encoding circuit, capable of operating as a chipless radio frequency identification (RFID) tag is presented. The structure consists of novel concentric trefoil-shaped slot resonators realized using Rogers RT/duroid® 5880 laminate, occupying a physical footprint of 13.55 × 13.55 mm2. Each resonating element is associated with a particular data bit, having a 1:1 resonator-to-bit correspondence. Bit sequences are configured through introducing modifications in the geometric structure either by addition or exclusion of each nested slot resonator. Such changes manifest directly in the electromagnetic signature of the tag as presence or absence of corresponding resonant peaks. The proposed 10-bit tag offers minimized inter-resonator mutual coupling and insensitivity to changes in polarization and incident angles thereby demonstrating orientation independent functionality. Moreover, error-free encoding is achieved through stabilizing the shift in resonant frequencies for a variety of different geometric configurations and orientation of the structure. The tag operates within the license-free ultra-wideband ranging from 5.4 to 10.4 GHz, providing spectral bit capacity and bit density of 2 bits/GHz and 5.44 bits/cm2 respectively.

Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user’s emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods.

A novel Radio Frequency Identification (RFID) based sensor supporting touch detection and localization features is proposed in this work. The formulated sensor leverages chipless variant of RFID technology for the design of a passive fully-printable frequency domain-based sensor-incorporated tag. The sensor is composed of square resonators arranged in a peculiar fashion laid down across a 3 × 2 grid. The proposed sensor incorporated-tag readily keeps track of human-digit position, allowing for tracking of finger-swipes which, in turn, can potentially be used for recognition of unlock patterns and security codes. Performance of the sensor is analyzed using its Radar Cross Section (RCS) response observable in the spectral domain. Each constituent resonant-element making up the sensor resonates at a single frequency represented by a distinct dip in the RCS response. The spectral dip drifts well outside of its allocated band upon occurrence of a touch event. A functional prototype of the sensor tag is fabricated on a 0.508 mm thick Rogers RT/Duroid® 5880 laminate is scrutinized of its electromagnetic performance. The sensor possesses a compact physical footprint equal to 45 mm × 55 mm. The obtained results solidify the suitability of the proposed sensor for deployment in secure access control settings prevalent in smart cities and connected home applications.