This paper presents a novel algorithm for the estimation of the heart rate variability (HRV) features using 24 GHz continuous-wave Doppler radar with quadrature architecture. The proposed algorithm combines frequency and time domain analysis for high-accuracy estimation of beat-to-beat intervals (BBIs). Initially, band pass filtered in-phase (I) and quadrature (Q) radar components are fused into a single combined signal that contains information on the heartbeats. Its frequency domain analysis is used for coarse heart rate estimation. At the same time, the combined signal is processed using a filter bank containing narrowband band pass filters with different center frequencies. One of the band pass filter outputs is selected as the valid output based on the coarse heart rate estimation. Zero crossings in the resulting filter bank output signal represent heartbeats that are used to extract the BBIs. Finally, four HRV features are calculated from the BBIs. The algorithm is tested on real data obtained from recordings on ten human subjects. The mean relative error of extracted BBIs compared to electrocardiogram (ECG) measurement is in the 1.02–2.07 % range. Furthermore, two time domain and two frequency domain HRV features were calculated from the BBIs. The obtained results show a high level of agreement between radar-extracted and ECG-extracted HRV features. Low computation complexity makes this algorithm suitable for real-time monitoring.

Modern communication standards, such as 5G new radio (5G NR), require a high speed decoder for highly irregular quasi-cyclic low density parity check (QC-LDPC) codes. A widely used approach in QC-LDPC decoders is a layered decoding schedule which processes the parity check matrix in parts, thus providing faster convergence. However, pipelined layered decoding architecture suffers from data hazards that reduce the throughput. This paper presents a novel architecture, which can facilitate any QC-LDPC decoding without stall cycles caused by pipeline hazards. The decoder conveniently incorporates both the layered and the flooding schedules in cases when hazards occur. The paper also presents the genetic algorithm based optimization of the decoding schedule for better signal-to-noise ratio (SNR) performance. The proposed architecture enables insertion of a large number of pipeline stages, thus providing high operating frequency. As a case study, the FPGA implementation for WiMAX, DVB-S2X, and 5G NR provided coded throughput of up to 1.77 Gbps, 4.32 Gbps, and 4.92 Gbps at 10 iterations, respectively. The results show a strong throughput increase of 30%-109% compared with the conventional layered decoder for 5G NR for the same SNR performance. The decoder provides highly efficient utilization of resources when compared with the state-of-the-art solutions.

The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from signs of life to complex mental states. The measurement of the ECG relies on electrodes attached to the skin to acquire the electrical activity of the heart, which imposes certain limitations. Recently, due to the advancement of wireless technology, it has become possible to pick up heart activity in a contactless manner. Among the possible ways to wirelessly obtain information related to heart activity, methods based on mm-wave radars proved to be the most accurate in detecting the small mechanical oscillations of the human chest resulting from heartbeats. In this paper, we presented a method based on a continuous-wave Doppler radar coupled with an artificial neural network (ANN) to detect heartbeats as individual events. To keep the method computationally simple, the ANN took the raw radar signal as input, while the output was minimally processed, ensuring low latency operation (<1 s). The performance of the proposed method was evaluated with respect to an ECG reference (“ground truth”) in an experiment involving 21 healthy volunteers, who were sitting on a cushioned seat and were refrained from making excessive body movements. The results indicated that the presented approach is viable for the fast detection of individual heartbeats without heavy signal preprocessing.

Quasi-cyclic low-density parity-check (QC–LDPC) codes are introduced as a physical channel coding solution for data channels in 5G new radio (5G NR). Depending on the use case scenario, this standard proposes the usage of a wide variety of codes, which imposes the need for high encoder flexibility. LDPC codes from 5G NR have a convenient structure and can be efficiently encoded using forward substitution and without computationally intensive multiplications with dense matrices. However, the state-of-the-art solutions for encoder hardware implementation can be inefficient since many hardware processing units stay idle during the encoding process. This paper proposes a novel partially parallel architecture that can provide high hardware usage efficiency (HUE) while achieving encoder flexibility and support for all 5G NR codes. The proposed architecture includes a flexible circular shifting network, which is capable of shifting a single large bit vector or multiple smaller bit vectors depending on the code. The encoder architecture was built around the shifter in a way that multiple parity check matrix elements can be processed in parallel for short codes, thus providing almost the same level of parallelism as for long codes. The processing schedule was optimized for minimal encoding time using the genetic algorithm. The optimized encoder provided high throughputs, low latency, and up-to-date the best HUE.

In this paper we propose a method for real-time blob detection in large images with low memory cost. The method is suitable for implementation on the specialized parallel hardware such as multi-core platforms, FPGA and ASIC. It uses parallelism to speed-up the blob detection. The input image is divided into blocks of equal sizes to which the maximally stable extremal regions (MSER) blob detector is applied in parallel. We propose the usage of multiresolution analysis for detection of large blobs which are not detected by processing the small blocks. This method can find its place in many applications such as medical imaging, text recognition, as well as video surveillance or wide area motion imagery (WAMI). We explored the possibilities of usage of detected blobs in the feature-based image alignment as well. When large images are processed, our approach is 10 to over 20 times more memory efficient than the state of the art hardware implementation of the MSER.

From the ancient mythology till the modern times, people were trying to build an artificial mechanical replica of themselves. Inspired by this long tradition of various engineering projects, we will hereby describe a partly humanoid robotic structure. Our robotic configuration is composed out of an anthropomimetic upper body, but instead of legs it uses a wheeled cart for the motion. In our research, this so-called semi-anthropomimetic structure has a four-wheeled cart. This work is aiming to analyze the behaviour of the robot that is exposed to different kind of external disturbances. Disturbances coming from the outside in the form of external forces (impulse and long term) simulate the interactions of the robot and its ambience. Necessary simulations were thoroughly executed (in that way analyzing robotic balance) and proper size of the cart is evaluated following the ZMP theoretical background.