Current Issue : October-December Volume : 2022 Issue Number : 4 Articles : 5 Articles
A real-time streaming feedforward active-noise-cancellation (ANC) system for an in-ear headphone was demonstrated in a real application scenario, by implementing a 10-layer dilated convolutional-neural-network (CNN) on a field programmable gate array (FPGA). A 16 × 16 systolic array was used in the FPGA, to speed up the model computation. The system latency was 170.6 μs, at the system clock frequency of 120 MHz. The CNN model used 3232 parameters. Due to the large input receptive field, of 327 ms, this work achieved total power reduction, of 14.8 dB and 14.3 dB at the noise incident direction of 0◦ and 90◦, respectively, and the noise attenuation bandwidth was 2000 Hz at both angles; all results were superior to those of the conventional FxLMS algorithm....
Field Programmable Gate Array (FPGA) accelerators have been widely adopted for artificial intelligence (AI) applications on edge devices (Edge-AI) utilizing Deep Neural Networks (DNN) architectures. FPGAs have gained their reputation due to the greater energy efficiency and high parallelism than microcontrollers (MCU) and graphical processing units (GPU), while they are easier to develop and more reconfigurable than the Application Specific Integrated Circuit (ASIC). The development and building of AI applications on resource constraint devices such as FPGAs remains a challenge, however, due to the co-design approach, which requires a valuable expertise in low-level hardware design and in software development. This paper explores the efficacy and the dynamic deployment of hardware accelerated applications on the Kria KV260 development platform based on the Xilinx Kria K26 system-on-module (SoM), which includes a Zynq multiprocessor system-onchip (MPSoC). The platform supports the Python-based PYNQ framework and maintains a high level of versatility with the support of custom bitstreams (overlays). The demonstration proved the reconfigurabibilty and the overall ease of implementation with low-footprint machine learning (ML) algorithms....
Reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) are promising techniques to develop nextgeneration wireless systems. While RIS has huge potential to create massive device connectivity, NOMA exhibits its spectrum efficient communication among multiple access approaches. RIS is a passive device made up of low-cost meta-surfaces which can control the propagation of radio waves, and it is easily deployable in lots of applications in the Internet of Things. The full-duplex nature of RIS has also been a major reason for its consideration of major emerging and trending technologies. In this paper, we aim to investigate the secrecy performance of the RIS-NOMA-assisted Internet of Things (IoT) systems in the presence of two legitimate users who belong to a cluster, and those devices are associated with the existence of an eavesdropper situated close to such a cluster. This paper considers the devices in the presence of RIS and an eavesdropper. As main performance metrics, the closed-form expressions for secrecy outage probability (SOP) and strictly positive secrecy capacity (SPSC) are derived to evaluate the performance of legitimate users. Simulations are performed in support of the Monte-Carlo method, and the obtained results show that in most of the cases, the number of meta-surfaces in RIS and signal-to-noise ratio (SNR) levels at the source also plays a pivotal role in influencing the secure performance of the system....
Fuzzy C-Means (FCM) is a widely used clustering algorithm that performs well in various scientific applications. Implementing FCM involves a massive number of computations, and many parallelization techniques based on GPUs and multicore systems have been suggested. In this study, we present a method for optimizing the FCM algorithm for high-speed field-programmable gate technology (FPGA) using a high-level C-like programming language called open computing language (OpenCL).Themethod was designed to enable the high-level compiler/synthesis tool to manipulate a task-parallelism model and create an efficient design. Our experimental results (based on several datasets) show that the proposed method makes the FCM execution time more than 186 times faster than the conventional design running on a single-core CPU platform. Also, its processing power reached 89 giga floating points operations per second (GFLOPs)....
Artificial intelligence techniques for pneumatic robot manipulators have become of deep interest in industrial applications, such as non-high voltage environments, clean operations, and high power-to-weight ratio tasks. The principal advantages of this type of actuator are the implementation of clean energies, low cost, and easy maintenance. The disadvantages of working with pneumatic actuators are that they have non-linear characteristics. This paper proposes an intelligent controller embedded in a programmable logic device to minimize the non-linearities of the air behavior into a 3-degrees-of-freedom robot with pneumatic actuators. In this case, the device is suitable due to several electric valves, direct current motors signals, automatic controllers, and several neural networks. For every degree of freedom, three neurons adjust the gains for each controller. The learning process is constantly tuning the gain value to reach the minimum of the mean square error. Results plot a more appropriate behavior for a transitive time when the neurons work with the automatic controllers with a minimum mean error of ±1.2 mm....
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