Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
7 results
Search Results
Item A Multiple Llyods Approach for LiDAR Point Cloud Quantization and Communication(Institute of Electrical and Electronics Engineers Inc., 2023) Dayananda, B.N.; Achala, G.; Srihari, P.; Raju, M.K.; Vandana, G.S.; Pardhasaradhi, B.The development and usage of drones and LiDARs have become common in forestry, archaeology, surveillance, and intruders in recent years. Even though both domains reached a very mature state, the usage, integration of LiDAR scanning from drones, security, and low bandwidth constraints are still fantasy. This paper proposed to use LiDAR on a drone to scan the area, quantize the point cloud, and communicate it to the ground station through free space communication. The purpose of open space communication rather than cloud-based solutions is to avoid related security threats. In addition, this paper uses Lloyds-based quantization to achieve optimality in the quantization scheme. Before transmitting the point cloud, it is proposed to quantize it in parallel optimal quantizers. As a result of the proposed quantizer model, we calculated the RMSE, bandwidth, and choice of communication module for this particular scenario. The quantization loss is shown in terms of RMSE, bandwidth, and channel capacity requirements depicted with some bits. RMSE value obtained in this work for the Lloyds quantization method is 0.899. The LiDAR point cloud data is transmitted to ground station with data rate of 21Mbps utilizing free space optical communication. © 2023 IEEE.Item DSP Architectures of Covariance Intersection Fusion Algorithm for Automotive Sensor Fusion(Institute of Electrical and Electronics Engineers Inc., 2023) Praharshita, D.S.L.; Achala, G.; Srihari, P.; Shripathi Acharya, U.S.; Pardhasaradhi, B.The data fusion from sensors within the automotive vehicle is vital for improved accuracy and safety. The centralized and information matrix fusion (IMF) algorithms are famous for providing an optimal fusion estimate. However, the IMF is not viable in automotive sensor fusion applications due to the limited bandwidth and low hardware resources. Hence, distributed fusion technology is widely adopted in the automotive sensor applications to achieve high-speed and low-area realizations. This paper proposes three digital signal processing (DSP) architectures for covariance intersection (CI) fusion algorithm: Pipelined-traditional CI, adder-ladder CI, and pipelined adder-ladder CI. The proposed DSP architectures are evaluated with hardware resource consumption (multipliers, adders, and delays), maximum achievable frequency, and latency of the architecture. In addition, proposed CI algorithms for Digital Signal Processing (DSP) architectures are compared with IMF DSP architectures. The hardware resources and optimal pipeline stages required for CI with respect to N number of sensors are provided. The traditional pipeline algorithm requires N number of stages where as the proposed pipelined version of adder-ladder CI requires a N-1 pipeline stage with additional 7N-1 and 7N-3 delay elements for even and odd number of sensors to achieve the overall system operating frequency to an operation of multiplier. The proposed DSP architectures are suitable for automotive sensor fusion due to their high operating frequency and low hardware resources. © 2023 IEEE.Item Design of Barker-7 Radar Pulse Compressor Using DSP Architecture Minimization Techniques(Institute of Electrical and Electronics Engineers Inc., 2024) Anoop, K.; Dayananda, B.N.; Achala, G.; Srihari, P.; Pardhasaradhi, B.; Cenkarmaddi, L.R.Pulse compression algorithms play an important role in achieving better range resolution (RR) in automobile radar and ultrasonic sensors. The RR is the sensor's capacity to distinguish between two targets positioned in the same angular direction but at different distances. The Digital Signal Processing Architecture (DSPA) used in the pulse compressor is crucial for reaching high speeds or reducing hardware needs. This work presents two unique radar pulse compressor DSPAs for Barker-7 sequences, based on latency and area. One proposed design is the unfolding DSPA technology, which increases sample rate and speed. At the same time, another architecture, the folding DSPA approach, reduces hardware utilization and power consumption. The proposed DSPA design architectures are implemented and verified with the Artix-7 FPGA Kit. The unfolded Barker-7 pulse compressor increased the speed by 1.87 times as compared to the standard broadcasting Barker-7 realization, as demonstrated by the hardware implementation results. Folded Barker-7 architecture consumed 89% less power than existing Barker-7 broadcast architecture. The DSPAs proposed for the Barker-7 pulse compressor are ideal for ultrasonic, sonar, and radar applications that require high speed and low power. © 2024 IEEE.Item Depth Information Fusion Using Radar-LiDAR-Camera Experimental Setup for ADAS Applications(Institute of Electrical and Electronics Engineers Inc., 2024) Dayananda, N.B.; Srivastava, N.; Achala, G.; Nandagiri, A.; Srihari, P.; Pardhasaradhi, B.; Cenkarmaddi, L.R.Improved scene perception makes the safe driving of automotive vehicles (AVs) feasible. The most common automotive sensors for AV perception for detection, classification, a nd t racking a re t he Light Detection And Ranging (LiDAR), radar, and camera sensors. The most reliable sensors for determining range are LiDAR and radar. In this research, we consider the referencing from the camera-based object recognition to fuse the LiDAR and radar point cloud data. To minimize any unintended effects from sensor orientation and sampling time, all three sensors are installed, calibrated, and time-aligned for this experiment. Subsequently, the obtained camera sensor data is subjected to object detection using a MobileNet-based deep neural network (DNN). The radar and LiDAR point cloud data are projected with the two-dimensional bounding box width, length, and height used for object recognition. Following that, the range information from the radar and LiDAR is retrieved and combined using a weighted average fusion algorithm. This experiment is run on the ROS platform, using AWR1642 radar sensor and RealSense LiDAR camera L515 sensor. The object detection from the camera and conducting fusion on the radar and LiDAR sensor is a potential algorithm for the Advanced Driver Assistant System (ADAS) emergency brake assistant (EBA) function. © 2024 IEEE.Item High-Speed Strassen Matrix Multiplication Accelerators for 2D Kalman Filter(Institute of Electrical and Electronics Engineers Inc., 2024) Pudi, M.; Dayananda, B.N.; Achala, G.; Srihari, P.; Pardhasaradhi, B.; Cenkarmaddi, L.R.High-speed and low-area Kalman filter (KF) algorithms are critical in autonomous vehicles, robotics, military, target tracking, and other applications. KF requires a large number of matrix multiplications (complexity of order O(n3)), which increases the number of cycles needed to process data. There is a vital requirement to develop a matrix multiplication accelerator module to enhance the efficiency of the entire Kalman Filter (KF) process. This paper proposes two accelerator models for KF, namely pipelined Strassen matrix multiplication and hybrid Strassen matrix multiplication. We propose pipelining the existing Strassen matrix multiplication architecture to reduce critical time to a single multiplication operation. To accelerate the KF and target the low area, we present a hybrid Strassen matrix multiplication architecture that reduces higher dimensions using the Strassen algorithm and computes the 2 × 2 matrix using standard matrix multiplication. The Strassen algorithm, pipelined Strassen algorithm, and hybrid Strassen algorithm-based accelerators are designed for the Vertix-7 FPGA to determine the maximum possible frequency and space limitations. For demonstration purposes, a 2D-KF is studied with a four-dimensional state vector. © 2024 IEEE.Item On the Synthesis of Channel Codes for NAND flash devices in Space Application(Institute of Electrical and Electronics Engineers Inc., 2024) Achala, G.; Srihari, P.; Shripathi Acharya, U.S.NAND flash memory technology is an important component that is vital in enabling data storage and processing capabilities for deep space missions. Thus, it plays an important role in contributing to the success and scientific outcomes of exploration endeavors beyond Earth's orbit. Its non-volatile characteristic guarantees data integrity even when power is unavailable, a critical attribute for extended missions where power resources are constrained. To improve the data integrity in NAND flash devices used in space applications, appropriate channel codes have to be incorporated. In this work, we have synthesized channel codes for two different NAND flash memory architectures used in space applications. The designed codes provide improved bit error rates when compared with the state-of-the-art. By incorporating suitably designed Subfield Subcodes of Reed Solomon (SSRS) codes in flash memory devices, the device is able to return stored data at acceptable bit error rates even when the raw bit error rate is as high as 10-3 © 2024 IEEE.Item Real-time Radar Imaging with Time Domain Correlation and Doppler Beam Sharpening(Institute of Electrical and Electronics Engineers Inc., 2024) Kumar, S.A.; Achala, G.; Vandana, G.S.; Srihari, P.; Pardhasaradhi, B.; Cenkarmaddi, L.R.Imaging with radar serves numerous purposes across remote sensing, monitoring civil infrastructure, detecting passing vehicles, and recognizing vulnerable road users (VRUs) within Advanced Driver Assistance Systems (ADAS). In most ADAS applications, one among a variety of radars, the millimeter wave (mmWave) radars, are limited to acquiring range, azimuth, elevation, and Doppler information. Configuration of the mm-wave radar in imaging form by fully utilizing built-in sending and receiving models is proposed in the work presented in this paper. The mm-wave radar is placed on a mobile platform, and the time domain correlation (TDC) is applied, followed by Doppler beam sharpening (DBS), to obtain radar imaging. The proposed algorithm was demonstrated with the help of IWR1642 radar, and real-time experiments were conducted with targets like cars, bicycles, and bikes. The mm-wave radar equipment and platform were moved with an approximate velocity and acquired I-Q channel data, further processed with the TDC-DBS algorithm. The experimental findings demonstrate successful target detection across scenarios considered in our work. Notably, the MIMO configuration on a fast-moving platform, along with the TDC-DBS algorithm, yielded superior results compared to the TDC algorithm. This algorithm stands out as a promising choice for automotive industry applications, such as imaging guardrails, detecting passing vehicles, and identifying vulnerable road users using side-mounted radar configurations. © 2024 IEEE.