This paper addresses the challenges of guiding Autonomous Vehicles (AVs) through unprotected left turns at unsignalized, fully uncontrolled, chaotic intersections consisting of pedestrians and adversarial vehicles. These intersections present several challenges for AVs, including the absence of traffic signals or signs, the dynamic nature of the environment, including human interactions due to the presence of crosswalks, and the variability of intersection layouts. This study develops a containerized simulation environment based on a high-fidelity simulation tool, CARLA, and is made open-source at https: //github. com/faizansana/ intersection-carla-gym. This study proposes using a hierarchical Deep Reinforcement Learning (DRL) wherein the DRL policy sets the target speed for the low-level PID controllers used for actuation. To the best of our knowledge, this study stands as the pioneering effort in addressing pedes-trian interaction within a high-fidelity simulation environment, coexisting alongside adversarial vehicles within CARLA. It conducts a comprehensive comparative analysis of five model-free DRL algorithms in a hierarchical framework. It is observed that Recurrent PPO with a discretized action space has the highest success rate and lowest accident rate for unprotected left turns. However, all algorithms struggle to achieve acceptable success rates when pedestrians exist within the environment, emphasizing the importance of the high-fidelity simulation tool. The paper concludes by discussing potential extensions of the proposed system and future research directions.

Visible Light Communication (VLC) is a novel optical wireless communication technology which uses Light Emitting Diodes (LEDs) and Photodiodes for coherent detection and very-high-data rate data communication system. The stringent Line of Sight (LoS) requirement in VLC makes it very suitable for Indoor Positioning System (IPS), to be used for autonomous and smart city infrastructure. The current work aims to implement a real time IPS system using VLC link in Network Simulator (NS-3). The VLC module is implemented by modelling real-time attributes of LEDs, optical channel, and the photodiodes. The localization is carried out using trilateration scheme which measures received signal strength interference (RSSI) for position estimation of the target. Furthermore, a comparison is carried out between VLC link and other existing technology, Wi-Fi, as far as positioning accuracy and other important performance metrics are concerned. The simulation results show significant improvement for the VLC link over the Wi-Fi link.

The fourth Industrial Revolution is expected to lead to an era of technological innovation and digitization that would require connectivity by the users, anywhere and anytime. The fifth generation of wireless communication systems and the technologies therein are being explored to cater to high connectivity needs that encompass high data rates, very low latencies, energy‐efficient systems, etc. A multiuser environment is anticipated that would require multiple access techniques, such as Nonorthogonal Multiple Access (NOMA). The user data in the power domain NOMA is superimposed, at the transmitter base station, which is in turn subjected to Successive Interference Cancellation at the user end. In the multiuser downlink, the desired user’s signal is subjected to imperfect SIC due to incomplete cancellation of the undesired user’s signal. Pulse‐shaping of NOMA symbols using wavelet transform is proposed to mitigate the multiuser interference due to imperfect SIC. Closed‐form symbol error rate (SER) expression is derived for the wavelet NOMA system for a three‐user scenario. Analytical results show that wavelet transform pulse‐shaped NOMA performs better compared to Fourier transform pulse‐shaped NOMA symbols in mitigating SIC and thereby minimize the residual error due to imperfect SIC.

Visible Light Communication (VLC) is an emerging wireless optical technology in the field of data communication. The VLC systems have a wide light spectrum, which provides a high data rate and low Peak to Average Power Ratio (PAPR). One of the major issues in VLC systems is interference which is caused by daylight and other illumination sources. The other issue in VLC systems is that the transmission side provides data as complex values which causes transmission related issues and is hard to read at the receiver side due to a lot of channel noise. To solve these issues, we propose the hybrid approach of Pulse Width Modulation (PWM) and Orthogonal Frequency Division Multiplexing (OFDM) with the Code Division Multiple Access (CDMA). The CDMA is employed for multiplexing the multi-user data for transmission to accommodate a large number of users and avoid inter-user interference. The line of sight (LOS) and non-line of sight (NLoS) components of the optical channel are simulated in MATLAB. Finally, the system is also designed physically using NI modules for the real-time environment. The results show that the VLC systems with PWM-OFDM perform better as compared to simple OFDM systems.

One of the main abilities that the robots need to maintain is to efficiently communicate with people in a humanly manner. Thus, human activity recognition (HAR) would be an integral part of such a human-robot interaction system. One of the major challenges in HAR is that the individuals perform their activities in different manners. Furthermore, there is a very wide range of different types of activities that the robots would require to understand. Some activities are simple, quick and short (e.g., sit down), while many others are complex, have many details and span through a long range of time (e.g., wearing contact lens). In this paper, we model the recognition of activities into a sequence-labeling problem and propose a new probabilistic graphical model (PGM) that can recognize both short/long-range activities, by introducing a hierarchical classification model and including extra links and loopy conditions in our PGM. To optimize the PGM and obtain its parameters during training, we use a structured prediction technique, a general framework that involves latent structured support vector machines (LSSVM) and hidden-state conditional random fields (HCRF). We evaluate our method on two widely used datasets (CAD-60 & UT-Kinect) that contain both activity types. Our obtained results are promising and show that our method can recognize both types of activities effectively, while most of the previous works only focused on one of these two major types. We further explore distributed processing techniques, since our method can easily be distributed over processing nodes. We also propose an efficient divide-and-merge technique to further speedup the training step.

This study examines subcarrier and optimal power allocation in orthogonal frequency division multiple access based 5G device‐to‐device (D2D) networks. To improve spectrum efficiency, D2D users share same subcarriers with the legacy users using underlay approach. In this approach, it is challenging to design an efficient subcarrier and power allocation method for D2D networks which guarantees the quality of service requirements of legacy users. Therefore, the key constraint is to check the interference condition among D2D and legacy users while allocating the same resources to D2D users. In this study, the authors propose a throughput efficient subcarrier allocation (TESA) and geometric water‐filling based optimal power allocation (GWFOPA) method for multi‐class cellular D2D systems. First, the TESA method selects subcarriers and allocates power equally for D2D users according to their service classes while maintaining interference and data rate constraints. Then, the GWFOPA method is applied to optimise power in a computationally effective way. The objective of TESA and GWFOPA method is to maximise the data rate of each class while maintaining interference constraint and fairness among the D2D users. Finally, the authors present simulation results to evaluate performance of TESA and GWFOPA in terms of throughput, user data rate, and fairness.

Fifth generation (5G) communication networks can achieve high spectral efficiency using sparse code multiple access (SCMA) scheme when large number of users are trying to transmit their data simultaneously. The sparsity of SCMA codewords offers the possibility of applying a low‐complexity message passing algorithm as an alternative to maximum likelihood detector. However, the requirement of densely deployed 5G users is to opportunistically explore new frequencies via cognitive features to overcome spectrum scarcity challenges. In this study, spectrum sensing enables cognitive radio capabilities for the SCMA system applied in millimetre wave (mmWave) 5G communications. Proposed cognitive SCMA system can sense the spectrum holes and adapt the transmission in order to utilise the available subcarriers. Besides, wavelet packet transform based techniques are used instead of conventional Fourier‐based spectrum sensing (FSS) and orthogonal frequency‐division multiple access (OFDMA). Wavelet packet spectrum sensing offers more accurate estimation of frequency and power compared with FSS. On the other hand, wavelet packet multiple access is more flexible and robust against interference compared with OFDMA. The simulation results verify that the proposed method can significantly improve the performance of SCMA system in terms of probabilities of false alarm and detection, and symbol error rate.

This paper proposes an iterative detector for uncoded OFDM signals in cooperative networks, where the information symbols are simply partitioned through a time-domain matrix at the OFDM transmitter. We analytically show that our proposed iterative detector at the partner node converges and can completely recover the user's data from its partitioned version, if sufficient redundancy is inserted in the user's data. For efficient use of the redundancy in the user's data, a coded cooperative transmission based on superposition modulation is proposed. Additionally, a closed-form input-output relationship for the partitioning and reconstruction algorithm in the proposed cooperative scenario is derived. We also obtain closed-form expressions for symbol error rate performance of the proposed coded cooperative scenario over Rayleigh frequency-selective fading channels. Numerical comparisons shed light on the relative merits of the proposed coded cooperation under various inter-user and uplink channel conditions.

In this article, we present a convex optimization model to design a stacked hybrid memory system to improve performance and reduce energy consumption of the chip-multiprocessor (CMP). Our convex model optimizes numbers and placement of SRAM and STT-RAM memories on the memory layer, and efficiently maps applications/threads on cores in the core layer. Power consumption that is the main challenge in the dark silicon era is represented as a power constraint in this work and it is satisfied by the detailed optimization model in order to design a dark silicon aware 3D CMP. Experimental results show that the proposed architecture considerably improves the energy-delay product (EDP) and performance of the 3D CMP compared to the Baseline memory design.

The rapid growth of modern wireless applications results in spectrum and energy scarcity. Cognitive radio (CR) technology is pivotal to resolve spectrum shortage. However, energy consumption is a critical issue in CR networks (CRNs) due to the unique functionality of spectrum sensing. Besides RF energy harvesting has come up as a potential solution to provide energy to CR devices. In this paper, we first provide an overview of spectrum sensing in CRNs with RF energy harvesting. Then, we propose a framework for multi-band spectrum sensing in CRNs with RF energy harvesting. We formulate a problem to optimize sensing time for throughput maximization while protecting primary users and keeping a minimum level of residual energy. Simulation results show the performance of multi-band cooperative spectrum sensing in terms of average throughput and energy harvested.

Millimetre‐wave (mmWave) 5G communications is an emerging technology to enhance the capacity of existing systems by thousand‐fold improvement. Heterogeneous networks employing densely distributed small cells can optimise the available coverage and throughput of 5G systems. Efficiently utilising the spectrum bands by small cells is one of the approaches that will considerably increase the available data rate and capacity of the heterogeneous networks. This challenging task can be achieved by spectrum sensing capability of cognitive radios and new modulation techniques for data transmission. In this study, a wavelet‐based filter bank is proposed for spectrum sensing and modulation in 5G heterogeneous networks. The proposed technique can mitigate the spectral leakage and interference by adapting the subcarriers according to cognitive information provided by wavelet packet based spectrum sensing (WPSS) and lowering sidelobes using wavelet‐based filter bank multicarrier modulation. The performance improvement of WPSS compared with Fourier‐based spectrum sensing is verified in terms of power spectral density comparison and probabilities of detection and false alarm. Meanwhile, the bit error rate performance demonstrates the superiority of the proposed wavelet‐based system compared with its Fourier‐based counterpart over the 60 GHz mmWave channel.

Signals are sparsely represented by a set of over-complete basis vectors called dictionary. Atoms of a dictionary are either chosen from a predefined set of functions such as Discrete Cosine Transform (DCT), Wavelets, Gabor, Contourlets, and Curvelets or learned from an image itself. Recently, a nonlinear (NL) dictionary has been proposed by adding NL functions to the conventional DCT atoms. This paper presents performance of a novel NL dictionary for retinal image reconstruction and denoising. The NL dictionary demonstrates good performance and can be adapted if shorter execution time is required.

Complex signals such as images, audio and video recordings can be represented by a large over complete dictionary without distinguishable compromise on the representation quality. Large over complete dictionaries with more patterns can be used to increase the sparse coding as well as provide significant improvements in signal representation quality. The use of the over-complete dictionaries and sparse coding has been successfully applied in compression, de-noising, and pattern recognition applications within the last few decades. One particular dictionary, the Discrete Cosine Transform (DCT) dictionary has seen a great deal of success in image processing applications. However, we propose a novel non-linear over-complete dictionary that is sparser than the DCT dictionary while improving the quality of the signal representation. The proposed non-linear dictionary has demonstrated through experimental results to be superior to the DCT dictionary by achieving higher signal to noise ratio (SNR) in the reconstructed images.

The increasing demand to deploy efficient ways of identification has made Radio frequency Identification (RFID) technology ubiquitous. Due to the wireless nature of communication between the reader and the tag, this technology imposes major security and privacy threats. Massive work to design a powerful authentication protocol has been put to overcome various threats against privacy and security of the system. However, certain constraints on RFID tags such as limited computation capabilities, memory size and communication cost, has made most approaches fail to conduct fully secured RFID system. In this paper, we present a novel cryptographic scheme, Hacker Proof Authentication Protocol (HPAP), that allows mutual authentication between the reader and the tag as well as secure tags' information. We prove our protocol achieves full security by deploying tag static identifier, updated timestamp, a one way hash function and encryption keys with semi randomized nature as they are updated using Linear Feedback Shift Register (LFSR). Simulation using C#.NET shows that the protocol is secure against various attacks. Comparison against various existing RFID authentication protocols prove that our protocol maintain less storage, computation load and low-cost.

This paper presents a low power current reusing quadrature voltage controlled oscillator for use in implantable electronics operating in the 402 MHz to 405 MHz medical implant communication service (MICS) frequency band. The oscillator was designed in IBM CMOS8RF 0.13 mum CMOS technology and simulated using Cadence IC5.141. The current reuse structure draws half the bias current as a conventional quadrature voltage controlled oscillator while exhibiting performance comparable to or exceeding that of previously published designs. Simulation results show that the oscillator phase noise is -127 dBc/Hz and consumes 1 mW of power from a 1 V supply. When the supply voltage is scaled to 0.65 V and biased for the same current consumption, the power consumption is reduced to 650 muW with phase noise of -111 dBc/Hz, which is almost 50% less power consumption when compared to oscillators in the same frequency band. The tuning range of the oscillator is 401.25 MHz to 407.5 MHz, enough to cover the MICS frequency band.

One of the fundamental requirements of cognitive radio networks is to reliably detect the presence of licensed primary users. Therefore, spectrum sensing should be performed prior to allowing unlicensed users to access the vacant licensed bands. Multiple secondary users can cooperate to increase the reliability of spectrum sensing. Previous work on wideband spectrum sensing showed that multiband joint detection, which jointly detects the signal energy over multiple frequency bands, is efficient in improving the dynamic spectrum utilization and reducing interference to the primary users. In this paper, we investigate the integration of basic wideband spectrum sensing with both data (soft) and decision (hard) fusion techniques to improve the performance in the presence of multiple secondary users. We formulate the optimization problem for the multiband joint detection when cooperation is used for both hard and soft decision approaches. Numerical results show the significant improvement in the performance, in terms of the aggregate opportunistic throughput and false alarm probability, achieved by using cooperative sensing. Also, better performance was achieved when the data fusion approach was used.

In wireless multiuser OFDM systems, dynamic resource allocation has been shown to improve the performance by exploiting the multiuser diversity. In this paper we propose a subcarrier allocation algorithm to increase the total data rate for the downlink of a variable bit rate multiuser OFDM system with proportional rate constraints subject to bit error rate and total transmit power. We assume that the channel is quasi-static where the channel status does not vary within each transmission block and the base station has perfect knowledge of subchannel gains. The proposed algorithm is based on prioritizing the critical (most sensitive) user in the system and the variance of the subchannel gains for each user is used to define the sensitivity of the user to the subcarrier allocation. Simulation results show that this algorithm achieves higher capacity with acceptable proportional fairness compared to the previous suboptimal solutions proposed by Rhee et al. in [9] and Shen et al. in [12].

In this work, we propose an architecture for the wavelet-packet based best-basis algorithm for images using Shannon entropy as cost function. The algorithm for the logarithm implementation for integers using Taylor series is also proposed to implement Shannon entropy. The proposed architecture includes the architectures for the best-tree selection. The execution time of the proposed hardware for the best-basis algorithm for images is compared to its software implementation. The proposed best-basis architecture has been described in VHDL at the RTL level, simulated successfully for its functional correctness and implemented in an FPGA.

The best-basis algorithm has gained much importance on textured-based image compression and denoising of signals. In this paper, an architecture for the wavelet-packet based best-basis algorithm for images is proposed. The paper also describes the architecture for best-tree selection from 2D wavelet packet decomposition. The precision analysis of the proposed architecture is also discussed and the result shows that increase in the precision of input pixel greatly increases the signal-to-noise ratio (SNR) per pixel whereas increase in the precision of filter coefficient does not greatly help in improving the SNR value. The proposed architecture is described in VHDL at the RTL level, simulated successfully for its functional correctness and implemented in an FPGA

Wavelet-packet based best-basis selection has become a popular method for image compression particularly for the textured images. In this work, we propose architecture for the best basis algorithm for images using lifting-based wavelet transform and threshold function as cost function. The architecture for the best-basis selection from 2D wavelet packet is also described. The proposed architecture has been described in VHDL at the RTL level, simulated successfully for its functional correctness and implemented in FPGA

The work area of a team of small robots is limited by their inability to traverse a very common obstacle: stairs. We present a complete integrated control architecture and communication strategy for a system of reconfigurable robots that can climb stairs. A modular robot design is presented which allows the robots to dynamically reconfigure to traverse certain obstacles. This work investigates the implementation of a system of autonomous robots which can cooperatively reconfigure themselves to collectively traverse obstacles such as stairs. We present a complete behavior and communication system which facilitates this autonomous reconfiguration. The behavior and communication systems are validated by implementing them on a mobile robot platform synthesized specifically for this research. Experimental trials showed that the implementation of the behavior control systems was successful. Several successful stair climbing trials were accomplished. Improvements to the mechanical design are proposed

In this research, an architecture that performs both forward and inverse lifting-based discrete wavelet transform is proposed. The proposed architecture reduces the hardware requirement by exploiting the redundancy in the arithmetic operation involved in DWT computation. The proposed architecture does not require any extra memory to store intermediate results. The proposed architecture consists of predict module, update module, address generation module, control unit and a set of registers to establish data communication between predict and update modules. The symmetrical extension of images at the boundary to reduce distorted images has been incorporated in our proposed architecture as mentioned in JPEG2000. This architecture has been described in VHDL at the RTL level and simulated successfully using ModelSim simulation environment

We present an audio classification and retrieval technique that exploits the non-stationary behavior of music signals and extracts features that characterize their spectral change over time. Audio classification provides a solution to incorrect and inefficient manual labelling of audio files on computers by allowing users to extract music files based on content similarity rather than labels. In our technique, classification is performed using time-frequency analysis and sounds are classified into 6 music groups consisting of rock, classical, folk, jazz and pop. For each 5 second music segment, the features that are extracted include entropy, centroid, centroid ratio, bandwidth, silence ratio, energy ratio, and location of minimum and maximum energy. Using a database of 143 signals, a set of 10 time-frequency features are extracted and an accuracy of classification of around 93% using regular linear discriminant analysis or 92.3% using the leave-one-out method is achieved.

Local discriminant bases method is a powerful algorithmic framework for feature extraction and classification applications that is based on supervised training. It is considerably faster compared to more theoretically ideal feature extraction methods such as principal component analysis or projection pursuit. In this paper an optimization block is added to the original local discriminant bases algorithm to promote the difference between disjoint signal classes. This is done by optimally weighting the local discriminant basis using the steepest decent algorithm. The proposed method is particularly useful when background features in the signal space show strong correlation with regions of interest in the signal as in mammograms for instance.

This paper introduces an efficient method for the recognition of human faces in 2-dimensional digital images using a new feature extraction method. The proposed feature extraction method includes human face localization derived from the shape information using a new distance measure as facial candidate threshold (FCT) as well as Pseudo Zernike moment invariant (PZMI). Also we introduce a new parameter to define axis correction ratio (ACR) of images for disregarding irrelevant information of face images. In this paper the effect of the new parameters in disregarding irrelevant information in recognition rate is studied. Also we evaluate the effect of orders of PZMI in the proposed technique. Simulation results on the face database of Olivetti research laboratory (ORL) indicate that high order PZMI together with derived face localization and proposed technique for feature extraction contain very useful information about face recognition process. Recognition rate of 99.3% is obtained using this proposed technique.

Local discriminant basis algorithm (LDB) is a supervised scheme for feature extraction and nonstationary signal classification. Due to its fast computational time, O(n log n), and excellent time-frequency localization, it is a promising method for nonstationary signal analysis. An optimized version of local discriminant basis (OLDB) has been recently proposed that emphasizes certain regions of interest in different classes using initial LDB features. The optimization process is particularly useful when background structures show high correlation with desired features in signal or image space as in mammograms. In this paper the performance of OLDB algorithm is studied in a texture classification problem. Classification into more than two classes of signals or images is a challenging problem and OLDB is capable of obtaining 85% accuracy classifying grayscale 64/spl times/64 textured images into three classes using only the 280 top LDB features as studied in this paper.

Local discriminant bases method is a powerful algorithmic framework for feature extraction and classification applications that is based on supervised training. It is considerably faster compared to more theoretically ideal feature extraction methods such as principal component analysis or projection pursuit. In this paper an optimization block is added to original local discriminant bases algorithm to promote the difference between disjoint signal classes. This is done by optimally weighting the local discriminant basis using steepest decent algorithm. The proposed method is particularly useful when background features in the signal space show strong correlation with regions of interest in the signal as in mammograms for instance.

This paper provides the results of a simulation-based fault characterization study of CMOS/BiCMOS logic families. We show that most of the shorts cause $I_{DDQ}$ faults, while open defects result in delay or stuck-open faults. We propose a design-for-testability technique for detecting short and bridging faults in CMOS/BiCMOS logic circuits. The impact of this circuit modification on the behavior of the circuit in normal mode is investigated.

This paper provides a simulation-based study of the delay fault testing in logic circuits. It is shown that delay testing is necessary in order to achieve a high defect coverage. By detecting delayed time response in a transistor circuit, three types of faults are detected: (1) faults which cause delayed transitions at the output node due to some open defects, (2) faults which cause an intermediate voltage level at the output node, and (3) most stuck-at faults which halt the circuit at '1' or '0'. An on-line checker is presented which enables the concurrent detection of delay faults. Since one checker is used for each output signal, the area overhead is minimal. This technique does not degrade the speed of the circuit under test (CUT). We show that the test circuit is independent of the size of the CUT. Simulation results show that this technique can be adjusted to fit to any design style.

In this paper an efficient method for calculating the zero state response of linear time invariant circuits is given. The input, in general, is a complex exponential function of time, of which step and sinusoidal functions are special cases. The method gives an algebraic relationship between the response due to two arbitrary exponential inputs. If the transient response due to one input is known, then the response due to the other input can be computed by the solution of a system of linear equations. No approximation is made in the derivation, and they are valid for all time and all exponential inputs. The method is useful in reducing the computational costs of analyzing periodically switched linear networks, such as, switched capacitor and switched current circuits.