Showing 1–29 of 29 results for author: Ibrahim, M S

Privacy-Preserving Collaborative Medical Image Segmentation Using Latent Transform Networks

Authors: Saheed Ademola Bello, Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim, Shujaat Khan

Abstract: Collaborative training across multiple institutions is becoming essential for building reliable medical image segmentation models. However, privacy regulations, data silos, and uneven data availability prevent hospitals from sharing raw scans or annotations, limiting the ability to train generalizable models. Latent-space collaboration frameworks such as privacy-segmentation framework (SF) offer a… ▽ More Collaborative training across multiple institutions is becoming essential for building reliable medical image segmentation models. However, privacy regulations, data silos, and uneven data availability prevent hospitals from sharing raw scans or annotations, limiting the ability to train generalizable models. Latent-space collaboration frameworks such as privacy-segmentation framework (SF) offer a promising alternative, but such methods still face challenges in segmentation accuracy and vulnerability to latent inversion and membership-inference attacks. This work introduces a privacy-preserving collaborative medical image segmentation framework (PPCMI-SF) designed for heterogeneous medical datasets. The approach combines skip-connected autoencoders for images and masks with a keyed latent transform that applies client-specific orthogonal mixing and permutation to protect latent features before they are shared. A unified mapping network on the server-side performs multi-scale latent-to-latent translation, enabling segmentation inference without exposing raw data. Experiments on four datasets: PSFH ultrasound, ultrasound nerve segmentation, FUMPE CTA, and cardiac MRI show that the proposed PPCMI-SF consistently achieves high Dice scores and improved boundary accuracy, as reflected by lower 95th percentile Hausdorff distance (HD95) and average symmetric surface distance (ASD) compared to the current state-of-the-art and performs competitively with privacy-agnostic baselines. Privacy tests confirm strong resistance to inversion and membership attacks, and the overall system achieves real-time inference with low communication overhead. These results demonstrate that accurate and efficient medical image segmentation can be achieved without compromising data privacy in multi-institution settings. △ Less

Submitted 4 March, 2026; originally announced March 2026.

Comments: 14 pages, 8 figures

Flow-Based Path Planning for Multiple Homogenous UAVs for Outdoor Formation-Flying

Authors: Mahmud Suhaimi Ibrahim, Shantanu Rahman, Muhammad Samin Hasan, Minhaj Uddin Ahmad, Abdullah Abrar

Abstract: Collision-free path planning is the most crucial component in multi-UAV formation-flying (MFF). We use unlabeled homogenous quadcopters (UAVs) to demonstrate the use of a flow network to create complete (inter-UAV) collision-free paths. This procedure has three main parts: 1) Creating a flow network graph from physical GPS coordinates, 2) Finding a path of minimum cost (least distance) using any g… ▽ More Collision-free path planning is the most crucial component in multi-UAV formation-flying (MFF). We use unlabeled homogenous quadcopters (UAVs) to demonstrate the use of a flow network to create complete (inter-UAV) collision-free paths. This procedure has three main parts: 1) Creating a flow network graph from physical GPS coordinates, 2) Finding a path of minimum cost (least distance) using any graph-based path-finding algorithm, and 3) Implementing the Ford-Fulkerson Method to find the paths with the maximum flow (no collision). Simulations of up to 64 UAVs were conducted for various formations, followed by a practical experiment with 3 quadcopters for testing physical plausibility and feasibility. The results of these tests show the efficacy of this method's ability to produce safe, collision-free paths. △ Less

Submitted 24 November, 2025; originally announced November 2025.

Comments: 9 pages, 15 figures, conference

Journal ref: 2022 7th International Conference on Mechanical Engineering and Robotics Research (ICMERR)

Enhancing Group Recommendation using Soft Impute Singular Value Decomposition

Authors: Mubaraka Sani Ibrahim, Isah Charles Saidu, Lehel Csato

Abstract: The growing popularity of group activities increased the need to develop methods for providing recommendations to a group of users based on the collective preferences of the group members. Several group recommender systems have been proposed, but these methods often struggle due to sparsity and high-dimensionality of the available data, common in many real-world applications. In this paper, we pro… ▽ More The growing popularity of group activities increased the need to develop methods for providing recommendations to a group of users based on the collective preferences of the group members. Several group recommender systems have been proposed, but these methods often struggle due to sparsity and high-dimensionality of the available data, common in many real-world applications. In this paper, we propose a group recommender system called Group Soft-Impute SVD, which leverages soft-impute singular value decomposition to enhance group recommendations. This approach addresses the challenge of sparse high-dimensional data using low-rank matrix completion. We compared the performance of Group Soft-Impute SVD with Group MF based approaches and found that our method outperforms the baselines in recall for small user groups while achieving comparable results across all group sizes when tasked on Goodbooks, Movielens, and Synthetic datasets. Furthermore, our method recovers lower matrix ranks than the baselines, demonstrating its effectiveness in handling high-dimensional data. △ Less

Submitted 14 November, 2025; originally announced November 2025.

Comments: ((1) African University of Science and Technology (Abuja, Nigeria), (2) Baze University (Abuja, Nigeria), (3) Babes-Bolyai University (Cluj-Napoca, Romania))

Band Splitting in m-Type II radio Bursts and their Role in Coronal Parameter Diagnostics

Authors: Pooja Devi, Ramesh Chandra, Rositsa Miteva, M. Syed Ibrahim, Kamal Joshi

Abstract: Type II radio bursts are signatures of shock waves generated by solar eruptions, observed at radio wavelengths. While metric (m) type II bursts originate in the lower corona, their longer-wavelength (up to kilometers) counterparts extend into interplanetary space. A rare but valuable feature observed in some type II bursts is band splitting in their dynamic spectra, which provides crucial insights… ▽ More Type II radio bursts are signatures of shock waves generated by solar eruptions, observed at radio wavelengths. While metric (m) type II bursts originate in the lower corona, their longer-wavelength (up to kilometers) counterparts extend into interplanetary space. A rare but valuable feature observed in some type II bursts is band splitting in their dynamic spectra, which provides crucial insights into physical parameters such as shock speed, Alfvén Mach number, Alfvén speed, and coronal magnetic field strength (B). In this study, we investigate band-splitting in 44 m-type II radio bursts observed by the Radio Solar Telescope Network during solar cycle 24 (2009 -- 2019). These events exhibit splitting in both fundamental and harmonic bands and are analyzed under both perpendicular and parallel shocks. All events are associated to solar flares and 41 (93 \%) with the coronal mass ejections. Shock speeds, derived using a hybrid coronal density model proposed by \cite{Vrsnak2004}, range from $\approx$ 350 to 1727 \kms. The relative bandwidth (BDW) of the split bands remains constant with frequency and height. Alfvén Mach numbers indicate moderate shock strength (1.06 -- 3.38), while Alfvén speeds and $B$ vary from $\approx$ 230 -- 1294 \kms\ and $\approx$ 0.48 -- 7.13 G, respectively. Power-law relationships are established as $BDW \propto f_L^{-0.4}$ and $BDW \propto R^{\sim1}$, while the coronal magnetic field decreases with height as $B \propto R^{\sim-3}$. These results enhance our understanding of shock dynamics and magnetic field structures in the solar corona. △ Less

Submitted 3 October, 2025; originally announced October 2025.

Comments: 14 Figures, 2 tables

2025 EERI LFE Travel Study -- Mexico: Lessons in soft soils, subsidence, and site effects

Authors: Morgan D. Sanger, Kenny Buyco, Mohammed S. Ibrahim, P. Salvador Ramos, Andres A. Acosta

Abstract: The 1985 M8.1 Mexico City earthquake marked a turning point in Mexican earthquake engineering, underscoring the influence of soft soils, subsidence, and site effects on seismic performance in the Valley of Mexico. In the four decades since, both research and engineering practice have evolved significantly, shaped by subsequent events such as the 2017 M7.1 Puebla-Morelos earthquake. Integrating obs… ▽ More The 1985 M8.1 Mexico City earthquake marked a turning point in Mexican earthquake engineering, underscoring the influence of soft soils, subsidence, and site effects on seismic performance in the Valley of Mexico. In the four decades since, both research and engineering practice have evolved significantly, shaped by subsequent events such as the 2017 M7.1 Puebla-Morelos earthquake. Integrating observations made during the Learning from Earthquakes Travel Study program and desk study findings, this paper summarizes the progression of geotechnical knowledge and practice in Mexico City, through the lessons of 1985 and 2017, through the lens of site effects, soil zonation, subsidence and emerging directions and future trends. △ Less

Submitted 30 September, 2025; originally announced October 2025.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials

Authors: Rachel K. Luu, Jingyu Deng, Mohammed Shahrudin Ibrahim, Nam-Joon Cho, Ming Dao, Subra Suresh, Markus J. Buehler

Abstract: Large language models (LLMs) have reshaped the research landscape by enabling new approaches to knowledge retrieval and creative ideation. Yet their application in discipline-specific experimental science, particularly in highly multi-disciplinary domains like materials science, remains limited. We present a first-of-its-kind framework that integrates generative AI with literature from hitherto-un… ▽ More Large language models (LLMs) have reshaped the research landscape by enabling new approaches to knowledge retrieval and creative ideation. Yet their application in discipline-specific experimental science, particularly in highly multi-disciplinary domains like materials science, remains limited. We present a first-of-its-kind framework that integrates generative AI with literature from hitherto-unconnected fields such as plant science, biomimetics, and materials engineering to extract insights and design experiments for materials. We focus on humidity-responsive systems such as pollen-based materials and Rhapis excelsa (broadleaf lady palm) leaves, which exhibit self-actuation and adaptive performance. Using a suite of AI tools, including a fine-tuned model (BioinspiredLLM), Retrieval-Augmented Generation (RAG), agentic systems, and a Hierarchical Sampling strategy, we extract structure-property relationships and translate them into new classes of bioinspired materials. Structured inference protocols generate and evaluate hundreds of hypotheses from a single query, surfacing novel and experimentally tractable ideas. We validate our approach through real-world implementation: LLM-generated procedures, materials designs, and mechanical predictions were tested in the laboratory, culminating in the fabrication of a novel pollen-based adhesive with tunable morphology and measured shear strength, establishing a foundation for future plant-derived adhesive design. This work demonstrates how AI-assisted ideation can drive real-world materials design and enable effective human-AI collaboration. △ Less

Submitted 8 August, 2025; originally announced August 2025.

DSLR-CNN: Efficient CNN Acceleration using Digit-Serial Left-to-Right Arithmetic

Authors: Malik Zohaib Nisar, Muhammad Sohail Ibrahim, Saeid Gorgin, Muhammad Usman, Jeong-A Lee

Abstract: Digit-serial arithmetic has emerged as a viable approach for designing hardware accelerators, reducing interconnections, area utilization, and power consumption. However, conventional methods suffer from performance and latency issues. To address these challenges, we propose an accelerator design using left-to-right (LR) arithmetic, which performs computations in a most-significant digit first (MS… ▽ More Digit-serial arithmetic has emerged as a viable approach for designing hardware accelerators, reducing interconnections, area utilization, and power consumption. However, conventional methods suffer from performance and latency issues. To address these challenges, we propose an accelerator design using left-to-right (LR) arithmetic, which performs computations in a most-significant digit first (MSDF) manner, enabling digit-level pipelining. This leads to substantial performance improvements and reduced latency. The processing engine is designed for convolutional neural networks (CNNs), which includes low-latency LR multipliers and adders for digit-level parallelism. The proposed DSLR-CNN is implemented in Verilog and synthesized with Synopsys design compiler using GSCL 45nm technology, the DSLR-CNN accelerator was evaluated on AlexNet, VGG-16, and ResNet-18 networks. Results show significant improvements across key performance evaluation metrics, including response time, peak performance, power consumption, operational intensity, area efficiency, and energy efficiency. The peak performance measured in GOPS of the proposed design is 4.37x to 569.11x higher than contemporary designs, and it achieved 3.58x to 44.75x higher peak energy efficiency (TOPS/W), outperforming conventional bit-serial designs. △ Less

Submitted 3 January, 2025; originally announced January 2025.

Comments: Published in IEEE Access Volume 12, 2024

Journal ref: IEEE Access (2024)

USEFUSE: Uniform Stride for Enhanced Performance in Fused Layer Architecture of Deep Neural Networks

Authors: Muhammad Sohail Ibrahim, Muhammad Usman, Jeong-A Lee

Abstract: Convolutional Neural Networks (CNNs) are crucial in various applications, but their deployment on resource-constrained edge devices poses challenges. This study presents the Sum-of-Products (SOP) units for convolution, which utilize low-latency left-to-right bit-serial arithmetic to minimize response time and enhance overall performance. The study proposes a methodology for fusing multiple convolu… ▽ More Convolutional Neural Networks (CNNs) are crucial in various applications, but their deployment on resource-constrained edge devices poses challenges. This study presents the Sum-of-Products (SOP) units for convolution, which utilize low-latency left-to-right bit-serial arithmetic to minimize response time and enhance overall performance. The study proposes a methodology for fusing multiple convolution layers to reduce off-chip memory communication and increase overall performance. An effective mechanism detects and skips inefficient convolutions after ReLU layers, minimizing power consumption without compromising accuracy. Furthermore, efficient tile movement guarantees uniform access to the fusion pyramid. An analysis demonstrates the utile stride strategy improves operational intensity. Two designs cater to varied demands: one focuses on minimal response time for mission-critical applications, and another focuses on resource-constrained devices with comparable latency. This approach notably reduced redundant computations, improving the efficiency of CNN deployment on edge devices. △ Less

Submitted 13 May, 2025; v1 submitted 18 December, 2024; originally announced December 2024.

Comments: Accepted for publication in the Journal of Systems Architecture on 11 May, 2025

L2R-CIPU: Efficient CNN Computation with Left-to-Right Composite Inner Product Units

Authors: Malik Zohaib Nisar, Mohammad Sohail Ibrahim, Muhammad Usman, Jeong-A Lee

Abstract: This paper proposes a composite inner-product computation unit based on left-to-right (LR) arithmetic for the acceleration of convolution neural networks (CNN) on hardware. The efficacy of the proposed L2R-CIPU method has been shown on the VGG-16 network, and assessment is done on various performance metrics. The L2R-CIPU design achieves 1.06x to 6.22x greater performance, 4.8x to 15x more TOPS/W,… ▽ More This paper proposes a composite inner-product computation unit based on left-to-right (LR) arithmetic for the acceleration of convolution neural networks (CNN) on hardware. The efficacy of the proposed L2R-CIPU method has been shown on the VGG-16 network, and assessment is done on various performance metrics. The L2R-CIPU design achieves 1.06x to 6.22x greater performance, 4.8x to 15x more TOPS/W, and 4.51x to 53.45x higher TOPS/mm2 than prior architectures. △ Less

Submitted 8 July, 2024; v1 submitted 1 June, 2024; originally announced June 2024.

DSLOT-NN: Digit-Serial Left-to-Right Neural Network Accelerator

Authors: Muhammad Sohail Ibrahim, Muhammad Usman, Malik Zohaib Nisar, Jeong-A Lee

Abstract: We propose a Digit-Serial Left-tO-righT (DSLOT) arithmetic based processing technique called DSLOT-NN with aim to accelerate inference of the convolution operation in the deep neural networks (DNNs). The proposed work has the ability to assess and terminate the ineffective convolutions which results in massive power and energy savings. The processing engine is comprised of low-latency most-signifi… ▽ More We propose a Digit-Serial Left-tO-righT (DSLOT) arithmetic based processing technique called DSLOT-NN with aim to accelerate inference of the convolution operation in the deep neural networks (DNNs). The proposed work has the ability to assess and terminate the ineffective convolutions which results in massive power and energy savings. The processing engine is comprised of low-latency most-significant-digit-first (MSDF) (also called online) multipliers and adders that processes data from left-to-right, allowing the execution of subsequent operations in digit-pipelined manner. Use of online operators eliminates the need for the development of complex mechanism of identifying the negative activation, as the output with highest weight value is generated first, and the sign of the result can be identified as soon as first non-zero digit is generated. The precision of the online operators can be tuned at run-time, making them extremely useful in situations where accuracy can be compromised for power and energy savings. The proposed design has been implemented on Xilinx Virtex-7 FPGA and is compared with state-of-the-art Stripes on various performance metrics. The results show the proposed design presents power savings, has shorter cycle time, and approximately 50% higher OPS per watt. △ Less

Submitted 21 September, 2023; v1 submitted 12 September, 2023; originally announced September 2023.

Comments: Presented at 2023 26th Euromicro Conference on Digital System Design (DSD)

Unsupervised Learning for Pilot-free Transmission in 3GPP MIMO Systems

Authors: Omar M. Sleem, Mohamed Salah Ibrahim, Akshay Malhotra, Mihaela Beluri, Philip Pietraski

Abstract: Reference signals overhead reduction has recently evolved as an effective solution for improving the system spectral efficiency. This paper introduces a new downlink data structure that is free from demodulation reference signals (DM-RS), and hence does not require any channel estimation at the receiver. The new proposed data transmission structure involves a simple repetition step of part of the… ▽ More Reference signals overhead reduction has recently evolved as an effective solution for improving the system spectral efficiency. This paper introduces a new downlink data structure that is free from demodulation reference signals (DM-RS), and hence does not require any channel estimation at the receiver. The new proposed data transmission structure involves a simple repetition step of part of the user data across the different sub-bands. Exploiting the repetition structure at the user side, it is shown that reliable recovery is possible via canonical correlation analysis. This paper also proposes two effective mechanisms for boosting the CCA performance in OFDM systems; one for repetition pattern selection and another to deal with the severe frequency selectivity issues. The proposed approach exhibits favorable complexity-performance tradeoff, rendering it appealing for practical implementation. Numerical results, using a 3GPP link-level testbench, demonstrate the superiority of the proposed approach relative to the state-of-the-art methods. △ Less

Submitted 4 February, 2023; originally announced February 2023.

Anticancer Peptides Classification using Kernel Sparse Representation Classifier

Authors: Ehtisham Fazal, Muhammad Sohail Ibrahim, Seongyong Park, Imran Naseem, Abdul Wahab

Abstract: Cancer is one of the most challenging diseases because of its complexity, variability, and diversity of causes. It has been one of the major research topics over the past decades, yet it is still poorly understood. To this end, multifaceted therapeutic frameworks are indispensable. \emph{Anticancer peptides} (ACPs) are the most promising treatment option, but their large-scale identification and s… ▽ More Cancer is one of the most challenging diseases because of its complexity, variability, and diversity of causes. It has been one of the major research topics over the past decades, yet it is still poorly understood. To this end, multifaceted therapeutic frameworks are indispensable. \emph{Anticancer peptides} (ACPs) are the most promising treatment option, but their large-scale identification and synthesis require reliable prediction methods, which is still a problem. In this paper, we present an intuitive classification strategy that differs from the traditional \emph{black box} method and is based on the well-known statistical theory of \emph{sparse-representation classification} (SRC). Specifically, we create over-complete dictionary matrices by embedding the \emph{composition of the K-spaced amino acid pairs} (CKSAAP). Unlike the traditional SRC frameworks, we use an efficient \emph{matching pursuit} solver instead of the computationally expensive \emph{basis pursuit} solver in this strategy. Furthermore, the \emph{kernel principal component analysis} (KPCA) is employed to cope with non-linearity and dimension reduction of the feature space whereas the \emph{synthetic minority oversampling technique} (SMOTE) is used to balance the dictionary. The proposed method is evaluated on two benchmark datasets for well-known statistical parameters and is found to outperform the existing methods. The results show the highest sensitivity with the most balanced accuracy, which might be beneficial in understanding structural and chemical aspects and developing new ACPs. The Google-Colab implementation of the proposed method is available at the author's GitHub page (\href{https://github.com/ehtisham-Fazal/ACP-Kernel-SRC}{https://github.com/ehtisham-fazal/ACP-Kernel-SRC}). △ Less

Submitted 19 December, 2022; originally announced December 2022.

Vandermonde Constrained Tensor Decomposition for Hybrid Beamforming in Multi-Carrier MIMO Systems

Authors: Mohamed Salah Ibrahim, Akshay Malhotra, Mihaela Beluri, Arnab Roy, Shahab Hamidi-Rad

Abstract: Hybrid beamforming has evolved as a promising technology that offers the balance between system performance and design complexity in mmWave MIMO systems. Existing hybrid beamforming methods either impose unit-modulus constraints or a codebook constraint on the analog precoders/combiners, which in turn results in a performance-overhead tradeoff. This paper puts forth a tensor framework to handle th… ▽ More Hybrid beamforming has evolved as a promising technology that offers the balance between system performance and design complexity in mmWave MIMO systems. Existing hybrid beamforming methods either impose unit-modulus constraints or a codebook constraint on the analog precoders/combiners, which in turn results in a performance-overhead tradeoff. This paper puts forth a tensor framework to handle the wideband hybrid beamforming problem, with Vandermonde constraints on the analog precoders/combiners. The proposed method strikes the balance between performance, overhead and complexity. Numerical results on a 3GPP link-level test bench reveal the efficacy of the proposed approach relative to the codebook-based method while attaining the same feedback overhead. Moreover, the proposed method is shown to achieve comparable performance to the unit-modulus approaches, with substantial reductions in overhead. △ Less

Submitted 29 November, 2022; originally announced November 2022.

Noise Robust Named Entity Understanding for Voice Assistants

Authors: Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, Kushal Tayal, Roger Zheng, Peter Grasch, Jason D. Williams, Lin Li

Abstract: Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed framework improves NER accuracy by up to… ▽ More Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed framework improves NER accuracy by up to 3.13% and EL accuracy by up to 3.6% in F1 score. The features used also lead to better accuracies in other natural language understanding tasks, such as domain classification and semantic parsing. △ Less

Submitted 10 August, 2021; v1 submitted 29 May, 2020; originally announced May 2020.

Comments: NAACL 2021 Industry Track

MSC Class: 68T50 ACM Class: I.2.7

Cell-Edge Detection via Selective Cooperation and Generalized Canonical Correlation

Authors: Mohamed Salah Ibrahim, Ahmed S. Zamzam, Aritra Konar, Nicholas D. Sidiropoulos

Abstract: Improving the uplink quality of service for users located around the boundaries between cells is a key challenge in LTE systems. Relying on power control, existing approaches throttle the rates of cell-center users, while multi-user detection requires accurate channel estimates for the cell-edge users, which is another challenge due to their low received signal-to-noise ratio (SNR). Utilizing the… ▽ More Improving the uplink quality of service for users located around the boundaries between cells is a key challenge in LTE systems. Relying on power control, existing approaches throttle the rates of cell-center users, while multi-user detection requires accurate channel estimates for the cell-edge users, which is another challenge due to their low received signal-to-noise ratio (SNR). Utilizing the fact that cell-edge user signals are weak but common (received at roughly equal power) at different base stations (BSs), this paper establishes a connection between cell-edge user detection and generalized canonical correlation analysis (GCCA). It puts forth a GCCA-based method that leverages selective BS cooperation to recover the cell-edge user signal subspace even at low SNR. The cell-edge user signals can then be extracted from the resulting mixture via algebraic signal processing techniques. The paper includes theoretical analysis showing why GCCA recovers the correct subspace containing the cell-edge user signals under mild conditions. The proposed method can also identify the number of cell-edge users in the system, i.e., the common subspace dimension. Simulations reveal significant performance improvement relative to various multiuser detection techniques. Cell-edge detection performance is further studied as a function of how many / which BSs are selected, and it is shown that using the closest three BS is always the best choice. △ Less

Submitted 11 April, 2020; originally announced April 2020.

Design Multimedia Expert Diagnosing Diseases System Using Fuzzy Logic (MEDDSFL)

Authors: Mohammed Salah Ibrahim, Doaa Waleed Al-Dulaimee

Abstract: In this paper we designed an efficient expert system to diagnose diseases for human beings. The system depended on several clinical features for different diseases which will be used as knowledge base for this system. We used fuzzy logic system which is one of the most expert systems techniques that used in building knowledge base of expert systems. Fuzzy logic will be used to inference the result… ▽ More In this paper we designed an efficient expert system to diagnose diseases for human beings. The system depended on several clinical features for different diseases which will be used as knowledge base for this system. We used fuzzy logic system which is one of the most expert systems techniques that used in building knowledge base of expert systems. Fuzzy logic will be used to inference the results of disease diagnosing. We also provided the system with multimedia such as videos, pictures and information for most of disease that have been achieved in our system. The system implemented using Matlab ToolBox and fifteen diseases were studied. Five cases for normal, affected and unaffected people's different diseases have been tested on this system. The results show that system was able to predict the status whether a human has a disease or not accurately. All system results are reported in tables and discussed in detail. △ Less

Submitted 22 March, 2020; originally announced March 2020.

Comments: arXiv admin: text overlap with arXiv:1006.4544, arXiv:1401.0245 by other authors

Reliable Detection of Unknown Cell-Edge Users Via Canonical Correlation Analysis

Authors: Mohamed Salah Ibrahim, Nicholas D. Sidiropoulos

Abstract: Providing reliable service to users close to the edge between cells remains a challenge in cellular systems, even as 5G deployment is around the corner. These users are subject to significant signal attenuation, which also degrades their uplink channel estimates. Even joint detection using base station (BS) cooperation often fails to reliably detect such users, due to near-far power imbalance, and… ▽ More Providing reliable service to users close to the edge between cells remains a challenge in cellular systems, even as 5G deployment is around the corner. These users are subject to significant signal attenuation, which also degrades their uplink channel estimates. Even joint detection using base station (BS) cooperation often fails to reliably detect such users, due to near-far power imbalance, and channel estimation errors. Is it possible to bypass the channel estimation stage and design a detector that can reliably detect cell-edge user signals under significant near-far imbalance? This paper shows, perhaps surprisingly, that the answer is affirmative -- albeit not via traditional multiuser detection. Exploiting that cell-edge user signals are weak but {\em common} to different base stations, while cell-center users are unique to their serving BS, this paper establishes an elegant connection between cell-edge user detection and canonical correlation analysis (CCA) of the associated space-time baseband-equivalent matrices. It proves that CCA identifies the common subspace of these matrices, even under significant intra- and inter-cell interference. The resulting mixture of cell-edge user signals can subsequently be unraveled using a well-known algebraic signal processing technique. Interestingly, the proposed approach does not even require that the signals from the different base stations are synchronized -- the right synchronization can be automatically determined as well. Experimental results demonstrate that the proposed approach achieves order of magnitude BER improvements compared to `oracle' multiuser detection that assumes perfect knowledge of the cell-center user channels. △ Less

Submitted 4 March, 2020; originally announced March 2020.

Fast Algorithms for Joint Multicast Beamforming and Antenna Selection in Massive MIMO

Authors: Mohamed Salah Ibrahim, Aritra Konar, Nicholas D. Sidiropoulos

Abstract: Massive MIMO is currently a leading physical layer technology candidate that can dramatically enhance throughput in 5G systems, for both unicast and multicast transmission modalities. As antenna elements are becoming smaller and cheaper in the mmW range compared to radio frequency (RF) chains, it is crucial to perform antenna selection at the transmitter, such that the available RF chains are swit… ▽ More Massive MIMO is currently a leading physical layer technology candidate that can dramatically enhance throughput in 5G systems, for both unicast and multicast transmission modalities. As antenna elements are becoming smaller and cheaper in the mmW range compared to radio frequency (RF) chains, it is crucial to perform antenna selection at the transmitter, such that the available RF chains are switched to an appropriate subset of antennas. This paper considers the joint problem of multicast beamforming and antenna selection for a single multicast group in massive MIMO systems. The prior state-of-art for this problem relies on semi-definite relaxation (SDR), which cannot scale up to the massive MIMO regime. A successive convex approximation (SCA) based approach is proposed to tackle max-min fair joint multicast beamforming and antenna selection. The key idea of SCA is to successively approximate the non-convex problem by a class of non-smooth, convex optimization problems. Two fast and memory efficient first-order methods are proposed to solve each SCA subproblem. Simulations demonstrate that the proposed algorithms outperform the existing state-of-art approach in terms of solution quality and run time, in both traditional and especially in massive MIMO settings. △ Less

Submitted 4 March, 2020; originally announced March 2020.

Heart Segmentation From MRI Scans Using Convolutional Neural Network

Authors: Shakeel Muhammad Ibrahim, Muhammad Sohail Ibrahim, Muhammad Usman, Imran Naseem, Muhammad Moinuddin

Abstract: Heart is one of the vital organs of human body. A minor dysfunction of heart even for a short time interval can be fatal, therefore, efficient monitoring of its physiological state is essential for the patients with cardiovascular diseases. In the recent past, various computer assisted medical imaging systems have been proposed for the segmentation of the organ of interest. However, for the segmen… ▽ More Heart is one of the vital organs of human body. A minor dysfunction of heart even for a short time interval can be fatal, therefore, efficient monitoring of its physiological state is essential for the patients with cardiovascular diseases. In the recent past, various computer assisted medical imaging systems have been proposed for the segmentation of the organ of interest. However, for the segmentation of heart using MRI, only few methods have been proposed each with its own merits and demerits. For further advancement in this area of research, we analyze automated heart segmentation methods for magnetic resonance images. The analysis are based on deep learning methods that processes a full MR scan in a slice by slice fashion to predict desired mask for heart region. We design two encoder decoder type fully convolutional neural network models △ Less

Submitted 21 November, 2019; originally announced November 2019.

Comments: Accepted for oral presentation at 13th International Conference - Mathematics, Actuarial, Computer Science & Statistics (MACS 13) at IoBM, Karachi, Pakistan

Leveraging User Engagement Signals For Entity Labeling in a Virtual Assistant

Authors: Deepak Muralidharan, Justine Kao, Xiao Yang, Lin Li, Lavanya Viswanathan, Mubarak Seyed Ibrahim, Kevin Luikens, Stephen Pulman, Ashish Garg, Atish Kothari, Jason Williams

Abstract: Personal assistant AI systems such as Siri, Cortana, and Alexa have become widely used as a means to accomplish tasks through natural language commands. However, components in these systems generally rely on supervised machine learning algorithms that require large amounts of hand-annotated training data, which is expensive and time consuming to collect. The ability to incorporate unsupervised, we… ▽ More Personal assistant AI systems such as Siri, Cortana, and Alexa have become widely used as a means to accomplish tasks through natural language commands. However, components in these systems generally rely on supervised machine learning algorithms that require large amounts of hand-annotated training data, which is expensive and time consuming to collect. The ability to incorporate unsupervised, weakly supervised, or distantly supervised data holds significant promise in overcoming this bottleneck. In this paper, we describe a framework that leverages user engagement signals (user behaviors that demonstrate a positive or negative response to content) to automatically create granular entity labels for training data augmentation. Strategies such as multi-task learning and validation using an external knowledge base are employed to incorporate the engagement annotated data and to boost the model's accuracy on a sequence labeling task. Our results show that learning from data automatically labeled by user engagement signals achieves significant accuracy gains in a production deep learning system, when measured on both the sequence labeling task as well as on user facing results produced by the system end-to-end. We believe this is the first use of user engagement signals to help generate training data for a sequence labeling task on a large scale, and can be applied in practical settings to speed up new feature deployment when little human annotated data is available. △ Less

Submitted 18 September, 2019; originally announced September 2019.

Comments: NeurIPS 2018 Conversational AI Workshop

Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks

Authors: Alishba Sadiq, Muhammad Sohail Ibrahim, Muhammad Usman, Muhammad Zubair, Shujaat Khan

Abstract: Due to the dynamic nature, chaotic time series are difficult predict. In conventional signal processing approaches signals are treated either in time or in space domain only. Spatio-temporal analysis of signal provides more advantages over conventional uni-dimensional approaches by harnessing the information from both the temporal and spatial domains. Herein, we propose an spatio-temporal extensio… ▽ More Due to the dynamic nature, chaotic time series are difficult predict. In conventional signal processing approaches signals are treated either in time or in space domain only. Spatio-temporal analysis of signal provides more advantages over conventional uni-dimensional approaches by harnessing the information from both the temporal and spatial domains. Herein, we propose an spatio-temporal extension of RBF neural networks for the prediction of chaotic time series. The proposed algorithm utilizes the concept of time-space orthogonality and separately deals with the temporal dynamics and spatial non-linearity(complexity) of the chaotic series. The proposed RBF architecture is explored for the prediction of Mackey-Glass time series and results are compared with the standard RBF. The spatio-temporal RBF is shown to out perform the standard RBFNN by achieving significantly reduced estimation error. △ Less

Submitted 17 August, 2019; originally announced August 2019.

Comments: Published in: 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST). arXiv admin note: substantial text overlap with arXiv:1908.01321

Quantum Calculus-based Volterra LMS for Nonlinear Channel Estimation

Authors: Muhammad Usman, Muhammad Sohail Ibrahim, Jawwad Ahmad, Syed Saiq Hussain, Muhammad Moinuddin

Abstract: A novel adaptive filtering method called $q$-Volterra least mean square ($q$-VLMS) is presented in this paper. The $q$-VLMS is a nonlinear extension of conventional LMS and it is based on Jackson's derivative also known as $q$-calculus. In Volterra LMS, due to large variance of input signal the convergence speed is very low. With proper manipulation we successfully improved the convergence perform… ▽ More A novel adaptive filtering method called $q$-Volterra least mean square ($q$-VLMS) is presented in this paper. The $q$-VLMS is a nonlinear extension of conventional LMS and it is based on Jackson's derivative also known as $q$-calculus. In Volterra LMS, due to large variance of input signal the convergence speed is very low. With proper manipulation we successfully improved the convergence performance of the Volterra LMS. The proposed algorithm is analyzed for the step-size bounds and results of analysis are verified through computer simulations for nonlinear channel estimation problem. △ Less

Submitted 7 August, 2019; originally announced August 2019.

Semi-Supervised Semantic Image Segmentation with Self-correcting Networks

Authors: Mostafa S. Ibrahim, Arash Vahdat, Mani Ranjbar, William G. Macready

Abstract: Building a large image dataset with high-quality object masks for semantic segmentation is costly and time consuming. In this paper, we introduce a principled semi-supervised framework that only uses a small set of fully supervised images (having semantic segmentation labels and box labels) and a set of images with only object bounding box labels (we call it the weak set). Our framework trains the… ▽ More Building a large image dataset with high-quality object masks for semantic segmentation is costly and time consuming. In this paper, we introduce a principled semi-supervised framework that only uses a small set of fully supervised images (having semantic segmentation labels and box labels) and a set of images with only object bounding box labels (we call it the weak set). Our framework trains the primary segmentation model with the aid of an ancillary model that generates initial segmentation labels for the weak set and a self-correction module that improves the generated labels during training using the increasingly accurate primary model. We introduce two variants of the self-correction module using either linear or convolutional functions. Experiments on the PASCAL VOC 2012 and Cityscape datasets show that our models trained with a small fully supervised set perform similar to, or better than, models trained with a large fully supervised set while requiring ~7x less annotation effort. △ Less

Submitted 25 February, 2020; v1 submitted 16 November, 2018; originally announced November 2018.

Comments: Accepted to CVPR 2020

Mirror-Prox SCA Algorithm for Multicast Beamforming and Antenna Selection

Authors: Mohamed S. Ibrahim, Aritra Konar, Mingyi Hong, Nicholas D. Sidiropoulos

Abstract: This paper considers the (NP-)hard problem of joint multicast beamforming and antenna selection. Prior work has focused on using Semi-Definite relaxation (SDR) techniques in an attempt to obtain a high quality sub-optimal solution. However, SDR suffers from the drawback of having high computational complexity, as SDR lifts the problem to higher dimensional space, effectively squaring the number of… ▽ More This paper considers the (NP-)hard problem of joint multicast beamforming and antenna selection. Prior work has focused on using Semi-Definite relaxation (SDR) techniques in an attempt to obtain a high quality sub-optimal solution. However, SDR suffers from the drawback of having high computational complexity, as SDR lifts the problem to higher dimensional space, effectively squaring the number of variables. This paper proposes a high performance, low complexity Successive Convex Approximation (SCA) algorithm for max-min SNR "fair" joint multicast beamforming and antenna selection under a sum power constraint. The proposed approach relies on iteratively approximating the non-convex objective with a series of non-smooth convex subproblems, and then, a first order-based method called Saddle Point Mirror-Prox (SP-MP) is used to compute optimal solutions for each SCA subproblem. Simulations reveal that the SP-MP SCA algorithm provides a higher quality and lower complexity solution compared to the one obtained using SDR. △ Less

Submitted 1 March, 2018; originally announced March 2018.

Comments: 6 pages, 3 figures

On the Capacity Region of the Deterministic Y-Channel with Common and Private Messages

Authors: Mohamed S. Ibrahim, Mohammed Nafie, Yahya Mohasseb

Abstract: In multi user Gaussian relay networks, it is desirable to transmit private information to each user as well as common information to all of them. However, the capacity region of such networks with both kinds of information is not easy to characterize. The prior art used simple linear deterministic models in order to approximate the capacities of these Gaussian networks. This paper discusses the ca… ▽ More In multi user Gaussian relay networks, it is desirable to transmit private information to each user as well as common information to all of them. However, the capacity region of such networks with both kinds of information is not easy to characterize. The prior art used simple linear deterministic models in order to approximate the capacities of these Gaussian networks. This paper discusses the capacity region of the deterministic Y-channel with private and common messages. In this channel, each user aims at delivering two private messages to the other two users in addition to a common message directed towards both of them. As there is no direct link between the users, all messages must pass through an intermediate relay. We present outer-bounds on the rate region using genie aided and cut-set bounds. Then, we develop a greedy scheme to define an achievable region and show that at a certain number of levels at the relay, our achievable region coincides with the upper bound. Finally, we argue that these bounds for this setup are not sufficient to characterize the capacity region. △ Less

Submitted 12 January, 2018; originally announced January 2018.

Comments: 4 figures, 7 pages

Active Learning for Structured Prediction from Partially Labeled Data

Authors: Mehran Khodabandeh, Zhiwei Deng, Mostafa S. Ibrahim, Shinichi Satoh, Greg Mori

Abstract: We propose a general purpose active learning algorithm for structured prediction, gathering labeled data for training a model that outputs a set of related labels for an image or video. Active learning starts with a limited initial training set, then iterates querying a user for labels on unlabeled data and retraining the model. We propose a novel algorithm for selecting data for labeling, choosin… ▽ More We propose a general purpose active learning algorithm for structured prediction, gathering labeled data for training a model that outputs a set of related labels for an image or video. Active learning starts with a limited initial training set, then iterates querying a user for labels on unlabeled data and retraining the model. We propose a novel algorithm for selecting data for labeling, choosing examples to maximize expected information gain based on belief propagation inference. This is a general purpose method and can be applied to a variety of tasks or models. As a specific example we demonstrate this framework for learning to recognize human actions and group activities in video sequences. Experiments show that our proposed algorithm outperforms previous active learning methods and can achieve accuracy comparable to fully supervised methods while utilizing significantly less labeled data. △ Less

Submitted 9 June, 2017; v1 submitted 7 June, 2017; originally announced June 2017.

Generic Tubelet Proposals for Action Localization

Authors: Jiawei He, Mostafa S. Ibrahim, Zhiwei Deng, Greg Mori

Abstract: We develop a novel framework for action localization in videos. We propose the Tube Proposal Network (TPN), which can generate generic, class-independent, video-level tubelet proposals in videos. The generated tubelet proposals can be utilized in various video analysis tasks, including recognizing and localizing actions in videos. In particular, we integrate these generic tubelet proposals into a… ▽ More We develop a novel framework for action localization in videos. We propose the Tube Proposal Network (TPN), which can generate generic, class-independent, video-level tubelet proposals in videos. The generated tubelet proposals can be utilized in various video analysis tasks, including recognizing and localizing actions in videos. In particular, we integrate these generic tubelet proposals into a unified temporal deep network for action classification. Compared with other methods, our generic tubelet proposal method is accurate, general, and is fully differentiable under a smoothL1 loss function. We demonstrate the performance of our algorithm on the standard UCF-Sports, J-HMDB21, and UCF-101 datasets. Our class-independent TPN outperforms other tubelet generation methods, and our unified temporal deep network achieves state-of-the-art localization results on all three datasets. △ Less

Submitted 30 May, 2017; originally announced May 2017.

Hierarchical Deep Temporal Models for Group Activity Recognition

Authors: Mostafa S. Ibrahim, Srikanth Muralidharan, Zhiwei Deng, Arash Vahdat, Greg Mori

Abstract: In this paper we present an approach for classifying the activity performed by a group of people in a video sequence. This problem of group activity recognition can be addressed by examining individual person actions and their relations. Temporal dynamics exist both at the level of individual person actions as well as at the level of group activity. Given a video sequence as input, methods can be… ▽ More In this paper we present an approach for classifying the activity performed by a group of people in a video sequence. This problem of group activity recognition can be addressed by examining individual person actions and their relations. Temporal dynamics exist both at the level of individual person actions as well as at the level of group activity. Given a video sequence as input, methods can be developed to capture these dynamics at both person-level and group-level detail. We build a deep model to capture these dynamics based on LSTM (long short-term memory) models. In order to model both person-level and group-level dynamics, we present a 2-stage deep temporal model for the group activity recognition problem. In our approach, one LSTM model is designed to represent action dynamics of individual people in a video sequence and another LSTM model is designed to aggregate person-level information for group activity recognition. We collected a new dataset consisting of volleyball videos labeled with individual and group activities in order to evaluate our method. Experimental results on this new Volleyball Dataset and the standard benchmark Collective Activity Dataset demonstrate the efficacy of the proposed models. △ Less

Submitted 9 July, 2016; originally announced July 2016.

Comments: arXiv admin note: text overlap with arXiv:1511.06040

Security Analysis of Secure Force Algorithm for Wireless Sensor Networks

Authors: Shujaat Khan, Muhammad Sohail Ibrahim, Kafeel Ahmed Khan, Mansoor Ebrahim

Abstract: In Wireless Sensor Networks, the sensor nodes are battery powered small devices designed for long battery life. These devices also lack in terms of processing capability and memory. In order to provide high confidentiality to these resource constrained network nodes, a suitable security algorithm is needed to be deployed that can establish a balance between security level and processing overhead.… ▽ More In Wireless Sensor Networks, the sensor nodes are battery powered small devices designed for long battery life. These devices also lack in terms of processing capability and memory. In order to provide high confidentiality to these resource constrained network nodes, a suitable security algorithm is needed to be deployed that can establish a balance between security level and processing overhead. The objective of this research work is to perform a security analysis and performance evaluation of recently proposed Secure Force algorithm. This paper shows the comparison of Secure Force 64, 128, and 192 bit architecture on the basis of avalanche effect (key sensitivity), entropy change analysis, image histogram, and computational time. Moreover, based on the evaluation results, the paper also suggests the possible solutions for the weaknesses of the SF algorithm. △ Less

Submitted 8 September, 2015; v1 submitted 3 September, 2015; originally announced September 2015.

Comments: in Asian Journal of Engineering Science and Technology 2015