Miracle-dz/UniFAR-FTUs huggingface dataset:
A Power Efficiency Enhancements of a Multi-Bit Accelerator for Memory Prohibitive Deep Neural Networks

Convolutional Neural Networks (CNN) are widely employed in the contemporary artificial intelligence systems. However these models have millions of connections between the layers, that are both memory prohibitive and computationally expensive. Employing these models on an embedded mobile application is resource limited with high power consumption and significant bandwidth requirement to access the data from the off-chip DRAM. Reducing the data movement between the on-chip and off-chip DRAM is the main criteria to achieve high throughput and overall better energy efficiency. Our proposed multi-bit accelerator achieves these goals by employing the truncation of the partial sum (Psum) results of the preceding layer before feeding it into the next layer. We exhibit the architecture by inferencing 32-bits for the first convolution layers and sequentially truncate the bits on the MSB/LSB of integer and fractional part without any further training on the original network. At the last fully connected layer, the top-1 accuracy is maintained with the reduced bit width of 14 and top-5 accuracy upto 10-bit width. The computation engine consists of an systolic array of 1024 processing elements (PE). Large CNNs such as AlexNet, MobileNet, SqueezeNet and EfficientNet were used as benchmark CNN model and Virtex Ultrascale FPGA was used to test the architecture. The proposed truncation scheme has 49% power reduction and resource utilization was reduced by 73.25% for LUTs (Look-up tables), 68.76% for FFs (Flip-Flops), 74.60% for BRAMs (Block RAMs) and 79.425% for Digital Signal Processors (DSPs) when compared with the 32 bits architecture. The design achieves a performance of 223.69 GOPS on a Virtex Ultrascale FPGA, the design has a overall gain of $3.63 \,\, \times $ throughput when compared to other prior FPGA accelerators. In addition, the overall power consumption is $4.5 \,\, \times $ lower when compared to other prior architectures. The ASIC version of the accelerator was designed in 22nm FDSOI CMOS process to achieve a overall throughput of 2.03 TOPS/W with a total power consumption of 791 mW and with a area of 1 mm $\times \,\, 1.2$ mm.
Deep Generative Model based Channel Agnostic Communication System for Efficient Data Transmission

In this modern era, where there is a great shift in momentum towards AI and data science, data-driven approach has substantiated to have diverse talents. This type of approach specifically in the field of wireless communication has proven to be highly efficient in modelling an end to end wireless communication channel with a completely unknown channel state information (CSI). Though there has been some work done in this direction they are not focusing on high dimension data like audio and image. Further with the tremendous growth in multimedia technologies and with higher resolution data being transmitted, there is an increasing need for bandwidth. Hence in this paper, we propose a novel idea where a Conditional Generative Adversarial Network is used to represent the channel effects of an end to end wireless communication channel which adapts to different noise levels with having the memory of the channel state information (CSI). Further to address this issue with bandwidth, Variational Adversarial Network is used where the input data is encoded into a condensed latent space and impacted with noise which is then passed through a Generative Adversarial Network which would be able to extract the original information. This reduces a lot of bandwidth and thereby increasing the overall data rate. To strengthen our proposal, we provide a comparative analysis with our conditional GAN and with extensive experiment results with both quantitative and qualitative analysis on images and voice signals.
RefineDCN: An Improved Community Detection Algorithm Based on Center Finding

Detecting community structure is an important problem in complex networks. Recently, the community detection method based on centers and neighbors (DCN) has been proposed, which is divided into two stages: community center point detection and label propagation. It has a better result on community detection of simple undirected graph than other algorithms. However, when there are two community centers connected directly, the method of DCN fails to find both centers. Based on the DCN algorithm, this paper proposes an optimization method based on visually-aided user interactions. By showing the local structure of the discovered center point through the force-directed layout, the potential community centers that are missing in DCN can be detected. We further propose the hierarchical visual clustering to assist users to detect more community centers easier. In addition, to make the propagation of labels more stable, we propose the multi-label propagation strategy based on importance which also preserves the labels proportion during propagation. The experimental results on both artificial and real-world networks demonstrate that our improved algorithm RefineDCN obtains better community detection results than the DCN algorithm.
Value Decomposition based Multi-Task Multi-Agent Deep Reinforcement Learning in Vehicular Networks

With the development of intelligent transportation system (ITS), a multitude of novel vehicular applications have been emerging. There is an urgent need for simultaneously supporting multi-tasks across a group of vehicles in a vehicular network, forming a typical multi-task multi-agent (MTMA) environment. Deep Reinforcement Learning (DRL) is deemed a promising approach to solving the highly complicated MTMA problem. However, owing to the extraordinarily growing computational complexity as well as the explosively increasing dimension of state and action spaces in the MTMA environment, the value functions in the DRL are usually bulky and could be difficult to be learned efficiently. In this way, by virtue of the correlations among multiple vehicular tasks, we adopt the value-decomposition mechanism (VDM) to decompose the complicated value function into several small pieces and then compute each sub-function separately. The proposed paradigm can yield great speed-up in learning and help substantially with a smaller state and action space but without degrading the performance. In this work, we consider an MTMA environment with three vehicular tasks to demonstrate the effectiveness of the proposed mechanism with simulation results.
Current Reconstruction Considering Time-Sharing Sampling Errors change throughout the running heads

Because of the time-sharing sampling logic, the three-phase currents reconstructed by single dc-link shunt present significant errors in the permanent magnet synchronous motor drives. A traceback-prediction current correction method for sampled currents is proposed in this article to eliminate the reconstruction errors. According to the analysis of time-sharing sampling errors, the concept of equivalent current change rate during one pulsewidth modulation (PWM) period is introduced, and the phase current characteristic model based on state observer is established. Both the equivalent and instantaneous current change rates can be obtained by the observer-based calculation method. In order to improve the current reconstruction precision, the ultimate values of three-phase currents can be predicted by adopting the proposed traceback-prediction current correction method, which does not require major computation and is easy to implement with no restrictions on the PWM strategy. The effectiveness of the proposed method is verified by experimental results.
An Adaptive Dummy-based Mechanism to Protect Location Privacy in Smart Health Care System

Advances in the scientific and technological revolution have engendered the spurt development of smart health care system (SHS). In SHS, Patients' location in-formation is transmitted to the designated medical service provider by sensors and wearables attached to patients' bodies for professional and quality assessment. The location information combined with time slot can disclose patients' sensitive privacy, including mental status, sensitive location, financial circum-stance. Thus, it demands our devoted attention and instant action to protect patient's location privacy, especially sensitive locations in SHS. In the privacy mechanisms based on location anonymizer, they are vulnerable to suffer collusion attacks and subjected to essentially compromise. In this paper, an adaptive dummy-based mechanism is proposed to preserve patients' sensitive locations, consisting of dual strategies, which are considering the direct distance between the generated false trajectories and the sensitive positions and weighing the sensitive locations. Extensive experiments are implemented to evaluate performance. The validity and feasibility of our proposed approach in protecting sensitive locations are indicated by simulation consequences compared with existing methods.
Aerobika as an Evidence Based Physiotherapy Procedure for COPD and the Proposition of a Smart OPEP Device

The effectiveness of conventional physiotherapy interventions (CPT) in alleviating Chronic Obstructive Pulmonary Disease (COPD) symptoms has been proved, thus it helps reducing the long-term damage of the lung tissue, improving lung function and enhancing Health-Related Quality of Life (HRQoL). The efficacy of the Oscillatory Positive Expiratory Pressure (OPEP) devices including Aerobika have been proven experimentally. Such mechanical devices are proposed to be an alternative to CPT. Utilization of such devices could be a potential component of a treatment strategy to improve COPD symptom control and reduce risk of re-exacerbations and reduce the economic burden of treating COPD patients. The aim of this study is to review the Evidence Based Practice (EBP) of OPEPs generally and for Aerobika specifically, and to present a modified smart model of the Aerobika device, thus enhancing its functionality. Randomized control trials, systematic reviews and other literature articles have been examined for EBP of the CPT and OPEP devices, where the effectiveness of OPEP including the Aerobika in treating COPD is reviewed and presented in this paper. Literature suggest that a modified smart Aerobika is a promising device that can enhance its effectiveness and functionality. The proposed modified Aerobika has been established by adding a piezoresistive pressure sensor to provide pressure readings that allow for automatic selection of suitable resistance level. Thus far, an ultra-nano servo motor was used in the modified device to adjust the oscillating range. Utilization of OPEP and Aerobika by physiotherapy practitioners help enhancing Physiotherapy outcomes, thus, developing smart forms of such devices gives enhanced functionality, and should be included in the treatment strategies for COPD.
Simplified Decoding of Polar Codes by Identifying Reed-Muller Constituent Codes

The throughput of successive cancellation decoding of polar codes can be improved through simplified decoders that identify specific constituent codes in the decoding tree. The identified codes include rate-0, rate-1, repetition, and single parity-check codes. In this work, constituent codes that belong to the family of first-order Reed-Muller codes, and their sub-codes, are also identified in the decoding tree. Alternative decoding schemes that utilize the structure of Reed-Muller codes are incorporated into successive cancellation decoding. Simulation results show that such an approach can improve both the block error rate performance as well as the decoding latency of polar codes.
An Analytical Study of Low Energy Monitoring Networks for Large-Scale Data Centers

Environmental monitoring using wireless sensors plays a key role in detecting hotspots or over-cooling conditions in data centers (DCs). However, monitoring a large enterprise or cloud DCs requires the deployment of thousands of sensors distributively with an operational time over months or years. Low Energy Monitoring Network (LEMoNet) is a two-tier Bluetooth Low Energy (BLE) based protocol for DC monitoring that leverages multi-gateway packet reception in its top tier to mitigate the unreliable BLE communication in the low tier. In this paper, we develop an analytical model to study the scalability and energy efficiency of LeMoNet in large-scale DCs. The accuracy of the model is validated through extensive event-driven simulations. Evaluation results show that LEMoNet can achieve high reliability in a network of 4800 nodes at a duty cycle of 15 sec (or equivalently, at an aggregated traffic load of 66Kbps per advertisement channel).
Assessing Radiation Hardness of SIC MOS Structures

It is widely known that devices based on wide gap semiconductors show potential benefits in terms of saving mass, increasing power densities compared with standard Silicon ones [1]. The higher operating temperatures these components can withstand can also reduce the power budget currently used for cooling down power electronics. These factors are critical in space applications where, for example SiC devices are very promising. However, in this field reliability is a paramount requirement, and radiation conditions can compromise the usage of these new technologies.
Network Security Analysis Using HTTPS with SSL On General Election Quick Count Website

The general election is a democratic event held every five years to elect the candidate who will lead the country during the period. There are several pilot websites for data processing by the quick count survey institution. However, in terms of security, the sites are still lacking because of the applicability have not implemented security systems for the anticipation of the attacks were not responsible. In this proposal, the authors designed a security application quick count based crowd-sourcing. HTTPS and SSL is a security protocol that is commonly used to secure web or internet transactions. The use of HTTPS and SSL on a quick count of applications is expected to be able to anticipate and secure communications between the client and the server of the intruder’s attack or unauthorized access. A simple implementation of SSL installation is shown to be able to reduce the vulnerability level and prevent the SQL injection attack.
On the Suitability of RCM-LDGM Codes for Sending High Data Rates over Impulsive Noise Channels

In this paper, we look at the problem of sending high data rates over additive impulsive noise channels. We propose the use of parallel Rate Compatible Modulation - Low- Density Generator Matrix (RCM-LDGM) codes, with an adapted decoder when the channel state information is available at the receiver. In order to assess the performance of the proposed codes, a modified version of Middleton class A noise and Markov- Middleton impulsive noise models are considered. To that end, we first show that if the expected number of impulses per channel use tends to 0, the capacity of the impulsive noise channel tends asymptotically to the AWGN capacity. With this result in mind, we show by Monte Carlo simulation that the performance loss of the proposed RCM-LDGM coding strategy is negligible when compared to the AWGN scenario without impulsive noise.
Scheduling of Elective Surgery in Al-Shifa Hospital Using an Integrated AHP-LP Approach

Al-Shifa Medical Complex is the largest central hospital in Gaza Strip. Al-Shifa Hospital has sixteen surgery rooms, six of them are used for elective surgeries that include general and specialized surgeries. Approximately, 35 elective surgeries are booked per day, while on average, 27 elective surgeries are performed daily. The surgeries scheduling in Al- Shifa Hospital follows the First Come First Served (FCFS) rule, which means a patient who first comes is assigned the first available time slot. A quick analysis of the available data shows that only 8% of elective surgeries are performed as scheduled. This research aims to prioritize the patients surgery schedules based on their health status as represented by given criteria/sub-criteria and not only on FCFS bases. Criteria/sub-criteria were identified and a final list was obtained. Criteria included: disease severity, difficulty in performing daily activities, hospital readmissions, patient having other diseases, and the patient age. Analytic Hierarchy Process (AHP) tool was used to rank each criteria/sub-criteria. AHP findings show that disease severity, difficulty in performing daily activities are the most important criteria followed by hospital readmissions, patient having other diseases, and patient age respectively. A Linear Programing model (LP) is formulated to schedule surgeries for each department with an objective function of maximizing the number of surgeries of each department. As a result of LP analysis, it was observed that some of those patients whose surgeries were performed ahead of schedule based on FCFS have the highest priority. Further, some patients whose surgeries were behind schedule ranked lowest. Also the results show that the department whose patients have the highest priority is neurosurgery.
Joint Resource Allocation and Trajectory Optimization with QoS in NOMA UAV Networks

In this paper, we mainly studied the joint resource allocation and UAV trajectory optimization for maximizing the total energy efficiency in UAV-based non-orthogonal multiple access (NOMA) downlink wireless networks with the quality of service (QoS) requirement. To track the joint optimization problem, a heuristic algorithm based on matching theory in cellular networks is proposed firstly to allocate users which connect the UAV in each subperiod, then the transmit power allocation problem which considers the maximum transmit power and minimum user date rate is transformed to a convex optimization problem by logarithmic approximation, and solved to get an optimal solution. Meanwhile, the successive convex optimization is used in UAV trajectory optimization problem for its near-optimal solution. Finally, numerical results are provided to support the rationality of the proposed algorithm.
Bayesian-based channel quality estimation method for LoRaWAN with unpredictable interference

The “Internet of things” has become a common term, and low-power wide-area (LPWA) technology is attracting much attention as one of its elemental technologies. LPWA achieves wide-area communication without consuming much energy, allowing various data sensing and gathering applications. LoRa is an LPWA communication technology that uses unlicensed bands. Because it is possible to build a self-managed network with LoRa, many LoRa-based services will be scattered in the same area without an overall administrator. As a result, the communication performance of LoRa may degrade due to unintended radio interference. Unfortunately, many LPWA techniques, including LoRa, have low data rates, making it difficult to gather sufficient control information to avoid such degradation of communication performance. In this paper, we propose a method for estimating network congestion states through successive estimation using Bayesian updates of prior distributions. Computer simulations show the network state can be estimated by our proposed method with accumulating a little control information.
Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems

Existing techniques for incremental learning are computationally expensive and produce duplicate features leading to higher false positive and true negative rates. We propose a novel privacy-preserving intrusion detection pipeline for distributed incremental learning. Our pre-processing technique eliminates redundancies and selects unique features by following innovative extraction techniques. We use autoencoders with non-negativity constraints, which help us extract less redundant features. More importantly, the distributed intrusion detection model reduces the burden on the edge classifier and distributes the load among IoT and edge devices. Theoretical analysis and numerical experiments have shown lower space and time costs than state of the art techniques, with comparable classification accuracy. Extensive experiments with standard data sets and real-time streaming IoT traffic give encouraging results.
Cuckoo Search Algorithm Based An Optimal Control Strategy for Switched Nonlinear Systems

In this paper an optimal control strategy for switched nonlinear systems is addressed. A metaheuristic hybrid approach based on the Cuckoo Search algorithm is suggested for this issue. The main objective of the suggested approach consists to minimize a performance criterion which depends on the optimal switching instants to be found, over a finite time horizon. Lastly, a hydraulic two-tank system will be given to show the performance and the effectiveness of the suggest method.
Computing Resource Allocation and Offloading Method based on Simulated Annealing Algorithm

The integration of mobile edge computing (MEC) and 5G ultra-dense small cell network structure provides higher computing capability for smart mobile devices (SMDs). This paper proposes a joint optimization method for task offloading and computing resource allocation based on Simulated Annealing Algorithm (SAA) in small cell wireless network scenarios with multiple base stations (BSs) and multiple MEC servers. Firstly, this method jointly considers computing resource allocation and task offloading. Secondly, it uses the Lagrange multiplier method to solve the problem of computing resource allocation. Thirdly, it adjusts the offloading decision through the improved simulated annealing algorithm (improved-SAA) dynamically. This paper uses the task completion time and the total weighted energy consumption as the System Utility Function to measure the benefit of task offloading. Compared with hjTORA Algorithm, Greedy Algorithm, and Local Search Algorithm, the experimental results show that the algorithm has better utility value under the influencing factors such as the number of subcarriers and SMDs, communication data, calculation data and other factors, which verifies the effectiveness of the designed algorithm.
Model Predictive Control with Switching Frequency Minimization for Modified Packed U-cell Inverter

In this paper, a finite control set model predictive control (FCS-MPC) for single-phase cascaded modified packed U-cell (MPUC) inverter is presented. The considered multilevel inverter (MLI) topology requires less number of switches and produces more levels in the output waveform compared to the conventional and recent ones. Two control objectives are achieved in the proposed control algorithm, reference current tracking and switching frequency minimization. The proposed FCS-MPC considers only the effective different vectors of the MPUC with eliminating the redundant states of the converter to reduce the computational effort of the algorithm. By minimizing a user-defined cost function, the developed FCS-MPC algorithm achieves low mean absolute reference tracking error of the grid current, high inverter voltage quality and low average switching frequency of the switches. The trade-off between the average switching frequency and the tracking performance with the voltage quality is studied to determine the suitable range of the weighting factor λn for the proposed system. Simulation results are presented using MATLAB/Simulink to verify the effectiveness of the control system.
A disruptive M2M social paradigm for Industry 4.0

Machine-to-Machine (M2M) is one of the emerging technologies that has caught the attention of many researchers. The heterogeneous connected objects, constrained by interoperability, security and confidentiality issues, is driving the emergence of the concept of the Social Internet of Things (SIoT). The latter consists of providing semi-autonomous communicating objects (CO) with interaction capacities inspired by human social relations. The SIoT concept contributes to the evolution of interaction between industrial objects, by improving deterministic mechanisms towards intelligent interactions. We propose a new concept of M2M interactions inspired by social paradigms. These paradigms are deduced from a synthesis of several sociological approaches.